---
title: "My First Quarto Document"
author: "John Doe"
date: "2025-01-30"
format: html
---Basics of R Programming Language
R!
R is a powerful programming language and open-source software widely used for statistical computing and data analysis. This programming language is developed by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand. R has gained popularity among statisticians, data scientists, researchers, and analysts for its flexibility, extensibility, and robust statistical capabilities.
Why learn R?
Here are several compelling reasons to consider learning R:
- Statistical Analysis
- Data Visualisation
- Open Source
- Community Support
- Extensibility
- Integration with Other Languages
- Data Science and Machine Learning
- Widely Used in Academia and Industry
- Continuous Development
Getting Started with R
To begin working with R, users typically install an Integrated Development Environment (IDE) such as RStudio, which provides a user-friendly interface for coding, debugging, and visualising results. R scripts are written in the R language and can be executed interactively or saved for later use.
A look around RStudio
Open RStudio. You will see four windows (aka panes). Each window has a different function. The screenshot below shows an analogy linking the different RStudio windows to cooking.
Console Pane
On the left-hand side, you’ll find the console. This is where you can input commands (code that R can interpret), and the responses to your commands, known as output, are displayed here. While the console is handy for experimenting with code, it doesn’t save any of your entered commands. Therefore, relying exclusively on the console is not recommended.
History Pane
The history pane (located in the top right window) maintains a record of the commands that you have executed in the R console during your current R session. This includes both correct and incorrect commands.
You can navigate through your command history using the up and down arrow keys in the console. This allows you to quickly recall and re-run previous commands without retyping them.
Environment Pane
The environment pane (located in the top right window) provides an overview of the objects (variables, data frames, etc.) that currently exist in your R session. It displays the names, types, dimensions, and some content of these objects. This allows you to monitor the state of your workspace in real-time.
Plotting Pane
The plotting pane (located in the bottom right window) is where graphical output, such as plots and charts, is displayed when you create visualisations in R. The Plotting pane often includes tools for zooming, panning, and exporting plots, providing additional functionality for exploring and customising your visualisations. Help Pane:
Help Pane
The help pane (located in the bottom right window) is a valuable resource for accessing documentation and information about R functions, packages, and commands. When you type a function or command in the console and press the F1 key (Mac: fn + F1) the Help pane displays relevant documentation. Additionally, you can type a keyword in the text box at the top right corner of the Help Pane.
Files Pane
The files pane provides a file browser and file management interface within RStudio. It allows you to navigate through your project directories, view files, and manage your file system.
Packages Pane
This pane provides a user-friendly interface for managing R packages. It lists installed packages and allows you to load, unload, update, and install packages.
Viewer Pane
It is used to display dynamic content generated by R, such as HTML, Shiny applications, or interactive visualisations.
Working directory
Opening an RStudio session launches it from a specific location. This is the working directory. R looks in the working directory by default to read in data and save files. You can find out what the working directory is by using the command getwd(). This shows you the path to your working directory in the console. In Mac this is in the format /path/to/working/directory and in Windows C:\path\to\working\directory. It is often useful to have your data and R scripts in the same directory and set this as your working directory. We will do this now.
Make a folder for this course somewhere on your computer that you will be able to easily find. Name the folder for example, Intro_R_REDCap_course. Then, to set this folder as your working directory:
In RStudio click on the Files tab and then click on the three dots, as shown below.
In the window that appears, find the folder you created (e.g. Intro_R_REDCap_course), click on it, then click Open. The files tab will now show the contents of your new folder. Click on More → Set As Working Directory, as shown below.
Note: You can use an RStudio project as described here to automatically keep track of and set the working directory.
R Scripts
In RStudio, the Script pane (located at the top left window) serves as a dedicated space for writing, editing, and executing R scripts. It is where you compose and organise your R code, making it an essential area for creating reproducible and well-documented analyses.
RStudio provides syntax highlighting in the Script pane, making it easier to identify different components of your code. You can execute individual lines or selections of code from the Script pane. This helps in testing and debugging code without running the entire script.
Quarto Document
Quarto is an open-source scientific and technical publishing system that allows you to combine text, code, and output in a single document. It is the next-generation version of RMarkdown and is widely used for reproducible research, dynamic reports, and interactive documents.
With Quarto, you can:
- Write reports that integrate R code and results
- Create interactive documents (HTML, PDF, Word, and more)
- Publish research outputs with dynamic figures and tables
Why use Quarto?
- Reproducibility
- Combines analysis and documentation in one file
- Flexible Outputs
- Generate HTML, PDF, Word, and presentations
- Works with R, Python, and Julia
- Supports Markdown Syntax
- Easy formatting for text and visuals
In this workshop, we will be using Quarto documents to write R code.
Getting Started with a Quarto Document
Follow these steps to create a new Quarto document in RStudio:
Open a New Quarto Document
- Open RStudio
- Go to File → New File → Quarto Document
- A dialog box will appear:
- Title: Enter a document title as “Analysing REDCap Data using R”
- Format: Leave default format as HTML
- Engine: Leave default engine as knitr
- Click Create.
This creates a new .qmd file in RStudio, which is a Quarto document.
Save the File
- Click File → Save As
- Choose a meaningful filename, e.g., introR_workshop.qmd
- Click Save
Understanding the Structure of a Quarto Document
A Quarto document consists of three main sections:
- YAML Header (Metadata Section)
This section is enclosed at the top of the file using — and contains metadata. Example:
Common YAML options:
- title: Document title
- author: Name of the author
- date: Date of the document
- format: Output type (HTML, PDF, Word, etc.)
- Text and Markdown (Narrative Section)
Quarto supports Markdown, a simple way to format text.
Headings:
# Main Heading ## Subheading ### Smaller HeadingBold and Italic Text:
**Bold Text** *Italic Text*Lists:
- Bullet Point 1 - Bullet Point 2Hyperlinks and Images:
[Click here for Quarto docs](https://quarto.org/) 
- Code Blocks (Executable Section)
Quarto allows you to insert code chunks that run R scripts inside your document.
Example R Code Chunk:
```{r}
# Example calculation
x <- c(1, 2, 3, 4, 5)
sum(x)
```
To insert a code chunk, go to Code in the menu -> Insert Code Chunk or use the keyboard shortcuts Windows/Linux: Ctrl + Alt + I or Mac: ⌘ + Option + I. Code is written inside triple backticks and it is executed when you render the document.
- Running and Rendering a Quarto Document
To run a single code chunk click the Run button at the top of the chunk or use the keyboard shortcut Windows/Linux: Ctrl + Shift + Enter or Mac: ⌘ + Shift + Enter.
To generate an output file (HTML, PDF, or Word), click the Render button in RStudio. The document compiles and opens the rendered file.
Tip
If PDF output fails, install TinyTeX for LaTeX support:
install.packages("tinytex")Keyboard Shortcuts in Quarto (Windows & Mac)
| Action | Windows/Linux | Mac |
|---|---|---|
| Run a single code line | Ctrl + Enter |
Cmd + Enter |
| Run a single code chunk | Ctrl + Shift + Enter |
Cmd + Shift + Enter |
| Run all chunks above | Ctrl + Alt + P |
Cmd + Option + P |
| Render (Knit) document | Ctrl + Shift + K |
Cmd + Shift + K |
| Insert a new code chunk | Ctrl + Alt + I |
Cmd + Option + I |
| Comment/uncomment a line | Ctrl + Shift + C |
Cmd + Shift + C |
| Open Quarto Render menu | Ctrl + Shift + R |
Cmd + Shift + R |
| Open Quarto preview | Ctrl + Shift + O |
Cmd + Shift + O |
| Restart R session | Ctrl + Shift + F10 |
Cmd + Shift + F10 |
Executing Commands
Executing commands or running code is the process of submitting a command to your computer, which does some computation and returns an answer. In RStudio, there are several ways to execute commands:
- Select the line(s) of code using the mouse, and then click Run at the top right corner of the R text file.
- Select Run Lines from the Code menu.
- Click anywhere on the line of code and click Run.
- Select the line(s) you want to run. Press ⌘ + Return (Mac) or Ctrl + Return (Windows/Linux) to run the selected code.
We suggest the third option, which is fastest. This link provides a list of useful RStudio keyboard shortcuts that can be beneficial when coding and navigating the RStudio IDE.
When you type in, and then run the commands shown in the grey boxes below, you should see the result in the Console pane at bottom left.
Simple Maths in R
We can use R as a calculator to do simple maths.
3 + 5[1] 8More complex calculator functions are built in to R, which is the reason it is popular among mathematicians and statisticians. To use these functions, we need to call these functions.
Calling Functions
R has a large collection of built-in functions that are called like this:
function_name(argument1 = value1, argument2 = value2, ...)Let’s explore using seq() function to create a series of numbers.
Start by typing se and then press Tab. RStudio will suggest possible completions. Specify seq() by typing more or use the up/down arrows to select it. You’ll see a helpful tooltip-type information pop up, reminding you of the function’s arguments. If you need more assistance, press F1 (Windows/linux) or fn + Tab (Mac) to access the full documentation in the help tab at the lower right.
Now, type the arguments 1, 10 and press Return.
seq(1, 10) [1] 1 2 3 4 5 6 7 8 9 10You can explicitly specify arguments using the name = value format. However, if you don’t, R will try to resolve them based on their position.
seq(from = 1, to = 10) [1] 1 2 3 4 5 6 7 8 9 10In this example, it assumes that we want a sequence starting from 1 and ending at 10. Since we didn’t mention the step size, it defaults to the value defined in the function, which is 1 in this case.
seq(from = 1, to = 10, by = 2)[1] 1 3 5 7 9If you are using name = value format the order of the arguments does not matter.
seq(to = 10, by = 2, from = 1)[1] 1 3 5 7 9For frequently used functions, I might rely on positional resolution for the first one or two arguments. However, beyond that, I prefer to use the name = value format for clarity and precision.
To take the log of 100:
log(x = 100, base = 10)[1] 2To take the square root of 100:
sqrt(100) # this is the short-hand of sqrt(x = 100)[1] 10Notice that the square root function is abbreviated to sqrt(). This is to make writing R code faster, however the draw back is that some functions are hard to remember, or to interpret.
Getting Help
In R, the ? and ?? operators are used for accessing help documentation, but they behave slightly differently.
- The
?operator is used to access help documentation for a specific function or topic. When you type?followed by the name of a function, you get detailed information about that function. For example try:
?meanView Output
<!DOCTYPE html>
R: Arithmetic Mean
| mean | R Documentation |
Arithmetic Mean
Description
Generic function for the (trimmed) arithmetic mean.
Usage
mean(x, ...)
## Default S3 method:
mean(x, trim = 0, na.rm = FALSE, ...)
Arguments
x
|
An R object. Currently there are methods for numeric/logical vectors and date, date-time and time interval objects. Complex vectors are allowed for |
trim
|
the fraction (0 to 0.5) of observations to be trimmed from each end of |
na.rm
|
a logical evaluating to |
…
|
further arguments passed to or from other methods. |
Value
If trim is zero (the default), the arithmetic mean of the values in x is computed, as a numeric or complex vector of length one. If x is not logical (coerced to numeric), numeric (including integer) or complex, NA_real_ is returned, with a warning.
If trim is non-zero, a symmetrically trimmed mean is computed with a fraction of trim observations deleted from each end before the mean is computed.
References
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
See Also
weighted.mean, mean.POSIXct, colMeans for row and column means.
Examples
x <- c(0:10, 50)
xm <- mean(x)
c(xm, mean(x, trim = 0.10))
The above command displays the help documentation for the mean function, providing information about its usage, arguments, and examples.
- The
??operator is used for a broader search across help documentation. It performs a search for the specified term or keyword in the documentation.
??regressionThis will search for the term “regression” in the help documentation and return relevant results. It’s useful when you want to find functions, packages, or topics related to a specific term.
Tab completion
A very useful feature is Tab completion. You can start typing and use Tab to autocomplete code, for example, a function name.
R Packages
Many developers have built 1000s of functions and shared them with the R user community to help make everyone’s work easier and more efficient. These functions (short programs) are generally packaged up together in (wait for it) Packages. For example, the tidyverse package is a compilation of many different functions, all of which help with data transformation and visualisation. Packages also contain data, which is often included to assist new users with learning the available functions.
Installing Packages
Packages are hosted on repositories, with CRAN (Comprehensive R Archive Network) being the primary repository. To install packages from CRAN, you use the install.packages() function. For example:
install.packages("tidyverse")This will spit out a lot of text into the console as the package is being installed. Once complete you should have a message:
The downloaded binary packages are in... followed by a long directory name.
To remove an installed package:
remove.packages("tidyverse")Loading Packages
After installation, you need to load a package into your R session using the library() function. For example:
library(tidyverse)── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsThis makes the functions and datasets from the ‘tidyverse’ package available for use in your current session.
Tip
You only need to install a package once. Once installed, you don’t need to reinstall it in subsequent sessions. However, you do need to load the package at the beginning of each R session using the library() function before you can utilise its functions and features. This ensures that the package is actively available for use in your current session.
To view packages currently loaded into memory:
(.packages()) [1] "lubridate" "forcats" "stringr" "dplyr" "purrr" "readr"
[7] "tidyr" "tibble" "ggplot2" "tidyverse" "stats" "graphics"
[13] "grDevices" "utils" "datasets" "methods" "base" search() [1] ".GlobalEnv" "package:lubridate" "package:forcats"
[4] "package:stringr" "package:dplyr" "package:purrr"
[7] "package:readr" "package:tidyr" "package:tibble"
[10] "package:ggplot2" "package:tidyverse" "package:stats"
[13] "package:graphics" "package:grDevices" "package:utils"
[16] "package:datasets" "package:methods" "Autoloads"
[19] "package:base" Package Documentation
Each package comes with documentation that explains how to use its functions. You can access this information using the help() function or by using ? before the function name:
help(tidyverse)View Output
<!DOCTYPE html>
R: tidyverse: Easily Install and Load the ‘Tidyverse’
| tidyverse-package | R Documentation |
tidyverse: Easily Install and Load the ‘Tidyverse’
Description
The ‘tidyverse’ is a set of packages that work in harmony because they share common data representations and ‘API’ design. This package is designed to make it easy to install and load multiple ‘tidyverse’ packages in a single step. Learn more about the ‘tidyverse’ at https://www.tidyverse.org.
Author(s)
Maintainer: Hadley Wickham hadley@rstudio.com
Other contributors:
-
RStudio [copyright holder, funder]
See Also
Useful links:
or by using vignette (if the documentation is in the form of vignettes):
vignette(package="tidyverse")Variables
A variable is a bit of tricky concept, but very important for understanding R. Essentially, a variable is a symbol that we use in place of another value. Usually the other value is a larger/longer form of data. We can tell R to store a lot of data, for example, in a variable named x. When we execute the command x, R returns all of the data that we stored there.
For now however we’ll just use a tiny data set: the number 5. To store some data in a variable, we need to use a special symbol <-, which in our case tells R to assign the value 5 to the variable x. This is called the assignment operator. To insert the assignment operator press Option + - (Mac) or Alt + - (Windows/Linux).
Let’s see how this works.
Create a variable called x, that will contain the number 5.
x <- 5R won’t return anything in the console, but note that you now have a new entry in the environment pane. The variable name is at the left (x) and the value that is stored in that variable, is displayed on the right (5).
We can now use x in place of 5:
x + 10[1] 15x * 3[1] 15Variables are sometimes referred to as objects. In R there are different conventions about how to name variables, but most importantly they:
- cannot begin with a number
- should begin with an alphabetical letter
- they are case sensitive
- variables can take any name, but its best to use something that makes sense to you, and will likely make sense to others who may read your code.
It is wise to adapt a consistent convention for separating words in variables.
For example:
# i_use_snake_case
# other.people.use.periods
# evenOthersUseCamelCaseThe Pipe Operator (|> or %>%)
The pipe operator (|>) is a commonly used feature of the tidyverse. It was originally defined in the (cleverly named) magrittr package, but is also included in the dplyr, tidyverse packages. The |> symbol can seem confusing and intimidating at first. However, once you understand the basic idea, it can become addicting!
We suggest you use a shortcut: ⌘ + Shift + M (Mac) or Ctrl + Shift + M (Windows/Linux).
The |> symbol is placed between a value on the left and a function on the right. The |> simply takes the value to the left and passes it to the function on the right as the first argument. It acts as a “pipe”. That’s it!
Suppose we have a variable, x.
x <- 7The following are the exact same.
sqrt(x)[1] 2.645751x |> sqrt()[1] 2.645751We’ll continue to use |> throughout this tutorial to show how useful it can be for chaining various data manipulation steps during an analysis.
Chaining functions
R chaining allows you to streamline your data analysis workflow by sequentially applying multiple operations to your data using the pipe operator |>. We often need to perform several data manipulation or analysis operations in a sequence. Chaining allows you to apply these operations one after the other in a clear and concise manner.
Here’s a basic template for chaining operations using the pipe operator |>:
result <- data |>
operation1(...) |>
operation2(...) |>
operation3(...) |>
...
operationN(...)In this template:
datarepresents the input data frame or object.operation1,operation2, …,operationNrepresent the functions or operations you want to apply sequentially to the data.For example:select(),filter()ormutate()functions....represents any additional arguments or parameters that may be passed to each operation.
Each operation takes the output of the previous operation as its input, making it easy to chain multiple operations together. This improves the readability of your code by organising operations in a left-to-right fashion and it avoids creating intermediate variables to store the results of each operation.
Clearing the Environment
Take a look at the objects you have created in your workspace that is accumulated in the environment pane in the upper right corner of RStudio.
You can obtain a list of objects in your workspace using a couple of different R commands:
objects()Output
[1] "a" "b" "sum" "x" ls()Output
[1] "a" "b" "sum" "x" If you wish to remove a specific object, let’s say x1, you can use the following command:
rm(a)To remove all objects:
rm(list = ls())Alternatively, you can click the broom icon in RStudio’s Environment pane to clear everything.
For the sake of reproducibility, it’s crucial to regularly delete your objects and restart your R session. This ensures that your analysis can be replicated next week or even after upgrading your operating system. Restarting your R session helps identify and address any dependencies or configurations needed for your analysis to run successfully.
Case Study: Immunotherapy Dataset
In this workshop, we are using a dummy Immunotherapy dataset on REDCap filled with randomly generated data. Therefore, note that in some cases the data can make no sense. However, this will be useful for learning how to import data into R, data manipulation and basic visualisation.
This dataset contains 15 instruments or forms namely: Demographics, Melanoma Data, Adjuvant Therapy, Systemic Therapy for Advanced Disease, Melanoma CNS Metastases, Adverse Events, Baseline Visit, Checkpoint Inhibitor Treatment, Immune Related Adverse Events (irAEs), Pathology, PET irAE Imaging, PPI and Antibiotic use during treatment with CPIs, Response Data, and Mortality Data.
Importing REDCap Data
REDCap API
REDCap (Research Electronic Data Capture) provides an Application Programming Interface (API) that allows users to programmatically access and interact with their project data. The API enables automation of data retrieval, updates, and exports, reducing manual effort and ensuring reproducibility in data analysis.
Peter Mac REDCap Instance
What is the REDCap API?
The REDCap API is a web-based service that allows users to interact with REDCap programmatically. Instead of manually downloading CSV files, users can use the API to:
- Retrieve records from a REDCap project
- Import new data or update existing records
- Export metadata (variable names, field types)
- Pull longitudinal and repeating instrument data
- Generate reports dynamically
The API facilitates automated data retrieval, making it a powerful tool for integrating REDCap data into R-based workflows.
Example Use Case
A researcher can schedule a daily script in R to pull the latest REDCap data for real-time analysis instead of manually exporting files from the web interface.
Requesting an API Token in REDCap
To access the API, users must obtain an API token, which is a unique, secure key that authenticates requests. REDCap provides API tokens at the user-project level. This means that if three users on the same project need to use the API, each user will need to individually request an API token. Similarly, if one user wants an API token for three different projects, they will need to request an API token for each project.
Steps to Request an API Token for a Project:
- Log in to REDCap and navigate to your project.
- If your REDCap project has API access enabled, you will see it in the applications on the left side of the screen as follows. Otherwise contact REDCapServiceDesk@petermac.org.
Click on “API” under the Applications menu.
Click “Request API token” to send a token request to the REDCap administrative team.
Your REDCap administrator will review and approve your request.
Once approved, you will receive a unique API token (a long alphanumeric string).
Important
Keep your API token private and never share it. It grants full access to your REDCap project data.
Using the REDCap API
The best way to familiarise yourself with the REDCap API is to explore the API Playground.
Click on the “API Playground” link from the left-hand menu under “Applications.”
Once in the API Playground, there is a blue box with a dropdown menu labeled “API Method.” This dropdown includes all the API actions REDCap can take.
- If a project is in production, the methods listed in this dropdown will be limited so as not to affect real data in the project. This is noted in the green text under the “API Method” dropdown.
Select the method you need from the dropdown menu and complete the additional information. The additional information (e.g., “Format”, “Instrument”, etc.) will vary depending on which API method you choose and the project structure. In the above example, the researcher is asking to export project information as a CSV.
- To see all the API functions REDCap is capable of, and export a .zip file of sample code, click on the “REDCap API documentation” link that is available on both the “API” page and in the “API Playground.”
When you scroll further down the page, there is an open text box with a series of tabs on the top, with each tab corresponding to a coding language. Each tab will provide the API code in the indicated language.
To execute a real API request, click the “Execute Request” button, and it will display the API response in a textbox as follows.
On the API Playground, there is a button that will let you “Execute Request.” This will perform the API action you are programming and thus affect the data in your project. Use this button with a great amount of caution.
Security Considerations for API Access
Since the API token provides direct access to your REDCap project, it must be handled securely.
Best Practices for API Security:
- Never share your API token with anyone. Keep your API token private. – Never hardcode it in scripts.
- Do not test API tokens in browsers. Using an API token in plain text within a script is unsecure. An API token should be encrypted within a script, be called via secure environment variables, or otherwise be accessible from the script via other secure mechanisms.
- Before you share code anywhere, remove your API token.
- Enable logging and monitor API access regularly.
- Revoke unused API tokens if they are no longer needed.
- Regenerate your API token every 90 days, or at any point that you think your token has been compromised. To regenerate your token, go to the API page and select “Regenerate token.” If you are no longer using the API functionality on your project, delete your token.
Using an Environment Variable for API Token in R
You can save your API keys into a “hidden” file containing code that runs when you start R. That file is called the “.Renviron”. It can be a bit of a pain to find this file. So the best option is to install the usethis package, which contains helper functions, including a function to find this file.
install.packages("remotes")
remotes::install_cran("usethis")When it comes to add packages to your copy of R, the install_cran() function in the remotes package is superior to the usual install.packages() function because it will first check to see if you already have the latest version before bothering to download and install.
After installing usethis you can access your “.Renviron” file by typing this in your console.
usethis::edit_r_environ()It will cause the file to open. Create a name for your API key (for example: rcap_immuno_key) and add a line like this to your .Renviron file:
rcap_immuno_key="your_api_token_here"When you click the link you will be given the option to create an API Token for this project. Copy the token created in the previous section from REDCap website, and paste it in the .Renviron file as explained above. Instead of your_api_token_here in the .Renviron file, your token should be there within ““.
After adding the line, remember to save the file and completely restart R/RStudio. Once R restarts, you can access the key like this:
api_token <- Sys.getenv("rcap_immuno_key")Once you have an API token, you can test whether it works using httr in R.
Example: Checking Project Information
library(REDCapR)
# Define API URL and Token
url <- "https://redcap.petermac.org.au/api/"
token <- Sys.getenv("rcap_immuno_key") # Load token securely
# Test API connection
formData <- list("token"=token,
content='project',
format='csv',
returnFormat='json'
)
response <- httr::POST(url, body = formData, encode = "form")
result <- httr::content(response)
# Print project details
result| project_id | project_title | creation_time | production_time | in_production | project_language | purpose | purpose_other | project_notes | custom_record_label | secondary_unique_field | is_longitudinal | has_repeating_instruments_or_events | surveys_enabled | scheduling_enabled | record_autonumbering_enabled | randomization_enabled | ddp_enabled | project_irb_number | project_grant_number | project_pi_firstname | project_pi_lastname | display_today_now_button | missing_data_codes | external_modules | bypass_branching_erase_field_prompt |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1840 | Sample Immune checkpoint inhibitor related endocrine toxicity | 2025-01-21 11:52:37 | 2025-01-21 15:55:23 | 1 | English | 1 | For a workshop | A database with dymmy data to be used for an R workshop titled Analysing REDCap data using R for Peter Mac employees | NA | NA | 1 | 1 | 1 | 0 | 0 | 0 | 0 | NA | NA | Anna | Galligan | 1 | NA | sticky_matrix_headers,data_dictionary_revisions,annotated_pdf,record_logging_link,data_driven_project_banner,project_autocomplete | 0 |
If the request is successful, you should see metadata about your REDCap project as shown above.
Importing REDCap Data via API
Once you have set up your API token securely, you can use R to retrieve data directly from REDCap. The REDCapR package provides an interface to streamline API calls from R, making it easy to import records from a REDCap project.
Reading REDCap Data
# If this fails, run install.packages("REDCapR") or
# remotes::install_github(repo="OuhscBbmc/REDCapR")
requireNamespace("REDCapR")Set project-wide values
There is some information that is specific to the REDCap project, as opposed to an individual operation. This includes:
- the uniform resource identifier (uri) of the server
- the token for the user’s project.
library(REDCapR)
# Define API URL and Token
uri <- "https://redcap.petermac.org.au/api/"
token <- Sys.getenv("rcap_immuno_key") # Load token securelyRead all records and fields
By default, the redcap_read() function retrieves the entire dataset from a REDCap project if no filtering parameters (such as records or fields) are specified.
# Read the entire dataset
immuno_all_rows_all_fields <- redcap_read(redcap_uri = uri, token = token)$data
# print the top 6 rows
head(immuno_all_rows_all_fields)| record_id | redcap_event_name | redcap_repeat_instrument | redcap_repeat_instance | ur | last_name | first_name | sex | dob | height | weight | bmi | coenrolled___1 | coenrolled___2 | coenrolled___3 | coenrolled___4 | clinical_trial | clinical_trial_description | medical_history___1 | medical_history___2 | medical_history___7 | medical_history___8 | medical_history___5 | medical_history___3 | medical_history___4 | medical_history___6 | medical_history___99 | medical_history_other | autoimmune_disease | autoimmune_disease_select___1 | autoimmune_disease_select___2 | autoimmune_disease_select___3 | autoimmune_disease_select___4 | autoimmune_disease_select___5 | autoimmune_disease_select___6 | autoimmune_disease_select___9 | autoimmune_disease_other | rheumatoid_arthritis | smoking | demographics_complete | mel_type___1 | mel_type___2 | mel_type___3 | mel_type___4 | mel_type___5 | mel_type___6 | mel_type_cutaneous | mel_mutation___1 | mel_mutation___2 | mel_mutation___3 | mel_mutation___4 | mel_mutation___5 | mel_mutation___9 | mel_mutation_braf | mel_mutation_nras | mel_mutation_kit | mel_mutation_other | mel_first_date | mel_first_stage | resct1stdiag | mel_date_diag | stage_diagnosis | dt_advanced_dis | melanoma_data_complete | adj_given | adj_path_done | adj_path_date | adj_path_hb | adj_path_wcc | adj_path_neut_lymph | adj_path_creat | adj_path_glucose | adj_path_lipase | adj_path_a1c_dcct | adj_path_a1c_ifcc | adj_path_insulin | adj_path_cpep | adj_path_islet_ab___1 | adj_path_islet_ab___2 | adj_path_islet_ab___3 | adj_path_islet_ab___4 | adj_path_gad_ab | adj_path_ia2_ab | adj_path_insulin_ab | adj_path_znt8_ab | adj_path_cortisol | adj_path_acth | adj_path_tsh | adj_path_ft4 | adj_path_ft3 | adj_path_tpo_ab | adj_path_tg_ab | adj_path_trab | adj_path_fsh | adj_path_lh | adj_path_testosterone | adj_path_oestradiol | adj_path_igf1 | adj_path_gh | adj_path_prolactin | adj_path_alt | adj_path_albumin | adj_path_alp | adj_path_bilirubin | adj_path_ferritin | adj_path_crp | adj_path_vitd | adj_path_troponin | adj_path_calprotectin | adj_stage | adj_type | adj_summary | adj_type_trialid | adjstartdate | adj_start | adj_medication___1 | adj_cessation | adj_recurrence | adj_recurrence_date | timrecurrence | adj_resection | adjuvant_therapy_complete | sys_path_done | sys_path_date | sys_path_hb | sys_path_wcc | sys_path_neut_lymph | sys_path_creat | sys_path_glucose | sys_path_lipase | sys_path_a1c_dcct | sys_path_a1c_ifcc | sys_path_insulin | sys_path_cpep | sys_path_islet_ab___1 | sys_path_islet_ab___2 | sys_path_islet_ab___3 | sys_path_islet_ab___4 | sys_path_gad_ab | sys_path_ia2_ab | sys_path_insulin_ab | sys_path_znt8_ab | sys_path_cortisol | sys_path_acth | sys_path_tsh | sys_path_ft4 | sys_path_ft3 | sys_path_tpo_ab | sys_path_tg_ab | sys_path_trab | sys_path_fsh | sys_path_lh | sys_path_testosterone | sys_path_oestradiol | sys_path_igf1 | sys_path_gh | sys_path_prolactin | sys_path_alt | sys_path_albumin | sys_path_alp | sys_path_bilirubin | sys_path_ferritin | sys_path_crp | sys_path_vitd | sys_path_troponin | sys_path_calprotectin | systemic_type | systemic_summary | systemic_chemo | systemic_ici | systemic_mapk | systemic_trial | systemic_trial_number | systemic_type_other | systemic_stage | systemic_ecog | systemic_disease_sites___1 | systemic_disease_sites___2 | systemic_disease_sites___3 | systemic_disease_sites___4 | systemic_disease_sites___5 | systemic_disease_sites___6 | systemic_disease_sites___7 | systemic_disease_sites___8 | systemic_disease_sites___9 | systemic_disease_sites___10 | systemic_disease_sites___11 | systemic_disease_sites___12 | systemic_ppi | systemic_antibiotics | systemic_steroids | systemic_ldh | systemic_ldh_value | systemic_creatinine_units | systemic_creatinine_uln | systemic_egfr | systemic_start | systemic_cycles | systemic_cease_reason | rest_pet | rest_ct | first_response | st_resp_ct | dt_first_response | best_response_percist | best_resp_recist | dt_best_response | systemic_percist_response_time | systemic_progression | systemic_progression_type | systemic_progression_date | systemic_progression_time | systemic_progression_imaging | pseudoprogression | systemic_prog_imaging_type___1 | systemic_prog_imaging_type___2 | systemic_prog_imaging_type___3 | systemic_prog_ct_date | systemic_prog_pet_date | systemic_prog_mri_date | systemic_progression_clinically | sites_progression___1 | sites_progression___2 | sites_progression___3 | sites_progression___4 | sites_progression___5 | sites_progression___6 | sites_progression___7 | sites_progression___8 | sites_progression___9 | sites_progression___10 | sites_progression___11 | sites_progression___12 | site_1_met_at_recur___1 | site_1_met_at_recur___2 | site_1_met_at_recur___3 | site_1_met_at_recur___4 | site_1_met_at_recur___5 | site_1_met_at_recur___6 | site_1_met_at_recur___7 | site_1_met_at_recur___8 | site_1_met_at_recur___9 | site_1_met_at_recur___10 | site_1_met_at_recur___11 | site_1_met_at_recur___12 | biopsy_confirmed | systemic_oligorecurrence | systemic_oligo_treatment | treat_intent_olig | systemic_oligo_treatment_date | systemic_oligo_treatment_response | systemic_oligo_treatment_systemic | date_last_syst_tx | tx_ongoing | reason_cessation | p_treatment___1 | p_treatment___2 | p_treatment___3 | p_treatment___4 | type_io___1 | type_io___2 | type_io___3 | dis_free_io___1 | dis_free_io___2 | dis_free_io___3 | dis_free_io___4 | dur_res_io | res_pemb_nivo___1 | res_pemb_nivo___2 | res_pemb_nivo___3 | res_pemb_nivo___4 | res_niv_pem | res_pem___1 | res_pem___2 | res_pem___3 | res_pem___4 | dur_res_pem | braf_mek | targ_dis_free___1 | targ_dis_free___2 | targ_dis_free___3 | targ_dis_free___4 | dur_braf_mek | site_disease_prog___1 | site_disease_prog___2 | site_disease_prog___3 | site_disease_prog___4 | site_disease_prog___5 | site_disease_prog___6 | site_disease_prog___7 | site_disease_prog___8 | site_disease_prog___9 | site_disease_prog___10 | site_disease_prog___11 | p_treatment_info | prior_treatment_complete | cnsmets_date | cnsmets_number | number_cns_mets | cnsmets_largest | cns_symptoms | surgery_brainmets | cnsmets_radiotherapy | cnsmets_glucocorticoids | cnsmets_brafmek | cnsmets_bevacizumab | cns_diag | cns_symptoms_type | resp_io_brainmet___1 | resp_io_brainmet___2 | resp_io_brainmet___3 | resp_io_brainmet___4 | cns_steroid | cns_braf_mek | single_double | melanoma_cns_metastases_complete | ae_any | ae_adj_sql | ae_systemic_sql | ae_type | ae_type_sql | ae_type_sql_select | ae_endocrine | ae_gastrointestinal | ae_haematological | ae_neurological | ae_skin | ae_onset_date | ae_ctcae | ae_kdigo | ae_investigations___1 | ae_investigations___2 | ae_investigations___3 | ae_investigations___4 | ae_investigations___5 | ae_investigations___6 | ae_investigations___7 | ae_investigations___8 | ae_investigations___9 | ae_investigations___10 | ae_investigations___11 | ae_autoantibodies_date | ae_autoantibodies_result | ae_biopsy_date | ae_biopsy_result | ae_csf_date | ae_csf_result | ae_ecg_date | ae_ecg_result | ae_echo_date | ae_echo_result | ae_endoscopy_date | ae_endoscopy_result | ae_fcp_date | ae_fcp_result | ae_fmcs_date | ae_fmcs_result | ae_mri_date | ae_mri_result | ae_ncs_date | ae_ncs_result | ae_urin_date | ae_urin_result | ae_treatment___11 | ae_treatment___10 | ae_treatment___1 | ae_treatment___2 | ae_treatment___3 | ae_treatment___4 | ae_treatment___5 | ae_treatment___15 | ae_treatment___6 | ae_treatment___7 | ae_treatment___8 | ae_treatment___9 | ae_treatment___14 | ae_treatment___12 | ae_treatment___13 | ae_summary | adverse_events_complete | b_date | b_ecog | b_autoim___1 | b_autoim___2 | b_autoim___3 | b_autoim___4 | b_autoim___5 | b_autoim___6 | b_autoim___7 | b_autoim___8 | b_autoim___9 | b_autoim___10 | b_autoim___11 | b_autoim___12 | b_autoim___13 | b_autoim_type | b_fhx | b_endo___1 | b_endo___2 | b_endo___3 | b_endo___4 | b_endo___5 | b_endo___6 | b_endo___7 | b_endo___8 | b_endo___9 | b_endo___10 | b_endo___11 | b_endo___12 | b_endo_type | hla | b_cancer | b_cancer_hx | b_pmhx | b_meds | b_ppi | b_steroid | b_steroid_type | baseline_visit_complete | drug_name | drug_date | drug_number | drug_comment | checkpoint_inhibitor_treatment_complete | irae_type___1 | irae_type___2 | irae_type___3 | irae_type___4 | irae_type___5 | irae_type___6 | irae_type___7 | irae_type___8 | irae_type___9 | irae_type___10 | endo_irae___1 | endo_irae___2 | endo_irae___3 | endo_irae___4 | hypophysitis_date | hypophysitis_time | thyroiditis_date | thyroiditis_time | pancreatitis_date | adrenalitis_date | hypophysitis_cycle | thyroid_cycle | pancreas_cycle | adrenal_cycle | endo_irae_comment | skin_date | skin_cycle | skin_type___1 | skin_type___2 | skin_type___3 | skin_type___4 | skin_type___5 | skin_type___6 | skin_type___7 | skin_type___8 | skin_type___9 | skin_type___10 | skin_grade | skin_histo | rheum_date | rheum_cycle | rheum_type___1 | rheum_type___2 | rheum_type___3 | rheum_type___4 | rheum_type___5 | rheum_type___6 | rheum_type___7 | rheum_grade | rheum_path | gastro_date | gastro_cycle | gastro_type___1 | gastro_type___2 | gastro_type___3 | gastro_type___4 | gastro_type___5 | gastro_grade | gastro_endoscopy | gastro_histo | gastro_radiol | gastro_cdt | gastro_stool | gastro_path | liver_date | liver_cycle | liver_grade | liver_path | liver_histo | liver_radiol | renal_date | renal_cycle | renal_type___1 | renal_type___2 | renal_type___3 | renal_type___4 | renal_type___5 | renal_type___6 | renal_grade | renal_path | renal_urine | renal_histo | pulm_date | pulm_cycle | pulm_type___1 | pulm_type___2 | pulm_type___3 | pulm_type___4 | pulm_grade | pulm_radiol | pulm_histo | pulm_path | cardiac_date | cardiac_cycle | cardiac_type___1 | cardiac_type___2 | cardiac_type___3 | cardiac_type___4 | cardiac_type___5 | cardiac_grade | cardiac_tropck | cardiac_ecg | cardiac_echo | cardiac_radiol | cardiac_histo | neuro_date | neuro_cycle | neuro_type___1 | neuro_type___2 | neuro_type___3 | neuro_type___4 | neuro_type___5 | neuro_type___6 | neuro_type___7 | neuro_type___8 | neuro_type___9 | neuro_type___10 | neuro_type___11 | neuro_type___12 | neuro_grade | neuro_radiol | neuro_path | irae_steroids | irae_details | irae_emergency | immune_related_adverse_events_iraes_complete | path_date | hb | wcc | neut_lymph | creat | glucose | lipase | a1c_dcct | a1c_ifcc | insulin | cpep | islet_ab___1 | islet_ab___2 | islet_ab___3 | islet_ab___4 | gad_ab | ia2_ab | insulin_ab | znt8_ab | cortisol | acth | tsh | ft4 | ft3 | tpo_ab | tg_ab | trab | fsh | lh | testosterone | oestradiol | igf1 | gh | prolactin | alt | albumin | alp | bilirubin | ferritin | crp | vitd | troponin | calprotectin | pathology_complete | pit_image_date | image_type | pit_suspected | pit_size | pit_appearance | hypophysitis_image | pit_alt_image | image_comment | ctmri_imaging_complete | ur_pet | pet_date | pet_timing___1 | pet_timing___2 | pet_timing___3 | pet_timing___4 | pet_bsl | pet_uptake_time | sul_peak | suv_max | pet_steroid | b_pet_metastases___1 | b_pet_metastases___2 | b_pet_metastases___3 | b_pet_metastases___4 | b_pet_metastases___5 | b_pet_metastases___6 | b_pet_metastases___7 | b_pet_metastases___8 | b_pet_metastases___9 | b_pet_metastases___10 | pet_endo___1 | pet_endo___2 | pet_endo___3 | pet_endo___4 | pet_endo___5 | pet_endo___6 | pet_endo___7 | pet_endo___8 | pet_endo___9 | pet_endo___10 | pet_endo___11 | pit_pet_fdg | pit_pet_suv | pit_suv_change | hypophysitis_pet | pet_pit_ct | thyr_pet_fdg | thyr_pet_suv | thyr_suv_change | thyroiditis_pet | pet_thyr_ct | panc_pet_fdg | panc_pet_suv | panc_suv_change | pancreatitis_pet | pet_panc_ct | adrenal_pet_fdg | adrenal_pet_suv | adrenal_suv_change | adrenalitis_pet | pet_adrenal_ct | cns_pet_fdg | cns_pet_suv | brain_suv_change | encephalitis_pet | pet_cns_ct | rheum_pet | rheum_suvmax | pet_rheum_ct | liver_pet_fdg | liver_pet_suv | liver_suv_change | hepatitis_pet | pet_liver_ct | uppergi_pet_fdg | uppergi_pet_suv | uppergi_suv_change | gastritis_pet | pet_uppergi_ct | ilium_pet_fdg | ilium_pet_suv | ilium_suv_change | ileitis_pet | pet_ilium_ct | colon_pet_fdg | colon_pet_suv | colon_suv_change | colitis_pet | pet_colon_ct | pet_comments | sul_change | suv_change | percist_pet | eortc_pet | recist_pet | residual_disease___1 | residual_disease___2 | residual_disease___3 | residual_disease___4 | residual_disease___5 | residual_disease___6 | residual_disease___7 | residual_disease___8 | residual_disease___9 | residual_disease___10 | pet_metastasis | pet_metastasis_number | site_progression___1 | site_progression___2 | site_progression___3 | site_progression___4 | site_progression___5 | site_progression___6 | site_progression___7 | site_progression___8 | site_progression___9 | site_progression___10 | pet_imaging_complete | antibiotic_oral | antibiotic_iv | antibiotic_type | ppi | ppi_and_antibiotic_use_during_treatment_with_cpis_complete | responce_cpi | best_res_pet | time_to_best_response | response_dur_cpi | overall_response | progression_cpi | progression_adrenal | progression_site___1 | progression_site___2 | progression_site___3 | progression_site___4 | progression_site___5 | progression_site___6 | progression_site___7 | progression_site___8 | progression_site___9 | progression_site___10 | progression_site___11 | other_site | subsequent_rx | response_data_complete | mortality | mortality_date | ongoing_survelliance | date_last_scan | mortality_treatment_time | mortality_cause | other_cause_death | mortality_data_complete |
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| 1 | baseline_arm_1 | NA | NA | 2810493 | Jackson | Hannah | 2 | 1943-02-12 | 154 | 163 | 68.73 | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 1 | baseline_arm_1 | prior_treatment | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2 | Immune checkpoint inhibition: 2019-04-15 | NA | 2 | NA | 0 | NA | NA | 6 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 9 | NA | NA | NA | NA | 2019-04-15 | 2 | 3 | 1 | NA | 3 | NA | 2019-07-09 | 3 | NA | 2019-07-09 | 2.8 | 0 | NA | NA | NA | NA | NA | 0 | 0 | 0 | NA | NA | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | 0 | 10/5/19 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 2 | baseline_arm_1 | NA | NA | 6408685 | Howard | Samantha | 2 | 2008-01-29 | 181 | 97 | 29.61 | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 3 | baseline_arm_1 | NA | NA | 9994173 | Martinez | Noah | 2 | 1940-11-06 | 199 | 123 | 31.06 | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 3 | end_arm_1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1 | 2017-01-10 | 2 | 2016-10-24 | 4.7 | NA | NA | 2 |
| 3 | baseline_arm_1 | prior_treatment | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2 | Immune checkpoint inhibition: 2016-08-19 | NA | 3 | NA | 0 | NA | NA | 5 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | 9 | NA | NA | NA | NA | 2016-08-19 | NA | NA | 1 | NA | 4 | NA | 2016-10-24 | 4 | NA | NA | NA | 1 | 1 | 2016-10-24 | 2.2 | NA | 2 | 0 | 0 | 0 | NA | NA | NA | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | NA | NA | NA | NA | 0 | 21/10/16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
Read a subset of records
In many cases, you may only need data for a specific subset of records (e.g., certain patients). You can achieve this by specifying a list of record IDs in the records argument of redcap_read() as follows.
Pass an array (where each element is a record ID) to the records parameter:
# Define the specific records to retrieve
selected_records <- c(385, 490, 500) # Replace with actual record IDs
# Read only the selected records
immuno_some_records <- redcap_read(
redcap_uri = uri,
token = token,
records = selected_records
)$data
# print all rows
immuno_some_records| record_id | redcap_event_name | redcap_repeat_instrument | redcap_repeat_instance | ur | last_name | first_name | sex | dob | height | weight | bmi | coenrolled___1 | coenrolled___2 | coenrolled___3 | coenrolled___4 | clinical_trial | clinical_trial_description | medical_history___1 | medical_history___2 | medical_history___7 | medical_history___8 | medical_history___5 | medical_history___3 | medical_history___4 | medical_history___6 | medical_history___99 | medical_history_other | autoimmune_disease | autoimmune_disease_select___1 | autoimmune_disease_select___2 | autoimmune_disease_select___3 | autoimmune_disease_select___4 | autoimmune_disease_select___5 | autoimmune_disease_select___6 | autoimmune_disease_select___9 | autoimmune_disease_other | rheumatoid_arthritis | smoking | demographics_complete | mel_type___1 | mel_type___2 | mel_type___3 | mel_type___4 | mel_type___5 | mel_type___6 | mel_type_cutaneous | mel_mutation___1 | mel_mutation___2 | mel_mutation___3 | mel_mutation___4 | mel_mutation___5 | mel_mutation___9 | mel_mutation_braf | mel_mutation_nras | mel_mutation_kit | mel_mutation_other | mel_first_date | mel_first_stage | resct1stdiag | mel_date_diag | stage_diagnosis | dt_advanced_dis | melanoma_data_complete | adj_given | adj_path_done | adj_path_date | adj_path_hb | adj_path_wcc | adj_path_neut_lymph | adj_path_creat | adj_path_glucose | adj_path_lipase | adj_path_a1c_dcct | adj_path_a1c_ifcc | adj_path_insulin | adj_path_cpep | adj_path_islet_ab___1 | adj_path_islet_ab___2 | adj_path_islet_ab___3 | adj_path_islet_ab___4 | adj_path_gad_ab | adj_path_ia2_ab | adj_path_insulin_ab | adj_path_znt8_ab | adj_path_cortisol | adj_path_acth | adj_path_tsh | adj_path_ft4 | adj_path_ft3 | adj_path_tpo_ab | adj_path_tg_ab | adj_path_trab | adj_path_fsh | adj_path_lh | adj_path_testosterone | adj_path_oestradiol | adj_path_igf1 | adj_path_gh | adj_path_prolactin | adj_path_alt | adj_path_albumin | adj_path_alp | adj_path_bilirubin | adj_path_ferritin | adj_path_crp | adj_path_vitd | adj_path_troponin | adj_path_calprotectin | adj_stage | adj_type | adj_summary | adj_type_trialid | adjstartdate | adj_start | adj_medication___1 | adj_cessation | adj_recurrence | adj_recurrence_date | timrecurrence | adj_resection | adjuvant_therapy_complete | sys_path_done | sys_path_date | sys_path_hb | sys_path_wcc | sys_path_neut_lymph | sys_path_creat | sys_path_glucose | sys_path_lipase | sys_path_a1c_dcct | sys_path_a1c_ifcc | sys_path_insulin | sys_path_cpep | sys_path_islet_ab___1 | sys_path_islet_ab___2 | sys_path_islet_ab___3 | sys_path_islet_ab___4 | sys_path_gad_ab | sys_path_ia2_ab | sys_path_insulin_ab | sys_path_znt8_ab | sys_path_cortisol | sys_path_acth | sys_path_tsh | sys_path_ft4 | sys_path_ft3 | sys_path_tpo_ab | sys_path_tg_ab | sys_path_trab | sys_path_fsh | sys_path_lh | sys_path_testosterone | sys_path_oestradiol | sys_path_igf1 | sys_path_gh | sys_path_prolactin | sys_path_alt | sys_path_albumin | sys_path_alp | sys_path_bilirubin | sys_path_ferritin | sys_path_crp | sys_path_vitd | sys_path_troponin | sys_path_calprotectin | systemic_type | systemic_summary | systemic_chemo | systemic_ici | systemic_mapk | systemic_trial | systemic_trial_number | systemic_type_other | systemic_stage | systemic_ecog | systemic_disease_sites___1 | systemic_disease_sites___2 | systemic_disease_sites___3 | systemic_disease_sites___4 | systemic_disease_sites___5 | systemic_disease_sites___6 | systemic_disease_sites___7 | systemic_disease_sites___8 | systemic_disease_sites___9 | systemic_disease_sites___10 | systemic_disease_sites___11 | systemic_disease_sites___12 | systemic_ppi | systemic_antibiotics | systemic_steroids | systemic_ldh | systemic_ldh_value | systemic_creatinine_units | systemic_creatinine_uln | systemic_egfr | systemic_start | systemic_cycles | systemic_cease_reason | rest_pet | rest_ct | first_response | st_resp_ct | dt_first_response | best_response_percist | best_resp_recist | dt_best_response | systemic_percist_response_time | systemic_progression | systemic_progression_type | systemic_progression_date | systemic_progression_time | systemic_progression_imaging | pseudoprogression | systemic_prog_imaging_type___1 | systemic_prog_imaging_type___2 | systemic_prog_imaging_type___3 | systemic_prog_ct_date | systemic_prog_pet_date | systemic_prog_mri_date | systemic_progression_clinically | sites_progression___1 | sites_progression___2 | sites_progression___3 | sites_progression___4 | sites_progression___5 | sites_progression___6 | sites_progression___7 | sites_progression___8 | sites_progression___9 | sites_progression___10 | sites_progression___11 | sites_progression___12 | site_1_met_at_recur___1 | site_1_met_at_recur___2 | site_1_met_at_recur___3 | site_1_met_at_recur___4 | site_1_met_at_recur___5 | site_1_met_at_recur___6 | site_1_met_at_recur___7 | site_1_met_at_recur___8 | site_1_met_at_recur___9 | site_1_met_at_recur___10 | site_1_met_at_recur___11 | site_1_met_at_recur___12 | biopsy_confirmed | systemic_oligorecurrence | systemic_oligo_treatment | treat_intent_olig | systemic_oligo_treatment_date | systemic_oligo_treatment_response | systemic_oligo_treatment_systemic | date_last_syst_tx | tx_ongoing | reason_cessation | p_treatment___1 | p_treatment___2 | p_treatment___3 | p_treatment___4 | type_io___1 | type_io___2 | type_io___3 | dis_free_io___1 | dis_free_io___2 | dis_free_io___3 | dis_free_io___4 | dur_res_io | res_pemb_nivo___1 | res_pemb_nivo___2 | res_pemb_nivo___3 | res_pemb_nivo___4 | res_niv_pem | res_pem___1 | res_pem___2 | res_pem___3 | res_pem___4 | dur_res_pem | braf_mek | targ_dis_free___1 | targ_dis_free___2 | targ_dis_free___3 | targ_dis_free___4 | dur_braf_mek | site_disease_prog___1 | site_disease_prog___2 | site_disease_prog___3 | site_disease_prog___4 | site_disease_prog___5 | site_disease_prog___6 | site_disease_prog___7 | site_disease_prog___8 | site_disease_prog___9 | site_disease_prog___10 | site_disease_prog___11 | p_treatment_info | prior_treatment_complete | cnsmets_date | cnsmets_number | number_cns_mets | cnsmets_largest | cns_symptoms | surgery_brainmets | cnsmets_radiotherapy | cnsmets_glucocorticoids | cnsmets_brafmek | cnsmets_bevacizumab | cns_diag | cns_symptoms_type | resp_io_brainmet___1 | resp_io_brainmet___2 | resp_io_brainmet___3 | resp_io_brainmet___4 | cns_steroid | cns_braf_mek | single_double | melanoma_cns_metastases_complete | ae_any | ae_adj_sql | ae_systemic_sql | ae_type | ae_type_sql | ae_type_sql_select | ae_endocrine | ae_gastrointestinal | ae_haematological | ae_neurological | ae_skin | ae_onset_date | ae_ctcae | ae_kdigo | ae_investigations___1 | ae_investigations___2 | ae_investigations___3 | ae_investigations___4 | ae_investigations___5 | ae_investigations___6 | ae_investigations___7 | ae_investigations___8 | ae_investigations___9 | ae_investigations___10 | ae_investigations___11 | ae_autoantibodies_date | ae_autoantibodies_result | ae_biopsy_date | ae_biopsy_result | ae_csf_date | ae_csf_result | ae_ecg_date | ae_ecg_result | ae_echo_date | ae_echo_result | ae_endoscopy_date | ae_endoscopy_result | ae_fcp_date | ae_fcp_result | ae_fmcs_date | ae_fmcs_result | ae_mri_date | ae_mri_result | ae_ncs_date | ae_ncs_result | ae_urin_date | ae_urin_result | ae_treatment___11 | ae_treatment___10 | ae_treatment___1 | ae_treatment___2 | ae_treatment___3 | ae_treatment___4 | ae_treatment___5 | ae_treatment___15 | ae_treatment___6 | ae_treatment___7 | ae_treatment___8 | ae_treatment___9 | ae_treatment___14 | ae_treatment___12 | ae_treatment___13 | ae_summary | adverse_events_complete | b_date | b_ecog | b_autoim___1 | b_autoim___2 | b_autoim___3 | b_autoim___4 | b_autoim___5 | b_autoim___6 | b_autoim___7 | b_autoim___8 | b_autoim___9 | b_autoim___10 | b_autoim___11 | b_autoim___12 | b_autoim___13 | b_autoim_type | b_fhx | b_endo___1 | b_endo___2 | b_endo___3 | b_endo___4 | b_endo___5 | b_endo___6 | b_endo___7 | b_endo___8 | b_endo___9 | b_endo___10 | b_endo___11 | b_endo___12 | b_endo_type | hla | b_cancer | b_cancer_hx | b_pmhx | b_meds | b_ppi | b_steroid | b_steroid_type | baseline_visit_complete | drug_name | drug_date | drug_number | drug_comment | checkpoint_inhibitor_treatment_complete | irae_type___1 | irae_type___2 | irae_type___3 | irae_type___4 | irae_type___5 | irae_type___6 | irae_type___7 | irae_type___8 | irae_type___9 | irae_type___10 | endo_irae___1 | endo_irae___2 | endo_irae___3 | endo_irae___4 | hypophysitis_date | hypophysitis_time | thyroiditis_date | thyroiditis_time | pancreatitis_date | adrenalitis_date | hypophysitis_cycle | thyroid_cycle | pancreas_cycle | adrenal_cycle | endo_irae_comment | skin_date | skin_cycle | skin_type___1 | skin_type___2 | skin_type___3 | skin_type___4 | skin_type___5 | skin_type___6 | skin_type___7 | skin_type___8 | skin_type___9 | skin_type___10 | skin_grade | skin_histo | rheum_date | rheum_cycle | rheum_type___1 | rheum_type___2 | rheum_type___3 | rheum_type___4 | rheum_type___5 | rheum_type___6 | rheum_type___7 | rheum_grade | rheum_path | gastro_date | gastro_cycle | gastro_type___1 | gastro_type___2 | gastro_type___3 | gastro_type___4 | gastro_type___5 | gastro_grade | gastro_endoscopy | gastro_histo | gastro_radiol | gastro_cdt | gastro_stool | gastro_path | liver_date | liver_cycle | liver_grade | liver_path | liver_histo | liver_radiol | renal_date | renal_cycle | renal_type___1 | renal_type___2 | renal_type___3 | renal_type___4 | renal_type___5 | renal_type___6 | renal_grade | renal_path | renal_urine | renal_histo | pulm_date | pulm_cycle | pulm_type___1 | pulm_type___2 | pulm_type___3 | pulm_type___4 | pulm_grade | pulm_radiol | pulm_histo | pulm_path | cardiac_date | cardiac_cycle | cardiac_type___1 | cardiac_type___2 | cardiac_type___3 | cardiac_type___4 | cardiac_type___5 | cardiac_grade | cardiac_tropck | cardiac_ecg | cardiac_echo | cardiac_radiol | cardiac_histo | neuro_date | neuro_cycle | neuro_type___1 | neuro_type___2 | neuro_type___3 | neuro_type___4 | neuro_type___5 | neuro_type___6 | neuro_type___7 | neuro_type___8 | neuro_type___9 | neuro_type___10 | neuro_type___11 | neuro_type___12 | neuro_grade | neuro_radiol | neuro_path | irae_steroids | irae_details | irae_emergency | immune_related_adverse_events_iraes_complete | path_date | hb | wcc | neut_lymph | creat | glucose | lipase | a1c_dcct | a1c_ifcc | insulin | cpep | islet_ab___1 | islet_ab___2 | islet_ab___3 | islet_ab___4 | gad_ab | ia2_ab | insulin_ab | znt8_ab | cortisol | acth | tsh | ft4 | ft3 | tpo_ab | tg_ab | trab | fsh | lh | testosterone | oestradiol | igf1 | gh | prolactin | alt | albumin | alp | bilirubin | ferritin | crp | vitd | troponin | calprotectin | pathology_complete | pit_image_date | image_type | pit_suspected | pit_size | pit_appearance | hypophysitis_image | pit_alt_image | image_comment | ctmri_imaging_complete | ur_pet | pet_date | pet_timing___1 | pet_timing___2 | pet_timing___3 | pet_timing___4 | pet_bsl | pet_uptake_time | sul_peak | suv_max | pet_steroid | b_pet_metastases___1 | b_pet_metastases___2 | b_pet_metastases___3 | b_pet_metastases___4 | b_pet_metastases___5 | b_pet_metastases___6 | b_pet_metastases___7 | b_pet_metastases___8 | b_pet_metastases___9 | b_pet_metastases___10 | pet_endo___1 | pet_endo___2 | pet_endo___3 | pet_endo___4 | pet_endo___5 | pet_endo___6 | pet_endo___7 | pet_endo___8 | pet_endo___9 | pet_endo___10 | pet_endo___11 | pit_pet_fdg | pit_pet_suv | pit_suv_change | hypophysitis_pet | pet_pit_ct | thyr_pet_fdg | thyr_pet_suv | thyr_suv_change | thyroiditis_pet | pet_thyr_ct | panc_pet_fdg | panc_pet_suv | panc_suv_change | pancreatitis_pet | pet_panc_ct | adrenal_pet_fdg | adrenal_pet_suv | adrenal_suv_change | adrenalitis_pet | pet_adrenal_ct | cns_pet_fdg | cns_pet_suv | brain_suv_change | encephalitis_pet | pet_cns_ct | rheum_pet | rheum_suvmax | pet_rheum_ct | liver_pet_fdg | liver_pet_suv | liver_suv_change | hepatitis_pet | pet_liver_ct | uppergi_pet_fdg | uppergi_pet_suv | uppergi_suv_change | gastritis_pet | pet_uppergi_ct | ilium_pet_fdg | ilium_pet_suv | ilium_suv_change | ileitis_pet | pet_ilium_ct | colon_pet_fdg | colon_pet_suv | colon_suv_change | colitis_pet | pet_colon_ct | pet_comments | sul_change | suv_change | percist_pet | eortc_pet | recist_pet | residual_disease___1 | residual_disease___2 | residual_disease___3 | residual_disease___4 | residual_disease___5 | residual_disease___6 | residual_disease___7 | residual_disease___8 | residual_disease___9 | residual_disease___10 | pet_metastasis | pet_metastasis_number | site_progression___1 | site_progression___2 | site_progression___3 | site_progression___4 | site_progression___5 | site_progression___6 | site_progression___7 | site_progression___8 | site_progression___9 | site_progression___10 | pet_imaging_complete | antibiotic_oral | antibiotic_iv | antibiotic_type | ppi | ppi_and_antibiotic_use_during_treatment_with_cpis_complete | responce_cpi | best_res_pet | time_to_best_response | response_dur_cpi | overall_response | progression_cpi | progression_adrenal | progression_site___1 | progression_site___2 | progression_site___3 | progression_site___4 | progression_site___5 | progression_site___6 | progression_site___7 | progression_site___8 | progression_site___9 | progression_site___10 | progression_site___11 | other_site | subsequent_rx | response_data_complete | mortality | mortality_date | ongoing_survelliance | date_last_scan | mortality_treatment_time | mortality_cause | other_cause_death | mortality_data_complete |
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| 385 | baseline_arm_1 | NA | NA | 5454920 | Carter | Rachel | 1 | 1956-08-28 | 146 | 151 | 70.84 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | 2 | 2 | 0 | 0 | 0 | 0 | 1 | 0 | NA | 0 | 0 | 0 | 1 | 0 | 0 | NA | NA | NA | NA | 2014-06-23 | 4 | 0 | 2017-12-05 | 2 | 23/5/14 | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2014-05-23 | 1 | NA | NA | 1 | 1 | 1 | 1 | 0 | 0 | 1 | confusion | 0 | 0 | 0 | 0 | 0 | 0 | NA | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 385 | end_arm_1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1 | 2017-07-02 | NA | NA | 11.3 | 1 | NA | 2 |
| 385 | baseline_arm_1 | prior_treatment | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2 | Immune checkpoint inhibition: 2016-07-22 | NA | 3 | NA | 0 | NA | NA | 6 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | 328 | 83 | 110 | 77 | 2016-07-22 | NA | 2 | 1 | NA | 4 | NA | NA | NA | NA | NA | NA | 1 | 3 | 2016-09-05 | 1.5 | 1 | NA | 0 | 1 | 1 | NA | 2016-10-10 | 2016-09-05 | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | NA | NA | NA | NA | NA | 14/10/16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 385 | baseline_arm_1 | prior_treatment | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2 | Immune checkpoint inhibition: 2016-10-14 | NA | 1 | NA | 0 | NA | NA | 6 | 2 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | 2016-10-14 | NA | 2 | 1 | NA | 4 | NA | 2017-03-30 | 4 | NA | NA | NA | 1 | 2 | 2016-11-04 | 0.7 | 1 | NA | 0 | 0 | 1 | NA | NA | 2016-11-04 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 2 | NA | 2016-11-11 | 4 | 0 | 14/10/26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 490 | baseline_arm_1 | NA | NA | 1088816 | Garcia | Claire | 1 | 1985-06-09 | 197 | 52 | 13.40 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | |
| 490 | end_arm_1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1 | 2018-12-11 | 2 | NA | 24.3 | 1 | NA | 2 |
| 490 | baseline_arm_1 | adjuvant_therapy | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | Trial: | NA | NA | NA | 0 | NA | NA | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 490 | baseline_arm_1 | prior_treatment | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2 | Immune checkpoint inhibition: 2016-12-02 | NA | 3 | NA | 1 | Keynote 054 - cross over | NA | 5 | 1 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | NA | NA | NA | NA | 2016-12-02 | NA | NA | 2 | 1 | NA | 1 | 2017-02-22 | NA | 1 | NA | NA | 1 | 1 | 2017-05-18 | 5.5 | 1 | NA | 1 | 0 | 0 | 2017-05-18 | NA | NA | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | NA | NA | NA | NA | 0 | 29/4/17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 490 | baseline_arm_1 | prior_treatment | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3 | MAPK inhibitor therapy: 2017-05-19 | NA | NA | 1 | 0 | NA | NA | 5 | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 9 | NA | NA | NA | NA | 2017-05-19 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | NA | NA | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 500 | baseline_arm_1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 500 | baseline_arm_1 | prior_treatment | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 3 | MAPK inhibitor therapy: 2017-11-01 | NA | NA | 1 | 0 | NA | NA | 6 | NA | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | 79 | 110 | 87 | 2017-11-01 | NA | 2 | 1 | 2 | 2 | NA | NA | NA | NA | 2018-01-09 | 2.3 | 1 | 2 | 2018-06-05 | 7.1 | 1 | 2 | 0 | 0 | 1 | NA | NA | 2018-06-05 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | NA | NA | NA | NA | 0 | 15/6/18 | 1 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 500 | baseline_arm_1 | prior_treatment | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 2 | Immune checkpoint inhibition: 2018-06-29 | NA | 2 | NA | 0 | NA | NA | 6 | NA | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | 2018-06-29 | 2 | 2 | 1 | NA | 4 | NA | 2018-09-12 | 4 | NA | 2018-09-12 | 2.5 | 1 | 3 | 2018-09-12 | 2.5 | 1 | 2 | 0 | 1 | 1 | NA | 2018-09-12 | 2018-09-12 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | NA | NA | NA | NA | NA | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | NA | NA | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | NA | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 500 | baseline_arm_1 | adverse_events | 1 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 1 | NA | Immune checkpoint inhibition: 2018-06-29 | 4 | Gastrointestinal | NA | NA | 8 | NA | NA | NA | 2018-09-01 | 3 | NA | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2018-09-01: Gastrointestinal | 2 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
Read a subset of fields
If you only need specific variables (e.g., record_id, dob, gender), you can specify a list of field names in the fields argument of redcap_read().
# Define the specific fields to retrieve
selected_fields <- c("first_name", "dob", "mortality_date") # Replace with actual record IDs
# Read only the selected fields
immuno_some_fields <- redcap_read(
redcap_uri = uri,
token = token,
fields = selected_fields
)$data
# print the top 6 rows
head(immuno_some_fields)| record_id | redcap_event_name | redcap_repeat_instrument | redcap_repeat_instance | first_name | dob | mortality_date |
|---|---|---|---|---|---|---|
| 1 | baseline_arm_1 | NA | NA | Hannah | 1943-02-12 | NA |
| 2 | baseline_arm_1 | NA | NA | Samantha | 2008-01-29 | NA |
| 3 | baseline_arm_1 | NA | NA | Noah | 1940-11-06 | NA |
| 3 | end_arm_1 | NA | NA | NA | NA | 2017-01-10 |
| 4 | baseline_arm_1 | NA | NA | Aiden | 1963-06-04 | NA |
| 4 | end_arm_1 | NA | NA | NA | NA | 2024-02-09 |
In all these cases, the data imported into R from REDCap is in its raw format. For example, a categorical variable like sex, which is expected to contain values such as “Male” or “Female,” may instead be represented as numeric codes (e.g., 1 for Male, 2 for Female). While these values can be manually recoded in R, doing so for large projects with multiple categorical variables can quickly become cumbersome and error-prone.
Furthermore, complex study designs—such as those used in clinical trials, cohort studies, and observational research—often involve longitudinal data or repeating instruments, adding another layer of complexity to data management.
- Longitudinal data is used when information is collected at multiple time points or study events (e.g., Baseline, Follow-up).
- Repeating instruments allow a single form to be completed multiple times per participant (e.g., recording multiple adverse events, medications, or hospital visits).
Handling these structured data formats in R requires additional steps for cleaning and organisation.
To address these challenges, the REDCapTidieR package extends the functionality of REDCapR, making it easier to analyse complex REDCap datasets.
Reading all REDCap data
Unlike REDCapR, which returns a single large dataframe, REDCapTidieR automatically structures and organises the data by breaking it into separate tibbles, each representing a different REDCap instrument. This makes it easier to work with studies involving multiple forms and events.
Before using REDCapTidieR, ensure it is installed along with its dependencies:
# If this fails, run install.packages("REDCapTidieR") or
# devtools::install_github("CHOP-CGTInformatics/REDCapTidieR")
requireNamespace("REDCapTidieR")To import the entire dataset while maintaining structured tables (or supertibble), use read_redcap():
# Load required packages
library(REDCapTidieR)
# Read entire REDCap project data
immuno <- read_redcap(redcap_uri = uri, token = token, raw_or_label = "raw")# print the structure of imported data
str(immuno, max.level = 2)suprtbl [15 × 11] (S3: redcap_supertbl/tbl_df/tbl/data.frame)
$ redcap_form_name : chr [1:15] "demographics" "melanoma_data" "adjuvant_therapy" "prior_treatment" ...
$ redcap_form_label: chr [1:15] "Demographics" "Melanoma Data" "Adjuvant Therapy" "Systemic Therapy for Advanced Disease" ...
$ redcap_data :List of 15
$ redcap_metadata :List of 15
$ redcap_events :List of 15
$ structure : chr [1:15] "nonrepeating" "nonrepeating" "repeating" "repeating" ...
$ data_rows : int [1:15] 498 498 288 765 498 231 144 177 144 441 ...
$ data_cols : int [1:15] 38 26 61 177 22 67 41 8 128 47 ...
$ data_size : 'lobstr_bytes' num [1:15] 181.33 kB 130.62 kB 117.23 kB 1.10 MB 97.36 kB ...
$ data_na_pct : 'formattable' num [1:15] 13% 22% 82% 41% 62% ...
..- attr(*, "formattable")=List of 4
$ form_complete_pct: 'formattable' num [1:15] 74% 74% 97% 96% 34% ...
..- attr(*, "formattable")=List of 4Exploring the Data
The supertibble object can be viewed with the RStudio Data Viewer. You can click on the table icon in the Environment tab to view of the supertibble in the data viewer. At a glance you see an overview of the instruments in the REDCap project.
immuno supertibbleYou can drill down into individual tables in the redcap_data and redcap_metadata columns. Note that in the demographics data tibble, each row represents a patient, identified by their record_id.
demographics data tibbleIn the pet_imaging data tibble, each row represents a PET scan information of a specific patient. Each row is identified by the combination of record_id and redcap_form_instance. This difference in granularity is because pet_imaging is a repeating instrument whereas demographics is a nonrepeating instrument.
pet_imaging data tibbleYou can also explore the metadata tibbles in the redcap_metadata column to find out about field labels, field types, and other field attributes.
demographics metadata tibbleExtracting data tibbles from the supertibble
REDCapTidieR provides three different functions to extract data tibbles from a supertibble.
Binding data tibbles into the environment
The bind_tibbles() function takes a supertibble and binds its data tibbles directly into the global environment. When you use bind_tibbles() while working interactively in the RStudio IDE, you will see data tibbles appear in the Environment pane.
immuno |> bind_tibbles()
bind_tibbles functionBy default, bind_tibbles() extracts all data tibbles from the supertibble. With the tbls argument you can specify a subset of data tibbles that should be extracted.
Extracting a list of data tibbles
The extract_tibbles() function takes a supertibble and returns a named list of data tibbles. The default is to extract all data tibbles. We use str here to show the structure of the list returned by extract_tibbles().
immuno_instrument_list <- immuno |>
extract_tibbles()
immuno_instrument_list |>
str(max.level = 1)List of 15
$ demographics : tibble [498 × 38] (S3: tbl_df/tbl/data.frame)
$ melanoma_data : tibble [498 × 26] (S3: tbl_df/tbl/data.frame)
$ adjuvant_therapy : tibble [288 × 61] (S3: tbl_df/tbl/data.frame)
$ prior_treatment : tibble [765 × 177] (S3: tbl_df/tbl/data.frame)
$ melanoma_cns_metastases : tibble [498 × 22] (S3: tbl_df/tbl/data.frame)
$ adverse_events : tibble [231 × 67] (S3: tbl_df/tbl/data.frame)
$ baseline_visit : tibble [144 × 41] (S3: tbl_df/tbl/data.frame)
$ checkpoint_inhibitor_treatment : tibble [177 × 8] (S3: tbl_df/tbl/data.frame)
$ immune_related_adverse_events_iraes : tibble [144 × 128] (S3: tbl_df/tbl/data.frame)
$ pathology : tibble [441 × 47] (S3: tbl_df/tbl/data.frame)
$ ctmri_imaging : tibble [332 × 12] (S3: tbl_df/tbl/data.frame)
$ pet_imaging : tibble [35 × 112] (S3: tbl_df/tbl/data.frame)
$ ppi_and_antibiotic_use_during_treatment_with_cpis: tibble [23 × 7] (S3: tbl_df/tbl/data.frame)
$ response_data : tibble [144 × 23] (S3: tbl_df/tbl/data.frame)
$ mortality_data : tibble [381 × 10] (S3: tbl_df/tbl/data.frame)Adding variable labels with the labelled package
REDCapTidieR package allows you to attach labels to variables in the supertibble. Variable labels can make data exploration easier.
immuno |>
make_labelled() |>
bind_tibbles()The make_labelled() function takes a supertibble and returns a supertibble with variable labels applied to the variables of the supertibble as well as to the variables of all data and metadata tibbles in the redcap_data and redcap_metadata columns of the supertibble.
The RStudio Data Viewer shows variable labels below variable names.
You can use the labelled::look_for() function to explore the variable labels of a tibble.
labelled::look_for(mortality_data)| pos | variable | label | col_type | missing | levels | value_labels |
|---|---|---|---|---|---|---|
| 1 | record_id | Record ID | dbl | 0 | NULL | NULL |
| 2 | redcap_event | REDCap Event | chr | 0 | NULL | NULL |
| 3 | mortality | Has participant deceased? | dbl | 14 | NULL | NULL |
| 4 | mortality_date | Date of last follow up or death | date | 10 | NULL | NULL |
| 5 | ongoing_survelliance | Ongoing melanoma imaging surveillance? | dbl | 71 | NULL | NULL |
| 6 | date_last_scan | Date of last scan | date | 113 | NULL | NULL |
| 7 | mortality_treatment_time | Time since first treatment dose (months) | dbl | 60 | NULL | NULL |
| 8 | mortality_cause | Cause of Death | dbl | 156 | NULL | NULL |
| 9 | other_cause_death | Cause of death if not melanoma | lgl | 381 | NULL | NULL |
| 10 | form_status_complete | REDCap Instrument Completed? | dbl | 0 | NULL | NULL |
These labels are the REDCap field labels that prompt data entry in the REDCap instrument. REDCapTidieR places them into the field_label variable of the instrument’s metadata tibble. Below you can see that the field labels of the REDCap instrument for mortality_data are the same as the labels above.
mortality_data instrumentIn the demographics instrument, a label has a trailing colon : (check the label of autoimmune_disease_select___9 variable below). This won’t look good as a variable label so let’s remove it.
labelled::look_for(demographics)| pos | variable | label | col_type | missing | levels | value_labels |
|---|---|---|---|---|---|---|
| 1 | record_id | Record ID | dbl | 0 | NULL | NULL |
| 2 | redcap_event | REDCap Event | chr | 0 | NULL | NULL |
| 3 | ur | UR Number | dbl | 8 | NULL | NULL |
| 4 | last_name | Last Name | chr | 8 | NULL | NULL |
| 5 | first_name | First Name | chr | 8 | NULL | NULL |
| 6 | sex | Gender | dbl | 8 | NULL | NULL |
| 7 | dob | Date of Birth | date | 8 | NULL | NULL |
| 8 | height | Height | dbl | 8 | NULL | NULL |
| 9 | weight | Weight | dbl | 8 | NULL | NULL |
| 10 | bmi | BMI | dbl | 8 | NULL | NULL |
| 11 | coenrolled___1 | Other Studies Enrolled In: MetaMel | dbl | 0 | NULL | NULL |
| 12 | coenrolled___2 | Other Studies Enrolled In: Micromac | dbl | 0 | NULL | NULL |
| 13 | coenrolled___3 | Other Studies Enrolled In: MRV | dbl | 0 | NULL | NULL |
| 14 | coenrolled___4 | Other Studies Enrolled In: SUMMA | dbl | 0 | NULL | NULL |
| 15 | clinical_trial | Participating in a Clinical Trial | dbl | 113 | NULL | NULL |
| 16 | clinical_trial_description | Describe Trial(s) | chr | 401 | NULL | NULL |
| 17 | medical_history___1 | Medical History: Chronic kidney disease | dbl | 0 | NULL | NULL |
| 18 | medical_history___2 | Medical History: Diabetes | dbl | 0 | NULL | NULL |
| 19 | medical_history___7 | Medical History: Diabetes - Type 1 | dbl | 0 | NULL | NULL |
| 20 | medical_history___8 | Medical History: Diabetes - Type 2 | dbl | 0 | NULL | NULL |
| 21 | medical_history___5 | Medical History: GN | dbl | 0 | NULL | NULL |
| 22 | medical_history___3 | Medical History: Hypertension | dbl | 0 | NULL | NULL |
| 23 | medical_history___4 | Medical History: Ischaemic heart disease | dbl | 0 | NULL | NULL |
| 24 | medical_history___6 | Medical History: Vasculitis | dbl | 0 | NULL | NULL |
| 25 | medical_history___99 | Medical History: Other | dbl | 0 | NULL | NULL |
| 26 | medical_history_other | Medical History Other Unknown | chr | 415 | NULL | NULL |
| 27 | autoimmune_disease | History of Idiopathic Autoimmune Disease | dbl | 102 | NULL | NULL |
| 28 | autoimmune_disease_select___1 | Select Autoimmune Disease(s): Connective tissue disease | dbl | 0 | NULL | NULL |
| 29 | autoimmune_disease_select___2 | Select Autoimmune Disease(s): Inflammatory arthritis | dbl | 0 | NULL | NULL |
| 30 | autoimmune_disease_select___3 | Select Autoimmune Disease(s): Inflammatory bowel disease | dbl | 0 | NULL | NULL |
| 31 | autoimmune_disease_select___4 | Select Autoimmune Disease(s): Interstitial lung disease | dbl | 0 | NULL | NULL |
| 32 | autoimmune_disease_select___5 | Select Autoimmune Disease(s): Multiple sclerosis | dbl | 0 | NULL | NULL |
| 33 | autoimmune_disease_select___6 | Select Autoimmune Disease(s): Sarcoidosis | dbl | 0 | NULL | NULL |
| 34 | autoimmune_disease_select___9 | Select Autoimmune Disease(s): Other, specify: | dbl | 0 | NULL | NULL |
| 35 | autoimmune_disease_other | Describe Autoimmune Disease | lgl | 498 | NULL | NULL |
| 36 | rheumatoid_arthritis | Rheumatoid arthritis | lgl | 498 | NULL | NULL |
| 37 | smoking | Smoking History | dbl | 100 | NULL | NULL |
| 38 | form_status_complete | REDCap Instrument Completed? | dbl | 0 | NULL | NULL |
The make_labelled() function has a format_labels argument that you can use to preprocess labels before applying them to variables.
immuno |>
make_labelled(format_labels = ~ gsub(":", "", .)) |>
bind_tibbles()
labelled::look_for(demographics, "autoimmune")| pos | variable | label | col_type | missing | levels | value_labels |
|---|---|---|---|---|---|---|
| 27 | autoimmune_disease | History of Idiopathic Autoimmune Disease | dbl | 102 | NULL | NULL |
| 28 | autoimmune_disease_select___1 | Select Autoimmune Disease(s) Connective tissue disease | dbl | 0 | NULL | NULL |
| 29 | autoimmune_disease_select___2 | Select Autoimmune Disease(s) Inflammatory arthritis | dbl | 0 | NULL | NULL |
| 30 | autoimmune_disease_select___3 | Select Autoimmune Disease(s) Inflammatory bowel disease | dbl | 0 | NULL | NULL |
| 31 | autoimmune_disease_select___4 | Select Autoimmune Disease(s) Interstitial lung disease | dbl | 0 | NULL | NULL |
| 32 | autoimmune_disease_select___5 | Select Autoimmune Disease(s) Multiple sclerosis | dbl | 0 | NULL | NULL |
| 33 | autoimmune_disease_select___6 | Select Autoimmune Disease(s) Sarcoidosis | dbl | 0 | NULL | NULL |
| 34 | autoimmune_disease_select___9 | Select Autoimmune Disease(s) Other, specify | dbl | 0 | NULL | NULL |
| 35 | autoimmune_disease_other | Describe Autoimmune Disease | lgl | 498 | NULL | NULL |
This remove all colons in labels.
Renaming column names using labels
Some columns associated with checkbox fields in REDCap forms often have less intuitive names. For example, in the melanoma_data instrument, the melanoma type columns are named as mel_type___1, mel_type___2, mel_type___3, etc. These names correspond to different melanoma subtypes but are not easily interpretable.
To improve readability, these columns can be renamed using their corresponding labels, making the dataset more intuitive for analysis. The following function automates this renaming process by extracting variable labels and applying them to the column names.
# This function rename checkbox columns using the labels
rename_checkbox_columns <- function(instrument, column_name_prefix) {
# List of column names to rename
col_names_to_rename <- labelled::look_for(instrument, column_name_prefix)$variable
# New names for the selected columns
new_names <- labelled::look_for(instrument, column_name_prefix)$label
new_names <- str_replace_all(new_names, " ", "_")
new_names <- str_replace_all(new_names, ":", "")
# Rename the columns
names(instrument)[names(instrument) %in% col_names_to_rename] <- new_names
return(instrument)
}melanoma_data <- rename_checkbox_columns(melanoma_data, "mel_type___")
head(melanoma_data)| record_id | redcap_event | Melanoma_type_Cutaneous | Melanoma_type_Mucosal | Melanoma_type_Acral | Melanoma_type_Uveal | Melanoma_type_Unknown_primary | Melanoma_type_Pathology_not_available | mel_type_cutaneous | mel_mutation___1 | mel_mutation___2 | mel_mutation___3 | mel_mutation___4 | mel_mutation___5 | mel_mutation___9 | mel_mutation_braf | mel_mutation_nras | mel_mutation_kit | mel_mutation_other | mel_first_date | mel_first_stage | resct1stdiag | mel_date_diag | stage_diagnosis | dt_advanced_dis | form_status_complete |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | baseline | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 |
| 2 | baseline | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 |
| 3 | baseline | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 |
| 4 | baseline | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 |
| 5 | baseline | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | V600M | NA | NA | NA | 2015-09-01 | 2 | 1 | 2016-03-10 | 1 | 27/10/16 | 0 |
| 6 | baseline | 0 | 0 | 0 | 0 | 0 | 0 | NA | 0 | 0 | 0 | 0 | 0 | 0 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | 0 |
Viewing the Data
This section demonstrates different ways to get to know the immuno dataset and its instruments.
When the name of the object is typed, the first few lines along with some information, such as the number of rows are displayed:
immunoSince this is a large dataset, the output is not shown here. But you can try executing the above code.
To view any column displayed above in the immuno object, you can specify the column number within [[]] or column name followed by $.
- For example to view column 1:
immuno[[1]] [1] "demographics"
[2] "melanoma_data"
[3] "adjuvant_therapy"
[4] "prior_treatment"
[5] "melanoma_cns_metastases"
[6] "adverse_events"
[7] "baseline_visit"
[8] "checkpoint_inhibitor_treatment"
[9] "immune_related_adverse_events_iraes"
[10] "pathology"
[11] "ctmri_imaging"
[12] "pet_imaging"
[13] "ppi_and_antibiotic_use_during_treatment_with_cpis"
[14] "response_data"
[15] "mortality_data" - For example to view
redcap_form_namecolumn:
immuno$redcap_form_name [1] "demographics"
[2] "melanoma_data"
[3] "adjuvant_therapy"
[4] "prior_treatment"
[5] "melanoma_cns_metastases"
[6] "adverse_events"
[7] "baseline_visit"
[8] "checkpoint_inhibitor_treatment"
[9] "immune_related_adverse_events_iraes"
[10] "pathology"
[11] "ctmri_imaging"
[12] "pet_imaging"
[13] "ppi_and_antibiotic_use_during_treatment_with_cpis"
[14] "response_data"
[15] "mortality_data" A similar method can be used to access the patient data in all instruments using the redcap_data column or the 3rd column in this case. However, this displays patient data of all the instruments one after the other, making it difficult to read. A better way is to view a single instrument as follows.
For example, to view the mortality_data instrument, we can access the redcap_data column first (i.e., immuno$redcap_data or immuno[[3]]) and then access the 15th instrument:
head(immuno$redcap_data[[15]]) # same as immuno[[3]][[15]]| record_id | redcap_event | mortality | mortality_date | ongoing_survelliance | date_last_scan | mortality_treatment_time | mortality_cause | other_cause_death | form_status_complete |
|---|---|---|---|---|---|---|---|---|---|
| 3 | end | 1 | 2017-01-10 | 2 | 2016-10-24 | 4.7 | NA | NA | 2 |
| 4 | end | 0 | 2024-02-09 | 2 | 2023-06-29 | NA | NA | NA | 2 |
| 7 | end | 0 | 2024-10-15 | 2 | 2021-09-01 | NA | NA | NA | 2 |
| 8 | end | 0 | 2024-09-20 | 1 | 2024-09-09 | NA | NA | NA | 2 |
| 12 | end | 1 | 2016-11-18 | NA | 2016-01-04 | NA | 1 | NA | 2 |
| 16 | end | 0 | 2024-04-26 | 1 | 2023-07-14 | NA | 1 | NA | 2 |
The dim() function prints the dimensions (rows x columns):
dim(immuno)[1] 15 11dim(immuno$redcap_data[[15]])[1] 381 10This information is available at the environment pane in the top right panel as the number of observations (rows) and variables (columns).
The nrow() function prints the number of rows while ncol() prints the number of columns:
nrow(immuno$redcap_data[[15]])[1] 381ncol(immuno$redcap_data[[15]])[1] 10The View() function gives a spreadsheet-like view of the data frame:
View(immuno)By clicking the object on the environment tab also gives a spreadsheet-like view of the object: 
The head() function prints the top 6 rows of a data frame:
head(immuno$redcap_data[[15]])| record_id | redcap_event | mortality | mortality_date | ongoing_survelliance | date_last_scan | mortality_treatment_time | mortality_cause | other_cause_death | form_status_complete |
|---|---|---|---|---|---|---|---|---|---|
| 3 | end | 1 | 2017-01-10 | 2 | 2016-10-24 | 4.7 | NA | NA | 2 |
| 4 | end | 0 | 2024-02-09 | 2 | 2023-06-29 | NA | NA | NA | 2 |
| 7 | end | 0 | 2024-10-15 | 2 | 2021-09-01 | NA | NA | NA | 2 |
| 8 | end | 0 | 2024-09-20 | 1 | 2024-09-09 | NA | NA | NA | 2 |
| 12 | end | 1 | 2016-11-18 | NA | 2016-01-04 | NA | 1 | NA | 2 |
| 16 | end | 0 | 2024-04-26 | 1 | 2023-07-14 | NA | 1 | NA | 2 |
Similarly, the tail() function prints the bottom 6 rows of the data frame:
tail(immuno$redcap_data[[15]])| record_id | redcap_event | mortality | mortality_date | ongoing_survelliance | date_last_scan | mortality_treatment_time | mortality_cause | other_cause_death | form_status_complete |
|---|---|---|---|---|---|---|---|---|---|
| 492 | end | 1 | 2017-10-18 | NA | NA | 6.8 | 1 | NA | 2 |
| 493 | end | 1 | 2024-06-26 | 2 | NA | 32.5 | 1 | NA | 2 |
| 494 | end | 1 | 2016-02-12 | 2 | NA | 6.1 | 1 | NA | 2 |
| 496 | end | 1 | 2018-06-28 | 2 | NA | NA | 1 | NA | 2 |
| 497 | end | 1 | 2016-08-04 | 2 | NA | 1.1 | 1 | NA | 2 |
| 499 | end | NA | 2018-10-09 | NA | NA | NA | NA | NA | 2 |
The colnames() function displays all the column names:
colnames(immuno$redcap_data[[15]]) [1] "record_id" "redcap_event"
[3] "mortality" "mortality_date"
[5] "ongoing_survelliance" "date_last_scan"
[7] "mortality_treatment_time" "mortality_cause"
[9] "other_cause_death" "form_status_complete" The $ symbol allows access to individual columns. To display mortality_date column:
head(immuno$redcap_data[[15]]$mortality_date)[1] "2017-01-10" "2024-02-09" "2024-10-15" "2024-09-20" "2016-11-18"
[6] "2024-04-26"The str() function shows the structure of the data:
str(immuno$redcap_data[[15]])tibble [381 × 10] (S3: tbl_df/tbl/data.frame)
$ record_id : num [1:381] 3 4 7 8 12 16 23 24 28 29 ...
$ redcap_event : chr [1:381] "end" "end" "end" "end" ...
$ mortality : num [1:381] 1 0 0 0 1 0 1 1 0 1 ...
$ mortality_date : Date[1:381], format: "2017-01-10" "2024-02-09" ...
$ ongoing_survelliance : num [1:381] 2 2 2 1 NA 1 NA 2 1 2 ...
$ date_last_scan : Date[1:381], format: "2016-10-24" "2023-06-29" ...
$ mortality_treatment_time: num [1:381] 4.7 NA NA NA NA NA NA 17.2 80.4 9.7 ...
$ mortality_cause : num [1:381] NA NA NA NA 1 1 1 1 1 1 ...
$ other_cause_death : logi [1:381] NA NA NA NA NA NA ...
$ form_status_complete : num [1:381] 2 2 2 2 2 2 2 2 2 2 ...The glimpse()function (dplyr package) displays a compact summary of the data frame, showing you key details such as the data types of each column, the first few values, and the total number of observations.
glimpse(immuno$redcap_data[[15]])Rows: 381
Columns: 10
$ record_id <dbl> 3, 4, 7, 8, 12, 16, 23, 24, 28, 29, 31, 37, 3…
$ redcap_event <chr> "end", "end", "end", "end", "end", "end", "en…
$ mortality <dbl> 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, …
$ mortality_date <date> 2017-01-10, 2024-02-09, 2024-10-15, 2024-09-…
$ ongoing_survelliance <dbl> 2, 2, 2, 1, NA, 1, NA, 2, 1, 2, NA, 2, 1, 1, …
$ date_last_scan <date> 2016-10-24, 2023-06-29, 2021-09-01, 2024-09-…
$ mortality_treatment_time <dbl> 4.7, NA, NA, NA, NA, NA, NA, 17.2, 80.4, 9.7,…
$ mortality_cause <dbl> NA, NA, NA, NA, 1, 1, 1, 1, 1, 1, 1, 1, NA, N…
$ other_cause_death <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ form_status_complete <dbl> 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, …The summary() function generates summary statistics:
summary(immuno$redcap_data[[15]]) record_id redcap_event mortality mortality_date
Min. : 3.0 Length:381 Min. :0.0000 Min. :2013-11-15
1st Qu.:150.0 Class :character 1st Qu.:0.0000 1st Qu.:2018-08-02
Median :270.0 Mode :character Median :1.0000 Median :2021-07-20
Mean :265.2 Mean :0.5313 Mean :2021-04-11
3rd Qu.:382.0 3rd Qu.:1.0000 3rd Qu.:2024-02-15
Max. :499.0 Max. :1.0000 Max. :2024-10-15
NA's :14 NA's :10
ongoing_survelliance date_last_scan mortality_treatment_time
Min. :1.000 Min. :0624-01-02 Min. : 0.10
1st Qu.:1.000 1st Qu.:2020-05-30 1st Qu.: 11.70
Median :2.000 Median :2023-07-06 Median : 33.40
Mean :1.597 Mean :2016-11-19 Mean : 40.41
3rd Qu.:2.000 3rd Qu.:2024-02-09 3rd Qu.: 62.30
Max. :2.000 Max. :2028-07-06 Max. :131.50
NA's :71 NA's :113 NA's :60
mortality_cause other_cause_death form_status_complete
Min. :1.000 Mode:logical Min. :0.000
1st Qu.:1.000 NA's:381 1st Qu.:2.000
Median :1.000 Median :2.000
Mean :1.173 Mean :1.995
3rd Qu.:1.000 3rd Qu.:2.000
Max. :3.000 Max. :2.000
NA's :156 A statistical overview can be obtained using the skim() function in skimr package:
library(skimr)
skim(immuno$redcap_data[[15]])| Name | immuno$redcap_data[[15]] |
| Number of rows | 381 |
| Number of columns | 10 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| Date | 2 |
| logical | 1 |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| redcap_event | 0 | 1 | 3 | 3 | 0 | 1 | 0 |
Variable type: Date
| skim_variable | n_missing | complete_rate | min | max | median | n_unique |
|---|---|---|---|---|---|---|
| mortality_date | 10 | 0.97 | 2013-11-15 | 2024-10-15 | 2021-07-20 | 293 |
| date_last_scan | 113 | 0.70 | 0624-01-02 | 2028-07-06 | 2023-07-06 | 218 |
Variable type: logical
| skim_variable | n_missing | complete_rate | mean | count |
|---|---|---|---|---|
| other_cause_death | 381 | 0 | NaN | : |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| record_id | 0 | 1.00 | 265.17 | 134.35 | 3.0 | 150.0 | 270.0 | 382.0 | 499.0 | ▅▇▇▇▇ |
| mortality | 14 | 0.96 | 0.53 | 0.50 | 0.0 | 0.0 | 1.0 | 1.0 | 1.0 | ▇▁▁▁▇ |
| ongoing_survelliance | 71 | 0.81 | 1.60 | 0.49 | 1.0 | 1.0 | 2.0 | 2.0 | 2.0 | ▆▁▁▁▇ |
| mortality_treatment_time | 60 | 0.84 | 40.41 | 31.58 | 0.1 | 11.7 | 33.4 | 62.3 | 131.5 | ▇▅▃▂▁ |
| mortality_cause | 156 | 0.59 | 1.17 | 0.54 | 1.0 | 1.0 | 1.0 | 1.0 | 3.0 | ▇▁▁▁▁ |
| form_status_complete | 0 | 1.00 | 1.99 | 0.10 | 0.0 | 2.0 | 2.0 | 2.0 | 2.0 | ▁▁▁▁▇ |
Writing Data to a File
Writing data to a file is a fundamental operation in programming and data analysis. It involves taking data from within a program or environment and storing it in a file on a disk for later use or sharing. This section explains the basics of writing a data file using the readr package.
The write_csv() and write_tsv() functions are part of the readr package, which is designed for writing delimited files like CSV (comma-separated values) and TSV (tab-separated values). These functions are used to write data frames into CSV and TSV files, respectively.
We first provide the variable name of the data frame followed by the file name (ideally including the full folder location).
To write a CSV file:
# on Mac:
write_csv(cms_data, "~/Desktop/cms_data.csv")
# on Windows
write_csv(cms_data, "C:/Users/srajapaksa/Desktop/cms_data.csv")To write a TSV file:
# on Mac:
write_tsv(cms_data, "~/Desktop/cms_data.csv")
# on Windows
write_tsv(cms_data, "C:/Users/srajapaksa/Desktop/cms_data.csv")Data manipulation with `dplyr` functions
Common tasks in working with data include actions like filtering rows or columns, performing calculations, or adding new columns. This sort of operations is known as data manipulation. It is the process of cleaning, organising, and transforming raw data into a more structured and usable format for analysis.
In this workshop, we’ll guide you through the process of data manipulation in R, starting with the tidyverse. The tidyverse is a collection of packages that align with a data science philosophy developed by Hadley Wickham and the RStudio team. Many users find it to be a more intuitive way to grasp R concepts.
You’ll primarily use five key dplyr functions for data manipulations:
filter(): pick observations based on their values.select(): pick variables by their names.mutate(): create new variables using functions applied to existing variables.summarise(): collapse multiple values into a single summary.arrange(): reorder the rows based on specified criteria.
If you’ve already installed the tidyverse package (if not, you can do so by running the command: install.packages("tidyverse")), let’s proceed to load it into our R session first:
library(tidyverse)Next, load the pre-processed RDS Object:
immuno_dataset <- readRDS("data/Sample_immuno_dataset.rds")RDS (R Data Serialisation) files are used to save and load single R objects while preserving their structure, labels, and attributes. The .rds format is useful for storing dataframes, lists, models, and other complex objects. For convenience, the previously loaded REDCap dataset has been pre-processed and saved as an .rds file. This pre-processed version will be used throughout the remainder of the workshop.
filter()
The filter() function takes logical expressions and returns the rows for which all are TRUE.
Example 1: Find all records from the melanoma_data data frame where the melanoma type is cutaneous.
immuno_dataset$redcap_data$melanoma_data |>
filter(melanoma_type == "cutaneous") |>
head()| record_id | redcap_event | melanoma_type | melanoma_molecular_mutation | mel_first_date | mel_first_stage | resct1stdiag | mel_date_diag | stage_diagnosis | dt_advanced_dis |
|---|---|---|---|---|---|---|---|---|---|
| 16 | baseline | cutaneous | nras | 2019-06-01 | Stage III | TRUE | 2017-01-01 | yes | 01062017 |
| 24 | baseline | cutaneous | wild_type | 2014-03-27 | Stage II | TRUE | 2015-01-01 | yes | 1/9/15 |
| 28 | baseline | cutaneous | braf | 2015-09-01 | Stage II | TRUE | 2016-03-10 | yes | 27/10/16 |
| 57 | baseline | cutaneous | nras | 2019-12-01 | Stage I | TRUE | 2020-07-09 | yes | 08/01/2021 |
| 58 | baseline | cutaneous | wild_type | 2013-10-01 | Stage I | TRUE | 2015-09-14 | yes | 6/3/17 |
| 60 | baseline | cutaneous | braf | 2017-03-01 | Stage III | TRUE | 2019-11-01 | no | 1/11/19 |
Here we are sending the immuno_dataset$redcap_data$melanoma_data data frame into the function filter() which tests each value in melanoma_type column for the value “cutaneous” and returns the rows where this condition is TRUE.
You can check the dimension (number of rows and number of columns) of the resulting data frame by using the dim() function as follows:
immuno_dataset$redcap_data$melanoma_data |> filter(melanoma_type == "cutaneous") |> dim()[1] 265 10Example 2: Identify records in mortality_data where time since first treatment dose (mortality_treatment_time) exceeds 1 year. mortality_treatment_time is given in months.
immuno_dataset$redcap_data$mortality_data |>
filter(mortality_treatment_time > 12) |>
head()| record_id | redcap_event | mortality | mortality_date | ongoing_survelliance | date_last_scan | mortality_treatment_time | mortality_cause |
|---|---|---|---|---|---|---|---|
| 24 | end | TRUE | 2017-03-03 | no | NA | 17.2 | melanoma progression |
| 28 | end | FALSE | 2023-08-18 | yes | 2022-08-03 | 80.4 | melanoma progression |
| 31 | end | TRUE | 2017-11-09 | NA | NA | 54.3 | melanoma progression |
| 38 | end | FALSE | 2024-04-15 | yes | 2024-06-01 | 109.6 | NA |
| 43 | end | FALSE | 2024-06-12 | yes | 2024-06-13 | 61.9 | NA |
| 48 | end | FALSE | 2023-01-26 | yes | NA | 89.0 | NA |
We can use logical operators like and &, or | to combine multiple conditions as follows.
Example 3: Find all the records in mortality_data where cause of death (mortality_cause) is categorised as “melanoma progression” and has a date of last follow up or death (mortality_date) before “2023-01-01”
immuno_dataset$redcap_data$mortality_data |>
filter(mortality_cause == "melanoma progression" & mortality_date > as.Date("2023-01-01")) |>
head()| record_id | redcap_event | mortality | mortality_date | ongoing_survelliance | date_last_scan | mortality_treatment_time | mortality_cause |
|---|---|---|---|---|---|---|---|
| 16 | end | FALSE | 2024-04-26 | yes | 2023-07-14 | NA | melanoma progression |
| 28 | end | FALSE | 2023-08-18 | yes | 2022-08-03 | 80.4 | melanoma progression |
| 68 | end | FALSE | 2024-01-19 | yes | 2023-04-18 | 46.1 | melanoma progression |
| 72 | end | FALSE | 2024-03-21 | yes | 2024-03-15 | 53.6 | melanoma progression |
| 77 | end | FALSE | 2024-07-05 | yes | 2024-06-28 | 91.1 | melanoma progression |
| 87 | end | TRUE | 2024-01-28 | no | 2015-10-17 | 24.9 | melanoma progression |
Example 4: Find the records in melanoma_data where melanoma_molecular_mutation is either braf or nras.
immuno_dataset$redcap_data$melanoma_data |>
filter(melanoma_molecular_mutation == "braf" | melanoma_molecular_mutation == "braf") |>
head()| record_id | redcap_event | melanoma_type | melanoma_molecular_mutation | mel_first_date | mel_first_stage | resct1stdiag | mel_date_diag | stage_diagnosis | dt_advanced_dis |
|---|---|---|---|---|---|---|---|---|---|
| 28 | baseline | cutaneous | braf | 2015-09-01 | Stage II | TRUE | 2016-03-10 | yes | 27/10/16 |
| 60 | baseline | cutaneous | braf | 2017-03-01 | Stage III | TRUE | 2019-11-01 | no | 1/11/19 |
| 70 | baseline | cutaneous | braf | 2012-01-01 | Stage unknown as pathology unavailable | TRUE | 2017-08-18 | no | NA |
| 79 | baseline | cutaneous | braf | 2015-04-01 | Stage unknown as pathology unavailable | TRUE | 2018-12-01 | no | 1/12/18 |
| 80 | baseline | cutaneous | braf | 2013-01-01 | Stage I | TRUE | 2015-10-01 | yes | 8/10/15 |
| 85 | baseline | cutaneous | braf | 2012-07-01 | Stage II | TRUE | 2014-12-04 | no | 4/12/14 |
Example 5: Retrieve records where mortality_cause is due to “treatment toxicity” and mortality_treatment_time is greater than 4 months but less than or equal to 10 months.
immuno_dataset$redcap_data$mortality_data |>
filter(
mortality_cause == "treatment toxicity" &
mortality_treatment_time > 4 &
mortality_treatment_time <= 10) | record_id | redcap_event | mortality | mortality_date | ongoing_survelliance | date_last_scan | mortality_treatment_time | mortality_cause |
|---|---|---|---|---|---|---|---|
| 414 | end | TRUE | 2019-02-18 | no | 2019-01-31 | 7.0 | treatment toxicity |
| 429 | end | TRUE | 2020-04-22 | no | NA | 5.2 | treatment toxicity |
%in% helper
The %in% function is used to determine whether elements of one vector are present in another vector. It returns a logical vector indicating whether each element of the first vector is found in the second vector.
When we want to filter a subset of rows that may contain multiple different values, it’s more efficient to provide a vector of the values of interest instead of combining multiple OR commands.
Example 6: Retrieve records where melanoma_type is acral, cutaneous or muscosal.
immuno_dataset$redcap_data$melanoma_data |>
filter(melanoma_type %in% c("acral", "cutaneous", "muscosal")) |>
head()| record_id | redcap_event | melanoma_type | melanoma_molecular_mutation | mel_first_date | mel_first_stage | resct1stdiag | mel_date_diag | stage_diagnosis | dt_advanced_dis |
|---|---|---|---|---|---|---|---|---|---|
| 16 | baseline | cutaneous | nras | 2019-06-01 | Stage III | TRUE | 2017-01-01 | yes | 01062017 |
| 24 | baseline | cutaneous | wild_type | 2014-03-27 | Stage II | TRUE | 2015-01-01 | yes | 1/9/15 |
| 28 | baseline | cutaneous | braf | 2015-09-01 | Stage II | TRUE | 2016-03-10 | yes | 27/10/16 |
| 44 | baseline | acral | NA | 2012-04-20 | Stage II | TRUE | 2016-03-30 | no | 30/3/16 |
| 57 | baseline | cutaneous | nras | 2019-12-01 | Stage I | TRUE | 2020-07-09 | yes | 08/01/2021 |
| 58 | baseline | cutaneous | wild_type | 2013-10-01 | Stage I | TRUE | 2015-09-14 | yes | 6/3/17 |
select()
The select() function returns a subset of the variables or columns.
This function can accept column names (even without quotation marks) or the column position number starting from the left. Unlike in base R (we explore before), commands within the brackets in select() do not need to be concatenated using c().
Example 1: Extract the record ID, Echo date (ae_echo_date) and MRI date (ae_mri_date) columns from adverse_events data frame.
immuno_dataset$redcap_data$adverse_events |>
select(record_id, ae_echo_date, ae_mri_date) |>
filter(!is.na(ae_echo_date)) # filter non-missing values in ae_echo_date column| record_id | ae_echo_date | ae_mri_date |
|---|---|---|
| 104 | 2022-10-19 | 2022-09-27 |
| 104 | 2022-10-19 | 2022-09-27 |
| 104 | 2022-10-19 | 2022-09-27 |
| 104 | 2022-10-19 | 2022-09-27 |
Using column positions:
immuno_dataset$redcap_data$adverse_events |>
select(1, 23, 30) |>
filter(!is.na(ae_echo_date)) # filter non-missing values in ae_echo_date column| record_id | ae_echo_date | ae_mri_date |
|---|---|---|
| 104 | 2022-10-19 | 2022-09-27 |
| 104 | 2022-10-19 | 2022-09-27 |
| 104 | 2022-10-19 | 2022-09-27 |
| 104 | 2022-10-19 | 2022-09-27 |
We can use the ‘-’ symbol to extract all columns except for specific ones:
immuno_dataset$redcap_data$demographics |>
select(-redcap_event, -ur, -sex, -dob, -height, -weight) |>
head()| record_id | last_name | first_name | bmi | other_studies_enrolled_in | medical_history | autoimmune_disease | select_autoimmune_diseases | smoking |
|---|---|---|---|---|---|---|---|---|
| 1 | Jackson | Hannah | 68.73 | NA | NA | NA | NA | NA |
| 2 | Howard | Samantha | 29.61 | NA | NA | NA | NA | NA |
| 3 | Martinez | Noah | 31.06 | NA | NA | NA | NA | NA |
| 4 | Lewis | Aiden | 51.51 | NA | NA | NA | NA | NA |
| 5 | Jenkins | Connor | 20.28 | NA | NA | FALSE | NA | Past smoker |
| 6 | Allen | Claire | 28.06 | NA | NA | NA | NA | NA |
Or use a combination of column names and positions:
immuno_dataset$redcap_data$demographics |>
select(1, medical_history, 15) |>
head()| record_id | medical_history | smoking |
|---|---|---|
| 1 | NA | NA |
| 2 | NA | NA |
| 3 | NA | NA |
| 4 | NA | NA |
| 5 | NA | Past smoker |
| 6 | NA | NA |
Useful helper functions
The select helper functions (check ?select_helpers) are a set of convenience functions provided by the dplyr package. These functions offer shortcuts for selecting columns based on specific criteria or patterns, making it easier to work with data frames.
Some commonly used select helper functions include:
starts_with(): selects columns that start with a specified prefix.
immuno_dataset$redcap_data$immune_related_adverse_events_iraes |>
select(starts_with('liver')) |>
head()| liver_date | liver_cycle | liver_grade | liver_path | liver_histo | liver_radiol |
|---|---|---|---|---|---|
| NA | NA | NA | NA | NA | NA |
| NA | NA | NA | NA | NA | NA |
| NA | NA | NA | NA | NA | NA |
| NA | NA | NA | NA | NA | NA |
| NA | NA | NA | NA | NA | NA |
| NA | NA | NA | NA | NA | NA |
ends_with(): selects columns that end with a specified suffix.
immuno_dataset$redcap_data$immune_related_adverse_events_iraes |>
select(ends_with('date')) |>
head()| hypophysitis_date | thyroiditis_date | pancreatitis_date | skin_date | rheum_date | gastro_date | liver_date | renal_date | pulm_date | neuro_date |
|---|---|---|---|---|---|---|---|---|---|
| 2017-01-20 | NA | NA | 2016-09-21 | NA | NA | NA | NA | NA | NA |
| 2017-01-20 | NA | NA | 2016-09-21 | NA | NA | NA | NA | NA | NA |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
contains(): selects columns that contain a specified substring.
immuno_dataset$redcap_data$immune_related_adverse_events_iraes |>
select(contains('skin')) |>
head()| skin_date | skin_cycle | characterise_skin/hair_irae | skin_grade | skin_histo |
|---|---|---|---|---|
| 2016-09-21 | NA | bullous_pemphigoid | 3 | Consistent with a pemphigoid-type reaction, possibly drug-related or consistent with Bullous Pemphigoid. |
| 2016-09-21 | NA | bullous_pemphigoid | 3 | Consistent with a pemphigoid-type reaction, possibly drug-related or consistent with Bullous Pemphigoid. |
| NA | NA | NA | NA | NA |
| NA | NA | NA | NA | NA |
| NA | NA | NA | NA | NA |
| NA | NA | NA | NA | NA |
everything(): Selects all columns.
This function returns all column names that have not been specified. It is often used when reordering all columns in a dataframe:
immuno_dataset$redcap_data$immune_related_adverse_events_iraes |>
select(1, starts_with("gastro"), everything()) |>
head()| record_id | gastro_date | gastro_cycle | gastro_grade | gastro_endoscopy | gastro_histo | gastro_radiol | gastro_cdt | gastro_stool | redcap_event | immune_related_adverse_event | endocrine_toxicity | hypophysitis_date | hypophysitis_time | thyroiditis_date | thyroiditis_time | pancreatitis_date | hypophysitis_cycle | thyroid_cycle | pancreas_cycle | skin_date | skin_cycle | characterise_skin/hair_irae | skin_grade | skin_histo | rheum_date | rheum_cycle | characterise_rheumatic_irae | rheum_grade | rheum_path | characterise_gastrointestinal_irae | liver_date | liver_cycle | liver_grade | liver_path | liver_histo | liver_radiol | renal_date | renal_cycle | characterise_renal_irae_ | renal_grade | renal_path | renal_histo | pulm_date | pulm_cycle | characterise_pulmonary_irae | pulm_grade | pulm_radiol | neuro_date | neuro_cycle | classify_neurological_irae | neuro_grade | neuro_radiol | irae_steroids | irae_details | irae_emergency |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 31 | NA | NA | NA | NA | NA | NA | NA | NA | legacy_data | endocrine | pituitary | 2017-01-20 | 234 | NA | NA | NA | NA | NA | NA | 2016-09-21 | NA | bullous_pemphigoid | 3 | Consistent with a pemphigoid-type reaction, possibly drug-related or consistent with Bullous Pemphigoid. | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | TRUE | ipi 2013 - no irae pembro 2015 - no irae ipi 2016 - Bullous pemphigoid - eruption right thigh, limbs,feet Plaque left cheek Derm Imp: bullous pemphigoid, ?secondary to ipilimumab ipi nivo 2017 - hypophysitis and recurrence of bullous pemphigoid. Steroids and IvIg | TRUE |
| 31 | NA | NA | NA | NA | NA | NA | NA | NA | legacy_data | skin/hair | pituitary | 2017-01-20 | 234 | NA | NA | NA | NA | NA | NA | 2016-09-21 | NA | bullous_pemphigoid | 3 | Consistent with a pemphigoid-type reaction, possibly drug-related or consistent with Bullous Pemphigoid. | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | TRUE | ipi 2013 - no irae pembro 2015 - no irae ipi 2016 - Bullous pemphigoid - eruption right thigh, limbs,feet Plaque left cheek Derm Imp: bullous pemphigoid, ?secondary to ipilimumab ipi nivo 2017 - hypophysitis and recurrence of bullous pemphigoid. Steroids and IvIg | TRUE |
| 74 | NA | NA | NA | NA | NA | NA | NA | NA | legacy_data | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 96 | NA | NA | NA | NA | NA | NA | NA | NA | legacy_data | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 97 | NA | NA | NA | NA | NA | NA | NA | NA | legacy_data | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 102 | NA | NA | NA | NA | NA | NA | NA | NA | legacy_data | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
Here the dimensions of the dataframe is not changed, merely the column order.
You can combine multiple helper functions to create more complex selection criteria. Additionally, you can use the ‘-’ symbol in front of the helper function to exclude the matched columns.
mutate()
The mutate() function adds new columns of data, thus ‘mutating’ the contents and dimensions of the input data frame.
Example 1: Calculate the BMI of patients (i.e, \(\text{BMI } = \frac{\text{nweight}}{\text{height in m} \times \text{ height in m}} \times 100\)).
Here we use the round() function to round off the result to the closest integer or numeric value as number of responses cannot contain decimal values.
immuno_dataset$redcap_data$demographics |>
mutate(bmi_new = weight / (height/100 * height/100)) |>
head()| record_id | redcap_event | ur | last_name | first_name | sex | dob | height | weight | bmi | other_studies_enrolled_in | medical_history | autoimmune_disease | select_autoimmune_diseases | smoking | bmi_new |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | baseline | 2810493 | Jackson | Hannah | Male | 1943-02-12 | 154 | 163 | 68.73 | NA | NA | NA | NA | NA | 68.72997 |
| 2 | baseline | 6408685 | Howard | Samantha | Male | 2008-01-29 | 181 | 97 | 29.61 | NA | NA | NA | NA | NA | 29.60838 |
| 3 | baseline | 9994173 | Martinez | Noah | Male | 1940-11-06 | 199 | 123 | 31.06 | NA | NA | NA | NA | NA | 31.05982 |
| 4 | baseline | 9580798 | Lewis | Aiden | Male | 1963-06-04 | 189 | 184 | 51.51 | NA | NA | NA | NA | NA | 51.51032 |
| 5 | baseline | 2653008 | Jenkins | Connor | Male | 2019-08-15 | 157 | 50 | 20.28 | NA | NA | FALSE | NA | Past smoker | 20.28480 |
| 6 | baseline | 931154 | Allen | Claire | Male | 1957-07-12 | 145 | 59 | 28.06 | NA | NA | NA | NA | NA | 28.06183 |
This creates a new column at the end of the data frame named bmi_new and computes the BMI. Because the number of columns is expanding, we can reduce the number of columns displayed using the select() function.
To do this, we need to use chaining which is discussed before.
Let’s use chaining to combine both select() and mutate() operations for the previous example:
immuno_dataset$redcap_data$demographics |>
select(record_id, weight, height, bmi) |>
mutate(bmi_new = weight / (height/100 * height/100)) |>
head()| record_id | weight | height | bmi | bmi_new |
|---|---|---|---|---|
| 1 | 163 | 154 | 68.73 | 68.72997 |
| 2 | 97 | 181 | 29.61 | 29.60838 |
| 3 | 123 | 199 | 31.06 | 31.05982 |
| 4 | 184 | 189 | 51.51 | 51.51032 |
| 5 | 50 | 157 | 20.28 | 20.28480 |
| 6 | 59 | 145 | 28.06 | 28.06183 |
case_when helper function
The case_when() function allows you to create conditional statements inside mutate(). It is a vectorized alternative to multiple ifelse() statements, making the code cleaner and easier to read. Each case is evaluated sequentially and the first match for each element determines the corresponding value in the output vector. If no cases match, the .default is used as a final “else” statment.
case_when(
condition1 ~ value1,
condition2 ~ value2,
condition3 ~ value3,
TRUE ~ default_value
)Each condition is checked in order, and the corresponding value is assigned if the condition is TRUE. The TRUE ~ default_value at the end acts as a fallback for any rows that do not match previous conditions.
Example 2: generates a new column bmi_category, which classifies patients based on their Body Mass Index (BMI) using the following categories:
- “Underweight”: BMI < 18.5
- “Normal Weight”: 18.5 ≤ BMI < 25
- “Overweight”: 25 ≤ BMI < 30
- “Obese”: BMI ≥ 30.
immuno_dataset$redcap_data$demographics |>
select(record_id, weight, height, bmi) |>
mutate(bmi_category = case_when(
bmi < 18.5 ~ "Underweight",
bmi >= 18.5 & bmi < 25 ~ "Normal Weight",
bmi >= 25 & bmi < 30 ~ "Overweight",
bmi > 30 ~ "Obese",
TRUE ~ "Unknown"
)) |> head()| record_id | weight | height | bmi | bmi_category |
|---|---|---|---|---|
| 1 | 163 | 154 | 68.73 | Obese |
| 2 | 97 | 181 | 29.61 | Overweight |
| 3 | 123 | 199 | 31.06 | Obese |
| 4 | 184 | 189 | 51.51 | Obese |
| 5 | 50 | 157 | 20.28 | Normal Weight |
| 6 | 59 | 145 | 28.06 | Overweight |
Here mutate() creates a new column bmi_category. case_when() assigns categories based on the BMI values and the final condition TRUE ensures any missing or unclassified values are labeled as “Unknown”.
summarise()
The summarise() function creates individual summary statistics from larger data sets.
The output of summarise()/summarize() differs qualitatively from the input. It results in a smaller dataframe with a reduced representation of the original data. While not strictly necessary, it’s advisable to assign new column names for the summary statistics generated by this function. This practice enhances clarity and organisation in your data analysis workflow.
Example 1: Calculate the mean number of creatinine (sys_path_creat) in prior_treatment data frame.
immuno_dataset$redcap_data$prior_treatment |>
summarise(mean_creatinine = mean(sys_path_creat))| mean_creatinine |
|---|
| NA |
This results in a data frame of size 1 row \(\times\) 1 col with a value of NA, indicating that the result is either Not Applicable or missing. This occurs because the column contains missing values, making the mean calculation invalid. To compute the mean creatinine level while excluding missing values, use the na.rm = TRUE argument in the mean() function.
immuno_dataset$redcap_data$prior_treatment |>
summarise(mean_creatinine = mean(sys_path_creat, na.rm = TRUE))| mean_creatinine |
|---|
| 69.14035 |
We can create additional summary statistics by adding them in a comma-separated sequence as follows:
immuno_dataset$redcap_data$prior_treatment |>
summarise(mean_creatinine = mean(sys_path_creat, na.rm = TRUE),
min_creatinine = min(sys_path_creat, na.rm = TRUE),
max_creatinine = max(sys_path_creat, na.rm = TRUE),
total_creatinine = sum(sys_path_creat, na.rm = TRUE))| mean_creatinine | min_creatinine | max_creatinine | total_creatinine |
|---|---|---|---|
| 69.14035 | 51 | 108 | 3941 |
n() helper function
This function counts the number of observations in a dataset. It does not take any arguments, but simply counts the rows.
immuno_dataset$redcap_data$prior_treatment |>
summarise(mean_creatinine = mean(sys_path_creat, na.rm = TRUE),
min_creatinine = min(sys_path_creat, na.rm = TRUE),
max_creatinine = max(sys_path_creat, na.rm = TRUE),
total_creatinine = sum(sys_path_creat, na.rm = TRUE),
n_rows = n())| mean_creatinine | min_creatinine | max_creatinine | total_creatinine | n_rows |
|---|---|---|---|---|
| 69.14035 | 51 | 108 | 3941 | 11627 |
arrange()
The arrange() function orders rows based on the values in a given column.
Example 1: Order the records based on the UR number in demographics.
immuno_dataset$redcap_data$demographics |>
arrange(ur) |>
head()| record_id | redcap_event | ur | last_name | first_name | sex | dob | height | weight | bmi | other_studies_enrolled_in | medical_history | autoimmune_disease | select_autoimmune_diseases | smoking |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 216 | baseline | 102028 | Johnson | Catherine | Female | 1979-08-09 | 190 | 103 | 28.53 | mrv | NA | TRUE | inflammatory_bowel_disease | Never smoked |
| 74 | baseline | 112616 | Peterson | Sophie | Male | 1929-09-08 | 161 | 71 | 27.39 | NA | hypertension | TRUE | inflammatory_arthritis | Never smoked |
| 98 | baseline | 113893 | Green | Nathan | Male | 1975-11-29 | 147 | 190 | 87.93 | NA | NA | FALSE | NA | Never smoked |
| 249 | baseline | 121563 | Jones | Jack | Male | 1998-06-06 | 193 | 68 | 18.26 | NA | NA | NA | NA | NA |
| 195 | baseline | 140072 | Foster | Charlotte | Female | 1963-05-19 | 195 | 135 | 35.50 | micromac | NA | FALSE | NA | Never smoked |
| 260 | baseline | 146543 | Barnes | Abigail | Female | 1927-01-16 | 167 | 158 | 56.65 | NA | NA | NA | NA | NA |
Example 2: Sort the records in mortality_data based on the mortality date first and then by last scan date (date_last_scan).
immuno_dataset$redcap_data$mortality_data |>
arrange(mortality_date, date_last_scan) |>
head()| record_id | redcap_event | mortality | mortality_date | ongoing_survelliance | date_last_scan | mortality_treatment_time | mortality_cause |
|---|---|---|---|---|---|---|---|
| 70 | end | TRUE | 2013-11-15 | no | NA | 45.7 | melanoma progression |
| 193 | end | TRUE | 2015-04-25 | no | NA | NA | melanoma progression |
| 331 | end | TRUE | 2015-05-29 | no | NA | 28.0 | melanoma progression |
| 84 | end | TRUE | 2015-06-19 | no | NA | 0.5 | melanoma progression |
| 349 | end | TRUE | 2015-07-25 | no | 2024-01-17 | 8.5 | melanoma progression |
| 85 | end | FALSE | 2015-08-05 | no | 2018-10-25 | 7.5 | melanoma progression |
desc() helper function
This function is used to sort data in descending order.
Example 3: Sort the records in mortality_data in descending order based on the mortality_treatment_time.
immuno_dataset$redcap_data$mortality_data |>
arrange(desc(mortality_treatment_time)) |>
head()| record_id | redcap_event | mortality | mortality_date | ongoing_survelliance | date_last_scan | mortality_treatment_time | mortality_cause |
|---|---|---|---|---|---|---|---|
| 188 | end | FALSE | 2024-09-06 | yes | 2023-12-22 | 131.5 | NA |
| 345 | end | TRUE | 2018-08-03 | no | NA | 115.0 | melanoma progression |
| 221 | end | FALSE | 2024-08-12 | yes | 2024-06-08 | 111.8 | NA |
| 38 | end | FALSE | 2024-04-15 | yes | 2024-06-01 | 109.6 | NA |
| 146 | end | FALSE | 2024-09-06 | no | 2020-08-06 | 108.6 | NA |
| 200 | end | FALSE | 2024-07-12 | no | 2022-01-04 | 108.3 | NA |
count() helper
The count() function is used to count the number of occurrences of unique values in one or more variables within a data frame. This function is particularly useful for summarising data and understanding the distribution of values within a dataset.
Example 1: Count the number of melanoma types in melanoma_data data frame.
immuno_dataset$redcap_data$melanoma_data |>
count(melanoma_type)| melanoma_type | n |
|---|---|
| acral | 7 |
| cutaneous | 265 |
| mucosal | 16 |
| pathology_not_available | 4 |
| unknown_primary | 64 |
| uveal | 21 |
| NA | 132 |
Example 2: Count the number of records observed in each melanoma type and melanoma molecular mutation.
immuno_dataset$redcap_data$melanoma_data |>
count(melanoma_type, melanoma_molecular_mutation)| melanoma_type | melanoma_molecular_mutation | n |
|---|---|---|
| acral | braf | 1 |
| acral | kit | 2 |
| acral | nras | 2 |
| acral | wild_type | 1 |
| acral | NA | 1 |
| cutaneous | braf | 110 |
| cutaneous | kit | 3 |
| cutaneous | nras | 54 |
| cutaneous | other | 1 |
| cutaneous | unknown | 3 |
| cutaneous | wild_type | 74 |
| cutaneous | NA | 20 |
| mucosal | braf | 1 |
| mucosal | kit | 3 |
| mucosal | nras | 3 |
| mucosal | wild_type | 9 |
| pathology_not_available | braf | 1 |
| pathology_not_available | nras | 1 |
| pathology_not_available | wild_type | 2 |
| unknown_primary | braf | 30 |
| unknown_primary | kit | 1 |
| unknown_primary | nras | 15 |
| unknown_primary | other | 1 |
| unknown_primary | wild_type | 16 |
| unknown_primary | NA | 1 |
| uveal | braf | 1 |
| uveal | nras | 1 |
| uveal | other | 2 |
| uveal | unknown | 1 |
| uveal | wild_type | 12 |
| uveal | NA | 4 |
| NA | braf | 2 |
| NA | wild_type | 2 |
| NA | NA | 128 |
Visualising Data
ggplot2 package simplifies the creation of plots. This package offers a streamlined interface for defining variables to plot, configuring their display, and adjusting visual attributes. Consequently, adapting to changes in the data or transitioning between plot types requires only minimal modifications. This feature facilitates the creation of high-quality plots suitable for publication with minimal manual adjustments.
If you’ve already installed the tidyverse package (if not, you can do so by running the command: install.packages("tidyverse")), let’s proceed to load it into our R session first:
library(tidyverse)Next, load the pre-processed RDS Object:
immuno_dataset <- readRDS("data/Sample_immuno_dataset.rds")Building a Basic Plot
The construction of ggplot graphics is incremental, allowing for the addition of new elements in layers. This approach grants users extensive flexibility and customisation options, enabling the creation of tailored plots to suit specific needs.
To build a ggplot, the following basic templates can be used for different types of plots.
Three things are required for a ggplot:
1. The data
We first specify the data frame that contains the relevant data to create a plot. Here we are sending the immuno_dataset$redcap_data$melanoma_data to the ggplot() function.
# render plot background
ggplot(immuno_dataset$redcap_data$demographics)
This command results in an empty gray panel. We must specify how various columns of the data frame should be depicted in the plot.
2. Aesthetics aes()
Next, we specify the columns in the data we want to map to visual properties (called aesthetics or aes in ggplot2). e.g. the columns for x values, y values and colours.
Since we are interested in generating a scatter plot, each point will have an x and a y coordinate. Therefore, we need to specify the x-axis to represent the year and y-axis to represent the count.
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = height))
This results in a plot which includes the grid lines, the variables and the scales for x and y axes. However, the plot is empty or lacks data points.
3. Geometric Representation geom_()
Finally, we specify the type of plot (the geom). There are different types of geoms:
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The range of geoms available in ggplot2 can be obtained by navigating to the ggplot2 package in the Packages tab pane in RStudio (bottom right-hand corner) and scrolling down the list of functions sorted alphabetically to the geom_... functions.
Since we are interested in creating a scatter plot, the geometric representation of the data will be in point form. Therefore we use the geom_point() function.
To plot the expression of estrogen receptor alpha (ESR1) against that of the transcription factor, GATA3:
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point() Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).
Notice that we use the + sign to add a layer of points to the plot. This concept bears resemblance to Adobe Photoshop, where layers of images can be rearranged and edited independently. In ggplot, each layer is added over the plot in accordance with its position in the code using the + sign.
A note about
|> and +
ggplot2 package was developed prior to the introduction of the pipe operator. In ggplot2, the + sign functions analogously to the pipe operator in other tidyverse functions, enabling code to be written from left to right.
Customising Plots
Adding Colour
The above plot could be made more informative. For instance, the additional information regarding the gender (i.e., sex column) could be incorporated into the plot. To do this, we can utilise aes() and specify which column in the data frame should be represented as the color of the points.
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi, color = sex)) +
geom_point() Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).
Notice that we specify the colour = sex argument in the aes() mapping inside the geom_() function instead of ggplot() function. Aesthetic mappings can be set in both ggplot() and individual geom() layers and we will discuss the difference in the Section: Adding Layers.
To colour points based on a continuous variable, for example: height:
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(aes(colour = height)) Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).
In ggplot2, a color scale is used for continuous variables, while discrete or categorical values are represented using discrete colors.
Note that some patient samples lack values, leading ggplot2 to remove those points with missing values for bmi and weight.
Adding Shape
Let’s add shape to points.
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(aes(shape = smoking)) Warning: Removed 105 rows containing missing values or values outside the scale range
(`geom_point()`).
Note that some patient samples have not been classified and ggplot has removed those points with missing values for the smoking categories.
Some aesthetics like shape can only be used with categorical variables:
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(aes(shape = height)) Error in `geom_point()`:
! Problem while computing aesthetics.
ℹ Error occurred in the 1st layer.
Caused by error in `scale_f()`:
! A continuous variable cannot be mapped to the shape aesthetic.
ℹ Choose a different aesthetic or use `scale_shape_binned()`.The shape argument allows you to customise the appearance of all data points by assigning an integer associated with predefined shapes shown below:
To use asterix instead of points in the plot:
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(shape = 8) Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).
It would be useful to be able to change the shape of all the points. We can do so by setting the size to a single value rather than mapping it to one of the variables in the data set - this has to be done outside the aesthetic mappings (i.e. outside the aes() bit) as above.
Aesthetic Setting vs. Mapping
Instead of mapping an aesthetic property to a variable, you can set it to a single value by specifying it in the layer parameters (outside aes()). We map an aesthetic to a variable (e.g., aes(shape = THREEGENE)) or set it to a constant (e.g., shape = 8). If you want appearance to be governed by a variable in your data frame, put the specification inside aes(); if you want to override the default size or colour, put the value outside of aes().
# size outside aes()
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(shape = 8) Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).# size inside aes()
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(aes(shape = smoking)) Warning: Removed 105 rows containing missing values or values outside the scale range
(`geom_point()`).
The above plots are created with similar code, but have rather different outputs. The first plot sets the size to a value and the second plot maps (not sets) the size to the three-gene classifier variable.
It is usually preferable to use colours to distinguish between different categories but sometimes colour and shape are used together when we want to show which group a data point belongs to in two different categorical variables.
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(aes(colour = sex, shape = smoking))Warning: Removed 105 rows containing missing values or values outside the scale range
(`geom_point()`).
Adding Size and Transparency
We can adjust the size and/or transparency of the points.
Let’s first increase the size of points.
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(aes(colour = sex), size = 2)Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).
Note that here we add the size argument outside of the the aesthetic mapping.
Size is not usually a good aesthetic to map to a variable and hence is not advised.
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(aes(colour = sex, size = smoking))Warning: Using size for a discrete variable is not advised.Warning: Removed 105 rows containing missing values or values outside the scale range
(`geom_point()`).
Because this value is discrete, the default size scale uses evenly spaced sizes for points categorised on smoking categories.
Transparency can be useful when we have a large number of points as we can more easily tell when points are overlaid, but like size, it is not usually mapped to a variable and sits outside the aes().
Let’s change the transparency of points.
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(aes(colour = sex), alpha = 0.5)Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).
Adding Layers
We can add another layer to this plot using a different geometric representation (or geom_ function) we discussed previously.
Let’s add trend lines to this plot using the geom_smooth() function which provide a summary of the data.
ggplot(immuno_dataset$redcap_data$demographics) +
geom_point(aes(x = weight, y = bmi)) +
geom_smooth(aes(x = weight, y = bmi))`geom_smooth()` using method = 'loess' and formula = 'y ~ x'Warning: Removed 8 rows containing non-finite outside the scale range
(`stat_smooth()`).Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).
Note that the shaded area surrounding blue line represents the standard error bounds on the fitted model.
There is some annoying duplication of code used to create this plot. We’ve repeated the exact same aesthetic mapping for both geoms. We can avoid this by putting the mappings in the ggplot() function instead.
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point() +
geom_smooth()
Geom layers specified earlier in the command are drawn first, preceding subsequent geom layers. The sequence of geom layers specified in the command determines their order of appearance in the plot.
If you switch the order of the geom_point() and geom_smooth() functions above, you’ll notice a change in the regression line. Specifically, the regression line will now be plotted underneath the points.
Let’s make the plot look a bit prettier by reducing the size of the points and making them transparent. We’re not mapping size or alpha to any variables, just setting them to constant values, and we only want these settings to apply to the points, so we set them inside geom_point().
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(size = 0.5, alpha = 0.5) +
geom_smooth() `geom_smooth()` using method = 'loess' and formula = 'y ~ x'Warning: Removed 8 rows containing non-finite outside the scale range
(`stat_smooth()`).Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).
Aesthetic Specifications in Plot vs. Layers
Aesthetic mappings can be provided either in the initial ggplot() call, in individual layers, or through a combination of both approaches. When there’s only one layer in the plot, the method used to specify aesthetics doesn’t impact the result.
# colour argument inside ggplot()
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi, colour = smoking)) +
geom_point(size = 0.5, alpha = 0.5) +
geom_smooth()
# colour argument inside geom_point()
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(aes(colour = smoking), size = 0.5, alpha = 0.5) +
geom_smooth() 
In the left plot, since we specified the colour (i.e., colour = smoking) inside the ggplot() function, the geom_smooth() function will fit regression lines for each type of ER status and will have coloured regression lines as shown above. This is because, when aesthetic mappings are defined in ggplot(), at the global level, they’re passed down to each of the subsequent geom layers of the plot.
If we want to add colour only to the points and fit a regression line across all points, we could specify the colour inside geom_point() function (i.e., right plot).
Plot Labels
You can customise plots to include a title, a subtitle, a caption or a tagusing the labs() function.
ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(aes(colour = sex), size = 0.5, alpha = 0.5) +
geom_smooth() +
labs(
title = "Variation between BMI and Weight coloured by smoking categories of melanoma patients",
subtitle = "BMI vs Weight",
caption = "Variation between BMI and Weight",
tag = "Figure 1",
y = "Body Mass Index",
x = "Weight (kg)")
Themes
Themes control the overall appearance of the plot, including background color, grid lines, axis labels, and text styles. ggplot offers several built-in themes, and you can also create custom themes to match your preferences or the requirements of your publication. The default theme has a grey background.
weight_vs_bmi <- ggplot(immuno_dataset$redcap_data$demographics, aes(x = weight, y = bmi)) +
geom_point(aes(colour = smoking), size = 0.5, alpha = 0.5) +
geom_smooth()
weight_vs_bmi + theme_bw()`geom_smooth()` using method = 'loess' and formula = 'y ~ x'Warning: Removed 8 rows containing non-finite outside the scale range
(`stat_smooth()`).Warning: Removed 8 rows containing missing values or values outside the scale range
(`geom_point()`).
Try these themes yourselves: theme_classic(), theme_dark(), theme_grey() (default), theme_light(), theme_linedraw(), theme_minimal(), theme_void() and theme_test().
Bar chart
Let’s create a bar chart of the number of patients based on different melanoma type in melanoma_data instrument.
The geom_bar is the geom used to plot bar charts. It requires a single aesthetic mapping of the categorical variable of interest to x.
ggplot(immuno_dataset$redcap_data$melanoma_data) +
geom_bar(aes(x = melanoma_type))
The dark grey bars are a big ugly - what if we want each bar to be a different colour?
ggplot(immuno_dataset$redcap_data$melanoma_data) +
geom_bar(aes(x = melanoma_type, colour = melanoma_type))
Colouring the edges wasn’t quite what we had in mind. Look at the help for geom_bar to see what other aesthetic we should have used.
ggplot(immuno_dataset$redcap_data$melanoma_data) +
geom_bar(aes(x = melanoma_type, fill = melanoma_type))
What happens if we colour (fill) with something other than the melanoma_type?
ggplot(immuno_dataset$redcap_data$melanoma_data) +
geom_bar(aes(x = melanoma_type, fill = melanoma_molecular_mutation))
We get a stacked bar plot.
Note the similarity in what we did here to what we did with the scatter plot - there is a common grammar.
We can rearrange the three gene groups into adjacent (dodged) bars by specifying a different position within geom_bar():
ggplot(immuno_dataset$redcap_data$melanoma_data) +
geom_bar(aes(x = melanoma_type, fill = melanoma_molecular_mutation), position = 'dodge')
What if want all the bars to be the same colour but not dark grey, e.g. blue?
ggplot(immuno_dataset$redcap_data$melanoma_data) +
geom_bar(aes(x = melanoma_type, fill = "blue"))
That doesn’t look right - why not?
You can set the aesthetics to a fixed value but this needs to be outside the mapping, just like we did before for size and transparency in the scatter plots.
ggplot(immuno_dataset$redcap_data$melanoma_data) +
geom_bar(aes(x = melanoma_type), fill = "blue")
Setting this inside the aes() mapping told ggplot2 to map the colour aesthetic to some variable in the data frame, one that doesn’t really exist but which is created on-the-fly with a value of “blue” for every observation.
Box plot
Box plots (or box & whisker plots) are a particular favourite seen in many seminars and papers. Box plots summarise the distribution of a set of values by displaying the minimum and maximum values, the median (i.e. middle-ranked value), and the range of the middle 50% of values (inter-quartile range). The whisker line extending above and below the IQR box define Q3 + (1.5 x IQR), and Q1 - (1.5 x IQR) respectively.
To create a box plot from immuno dataset:
# join the melanoma_data instrument and mortality_data
mel_mort <- full_join(immuno_dataset$redcap_data$melanoma_data ,
immuno_dataset$redcap_data$mortality_data,
by = "record_id")
# keep only the non-missing rows of mortality_treatment_time column
mel_mort <- mel_mort |> filter(!is.na(mortality_treatment_time))
ggplot(mel_mort, aes(x = melanoma_type, y = mortality_treatment_time)) +
geom_boxplot()
See geom_boxplot help to explain how the box and whiskers are constructed and how it decides which points are outliers and should be displayed as points.
Let’s try a colour aesthetic to also look at how estrogen receptor expression differs between HER2 positive and negative tumours.
ggplot(mel_mort, aes(x = melanoma_type, y = mortality_treatment_time, color = melanoma_type)) +
geom_boxplot() 
Violin plot
A violin plot is used to visualise the distribution of a numeric variable across different categories. It combines aspects of a box plot and a kernel density plot.
The width of the violin at any given point represents the density of data at that point. Wider sections indicate a higher density of data points, while narrower sections indicate lower density. By default, violin plots are symmetric.
ggplot(mel_mort, aes(x = melanoma_type, y = mortality_treatment_time, color = melanoma_type)) +
geom_violin()
Histogram
The geom for creating histograms is, rather unsurprisingly, geom_histogram().
ggplot(immuno_dataset$redcap_data$response_data) +
geom_histogram(aes(x = time_to_best_response))`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.Warning: Removed 155 rows containing non-finite outside the scale range
(`stat_bin()`).
The warning message hints at picking a more optimal number of bins by specifying the binwidth argument.
ggplot(immuno_dataset$redcap_data$response_data) +
geom_histogram(aes(x = time_to_best_response), binwidth = 5)Warning: Removed 155 rows containing non-finite outside the scale range
(`stat_bin()`).
Or we can set the number of bins.
ggplot(immuno_dataset$redcap_data$response_data) +
geom_histogram(aes(x = time_to_best_response), bins = 20)Warning: Removed 155 rows containing non-finite outside the scale range
(`stat_bin()`).
These histograms are not very pleasing, aesthetically speaking - how about some better aesthetics?
ggplot(immuno_dataset$redcap_data$response_data) +
geom_histogram(
aes(x = time_to_best_response),
bins = 20,
colour = "darkblue",
fill = "grey")Warning: Removed 155 rows containing non-finite outside the scale range
(`stat_bin()`).
Density plot
Density plots are used to visualise the distribution of a continuous variable in a dataset. These are essentially smoothed histograms, where the area under the curve for each sub-group will sum to 1. This allows us to compare sub-groups of different size.
ggplot(immuno_dataset$redcap_data$response_data) +
geom_density(aes(x = time_to_best_response, colour = best_res_pet))Warning: Removed 155 rows containing non-finite outside the scale range
(`stat_density()`).Warning: Groups with fewer than two data points have been dropped.Warning in max(ids, na.rm = TRUE): no non-missing arguments to max; returning
-Inf
Saving plot images
Use ggsave() to save the last plot you displayed.
ggsave("time_to_best_response_density_plot.png")You can alter the width and height of the plot and can change the image file type.
ggsave("time_to_best_response_density_plot.pdf", width = 20, height = 12, units = "cm")You can also pass in a plot object you have created instead of using the last plot displayed. See the help page (?ggsave) for more details.
Comments
In R, any text following the hash symbol # is termed a comment. R disregards this text, considering it non-executable. Comments serve the purpose of documenting your code, aiding your future understanding of specific lines, and highlighting the intentions or challenges encountered.
RStudio makes it easy to comment or uncomment a paragraph: Select the lines you want to comment (to comment a set of lines) or placing the cursor at any location of a line (to comment a single line), press at the same time on your keyboard ⌘ + Shift + C (mac) or Ctrl + Shift + C (Windows/Linux).
Extensive use of comments is encouraged throughout this course.