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📚 Resources for Statistical Programmers

This page is dedicated to helping SAS programmers transition to R and other open-source tools used in clinical programming. Whether you're working in pharmaceutical trials, CRO environments, or data science for healthcare, this hub includes a growing collection of guides, articles, cheat sheets, videos, and white papers designed to make your transition smoother.

Explore topics like “R for SAS programmers”, “SAS to R migration”, “open-source alternatives to SAS”, and practical materials on admiral, tidyverse, GitHub, GitLab, and cloud platforms like Domino or Cnvrg.

All links are manually curated and categorized by topic. More content is added regularly - this is your evolving guide to mastering R statistical programming and modernizing your workflow.

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  • How to learn R as a SAS user
    This article guides SAS users through the transition to R by highlighting key differences in workflow and data handling, and encourages learning R from the ground up to fully leverage its open-source ecosystem. It emphasizes using tools like RStudio, tidyverse, and Shiny to build modern, efficient analytical workflows.
    Author: Isabella Velásquez, Phil Bowsher
  • What Are the Tidyverse Packages in R Language?
    There are eight core Tidyverse packages namely ggplot2, dplyr, tidyr, readr, purrr, tibble, stringr and forcats that are mentioned in this article.
    Author: Harkiran Kaur
    Date: July 2025
  • R in FDA Submissions: Lessons Learned from 5 FDA Pilots
    Pharma companies are actively testing that question through a series of R Consortium-led pilots with the FDA. Their efforts aim to identify what works, what doesn't, and how tools like Shiny, container tech, and WebAssembly can shape the future of open-source regulatory submissions.
    Author: Dario Radečić
    Date: May 2025
  • Learning R as a SAS user
    The article offers practical guidance for SAS users transitioning to R, highlighting tools and techniques like the Sassy package ecosystem, cheat sheets, videos, and data loading strategies to bridge the gap between SAS and tidyverse workflows. It also compiles community-sourced tips on missing values, macro alternatives, performance tuning, and package selection, making it a well-rounded resource for easing into R programming.
    Author: Ted Laderas
    Date: October 2024
  • Contributed Cheatsheet
    Cheatsheets contributed by the community: admiral, arrow, Base R, R Best Practice, Regular Expressions, and many more.
    Author: community
  • Real projects, real transition, really revolutionary: transitioning to R for biometrics work
    This blog post describes how a biometrics team successfully transitioned from SAS to R by immersing developers in R-focused work, leveraging packages like tidyverse, admiral, and pharmaverse for CDISC-compliant outputs, and maintaining rigorous QC through parallel programming and documentation; it highlights the power of RShiny for interactive data review.
    Author: Danielle Stephenson, Alyssa Wittle, Rebekah Osster
    Date: September 2024
  • Using R Programming for Clinical Trial Data Analysis
    Increasing use or R in clinical trials for tasks like study design, data management, statistical analysis (e.g. survival models, mixed-effects), and safety monitoring, while acknowledging that SAS remains dominant but R's versatility and growing ecosystem make it a compelling alternative.
    Author: Quanticate
    Date: September 2024
  • How to Load SAS Files in R: Transitioning from SAS to R with Seamless Data Integration
    In this article the author explores how to integrate SAS data into your R workflow, allowing you to harness the strengths of both tools.
    Author: Eduardo Almeida
    Date: October 2023
  • SAS to R Migration: How to Import, Process, and Export SAS Files in R
    The article is about how to safely and gradually transition from SAS to R in data analysis workflows.
    Author: Eduardo Almeida
    Date: October 2023
  • Getting Started with R Cheat Sheet
    This cheat sheet will cover an overview of getting started with R. Use it as a handy, high-level reference for a quick start with R. For more detailed R Cheat Sheets, follow the highlighted cheat sheets below.
    Author: Richie Cotton
    Date: June 2022
  • The Difference Between `logr`, `logrx`, and `whirl`
    All three packages are used for keeping a record of what happens when R scripts are run, but they differ in what they offer and who they’re for.
    Author: Pharmaverse
  • From SAS Macros to R Functions: A Seamless Transition for Efficient Programming
    The post outlines how SAS programmers can smoothly shift from using macros to writing R functions by drawing parallels in structure and intent, emphasizing that both automate workflows with parameters. It highlights key similarities and differences.
    Author: Brian Carter

  • R Made Easier for SAS Programmers
    SAS to R Tips and examples
    Author: Sunil Gupta
    Date: November 2024
    Format: pdf
  • Building ADaMs with pharmaverse R packages admiral, metacore/metatools and xportr
    The core ADaM programming was done with admiral, additional derivations with metatools/metacore, and xportr was used to write regulatory compliant xpt files.
    Author: Fanny Gautier, Ben Straub, Edoardo Mancini, Sadchla Mascary
    Date: October 2024
    Format: Interactive presentation
  • Data transformation with dplyr :: CHEAT SHEET
    The RStudio cheat sheet provides a collection of commonly used functions and workflows—like filter(), select(), mutate(), summarize(), group_by(), pivot_longer(), and pivot_wider()—to clean, reshape, and wrangle tabular data efficiently using the tidyverse. It offers quick-reference syntax and examples to streamline data-manipulation tasks in R.
    Author: Posit
    Date: May 2024
    Format: pdf
  • SAS Vs R in Pharma
    This cheat sheet mainly focus on data manipulation techniques frequently used in pharmaceutical industry.
    Author: Bharath Kumar
    Date: November 2022
    Format: pdf
  • Data visualization with ggplot2 :: CHEAT SHEET
    A cheat sheet or reference guide illustrating how to create effective visualizations using R (most likely using ggplot2).
    Author: Posit
    Date: August 2021
    Format: pdf
  • Getting Started in R - Tidyverse Edition
    beginner-friendly introduction to R and the tidyverse, walking users step-by-step through installing RStudio, and using real-world datasets to manipulate, visualize, and summarize data via tidyverse tools
    Author: Saghir Bashir
    Date: October 2019
    Format: pdf
  • Moving From SAS to R Webinar Presentation
    The document discusses the transition from using SAS to R for analytics, highlighting the advantages of R such as cost-effectiveness, flexibility, and familiarity among new analysts. It addresses common concerns about R, including code transparency and scalability, and offers strategies for a successful migration, including technical support and training.
    Author: Thomas W. Dinsmore, Seth Mottaghinejad
    Date: August 2014
    Format: pdf, pptx
  • Matplotlib :: CHEAT SHEET
    Matplotlib cheatsheets and handouts
    Author: The Matplotlib team
    Date: 2012-2024
    Format: pdf
  • Using R in a Regulatory Environment: some FDA perspectives
    How is R used for regulatory review work? This presentation reflects the views of the author and should not be construed to represent the FDA's views or policies.
    Author: Paul Schuette
    Date: NO DATE
    Format: pdf
  • Base R :: CHEAT SHEET
    A concise cheat sheet focused on regular expression syntax and functions in R, covering both base R and the stringr package.
    Author: Paul Vanderlaken
    Format: pdf
  • SAS and R
    The intent of this resource is to act as a knowledge hub for using R in a clinical study context.
    Author: Bayer Oncology SBU
    Format: Interactive presentation
  • Utiization of Python in clinical study by SASPy
    SASPy is the module, which provides Python Application Programming Interfaces (APIs) to the SAS system. By using SASPy, Python can establish SAS session and run analytics from Python.
    Author: Yuichi Nakajima
    Format: pdf

  • Advanced Programming with R: Leveraging Tidyverse and admiral for ADaM Data Set Creation with a Comparison to SAS
    This paper explores an advanced two-stage approach for deriving key Subject-Level Analysis Dataset (ADSL) variables using the R programming language, with direct comparisons to SAS
    Author: Joshua J. Cook, Richann Jean Watson
    Date: June 2025
    Format: pdf
  • Transitioning from SAS to R
    An introduction to R targeted for the SAS programmer
    Author: Ashley Tarasiewicz, Chelsea Dickens
    Date: May 2024
    Format: pdf
  • Piloting into the Future: Publicly available R-based Submissions to the FDA
    A publicly available drug submissions with the R language as the core analysis language has been lacking and limits wider adoption within the Pharma industry. The R Consortium R Submission Working Group (RCRSWG) seeks to test the concept that a R-based language submission language can be bundled into a submission package and transferred successfully to FDA reviewers.
    Author: Ben Straub
    Date: May 2024
    Format: pdf
  • Running Python Code Inside a SAS Program
    Did you know that you can execute Python code inside a SAS Program? With the SAS Viya Platform, you can call PROC PYTHON and pass variables and datasets easily between a Python call and a SAS program.
    Author: Jim Box
    Date: May 2023
    Format: pdf
  • For SAS programmers, it’s time to learn R!
    This paper covers the differences between SAS syntax and R syntax in clinical trial programming, which can help SAS-based peers quickly master the use of R methods and gain a competitive edge in the industry.
    Author: Sookie Kong
    Date: September 2023
    Format: pdf
  • Building an Internal R Package for Statistical Analysis and Reporting in Clinical Trials
    This paper outlines the essential components of an R package and the valuable tools to help create these components. An R package is similar to a well-built SAS macro library; this includes a collection of functions, instruction documentation, sample data, and testing code with validation evidence.
    Author: Huei-Ling Chen, Heng Zhou, Nan Xiao
    Date: May 2023
    Format: pdf
  • Enterprise-level Transition from SAS to Open-Source Programming for the whole department
    The paper is written for those who wants to learn the enterprise-level transition from SAS to open-source programming. The paper will introduce the transition project that the whole department of 150+ SAS programmers has completely moved from SAS to Open-source programming.
    Author: Kevin Lee, Manikandan Jeeva
    Date: July 2022
    Format: pdf
  • R syntax for SAS programmers
    This paper provides a side-by-side comparison of R and SAS syntax, using examples of a clinical programmer’s typical SAS code adjacent to the corresponding R syntax.
    Author: Max Cherny
    Date: May 2022
    Format: pdf
  • Python-izing the SAS Programmer: A Brief Introduction to the World of Objects
    This presentation offers SAS programmers a gentle introduction to object‑oriented programming in Python by mapping familiar SAS syntax and data types to Python objects, demonstrating how lists, dictionaries, and functions correspond to SAS constructs like arrays, formats, and data step logic.
    Author: Mike Molter
    Date: October 2021
    Format: pdf
  • Packages in R for Clinical Data Analysis, Let's Demystify
    The scope of this paper is to present the RStudio program that demonstrate the relevant Packages in R (like data.table, dplyr, tidyr and knitr etc.)
    Author: Nagalakshmi Kudipudi
    Date: May 2021
    Format: pdf

  • What’s in a message?
    Enhancing Error and Warning Messages for the Admiral R Package for ADaM Datasets
    Author: Ben Straub, Daniel D. Sjoberg
    Date: March 2025
    Format: pdf
  • Bridging the Gap: Creating and Debugging User-Defined R Functions from a SAS Programmer's Perspective
    This paper bridges that gap by providing a detailed comparison of SAS macros and R functions, exploring R’s robust debugging tools.
    Author: Hrideep Antony
    Date: March 2025
    Format: pdf
  • How Can Python Be Used in Clinical Trials?
    In this paper, we aim to provide a comprehensive overview of our research and highlight various examples of Python's utility. First, we will discuss the strengths and limitations of Python.
    Author: Andras Kasa, Matt Davies, Pierre Dostie
    Date: November 2023
    Format: pdf
  • Post-mortem Logs in R
    Outlined is code snippet in the R programming language that allows for a post-mortem analysis of the log files of program. The snippet will programmatically scan multiple logs generated by the {logrx} package.
    Author: Teckla Akinyi
    Date: February 2023
    Format: pdf
  • R String Manipulation Functions vs SAS Character Functions
    In this paper will do a comparative analysis of character functions between R and SAS. This is a better way of learning any new programming language.
    Author: Jagadish Katam
    Date: November 2021
    Format: pdf
  • An Introduction to Python: The SAS Programmers Guide
    This paper aims to explore several aspects of the Python programming language, providing learning resources to any SAS programmers interested in learning the language like myself, and to review the plausibility of future use of Python within the industry.
    Author: Bradley Harris
    Date: November 2019
    Format: pdf

  • Pharmaverse: packages
    The `Pharmaverse Packages` page provides a curated list of R packages geared toward clinical trial programming and regulatory data workflows. It highlights core tools like admiral for CDISC ADaM dataset creation, rtables for complex reporting tables, teal and its modules for interactive Shiny-based data exploration, and mmrm for mixed-model analyses—each package featuring descriptions, download metrics, and recent activity.
  • tidyverse: packages
    The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.
  • admiral
    A toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in R. ADaM datasets are a mandatory part of any New Drug or Biologics License Application submitted to the United States Food and Drug Administration (FDA).
  • metacore
    Create an immutable container holding metadata for the purpose of better enabling programming activities and functionality of other packages within the clinical programming workflow.
  • metatools
    Uses the metadata information stored in `metacore` objects to check and build metadata associated columns.
  • dplyr
    dplyr is an R package whose set of functions are designed to enable dataframe (a spreadsheet-like data structure) manipulation in an intuitive, user-friendly way. It is one of the core packages of the popular tidyverse set of packages in the R programming language.
  • purrr
    purrr enhances R’s functional programming (FP) toolkit by providing a complete and consistent set of tools for working with functions and vectors.
  • haven
    Haven enables R to read and write various data formats used by other statistical packages. Currently it supports: SAS, SPSS, Stata.
  • data.table
    data.table is an R package that provides an enhanced version of a data.frame, the standard data structure for storing data in base R.
  • ggplot2
    ggplot2 is a R package dedicated to data visualization. It allows to build almost any type of chart.
  • xportr
    xportr is designed for clinical programmers to create CDISC compliant xpt files- ADaM or SDTM.
  • diffdf
    diffdf compares two data.frame objects and provides a detailed summary of any differences that were found between them. The package has it’s origins in supporting QC workflows for product development within the pharmaceutical industry aiming at being a light weight alternative to SAS’s PROC COMPARE.
  • logrx
    The purpose of the logrx package is to generate a log upon execution of an R script which enables traceability and reproducibility of the executed code.
  • logr
    The logr package helps create log files for R scripts. The package provides easy logging, without the complexity of other logging systems.
  • arsenal
    The goal of `arsenal` is to make statistical reporting easy. It includes many functions which the useR will find useful to have in his/her "arsenal" of functions. Each of these functions is motivated by a local SAS macro or procedure of similar functionality.
  • cheatsheet
    A simple R package that downloads helpful R cheatsheets from the repository maintained by Posit.

  • Package `data.table`
    Fast aggregation of large data, fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write.
    Author: Tyson Barrett, Matt Dowle, Arun Srinivasan, Jan Gorecki, Michael Chirico, Toby Hocking, Benjamin Schwendinger, Ivan Krylov
    Date: July 2025
    Format: pdf
  • Package `purrr`
    A complete and consistent functional programming toolkit for R.
    Author: Hadley Wickham
    Date: July 2025
    Format: pdf
  • Package `admiral`
    Reference manual on the admiral package
    Author: Ben Straub
    Date: June 2025
    Format: pdf
  • Package `logrx`
    A utility to facilitate the logging and review of R programs in clinical trial programming workflows.
    Author: Nathan Kosiba, Thomas Bermudez, Ben Straub, Michael Rimler, Nicholas Masel, Sam Parmar
    Date: May 2025
    Format: pdf
  • Package `haven`
    Import foreign statistical formats (including sas7bdat) into R via the embedded 'ReadStat' C library
    Author: Hadley Wickham, Evan Miller, Danny Smith
    Date: May 2025
    Format: pdf
  • Package `ggplot2`
    Create Elegant Data Visualisations Using the Grammar of Graphics.
    Author: Hadley Wickham, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Kara Woo, Dewey Dunnington, Teun van den Brand
    Date: April 2025
    Format: pdf
  • Package `logr`
    Contains functions to help create log files. The package aims to overcome the difficulty of the base R sink() command.
    Author: David Bosak, Rikard Isaksson
    Date: March 2025
    Format: pdf
  • Package `xportr`
    Tools to build CDISC compliant data sets and check for CDISC compliance.
    Author: Eli Miller, Ben Straub, Zelos Zhu, Ethan Brockmann, Vedha Viyash, Andre Verissimo, Sophie Shapcott, Celine Piraux, Kangjie Zhang, Adrian Chan, Sadchla Mascary
    Date: January 2025
    Format: pdf
  • Package `diffdf`
    Functions for comparing two data.frames against each other.
    Author: Craig Gower-Page, Kieran Martin
    Date: September 2024
    Format: pdf
  • Package `metatools`
    Reference manual on the metatools package
    Author: Christina Fillmore
    Date: June 2024
    Format: pdf
  • Package `metacore`
    Reference manual on the metacore package
    Author: Christina Fillmore
    Date: May 2024
    Format: pdf
  • Package `dplyr`
    A Grammar of Data Manipulation
    Author: Hadley Wickham
    Date: November 2023
    Format: pdf
  • Package `sqldf`
    Provides an easy way to perform SQL selects on R data frames.
    Author: G. Grothendieck
    Date: October 2022
    Format: pdf
  • Package `arsenal`
    An Arsenal of 'R' functions for large-scale statistical summaries, which are streamlined to work within the latest reporting tools in 'R' and 'RStudio' and which use formulas and versatile summary statistics for summary tables and models.
    Author: Ethan Heinzen, Jason Sinnwell, Elizabeth Atkinson, Tina Gunderson, Gregory Dougherty
    Date: October 2022
    Format: pdf
  • Package `tidyverse`
    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.
    Author: Hadley Wickham
    Date: February 2023
    Format: pdf

  • numpy
    NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, random simulation and much more.
  • pandas
    pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
  • matplotlib
    Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible.
  • seaborn
    Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
  • saspy
    This module provides Python APIs to the SAS system. You can start a SAS session and run analytics from Python through a combination of object-oriented methods or explicit SAS code submission. You can move data between SAS data sets and Pandas dataframes and exchange values between python variables and SAS macro variables.

  • SDTM programming in R using {sdtm.oak} package
    SDTM programming in R using {sdtm.oak} package. Workshop recorded as part of the 2024 R/Pharma Workshop Series.
    Author: Rammprasad Ganapathy
    Date: March 2025
    Platform: YouTube
  • GSK's R Journey: From Pilot Projects to Enterprise Adoption
    Understand the obstacles GSK faces in shifting to end-to-end use of R due to existing processes and timelines, and learn about their internal goals to achieve parity between R and other proprietary languages in their biostatistics toolkit.
    Author: Posit
    Date: November 2024
    Platform: YouTube
  • Roche's End-to-End R Journey to Submission
    In this web event, the Roche team will share a pioneering experience of end-to-end R submission for a new drug application to the FDA, EMA, and NMPA.
    Author: Posit, Roche
    Date: September 2024
    Platform: YouTube
  • R-based Test Submission Package for FDA Evaluation: A Milestone in R-based Regulatory Submissions
    This demo highlights the efforts led by Joel Laxamana, a statistical programmer at Genentech, to develop an R-based approach for submitting clinical trial packages to the FDA
    Author: Joel Laxamana
    Date: August 2024
    Platform: YouTube
  • Best Clinical programming language for freshers
    Is sas relevant in 2024? R vs SAS vs Python.
    Author: Mr Seeker
    Date: July 2024
    Platform: YouTube
  • Clinical submissions with admiral: an R-based ADaM solution with Ben Straub
    This recording will provide an overview of admiral, an R-based solution for creating ADaM datasets.
    Author: Ben Straub
    Date: June 2024
    Platform: YouTube
  • Python for SAS Programmers (Data Camp 2024)
    This video is part of the 2024 Spring Data Camp series and provides information on Python for SAS Programmers
    Author: Colton Rathe
    Date: May 2024
    Platform: YouTube
  • R for SAS Programmers (Data Camp 2024)
    This video is part of the 2024 Spring Data Camp series and provides information an overview of R for SAS programmers.
    Author: Daniel Anderson
    Date: May 2024
    Platform: YouTube
  • Mastering ggplot2 in R: Transform Your Data into Compelling Visuals!
    Dr. Padilla introduces viewers to the ggplot function in R, a powerful tool for data visualization. After creating a basic plot with toy data, she explains layering concepts, the grammar of graphics, and how data can be encoded using color, shape, and size, demonstrating how to derive insights from visual patterns in the data.
    Author: Lace Padilla
    Date: September 2023
    Platform: YouTube
  • Clinical R Programming: The Full Course – Learn How to Use R for Clinical Research
    The R Tutorials for beginners start from zero and move gradually to more complex concepts. This Clinical R Programming Tutorial will take you from the basics of R programming language to more advanced concepts. R programming for data science for beginners, you will learn about data structures, data analysis, programming, graphs and charts, and fun applications of R Programming to give you a complete understanding.
    Author: https://greatonlinetraining.com/r
    Date: October 2022
    Platform: YouTube
  • Making the move to open source – a SAS to R migration
    In this webinar, the author considers the whys and hows: why this transition is typically much more than a technical migration – and shares his perspective on how to plan and execute a successful implementation, with practical advice and real-world examples.
    Author: Tom Bowling, Bruce Seymour
    Date: June 2022
    Platform: Ascent
  • Python Coding for SAS Programmers
    This video provides demonstrations and examples of using Python Coding for SAS programmers.
    Author: Derek Cruikshank
    Date: December 2023
    Platform: YouTube
  • How to import SAS data file in Python
    This video will help in understanding the method to read the statistical file i.e SAS file in Python
    Author: Urmisha Patel
    Date: May 2022
    Platform: YouTube
  • From SAS to R
    Joe Korzun, Data Scientist Consultant at Procogia presents on using SAS to R for medicine. Korzun starts by giving an introduction to SAS, R, and other R Packages, and also how they are currently used in the healthcare industry.
    Author: Joe Korszun
    Date: August 2021
    Platform: YouTube
  • Import SAS (sas7bdat) files into Python
    A quick and easy way to read .sas7bdat files from SAS into python.
    Author: Data Liam
    Date: January 2021
    Platform: YouTube

  • Tables in Clinical Trials with R
    It presents various aspects of creating tables with the R language to analyze and report clinical trials data. The book was initiated by the R Consortium working group R Tables for Regulatory Submissions (RTRS).
    Author: Karima Ahmad, Gabe Becker, Emily de la Rua, Christina Fillmore, David Gohel, Richard Iannone, James J. Kim, Alexandra Lauer, Duncan Murdoch, Joseph Rickert, Adrian Waddell, Sheng-Wei Wang, Yilong Zhang
    Date: June 2024
  • The Epidemiologist R Handbook: Transition to R
    SAS is commonly used at public health agencies and academic research fields. Although transitioning to a new language is rarely a simple process, understanding key differences between SAS and R may help you start to navigate the new language using your native language.
    Author: Neale Batra, Alex Spina, Paula Blomquist, Finlay Campbell, Henry Laurenson-Schafer, Isaac Florence, Natalie Fischer, Aminata Ndiaye, Liza Coyer, Jonathan Polonsky, Yurie Izawa, Chris Bailey, Daniel Molling, Isha Berry, Emma Buajitti, Mathilde Mousset, Sara Hollis, Wen Lin
    Date: September 2024
  • R Markdown: The Definitive Guide
    With R Markdown you can: Compile a single R Markdown document to a report in different formats, such as PDF, HTML, or Word., Create notebooks in which you can directly run code chunks interactively, Make slides for presentations, Produce dashboards with flexible, interactive, and attractive layouts.
    Author: Yihui Xie, J. J. Allaire, Garrett Grolemund
    Date: December 2023
  • Introduction to R for SAS programmers
    On this page you find the materials used in the workshop Introduction to R for SAS programmers taking place on January 17th, 2023.
    Author: Sadchla Mascary, Stefan Thoma, Zelos Zhu, Thomas Neitmann
    Date: January 2023
  • R for Fledglings
    We’ve been asked to provide a short introduction to R and its utility in natural resource management. In this short introduction, we can guarantee one thing: you won’t learn R in a few days. That would be like learning to speak French in a few days. To actually learn R, you need to practice...
    Author: Therese M. Donovan, Michelle Brown, Jonathan E. Katz
    Date: February 2021
  • Python for SAS users
    Python for SAS Users provides the most comprehensive set of examples currently available. It contains over 200 Python scripts and approximately 75 SAS programs that are analogs tothe Python scripts.
    Author: Sarah Chen, Randy Betancourt
    Date: September 2019
  • ggplot2: Elegant Graphics for Data Analysis
    While this book gives some details on the basics of ggplot2, its primary focus is explaining the Grammar of Graphics that ggplot2 uses, and describing the full details. It will help you understand the details of the underlying theory, giving you the power to tailor any plot specifically to your needs.
    Author: Hadley Wickham, Danielle Navarro, Thomas Lin Pedersen
    Date: 2009 (firts edition)

  • R sqldf: 7 Examples of How to Navigate R Data Frames with SQL
    R sqldf is a package that aims to bridge the gap for R newcomers with a decent SQL background. In plain English, you can play with R dataframes by writing SQL. No dplyr (or an equivalent). No hundreds of new methods you need to learn and relearn.
    Author: Dario Radečić
    Date: November 2024
  • How to write a SQL query in R?
    Sqldf package is a convenient R tool that allows the execution of SQL operations on R data frames
    Author: Geeksforgeeks
    Date: February 2023
  • Comparing Structured Query Language and R
    A hands-on tutorial that demonstrates how to perform database queries directly from R—showing how to connect to a database, write SQL queries, and import the results into R data frames for analysis.
    Author: RPubs

  • R vs Python
    R Programming Language and Python are both used extensively for Data Science. Both are very useful and open-source languages as well. For data analysis, statistical computing, and machine learning Both languages are strong tools with sizable communities and huge libraries for data science jobs.
    Author: Achint Kaur
    Date: July 2025
  • What Is the Difference Between SAS vs. R?
    SAS is easier to learn and ideal for large-scale data management, but R offers superior data visualization and is completely free, making it more attractive for small to midsize organizations and statistical analysts.
    Author: Indeed Editorial Team
    Date: June 2025
  • Transitioning Clinical Research from SAS to R
    A detailed article from ProCogia on the transition from SAS to R in clinical trials, with a special focus on Pharmaverse packages (e.g. admiral, xportr, sdtm.oak) for creating CDISC-compliant SDTM and ADaM datasets
    Author: Shuyu Fan
    Date: April 2025
  • Quantifying the Difference: Why R and SAS Don’t Always See Eye to Quantile
    Both R and SAS are powerful tools used for statistical analysis, but if you've ever compared the quantile results from both platforms, you may have noticed that they can produce different values.
    Author: Sarita Singh
    Date: March 2025
  • Enabling the pharmaceutical programming community to develop ADaM datasets in R: Four Perspectives From the Maintainers
    The article features maintainers from different companies discussing the admiral R package — a modular, collaborative tool designed to streamline ADaM dataset creation for clinical trials within the Pharmaverse ecosystem. They explore its real-world impact across pharma companies, the structure of its core and extension packages, and the community-driven vision behind its continued development.
    Author: Edoardo Mancini, Ben Straub, Stefan Bundfuss, Fanny Gautier
    Date: March 2025
  • Validation and QC process in Clinical programming: A Comparison of R Packages and SAS Proc Compare
    Comparison of SAS’s PROC COMPARE and various R packages (like diffdf, arsenal, compareDF, and all.equal()) for dataset QC in clinical programming, highlighting R’s flexibility, cost‑effectiveness, and growing suitability for regulatory validation workflows compared to SAS’s established, but more rigid, tool.
    Author: Hamza Rahal
    Date: October 2024
  • A Guide to R Package Validation in Pharma
    The post emphasizes the essential need for validating R packages in the pharmaceutical industry to ensure data accuracy, reproducibility, and compliance with GxP standards, as open-source usage grows for clinical trials.
    Author: Gift Kenneth
    Date: October 2024
  • The Similarities Between SAS Macros and R Functions/Packages in Clinical Programming and the need for Coexistence
    SAS macros and R functions (and packages) serve the same core purpose—enabling reusable, parameterized automation—making them ideal for standardizing clinical trial workflows
    Author: Hamza Rahal
    Date: September 2024
  • SAS Versus R
    SAS and R are important data analytics tools used in today’s tech world. Both tools are extensively used by Data Scientists and Data Analysts. Making a choice between SAS and R has been a longstanding debate in the world of Data Science.
    Author: Shailesh Bhagat
    Date: August 2024
  • Comparing SAS vs R for Clinical Trials Statistical Programming
    While R is gaining significant traction in clinical trial statistical programming due to its modern capabilities, a full replacement of SAS by 2024 remains unlikely, as SAS's entrenched regulatory role and legacy presence continue to sustain its dominance
    Author: NoyMed
    Date: 2024
  • Jupyter Notebook VS JupyterLab - a Comprehensive Guide
    Jupyter Notebook and JupyterLab are both open-source web applications that allow you to create and share documents containing live code, equations, visualizations, and narrative text. The primary purpose of these tools is to provide an interactive environment for data exploration, analysis, and visualization.
    Author: Saturn Cloud
    Date: December 2023
  • SAS to R Migration: Time for Innovative Change in Clinical Research
    Discussion of the shift in clinical research from SAS to R, driven by R’s advantages in speed, flexible analytics, data visualization, and open-source ecosystem, which make clinical trials more efficient and agile.
    Author: Pavan Vipparthi
    Date: October 2023
  • R Programming and Pharmaceutical Data Analysis (Packages for Clinical Trial Data)
    It highlights several open‑source R packages built by the pharmaceutical industry—tailored for clinical trial analysis and reporting—significantly expanding R’s role in life‑sciences workflows.
    Author: Indraneel Chakraborty
    Date: May 2023
  • SAS vs. R: Which Is Better?
    SAS excels at handling large-scale data processing with reliable performance and strong enterprise support, while R—being free and open-source—offers superior data visualization, customization, and easy collaboration.
    Author: Sakshi Gupta
    Date: October 2021
  • How SAS Users should Think about Python
    There are quite a few resources out there to help SAS users get started with writing some Python code. But for those of us who have SAS as a first language, switching to Python is quite a bit more than translating lines of code. It’s a change in world-view. Here’s how to view the world as a Pythonista.
    Author: John Curry
    Date: October 2021
  • Introduction to Python in Clinical Trials
    It introduces Python as a modern alternative to SAS for clinical trial programming - showing how it can handle tasks like CDISC SDTM mapping, SAS dataset metadata extraction, and general analytics workflows.
    Author: Allwyn Dsouza
    Date: September 2021
  • R Programming Datasets - Are they reliable & efficient for SAS Datasets?
    The article compares R and SAS for clinical trial datasets, noting that while SAS handles large data efficiently, R offers flexible and reliable tools that make it a strong alternative for data transformation and analysis.
    Author: Quanticate
    Date: August 2021
  • SAS, R, or Python Survey Results (2020)
    In the 2020 Burtch Works survey of over 1,000 data scientists and analytics professionals, Python emerged as the preferred tool (47% of votes), with R and SAS trailing behind.
    Date: December 2020
  • What is the difference between Jupyter Notebook and JupyterLab?
    Jupyter Notebook is a web-based interactive computational environment for creating Jupyter notebook documents. It supports several languages like Python (IPython), Julia, R etc. and is largely used for data analysis, data visualization and further interactive, exploratory computing.
    Author: krist (stackoverflow)
    Date: June 2018
  • SAS vs. R Discussion Prep
    A debate between SAS and R, focusing on their relative strengths, such as R's flexibility and cost-efficiency versus SAS's legacy reliability and ease of use
    Author: Posit Community
    Date: November 2017
  • Master R by Comparing with SAS Overview
    Practical cheat sheets, side‑by‑side comparisons of common tasks, and industry‑specific guidance (like pharmaceutical CDISC workflows) to replicate familiar SAS operations in a more streamlined, open‑source R environment
    Author: R-Guru
  • The Big Conversion: from SAS to R (and back)
    The article explores the challenges and realities of SAS-to-R (and R-to-SAS) code conversion in organizations where both tools may coexist. It emphasizes the importance of understanding legacy code, modularizing the conversion process, and validating results, while also encouraging data scientists to become fluent in both languages as the industry shifts toward greater interoperability.
    Author: Lee Medoff
    Date: November 2015
  • R vs SAS, why is SAS preferred by private companies?
    I learned R but it seems that companies are much more interested in SAS experience. What are the advantages of SAS over R?
    Author: StackExchange
    Date: August 2012

  • Keyboard Shortcuts in the RStudio :: CHEAT SHEET
    It provides a comprehensive reference for key shortcuts - such as console, source editor, help, git, plots, and terminal - covering actions like running code, navigating panes, finding functions, and accessing the command palette
    Author: RStudio
  • R and R Studio Reference Guides and 'Cheat Sheet' Compilation
    The following is a set of reference guides and "cheat sheets" that has been gathered from various reproduced, distributed, or copied without permission from the sheet designer / author (s).
    Author: These are resources on the Internet.
    Format: pdf
  • Package `rstudioapi`
    The rstudioapi package is designed to make it easy to conditionally access the RStudio API from CRAN packages, avoiding any potential problems with R CMD check. This package contains a handful of useful wrapper functions to access the API.
    Author: Kevin Ushey
    Date: January 2014
  • R-analyst Cheat sheet: Data Visualization in R
    R Programming offers a set of inbuilt functions & libraries (such as ggplot2, leaflet, lattice) to create visualizations & present data stories. Below is a guide to create basic and advanced visualizations in R
    Author: Analytics Vidhya
    Format: jpg

  • Git Cheat Sheet (GitHub)
    This cheat sheet features the most important and commonly used Git commands for easy reference.
    Author: GitHub
    Format: pdf
  • Git Cheat Sheet (GitLab)
    Git Cheat Sheet by GitLab
    Author: GitLab
    Format: pdf
  • GitLab vs GitHub: Key Differences in 2025
    Both GitLab and GitHub allow you to centrally store your Git repositories and collaborate on them via a web-based interface. However, while they offer similar basic functionality, they also have several distinguishing factors.
    Author: James Walker
    Date: April 2025
  • Complete Git Cheat Sheet
    This cheat sheet will give you the download on all things Git.
    Author: Richie Cotton
    Date: June 2022
  • Pro Git book
    Is a fully-updated guide to Git and its usage in the modern world. A book by Git experts to turn you into a Git expert, introduces the world of distributed version control, shows how to build a Git development workflow.
    Author: Scott Chacon, Ben Straub
    Date: November 2014
    Format: pdf, epub

  • pandas: Comparison with SAS
    For potential users coming from SAS this page is meant to demonstrate how different SAS operations would be performed in pandas.
    Author: NumFOCUS
    Date: 2025
  • Deep Dive into xportr
    This vignette will explore in detail all the possibilities of the {xportr} package for applying information from a metadata object to an R created dataset using the core {xportr} functions.
    Author: Eli Miller
    Date: October 2024
  • Introduction to diffdf
    The purpose of diffdf is to provide proc compare like functionality to R for use in second line programming.
    Author: Craig Gower, Kieran Martin
    Date: September 2024
  • Graphics in R with ggplot2
    It explains how to create common visualizations in R -- such as scatter plots, line charts, histograms, boxplots, barplots, density plots, and even raincloud plots -- using the ggplot2 package, based on the Grammar of Graphics framework.
    Author: Antoine Soetewey
    Date: August 2021
  • ggplot2 barplots : Quick start guide - R software and data visualization
    This R tutorial describes how to create different plots using R software and ggplot2 package.
    Author: Statistical tools for high-throughput data analysis
    Date: May 2020
  • A data.table and dplyr tour
    data.table and dplyr are two R packages that both aim at an easier and more efficient manipulation of data frames. Here is a quick overview of the main differences.
    Author: Atrebas
    Date: March 2019
  • Comparing two data frames
    Compare of dplyr::all_equal, janitor::compare_df_cols, vetr::alike, diffdf::diffdf
    Author: Hadley Wickham, Garrett Grolemund
    Date: 2016-2019
  • Top 50 ggplot2 Visualizations - The Master List (With Full R Code)
    What type of visualization to use for what sort of problem? This tutorial helps you choose the right type of chart for your specific objectives and how to implement it in R using ggplot2.
    Author: Selva Prabhakaran
    Date: 2016-2017

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