Master R by Comparing with SAS Overview (Join Us)

This section provides a comprehensive overview and review of the similarities and differences between SAS and R so that organizations can make more informed decisions in their SAS to R migration plans and vision.  At least 10 top pharma companies and regulatory agencies that make up the R Consortium along with CDISC have adapted R!

R is a powerful programming language that is cost effective (at least 40%) , reliable and custom built!  R is both challenging and creative since R packages are similar to putting the hundreds of pieces together in a jigsaw puzzle.  R is extensively used in public health projects, healthcare economics, exploratory/scientific analysis, trend identification, generation of Plots/Graphs, specific Stat analysis, and machine learning.  See pharmaverse section for gxp validation.

R is ideal for:

1. Smaller projects with budget constraints.
2. Research prioritizing compelling and interactive data visualizations.
3. Teams comfortable with open-source platforms and willing to invest in learning R.
4. Projects requiring flexibility and customization beyond standard functionalities.
5. Collaboration with researchers and institutions adopting R workflows.

R unique features include:

1. Flexibility and customization to develop and share R packages.
2. More quickly adapt cutting-edge statistical techniques.

R and Shiny Apps used by FDA include:

1. Baseline comparison of treatment groups
2. Data Anomaly Detection
3. Interactive Support of FDA Submissions
   

R and Shiny Apps used by Pharma Industry iinclude:

1. Clinical Data Validation
2. Interactive Clinical Reporting of Tables, Lists and Graphs
3. PSUR and DSUR Review and Reporting
4. Safety Data Review and Monitoring of Con Meds
5. Interactive-Visualization Patient Profile 

R generation of plots/graphs include:

1. Identifying the trends and getting a better understanding of data visually
2. Understanding the Randomization pattern and checking for bias
3. Check for Outliers in any lab values or other values of interest
4. Supports check stratification impact and subgroup analysis checks
5. Site-level analytics – such as identification of no. SAEs and deaths

R migration includes:

1. Team Training and Mentoring
2. Show Value
3. Build Credibility
4. Interactivity is the Key
5. Find Quick Wins
6. Excel to Shiny
7. 'Borrow Code' 

For sponsors who want to keep SAS for legacy studies, they may be interested to use R for custom graphs for data visualization or Shiny apps for user and clinical data interactions.  For sponsors who want to be submission ready, it makes sense to apply caution until all R packages have been installed, tested and can produce SDTMs, ADaMs and TLGs.  Sponsors may also want to leverage pharmaverse packages for source or qc purposes.

While most everything from SAS can be replicated in R, there is a steep learning curve since R concepts and process flow are more object oriented. R has meanings for special characters such as [], {} and () for example.  In addition, most of R syntax consists of functions which are similar to SAS functions and macro programs. So, knowing how to call SAS functions and macros will help to understand, write and execute R functions.  Like SAS macro programs, R functions can have positional, keyword and default parameters. See list of R packages install in SAS LSAF.