From SAS to R

After emerging from the academic world, R, the open-source successor of the S statistical language is currently seeing huge growth in its ecosystem, user community and enterprise use. It considerably gained in reliability and benefited from the support of huge IT actors like Microsoft which natively integrates it in its SQL Server solution.

It is now one of the two most used data science languages with Python. Its capabilities extend from data ingestion to the creation of interactive web applications, while also being capable of traditional statistics, machine learning, map visualization, etc., making it a very useful tool for data labs to master.

Many leading organizations (bank, insurance companies, pharmaceutical groups, etc.) have already completed their transition from SAS to the R ecosystem and have witnessed great benefits as a result. But many more are still in the process and this course is made for them.

Trainers

Romain
Romain
Head of Research
Romain
Head of Research
Mélanie
Mélanie
Data Scientist
Mélanie
Data Scientist

Training plan

1 – Generalities

Introduction to R
Global comparison of both tools
Evolution of R

2 – Development tools

R development tools
R GUI
R Studio IDE
Jupyter Notebook, etc.
Exemple on RStudio

3 – Syntax

Help in R
Advanced comparison between SAS and R
Objects in R,
Loop syntax
Create functions

4 – Packages

What is a package?
Installation and call
CRAN

5 – Data acquisition and manipulation

R / SAS file loading
Connection with a database
R / SAS manipulations
API exploration

6 – Data processing

R / SAS descriptive statistics
Supervised and unsupervised classification

7 – Visualization

Data visualization packages
Use of ggplot2
Shiny example

8 – Ecosystem

R and Big Data


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Training goals
  • Introduction to the open-source R language for SAS users
  • Reproducing in R familiar SAS statistical analysis and data manipulations
  • (Re)discovering the general programming concepts favorably replacing SAS macro language
  • Approaching more advanced use cases: interactive visualizations, Machine Learning, etc.
Duration
2 days
Needed skills
  • Prior knowledge in SAS (or SPSS)
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