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.
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.
3 - Syntax
Help in R, advanced comparison between SAS and R, objects in R, loop syntax, create functions
4 - Packages
What a package is, 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, exemple of Shiny
8 - Ecosystem
R and Big Data
- 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.
- Prior knowledge in SAS (or SPSS)