Data Analysis with the R Programming Language

Improving knowledge and skills in using the R programming language for:

• data processing;
• data comparison;
• data visualization;
• reporting.

• Data analysts;
• Risk and business analysts;
• Economists;
• Researchers;
• Auditors;
• Business controllers.

• Describe the steps of a typical data analysis project.
• Work effectively in the RStudio environment.
• Write code according to best practices.
• Interact with the outside world by importing and exporting data to and from R, including files on the Internet and SQL databases.
• Perform data “cleaning” to prepare it for further processing.
• Perform the most commonly used R programming language operations.
• Manipulate multiple tables simultaneously to answer questions and extract valuable information from data.
• Create elegant visualizations appropriate to the problem.
• Apply exploratory data analysis.
• Create and use new features. Control code execution and use loops.

There are no specific prerequisites.

Training materials and practical work examples prepared by BDA.

Not intended.

1. Basic elements of data analysis projects.
2. Recommendations for preparing the R and RStudio environment for work.
3. R programming best practices.
4. R data structures and functions.
5. Creating new functions.
6. Vector operations.
7. An effective approach to transforming and merging large tables.
8. The concept of clean data and data “cleaning”.
9. Introduction to working with text.
10. Choosing a data visualization type and visualization with ggplot2.
11. Exploratory data analysis (Explorary Data Analysis).
12. Importing external data from local files, the Internet, SQL database.
13. Exporting data.
14. R program execution control structures and cycles. Correlation analysis.