"Introduction to R for Data Analysis", GESIS Summer School in Survey Methodology 2022
Materials for the 2022 GESIS Summer School in Survey Methodology course "Introduction to R for Data Analysis"
Stefan Jünger ([email protected], @StefanJuenger); Veronika Batzdorfer ([email protected]; Johannes Breuer ([email protected], @MattEagle09)
The open source software package R is free of charge and offers standard data analysis procedures as well as a comprehensive repertoire of highly specialized processes and procedures, even for complex applications. After providing an introduction to the basic concepts and functionalities of R, we will go through a prototypical data analysis workflow in the course: import, wrangling, exploration, (basic) analysis, reporting.
- prior experience with quantitative data analysis, basic statistics, and regression
- experience with using other statistical packages (e.g., SPSS or Stata) is helpful, but not a requirement
By the end of the course participants should be:
- Comfortable with using
Rand RStudio - Able to import, wrangle, and explore their data with
R - Able to conduct basic visualizations and analyses of their data with
R - Able to generate reproducible research reports using
R Markdown
| Day | Time | Topic |
|---|---|---|
| Monday | 09:30 - 10:30 | Getting Started with R and RStudio |
| Monday | 10:30 - 10:45 | Break |
| Monday | 10:45 - 12:00 | Getting Started with R and RStudio |
| Monday | 12:00 - 13:00 | Lunch Break |
| Monday | 13:00 - 14:00 | Data Import & Export |
| Monday | 14:00 - 14:15 | Break |
| Monday | 14:15 - 15:30 | Data Import & Export |
| Day | Time | Topic |
|---|---|---|
| Tuesday | 09:30 - 10:30 | Data Wrangling - Part 1 |
| Tuesday | 10:30 - 10:45 | Break |
| Tuesday | 10:45 - 12:00 | Data Wrangling - Part 1 |
| Tuesday | 12:00 - 13:00 | Lunch Break |
| Tuesday | 13:00 - 14:00 | Data Wrangling - Part 2 |
| Tuesday | 14:00 - 14:15 | Break |
| Tuesday | 14:15 - 15:30 | Data Wrangling - Part 2 |
| Day | Time | Topic |
|---|---|---|
| Wednesday | 09:30 - 10:30 | Exploratory Data Analysis |
| Wednesday | 10:30 - 10:45 | Break |
| Wednesday | 10:45 - 12:00 | Exploratory Data Analysis |
| Wednesday | 12:00 - 13:00 | Lunch Break |
| Wednesday | 13:00 - 14:00 | Data Visualization - Part 1 |
| Wednesday | 14:00 - 14:15 | Break |
| Wednesday | 14:15 - 15:30 | Data Visualization - Part 1 |
| Day | Time | Topic |
|---|---|---|
| Thursday | 09:30 - 10:30 | Confirmatory Data Analysis |
| Thursday | 10:30 - 10:45 | Break |
| Thursday | 10:45 - 12:00 | Confirmatory Data Analysis |
| Thursday | 12:00 - 13:00 | Lunch Break |
| Thursday | 13:00 - 14:00 | Data Visualization - Part 2 |
| Thursday | 14:00 - 14:15 | Break |
| Thursday | 14:15 - 15:30 | Data Visualization - Part 2 |
| Day | Time | Topic |
|---|---|---|
| Friday | 09:30 - 10:30 | Reporting with R Markdown |
| Friday | 10:30 - 10:45 | Break |
| Friday | 10:45 - 12:30 | Reporting with R Markdown |
| Friday | 12:30 - 13:30 | Lunch Break |
| Friday | 13:30 - 14:30 | Outlook, Q&A |
1_2 Data Types, Import, & Export
Appendix - Setup and Workflow Help
1_2_3 Statistical Software Files
1_2_3 Statistical Software Files
2_2_1 Create & Transform Variables
2_2_3 Across & Aggregate Variables
2_2_4 Factors & Conditional Recoding
2_2_1 Create & Transform Variables
2_2_3 Across & Aggregate Variables
2_2_4 Factors & Conditional Recoding
Appendix - Exploring Missingness & Outliers