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"Introduction to R for Data Analysis", GESIS Summer School in Survey Methodology 2021

Materials for the 2021 GESIS Summer School in Survey Methodology course "Introduction to R for Data Analysis"

Johannes Breuer (johannes.breuer@gesis.org, @MattEagle09); Stefan Jünger (stefan.juenger@gesis.org, @StefanJuenger)

Please link to the workshop GitHub repository


Course description

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. In addition to 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.

Prerequisites

  • 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

Timetable

Day 1

Day Time Topic
Monday 10:30 - 11:30 Getting Started with R and RStudio
Monday 11:30 - 11:45 Break
Monday 11:45 - 12:45 Getting Started with R and RStudio
Monday 12:45 - 13:45 Lunch Break
Monday 13:45 - 15:00 Data Import & Export
Monday 15:00 - 15:15 Break
Monday 15:15 - 16:30 Data Import & Export

Day 2

Day Time Topic
Tuesday 10:00 - 11:15 Data Wrangling - Basics
Tuesday 11:15 - 11:30 Break
Tuesday 11:30 - 12:45 Data Wrangling - Basics
Tuesday 12:45 - 13:45 Lunch Break
Tuesday 13:45 - 15:00 Data Wrangling - Advanced
Tuesday 15:00 - 15:15 Break
Tuesday 15:15 - 16:30 Data Wrangling - Advanced

Day 3

Day Time Topic
Wednesday 10:00 - 11:15 Exploratory Data Analysis
Wednesday 11:15 - 11:30 Break
Wednesday 11:30 - 12:45 Exploratory Data Analysis
Wednesday 12:45 - 13:45 Lunch Break
Wednesday 13:45 - 15:00 Data Visualization - Part 1
Wednesday 15:00 - 15:15 Break
Wednesday 15:15 - 16:30 Data Visualization - Part 1

Day 4

Day Time Topic
Thursday 10:00 - 11:15 Confirmatory Data Analysis
Thursday 11:15 - 11:30 Break
Thursday 11:30 - 12:45 Confirmatory Data Analysis
Thursday 12:45 - 13:45 Lunch Break
Thursday 13:45 - 15:00 Data Visualization - Part 2
Thursday 15:00 - 15:15 Break
Thursday 15:15 - 16:30 Data Visualization - Part 2

Day 5

Day Time Topic
Friday 10:00 - 11:15 Reporting with R Markdown
Friday 11:15 - 11:30 Break
Friday 11:30 - 12:45 Reporting with R Markdown
Friday 12:45 - 13:45 Lunch Break
Friday 13:45 - 15:00 Advanced Use of R, Outlook, Q&A
Friday 15:00 - 15:15 Break
Friday 15:15 - 16:30 Advanced Use of R, Outlook, Q&A

Materials

Day 1

Slides

1_1 Getting Started

1_2 Data Types, Import, & Export

Appendix - Setup and Workflow Help

Exercises

1_1_1 First Steps

1_1_2 Packages Scripts

1_2_1 Data Types

1_2_2 Flat Files

1_2_3 Statistical Software Files

Solutions

1_1_1 First Steps

1_1_2 Packages Scripts

1_2_1 Data Types

1_2_2 Flat Files

1_2_3 Statistical Software Files

Day 2

Slides

2_1 Data Wrangling Basics

2_2 Data Wrangling Advanced

Appendix - Relational Data

Exercises

2_1_1 Select Rename

2_1_2 Filter Arrange

2_1_3 Mutate Recode Missings

2_2_1 Across the Tidyverse

2_2_2 Define your Cases

2_2_3 If I had a Function

2_2_4 Purrr Joy of Writing Loops

Solutions

2_1_1 Select Rename

2_1_2 Filter Arrange

2_1_3 Mutate Recode Missings

2_2_1 Across the Tidyverse

2_2_2 Define your Cases

2_2_3 If I had a Function

2_2_4 Purrr Joy of Writing Loops

Day 3

Slides

3_1 Exploratory Data Analysis

3_2 Data Visualization Part 1

Exercises

3_1_1 Summary Statistics

3_1_2 Frequencies Proportions

3_1_3 Crosstabs Correlations

3_2_1 A Simple Plot

3_2_2 Handling Multiple Plots

3_2_3 Plotting Repeats

3_2_4 GGood Plots

Solutions

3_1_1 Summary Statistics

3_1_2 Frequencies Proportions

3_1_3 Crosstabs Correlations

3_2_1 A Simple Plot

3_2_2 Handling Multiple Plots

3_2_3 Plotting Repeats

3_2_4 GGood Plots

Day 4

Slides

4_1 Confirmatory Data Analysis

4_2 Data Visualization Part 2

Exercises

4_1_1 t-test ANOVA

4_1_2 Regression Analysis

4_1_3 Regression Reporting

4_2_1 Plotting Diagnostics

4_2_2 Plotting a Regression

4_2_3 Combining Predictions

Solutions

4_1_1 t-test ANOVA

4_1_2 Regression Analysis

4_1_3 Regression Reporting

4_2_1 Plotting Diagnostics

4_2_2 Plotting a Regression

4_2_3 Combining Predictions

Day 5

Slides

5_1 Reporting with R Markdown

5_2 Outlook

Exercises

5_1_1 R Markdown