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"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 (stefan.juenger@gesis.org, @StefanJuenger); Veronika Batzdorfer (veronika.batzdorfer@gesis.org; Johannes Breuer (johannes.breuer@gesis.org, @MattEagle09)

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. 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.

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

Learning objectives

By the end of the course participants should be:

  • Comfortable with using R and 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

Timetable

Day 1

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 2

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 3

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 4

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 5

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

Materials

Day 1

Slides

1_1 Getting Started

1_2 Data Types, Import, & Export

Appendix - Setup and Workflow Help

Appendix - Labelled Data

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 Part 1

2_2 Data Wrangling Part 2

Appendix - Relational Data

Exercises

2_1_1 Select Rename

2_1_2 Filter Arrange

2_2_1 Create & Transform Variables

2_2_2 Missing Values

2_2_3 Across & Aggregate Variables

2_2_4 Factors & Conditional Recoding

Solutions

2_1_1 Select Rename

2_1_2 Filter Arrange

2_2_1 Create & Transform Variables

2_2_2 Missing Values

2_2_3 Across & Aggregate Variables

2_2_4 Factors & Conditional Recoding

Day 3

Slides

3_1 Exploratory Data Analysis

3_2 Data Visualization Part 1

Appendix - Exploring Missingness & Outliers

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

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