Descriptive Statistics

Welcome to the Descriptive Statistics module! In this module, you will learn about:

1. Summarizing data: How to calculate summary statistics for numeric data
2. Frequency tables: How to summarize categorical data
3. Summarizing data with tidyverse: How to calculate summary statistics for numeric data using tidyverse
4. Frequency tables with tidyverse: How to summarize categorical data using tidyverse
5. pivoting tables: How to reshape and summarize data

Each tutorial will comprise a series of interactive lessons with practice problems embedded throughout.

Learning Goals

After completing this module, you will be able to:

Summarizing Data

1. Calculate the mean, the standard deviation and the range of a variable
2. Create summary statistics using the summary() function
3. Use the describe() function
4. Account for missing values in your data when calculating summary statistics

Frequency Tables

1. Make frequency tables in R
2. Make proportion and cumulative proportion tables
3. Make readable tables including various frequency measures
4. Making cross tables between two variables

Summarizing Data with the Tidyverse

1. Use summarise() to compute descriptive statistics in a tidy, pipeline-based workflow
2. Apply summary functions to multiple variables at once using across()
3. Create grouped summaries with group_by()
4. Compute counts, proportions, and missingness with tidyverse tools

Frequency Tables with the Tidyverse

1. Use count() to generate tidy frequency tables
2. Add proportions and cumulative proportions using mutate()
3. Create grouped or conditional frequency tables

Pivoting tables

1. Understand when long or wide format is most appropriate for descriptive analyses
2. Use pivot_longer() to transform repeated measures into tidy long format
3. Use pivot_wider() to restructure summaries or prepare matrix-like data
4. Control naming conventions and handle missing values during pivoting

Click the button below to get started with the first tutorial.

Begin Tutorial