R Essential Training: Wrangling and Visualizing Data

Learn how to wrangle data and create meaningful visualizations with R, the programming language powering modern data science.

Introduction

• Make your data make sense
• Using the exercise files

1. What Is R?

• R in context
• Data science with R: A case study

2. Getting Started

• Installing R
• Environments for R
• Installing RStudio
• Navigating the RStudio environment
• Entering data
• Data types and structures
• Comments and headers
• Packages for R
• The tidyverse
• Piping commands with %>%

3. Importing Data

• R's built-in datasets
• Exploring sample datasets with pacman
• Importing data from a spreadsheet
• Importing XML data
• Importing JSON data
• Saving data in native R formats

4. Visualizing Data with ggplot2

• Introduction to ggplot2
• Using colors in R
• Using color palettes
• Creating bar charts
• Creating histograms
• Creating box plots
• Creating scatterplots
• Creating multiple graphs
• Creating cluster charts

5. Wrangling Data

• Creating tidy data
• Using tibbles
• Using data.table
• Converting data from wide to tall and from tall to wide
• Converting data from tables to rows
• Working with dates and times
• Working with list data
• Working with XML data
• Working with categorical variables
• Filtering cases and subgroups

6. Recoding Data

• Recoding categorical data
• Recoding quantitative data
• Transforming outliers
• Creating scale scores by counting
• Creating scale scores by averaging

Conclusion

• Next steps