Learn how to wrangle data and create meaningful visualizations with R, the programming language powering modern data science.
Overview
Syllabus
Introduction
- Make your data make sense
- Using the exercise files
- R in context
- Data science with R: A case study
- 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 %>%
- 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
- 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
- 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
- Recoding categorical data
- Recoding quantitative data
- Transforming outliers
- Creating scale scores by counting
- Creating scale scores by averaging
- Next steps
Taught by
Barton Poulson
