R Essential Training: Wrangling and Visualizing Data

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

7. An R for Data Science Case Study

• Data science with R: A case study

8. Exploring Data

• Computing frequencies
• Computing descriptive statistics
• Computing correlations
• Creating contingency tables
• Conducting a principal component analysis
• Conducting an item analysis
• Conducting a confirmatory factor analysis

9. Analyzing Data

• Comparing proportions
• Comparing one mean to a population: One-sample t-test
• Comparing paired means: Paired samples t-test
• Comparing two means: Independent samples t-test
• Comparing multiple means: One-factor analysis of variance
• Comparing means with multiple categorical predictors: Factorial analysis of variance

10. Predicting Outcomes

• Predicting outcomes with linear regression
• Predicting outcomes with lasso regression
• Predicting outcomes with quantile regression
• Predicting outcomes with logistic regression
• Predicting outcomes with Poisson or log-linear regression
• Assessing predictions with blocked-entry models

11. Clustering and Classifying Cases

• Grouping cases with hierarchical clustering
• Grouping cases with k-means clustering
• Classifying cases with k-nearest neighbors
• Classifying cases with decision tree analysis
• Creating ensemble models with random forest classification

Conclusion

• Next steps