Learning the R Tidyverse

Learn to integrate the tidyverse into your R workflow and get new tools for importing, filtering, visualizing, and modeling research and statistical data.

Introduction

• Welcome
• What you should know
• Exercise files

1. Getting Started with the tidyverse

• What is the tidyverse?
• Why use the tidyverse?
• Strengths of the tidyverse
• Set up R and RStudio for the tidyverse
• Maintain the tidyverse
• Prevent issues with plyr and dplyr

2. Being Tidy with RStudio Projects

• Why should you use projects in RStudio?
• Disable auto-saving of RData for reproducibility
• Create a new project

3. Introducing the %>% Operator

• What is the %>% operator?
• Identify where to use %>%
• Signficance of %>%
• Alternate options to %>%

4. Importing, Modifying, and Filtering Data

• Separate raw and clean data folders
• Import .xlsx files with readxl in R
• Import .csv files with readr into R
• Is it a data frame or a tibble?
• Select and filter data
• Convert strings to dates with mutate
• Separating columns into multiple columns
• Filter out NA values
• Export .csv files with readr
• Export .rdata objects for later

5. Summarizing and Tabulating Data in the tidyverse

• Sample data and cross-validation with dplyr
• Categorizing data with group_by
• Count members of subgroups within groups with n()
• Cumulative sums and more: cumsum, cumall, and cumany
• Create group summaries
• Remember to ungroup before moving on

6. Wide and Long Data

• Identify if data is wide or long
• The benefits of long (or tidy) data
• Convert data from wide to long
• Convert data from long to wide

7. select(), select_(), !!!, and Non-Standard Evaluation

• Non-standard evaluation and programming with the tidyverse
• Compare group_by and group_by_
• Tidy evaluation, quo, and !!

Conclusion

• Next steps