Data manipulation

Objective

• Use git branches in the “real world”
• Reproducibly clean, summarize, and organize dataframes using tidyverse packages
• Understand how R stores different data types (data frames, vector types, missing data)
• Know what the tidyverse is, how it differs from base R, and the philosophy behind using it here.
• Use the pipe to chain operations together
• Use dplyr functions to subset data (select, filter; logic and select fxns including where, ==, %in%, !) and manipulate data (mutate; lubridate; split-apply-combine)
• Use tidyr functions to reshape data (pivot_wider and pivot_longer)

Lesson outline

• Review from last week
• Warm-up: create a branch for today’s work
• Slides/discussion: using R for reproducible data analysis
  • Why use R?
  • What is the tidyverse and why use it?
  • Install dplyr and tidyr
• Live coding: How R thinks about data
  • Data carpentry R ecology revamp episode #2
  • Data frames
  • Vectors and data types
  • Missing data
• Live coding: dplyr and tidyr
  • Data carpentry R ecology revamp episode #3
  • Recent DC + R lesson
  • Chaining lines together with the pipe
    • %>%, |>
  • Subsetting and filtering data
    • incl. selection and pick https://dplyr.tidyverse.org/reference/dplyr_tidy_select.html
  • Adding columns
  • Split-apply-combine
  • Reshaping
• Live coding: advanced tidyverse topics
  • Options: across; dates; advanced joins; others?
• Live coding/discussion: getting help
  • reprex
• Live coding: practice modify-add-commit cycle
• Homework: None

Installation & materials

1. Slides
2. Install R packages ‘dplyr’, ‘tidyr’, ‘readr’
3. Data carpentry R ecology revamp episode #2
4. Data carpentry R ecology revamp episode #3