Learning Objectives

• explain why coding is advantageous for data management
• format R code for readability and clarity
• add comments and breaks to R code
• call R scripts from within R scripts (sourcing)
• write simple custom R functions
• properly organize an R project and workspace with RStudio
• read and write tables

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Day 1

Live coding: Intro to programming with R: vectors

• Introduction to R

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Day 2

Live coding: data tables and organizing a project with RStudio

• Data Carpentry lesson Starting with data:
  • function download.file()
  • inbuilt functions vs package functions
  • installing packages: the tidyverse package
  • function tidyverse::read_csv()
  • functions to read excel tables
  • functions to read google tables
  • functions to explore data structures
    • content: head(), tail(), and view()
    • summary: str() and summary()
    • size: dim(), nrow() and ncol()
    • names: names(), colnames(), rownames()
  • indexing data frames
  • subsetting data frames
  • Learners (in break out rooms) then instructor: do the challenge
  • Learners: do Activity 1
• Factors and formatting dates will be covered later.