Introduction to R

• Introduction to R:
  • Overview
  • Background
  • Features of the R statistical programming system
• Data entry:
  • Importing data
• Data types:
  • Numeric
  • Float
  • Binary
• R environment:
  • Introduction
  • Working directory
  • Creating/using scripts
  • Saving data and results.
• R graphics:
  • Brief introduction to {ggplot2}
  • Creating, editing and storing graphics
• Summary statistics:
  • Measures of location and spread
• Manipulating data in R:
  • Describing how data can be manipulated using logical operators and {dplyr}
• Vector operations:
  • Details of R’s vectors operations

Session 1

By the end of session 1, participants will…

• have a clear understanding of R/RStudio IDE and its background.
• be familiar with navigating the RStudio IDE.
• understand the core fundamentals of R.
• understand functions and arguments.
• be able to create vectors and applying functions.
• be exposed to the tibbles and {tidyverse} package.

Session 2

By the end of session 2, participants will…

• be able to comfortably import, export, and store data in R.
• have a basic introduction to graphics with {ggplot2}.
• have a basic understanding of manipulating data manipulation with {dplyr}.
• understand logical and relational data partitioning.

This course does not include:

• An advance usage of {ggplot2}.
• Advanced data analyses, wrangling and manipulation techniques. For data cleaning and manipulation see our Data Wrangling with Tidyverse course.
• A description of automated reporting using R Markdown, see our course Reporting with R Markdown.

No prior programming knowledge of any kind is assumed. This course is suitable for all fields of work. Previous attendees include biologists, statisticians, accountants, engineers & students, i.e., anyone who uses a spreadsheet!