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Introduction to R

Introduction to R

Course Level: Foundation (6 hours)

R is a versatile language for statistical computing and graphics. In this course you will learn the advantages of using R and how to get started. You will gain familiarity with the RStudio interface and learn the R basics. Also included is an introduction to the Tidyverse and how to use various packages for data storage, visualisation and manipulation. This course provides a great foundation to begin your R journey!

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Course Details

  • Course Outline
  • Learning Outcomes
  • Materials
  • Prior Knowledge

Course Outline

  • 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: D
    • Describing how data can be manipulated using logical operators and {dplyr}
  • Vector operations:
    • Details of R’s vectors operations

Learning Outcomes

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.

Materials

  • Page 1 of example course material for Introduction to R
  • Page 2 of example course material for Introduction to R
  • Page 3 of example course material for Introduction to R
  • Page 4 of example course material for Introduction to R
  • Page 5 of example course material for Introduction to R

Prior Knowledge

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!

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