Course Files

Below you’ll find the presentation files for the course and the exercises we’ll do during the hands on sessions.

Note: You also have access to the source material at the github repo.

We’ve also included links to materials to go further learning R at the bottom of the page!

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Presentation Files

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R Basics & Reading Data

PDF version

Markdown & Manipulating Data

PDF version

Summarizing Data

PDF version

It may help to have just the R code from the slides so you can easily run the code without typing it out. Below you’ll find .R scripts with just the R code from the notes (notice that there is some extra stuff as we often use R code to bring in images and things like that).

• R Basics & Reading Data R code
• Markdown & Manipulating Data R code
• Summarizing Data R code

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Exercises

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Exercise Sets

R Basics & Reading Data

• Exercises 1 - RStudio (Solutions: HTML, Rmd)
• Exercises 2 - Objects (Solutions: HTML, Rmd)
• Exercises 3 - Common Data Objects (Solutions: HTML, Rmd)
• Exercises 4 - Reading Data (Solutions: HTML, Rmd)

Markdown & Manipulating Data

• Exercises 5 - Markdown (Solutions: HTML, Rmd)
• Exercises 6 - dplyr (Solutions: HTML, Rmd)
• Exercises 7 - Creating Variables (Solutions: HTML, Rmd)
• Exercises 8 - tidyr (Solutions: HTML, Rmd)

Summarizing Data

• Exercises 9 - Numeric Summaries (Solutions: HTML, Rmd)
• Exercises 10 - Bar Plots (Solutions: HTML, Rmd)
• Exercises 11 - ggplot (Solutions: HTML, Rmd)

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Learning Resources

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Cheat Sheets

RStudio IDE

Base R

Data Import

RMarkdown

Data Transformation

Data Visualization

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Other Courses

Justin teaches the following three courses for Data Matters, a week-long series of one and two-day courses aimed at students and professionals in business, research, and government.

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Basics of R for Data Science and Statistics

The course provides a modern introduction to the R through the extremely popular suite of packages called the tidyverse. A rough outline is given below:

Day 1:

• Basics of how R stores data

• R Packages and the tidyverse

• Reading data from common formats into R (readr package)

• Using R Markdown for reproducibility (rmarkdown and knitr packages)

• Common data manipulations and creating new variables (dplyr package)

Day 2:

• Reshaping data for summarizing and modeling (tidyr package)

• Types of data and numeric summaries (including across groups)

• Creating publication ready graphs (ggplot2 package)

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Improving R Programs

The course provides a brief overview of R data structures followed by the following topics:

• Loops in R

• Vectorized functions (apply family of functions)

• How R functions work

• Function writing

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R for Automating Workflow & Sharing Work

Students will learn about the following topics:

• The R Markdown language

• How to automate reports with R Markdown

• Use of git and github for collaboration and version control

• Basic creation of websites through Markdown and github

• R Shiny web apps

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Data Computing eBook

This eBook (link)[https://dtkaplan.github.io/DataComputingEbook/] by Danny Kaplan and Matt Beckman is a free resource for an introduction to R course. The book has been used as a primary textbook for undergraduate courses that teach an introduction to R & tidyverse with no assumed prerequisites in statistics or programming. The book includes chapters devoted to basic RStudio IDE, data wrangling, visualization, and other topics useful for data processing & exploration.

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R Markdown: The Definitive Guide

This books has almost everything you could want to know about using R Markdown.

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R for Data Science

A commonly used introduction to the tidyverse.

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ggplot2: elegant graphics for data analysis

A book on using the ggplot2 package for plotting.

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