Lessons

Week 1

Under construction

Welcome! These first lessons will help you differentiate between different file types, absolute vs. relative folder paths, and installing R and RStudio:

• Intro to files and directories folders, files, and software
• Installing R & RStudio download r, rstudio, and other tools for the course

Week 2

This week we will cover the RStudio IDE. Most of your work in the course will be done in this environment, so it’s important to know to navigate the different panes and features.

• The RStudio IDE tips for managing your code, data, and files

Week 3

Under construction

I’ve written a small R package, goodenuffR, to help organize your code, data, and outputs. It’s based off of the excellent paper, Good enough practices in scientific computing,

• Project Organization with goodenuffR an introduction to the goodenuffr package

Week 4

Now that we’ve covered file organization and the RStudio IDE, we can move onto some R programming. These exercises cover basic R structures (vectors and functions), and help you understand how to extract pieces of your data in R.

• Introduction to R Programming an introduction to functions and objects
• Subsetting R Objects pull out the pieces from any object in r

Week 5

These lessons cover how to use the R Markdown, a “notebook interface to weave together narrative text and code to produce elegantly formatted output.”

• R Markdown in Practice extending r markdown

Week 6

So far, we’ve covered some basic R syntax. You’ve seen common R data objects (vectors, matrices, arrays, data frames, tibbles, and lists) and functions (str(), typeof(), class(), etc.).

The tidyverse is an “opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.” Ninety percent of the code you’ll see in this course uses the tidyverse philosophy, which makes it easier to write (and read).

• The Tidyverse an introduction to tidyverse (pipes, tibbles, and the grammar of graphics)

Week 7

Now that we’ve introduced the tidyverse (and the %>%), we’re going to extend this knowledge to ggplot2, a popular data visualization package.

• Data Visualization with ggplot2 (part 1) counting things, variable distributions, ridgelines, and violin plots

Week 8

Data are rarely collected in a way that’s fit for analysis or data visualization. In this lesson we introduce dplyr, a package designed to handle “most common data manipulation challenges.”

• Introduction to Data Manipulation introduction to wrangling data

Week 9

This lesson covers intermediate data visualization techniques like plotting trends (geom_line()), adding help-text to your graph (ggplot2::annotate()), labeling values to stand out (ggrepel::geom_label_repel()), and using reference lines for comparisons (ggplot2::geom_hline()).

• Data Visualization with ggplot2 (part 2) trends, adding text, labeling values, and reference lines

Week 10

tidyr has functions for generate sequences of data (full_seq()), creating an output for each combination of any input (expand_grid()), and completing missing combinations of data (complete()).

• Reshaping and Joining Data in R transpose, pivot, split, and combine data

Week 11

This lesson builds on our previous knowledge of ggplot2 and introduces facets (small multiples) with ggplot2::facet_wrap(), ggforce::facet_wrap_paginate(), and geofacet::facet_geo()

• Data Visualization with ggplot2 (part 3) advanced facetting and small multiples

Week 12

Now that we’ve covered how to manipulate and restructure data, we’ll use R’s functions for creating sequences (seq()), along with some of tidyr’s functions for dealing with missing or incomplete data (full_seq(), expand_grid(), and complete()).

• Advanced Data Transformation and Manipulation dealing with complex data structures

Week 13

Maps are a great way to give context to any data source. In this lesson, we’re going to collect data with the rtweet package and use it to build a map with ggplot2.

• Data Visualization with ggplot2 (part 4) building maps with ggplot2

Week 14

Under construction

Not every project requires a data visualization, so this week we’ll cover how to display data in tables.

• Displaying data in tables an introduction to creating tables in r

Week 15

• Scraping Wikipedia Tables advanced wrangling example

Week 16

• Case Study (Part 1) don’t mess with texas part 1: scrape html tables
• Case Study (Part 2) don’t mess with texas part 2: iterate and download with purrr
• Case Study (Part 3) don’t mess with texas part 3: split and wrangle the data
• Case Study (Part 4) don’t mess with texas part 3: manipulating images with magick

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