Course logistics, Intro, {ggplot2} review

Week 1

Monday January 6th, 2025

The {ggplot2} hex sticker which features a connected scatterplot with data points in varying shades of blue atop a gray gridded background. The word 'ggplot2' sits beneath the data.

Pre-lecture Prep

Please be sure to complete the following before class:

A. Install required packages

install.packages("palmerpenguins") # a package containing the `penguins` data set, which we'll use for plotting practice
install.packages("tidyverse") # a collection of packages used for data wrangling / manipulation and visualization (including {ggplot2})

B. Create your EDS-240-class-examples repository

We’ll be coding together quite a bit throughout this course. To stay organized, we’ll complete all of our in-class examples in one repository (repo). Create and clone a GitHub repository named EDS-240-class-examples (or something that makes sense for you). Step-by-step instructions are provided, below, if needed:

Create a remote (GitHub) repo:

  1. Navigate to your GitHub profile and click on Repositories (top menu bar)
  2. Click on the green New button in the upper right-hand corner
  3. Give it the name EDS-240-class-examples, and optionally, a short description. Select the radio button for Public repository (it should be selected by default). Initialize this repository with a README by checking the box and Add a .gitignore (select R from the .gitignore template drop down menu) boxes. Click Create repository.
  4. From your repo’s main page, click the green Code button and copy the URL to your clipboard

Clone your repo:

  1. Open up RStudio
  2. Click on the drop down menu next to the R project icon (top right corner)
  3. Click New Project… > Version Control > Git > paste your repo’s URL in the Repository URL: box and press Tab to auto fill the Project directory name field (it should automatically be named the same as your GitHub repo). Browse to select where on your computer you’d like your repo to be saved.
  4. Click Create Project

You should also create a folder for each week (e.g. week1/, week2/, week3/, etc.) inside your repository’s root directory to help keep all the various files that we’ll download / create organized. This consistent naming scheme will also make it easy to add large files (e.g. data files) to your .gitignore (you’ll do this for the first time in this week’s pre-discussion prep).

C. Download today’s .qmd template

Template .qmd files will be provided alongside (most) lecture slides to help keep us organized and on track throughout class. They are available for download directly from GitHub at the link(s) provided in the Lecture Materials table (below). Look for the download button at the top right corner of the file (it looks similar to this, ). Please download all necessary templates and save them to the appropriate EDS-240-data-viz-examples/weekX folder before each class.

Lecture Materials

Lecture slides Code-along template Code-along key
Lecture 1.1: course logistics & syllabus NA NA
Lecture 1.2: data viz intro NA NA
Lecture 1.3: {ggplot2} review 1.3 template 1.3 key


Pre-discussion Prep

Today’s discussion will focus on data cleaning / wrangling. Before coming to section, you’ll need to install some packages and download data. Find step-by-step instructions in the note, below (collapsed to save space):

A. Install required packages

install.packages("here") # for reproducible, relative file paths
install.packages("janitor") # tools for cleaning dirty data
install.packages("usdata") # demographic data on the United States at the county and state levels spanning multiple years.

B. Download necessary data

Download fracking data (registryupload_1.csv) from Google Drive and save it to your class repo in the following location: EDS-240-class-examples/week1/data/registryupload_1.csv

Why are we downloading from Google Drive and not FracFocus?

These data were originally downloaded as a zip file from FracFocus in November 2023 when they were still quite messy. Since then, FracFocus has published cleaner versions of these data, which is great, but also defeats the purpose of this exercise . For this reason, we’ll be working with this saved version of the data, rather than downloading directly from FracFocus’s data download page.

C. Add data to .gitignore

We don’t want to push any data in our repo to GitHub (GitHub enforces file size limits, and you’ll run into some serious headaches when you try to push a file that’s too big). To avoid doing this, we can add our data files to our .gitignore file, which, as the name implies, is a collection of files that we want Git to ignore. Open up your .gitignore file and add the following line (anywhere is fine, but it makes sense to add it beneath the last line):

.gitignore
# ignore any folder named `data/` within folders named `week__` (* acts as a wildcard)
/week*/data/

Notice that when you Save your modified .gitignore file, untracked data files at the specified file path disappear from your Git tab in RStudio, and a modified .gitignore file appears in it’s place (this is what we want!). Stage / add, commit, and push .gitignore.

An example repo with three folders: week1/, week2/, and other-stuff/. Each of those folders has a subfolder named data/, and each data/ folder contains a .csv file. Adding the line, /week*/data/ to the .gitignore tells Git to ignore folders (and their contents) named data/ that are within a parent folder named week__ (the * acts as a wildcard). If we wanted to ignore all three data/ folders (including other-stuff/data/), we can instead include /*/data/ to our .gitignore.

D. Prepare a week1-discussion.qmd file

Add a new blank file to your class repo – you’ll complete your discussion exercises here. For organization’s sake, it should live in the following location: EDS-240-class-examples/week1/week1-discussion.qmd. Fill out the necessary YAML information (title, author, date + any other information you find helpful).


Discussion Materials

Discussion Slides Exercise instructions Exercise solutions
D1 slides: data wrangling D1 exercise: wrangle fracking data D1 solution


Assignment Reminders

Assignment Type Assignment Title Date Assigned Date Due
EOC End-of-class survey (week 1) Mon 01/06/2024 Mon 01/06/2024, 11:55pm PT
SR Pre-course reflection (SR #1) Mon 01/06/2024 Sat 01/11/2024, 11:59pm PT
HW Homework Assignment #1 Mon 01/06/2024 Sat 01/18/2024, 11:59pm PT