Learning goals

⊕Git is a version control system (like “Track Changes” features from Microsoft Word on steroids) and GitHub is the home for your Git-based projects on the internet (like DropBox but much, much better).

• Get practice using Git and GitHub.
• Get acquainted with R and RStudio, which we will be using throughout the course to analyze data as well as to learn the statistical concepts discussed in the course.
• Appreciate the value of visualization in exploring the relationship between variables.

⊕R is the name of the programming language itself and RStudio is a convenient interface.

• Start using R for building plots and calculating summary statistics.

Starting slow

As the labs progress, you are encouraged to explore beyond what the labs dictate; a willingness to experiment will make you a much better programmer. Before we get to that stage, however, you need to build some basic fluency in R. Today we begin with the fundamental building blocks of R and RStudio: the interface, reading in data, and basic commands.

And to make versioning simpler, this is a solo lab. Additionally, we want to make sure everyone gets a significant amount of time at the steering wheel. In future labs you’ll learn about collaborating on GitHub and produce a single lab report for your team.

Pre-Lab work

You should have already received an invitation to join the GitHub organization for this course — stat408-s22. You need to accept the invitation before moving on to the next step. If you did not receive an invitation, send an email to your instructor with your GitHub username.

This lab also assumes you already have R/RStudio and Git installed on your computer. If you need to install either of these, navigate here:

• Install R and RStudio
• Install Git

Setup

The next three sections will configure Git to work with RStudio on your computer by following the instructions from Chapters 7, 9 and 12 of Happy Git with R. If you already completed these steps, skip to the “Getting Started” section.

git config --global user.name 'Jane Doe'
git config --global user.email 'jane@example.com'

git config --global --list
where git

install.packages("usethis")

usethis::create_github_token()

gitcreds::gitcreds_set()

Getting started

For each lab in this course you will start with a GitHub repository (which we’ll refer to as “repo” going forward) that I created for you and that contains the starter documents you will build upon when working on your assignment. The first step is always to bring these files into RStudio so that you can edit them, run them, view your results, and interpret them.

Then you will work in RStudio on the data analysis, making commits along the way (snapshots of your changes) and finally push all your work back to GitHub.

The next few steps will walk you through the process of getting information of the repo to be cloned, launching RStudio and cloning your repo, and getting started with the analysis.

• Click on the link that you should have received in your email to navigate to your GitHub repo for the lab.

• On GitHub, click on the green Clone or download button, select Use HTTPS (this might already be selected by default, and if it is, you’ll see the text Clone with HTTPS as in the image to the right). Click on the clipboard icon to copy the repo URL.

• Launch RStudio, go to File > New Project… and choose “Version Control.”

• Then choose “Git”.

• Copy and paste the URL of your lab repo into the dialog box next to “Repository URL”, name the project directory the same name as the repo name, and specify where you’d like to store the repo on your local computer for “Create project as subdirectory of”.

• Hit Create Project, and you’re good to go!

Warm up

Open the R Markdown (Rmd) file called lab-01-hello-r.Rmd. It will likely ask you if you would like to install the packages that are required, click Install.

Before we introduce the data, let’s warm up with some simple exercises.

⊕The top portion of your R Markdown file (between the three dashed lines) is called YAML. It stands for “YAML Ain’t Markup Language”. It is a human friendly data serialization standard for all programming languages. All you need to know is that this area is called the YAML (we will refer to it as such) and that it contains meta information about your document.

YAML

Open the R Markdown (Rmd) file in your project, change the author name to your name, and knit the document.

Committing changes

Then go to the Git pane in your RStudio.

If you have made changes to your Rmd file, you should see it listed here. Click on it to select it in this list and then click on Diff. This shows you the difference between the last committed state of the document and its current state that includes your changes. If you’re happy with these changes, write “Update author name” in the Commit message box and hit Commit.

You don’t have to commit after every change, this would get quite cumbersome. You should consider committing states that are meaningful to you for inspection, comparison, or restoration. In the first few assignments we will tell you exactly when to commit and in some cases, what commit message to use. As the semester progresses we will let you make these decisions.

Pushing changes

Now that you have made an update and committed this change, it’s time to push these changes to the web! Or more specifically, to your repo on GitHub. Why? So that others can see your changes. And by others, we mean the course teaching team (your repos in this course are private to you and us, only).

In order to push your changes to GitHub, click on Push.

In your web browser, navigate to your GitHub repo for this lab. You should be able to see your changes there!

install.packages(c("datasauRus", "tidyverse"))

library(tidyverse) 
library(datasauRus)

Exercises

1. According to the help file, how many rows and how many columns does the datasaurus_dozen file have? What are the variables included in the data frame? Check these answers by examining the data set using the base R functions dim and names. Add your responses to your lab report.

Let’s take a look at what these datasets are. To do so we can make a frequency table of the dataset variable:

table(datasaurus_dozen$dataset)

## 
##       away   bullseye     circle       dino       dots    h_lines high_lines 
##        142        142        142        142        142        142        142 
## slant_down   slant_up       star    v_lines wide_lines    x_shape 
##        142        142        142        142        142        142

Soon we will learn the “tidyverse” — a suite of R packages that share an underlying design philosophy, grammar, and data structures. As a preview, here is the “tidy” code for achieving the same results:

datasaurus_dozen %>%
  count(dataset)

## # A tibble: 13 × 2
##    dataset        n
##    <chr>      <int>
##  1 away         142
##  2 bullseye     142
##  3 circle       142
##  4 dino         142
##  5 dots         142
##  6 h_lines      142
##  7 high_lines   142
##  8 slant_down   142
##  9 slant_up     142
## 10 star         142
## 11 v_lines      142
## 12 wide_lines   142
## 13 x_shape      142

⊕Matejka, Justin, and George Fitzmaurice. “Same stats, different graphs: Generating datasets with varied appearance and identical statistics through simulated annealing.” Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM, 2017.

The original Datasaurus (dino) was created by Alberto Cairo in this great blog post. The other Dozen were generated using simulated annealing and the process is described in the paper Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing by Justin Matejka and George Fitzmaurice. In the paper, the authors simulate a variety of datasets that have the same summary statistics as the Datasaurus but have very different distributions.

🧶 ✅ ⬆️ Knit, commit, and push your changes to GitHub with the commit message “Added answer for Ex 1”. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

2. Plot y vs. x for the dino dataset. Then, calculate the correlation coefficient between x and y for this dataset. Use the base R plot function and the cor function for calculating correlation. Hint: You will first need to subset the datasaurus_dozen data set.

Below is the “tidy” code which would achieve the same results.

Start with the datasaurus_dozen and pipe it into the filter function to filter for observations where dataset == "dino". Store the resulting filtered data frame as a new data frame called dino_data.

dino_data <- datasaurus_dozen %>%
  filter(dataset == "dino")

There is a lot going on here, so let’s slow down and unpack it a bit.

First, the pipe operator: %>%, takes what comes before it and sends it as the first argument to what comes after it. So here, we’re saying filter the datasaurus_dozen data frame for observations where dataset == "dino".

Second, the assignment operator: <-, assigns the name dino_data to the filtered data frame.

Next, we need to visualize these data. We will use the ggplot function for this. Its first argument is the data you’re visualizing. Next we define the aesthetic mappings. In other words, the columns of the data that get mapped to certain aesthetic features of the plot, e.g. the x axis will represent the variable called x and the y axis will represent the variable called y. Then, we add another layer to this plot where we define which geometric shapes we want to use to represent each observation in the data. In this case we want these to be points, hence geom_point.

ggplot(data = dino_data, mapping = aes(x = x, y = y)) +
  geom_point()

If this seems like a lot, it is. And you will learn about the philosophy of building data visualizations in layer in detail next few weeks. For now, follow along with the code that is provided.

For the second part of this exercise, we need to calculate a summary statistic: the correlation coefficient. Correlation coefficient, often referred to as \(r\) in statistics, measures the linear association between two variables. You will see that some of the pairs of variables we plot do not have a linear relationship between them. This is exactly why we want to visualize first: visualize to assess the form of the relationship, and calculate \(r\) only if relevant. In this case, calculating a correlation coefficient really doesn’t make sense since the relationship between x and y is definitely not linear – it’s dinosaurial!

But, for illustrative purposes, let’s calculate the correlation coefficient between x and y.

⊕Start with dino_data and calculate a summary statistic that we will call r as the correlation between x and y.

dino_data %>%
  summarize(r = cor(x, y))

## # A tibble: 1 × 1
##         r
##     <dbl>
## 1 -0.0645

🧶 ✅ ⬆️ Knit, commit, and push your changes to GitHub with the commit message “Added answer for Ex 2”. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

3. Plot y vs. x for the star dataset. You can (and should) reuse code we introduced above, just replace the dataset name with the desired dataset. Then, calculate the correlation coefficient between x and y for this dataset. How does this value compare to the r of dino?

🧶 ✅ ⬆️ This is another good place to pause, knit, commit changes with the commit message “Added answer for Ex 3”, and push. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

4. Plot y vs. x for the circle dataset. You can (and should) reuse code we introduced above, just replace the dataset name with the desired dataset. Then, calculate the correlation coefficient between x and y for this dataset. How does this value compare to the r of dino?

🧶 ✅ ⬆️ You should pause again, commit changes with the commit message “Added answer for Ex 4”, and push. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

⊕Facet by the dataset variable, placing the plots in a 3 column grid, and don’t add a legend.

5. Finally, let’s plot all datasets at once. In order to do this we will make use of faceting.

ggplot(datasaurus_dozen, aes(x = x, y = y, color = dataset))+
  geom_point()+
  facet_wrap(~ dataset, ncol = 3) +
  theme(legend.position = "none")

And we can use the group_by function to generate all the summary correlation coefficients.

datasaurus_dozen %>%
  group_by(dataset) %>%
  summarize(r = cor(x, y)) %>%
  print(13)

You’re done with the data analysis exercises, but we’d like you to do two more things:

Attribution

This lab is adapted from material in the Data Science in a Box course by Mine Çetinkaya-Rundel licensed under a Creative Commons Attribution Share Alike 4.0 International. Visit here for more information about the license.