Lab 01 - Hello R!

due Tue, May 18 at 11:59p

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

This lab will go through much of the same workflow we’ve demonstrated in class. The main goal is to reinforce our demo of R and RStudio, which we will be using throughout the course both to learn the statistical concepts discussed in the course and to analyze real data and come to informed conclusions.

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

An additional goal is to reinforce git and GitHub, the collaboration and version control system that we will be using throughout the course.

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.

To make versioning simpler, this and the next lab are solo labs. In the future, you’ll learn about collaborating on GitHub and producing a single lab report for your lab team, but for now, concentrate on getting the basics down.

Your lab TA will lead you through the Getting Started, Packages, and Warm up sections.

Getting started

Clone the repo & start new RStudio project

Configure git

There is one more piece of housekeeping we need to take care of before we get started. Specifically, we need to configure your git so that RStudio can communicate with GitHub. This requires two pieces of information: your name and email address.

To do so, you will use the use_git_config function from the usethis package.

Type the following lines of code in the console in RStudio filling in your name and email address.

The email address is the one tied to your GitHub account.

For example, mine would be

If you get the error message

then you need to install the usethis package. Run the following code in the console to install the package. Then, rerun the use_git_config function with your GitHub username and email address associated with your GitHub account.

Once you run the configuration code, your values for user.name and user.email will display in the console. If your user.name and user.email are correct, you’re good to go! Otherwise, run the code again with the necessary changes.

RStudio & R Markdown

Below are the general components of an RStudio project.

Below are the general components of an RMarkdown file.

To get more details about the RStudio project, RMarkdown file, and just R in general, read Getting Started in Data Visualization by Kieran Healy.

Warm up

Before we introduce the data, let’s warm up with some simple exercises. We’re going to go through our first commit and push.

YAML:

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.

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

Commiting changes:

Now, go to the Git pane in your RStudio instance. This will be in the top right hand corner in a separate tab.

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. You should see deletions in red and additions in green. As well, you can see exactly which lines were changed.

If you’re happy with these changes, we’ll prepare the changes to be pushed to your remote repository. First, stage your changes by checking the appropriate box on the files you want to prepare. Next, write a meaningful commit message (for instance, “Updated author name”) in the Commit message box. Finally, click Commit. Note that every commit needs to have a commit message associated with it.

Of course, you don’t have to commit after every change, as 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 so that others can see your changes. 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, you must have staged your commit to be pushed. click on Push. This will prompt a dialogue box where you first need to enter your user name, and then your password. Don’t worry, we will soon teach you how to save your password so you don’t have to enter it every time, but for now assignment you’ll have to manually enter each time you push in order to gain some experience with the process.

Packages

In this lab we will work with two packages: datasauRus which contains the dataset, and tidyverse which is a collection of packages for doing data analysis in a “tidy” way.

If you want, you can Knit your template document and see the results.

Note, if you need to install the packages, you can install the tidyverse and datasauRus packages in the console (watch out for capitalization), but if you loaded the base project when creating this RStudio Cloud project, the packages should already be installed and only need to be loaded (remember that the console and the R Markdown environments are separate!). Let’s load these packages now:

library(tidyverse) 
library(datasauRus)

Data

If it’s confusing that the data frame is called datasaurus_dozen when it contains 13 datasets, you’re not alone! Have you heard of a baker’s dozen?

The data frame we will be working with today is called datasaurus_dozen and it’s in the datasauRus package. Actually, this single data frame contains 13 datasets, designed to show us why data visualization is important and how summary statistics alone can be misleading. The different datasets are marked by the dataset variable.

To find out more about the dataset, type the following in your console.

?datasaurus_dozen

A question mark before the name of an object will always bring up its help file. This command must be run in the console; alternatively, you can use

help(datasaurus_dozen)
  1. Based on 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? Add your responses to your lab report. When you’re done, commit your changes with the commit message “Added answer for Ex 1”, and push.

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

datasaurus_dozen %>%
  count(dataset) %>%
  print(13)
## # A tibble: 13 x 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 the same summary statistics to the Datasaurus but have very different distributions.

Data visualization and summary

  1. Plot y vs. x for the dino dataset. Then, calculate the correlation coefficient between x and y for this dataset.

Below is the code you will need to complete this exercise. Basically, the answer is already given, but you need to include relevant bits in your Rmd document and successfully knit it and view the 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()

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)!

For illustrative purposes only, 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 x 1
##         r
##     <dbl>
## 1 -0.0645

This is a good place to pause, knit and commit changes with the commit message “Added answer for Ex 2.” Push these changes when you’re done.

  1. 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.

  1. 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.

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

Finally, let’s plot all datasets at once. In order to do this we will make use of faceting, given by the code below:

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. We’ll go through these functions next week when we learn about data wrangling.

datasaurus_dozen %>%
  group_by(dataset) %>%
  summarize(r = cor(x, y))
  1. Include the faceted plot and the summary of the correlation coefficients in your lab write-up by including relevant code in R chunks (give them appropriate names). In the narrative below the code chunks, briefly comment on what you notice about the plots and the correlations between x and y values within each of them (one or two sentences is fine!).

You’re done with the data analysis exercises, but we’d like to do one more thing to customize the look of the report.

Resize your figures

We can customize the output from a particular R chunk by including options in the header that will override any global settings.

  1. In the R chunks you wrote for Exercises 2-5, customize the settings by modifying the options in the R chunks used to create those figures.

For Exercises 2, 3, and 4, we want square figures. We can use fig.height and fig.width in the options to adjust the height and width of figures. Modify the chunks in Exercises 2-4 to be as follows:

```{r ex2-chunk-name, fig.height=5, fig.width=5}

```

For Exercise 5, modify your figure to have fig.height of 10 and fig.width of 6.

Now, save and knit.

Once you’ve created this .pdf file, you’re done!

Commit all remaining changes, use the commit message “Done with Lab 1!” and push.

Submission

In this class, we’ll be submitting .pdf documents to Gradescope. Once you are fully satisfied with your lab, Knit to .pdf to create a .pdf document. You may notice that the formatting/theme of the report has changed – this is expected.

Before you wrap up the assignment, make sure all documents are updated on your GitHub repo. we will be checking these to make sure you have been practicing how to commit and push changes.

Remember – you must turn in a .pdf file to the Gradescope page before the submission deadline for full credit.

Once your work is finalized in your GitHub repo, you will submit it to Gradescope. Your assignment must be submitted on Gradescope by the deadline to be considered “on time”.

To submit your assignment: