Lab #01: Hello R!

due Sun, Jan 24 11:59 PM

This lab will go through the same workflow we demonstrated in class. We will continue our work with R / RStudio and git / GitHub.

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

In future labs, you will be encouraged to explore independently. But first, you need to build basic fluency in R.

Your lab TA will lead you through the Getting Started section

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. We need to configure 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 the email address associated with your GitHub account.

For example, mine would be

Then hit enter to run the code.

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.

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. Examine the knitted document.

Commiting changes

Now, go to the Git tab in your RStudio instance. This is in the upper-right pane.

If you have made changes to your lab01.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 including changes. You should see deletions in red and additions in green.

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 box next to lab01.Rmd. 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.

You don’t have to commit after every change, as this would get quite tedious. You should commit 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 your repo on GitHub.

In order to push your changes to GitHub, you must have staged your commit to be pushed. click on the green up arrow labeled 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) by running the code below.

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

But 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)

Datasaurus Data

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 and hit enter.

?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 exercise 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 using the code below.

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

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. The correlation coefficient (r) measures the strength and direction of 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.

For illustrative purposes only, let’s calculate the correlation coefficient between x and y.

dino_data %>%
  summarize(r = cor(x, y))
## # A tibble: 1 x 1
##         r
##     <dbl>
## 1 -0.0645
Now pause, knit and commit changes with the commit message “added answer for exercise 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?
Now pause, knit, commit changes with the commit message “Added answer for Ex 3”, 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 2-4, customize the settings by modifying the options in the R chunks used to create those figures.

For Exercises 2 and 3, 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 and 3 to be as follows:

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

```

For Exercise 4, 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: