Lab 01 - Hello R!

RStudio intro

Quarto intro

Scatterplots

Correlation

Goals

The main goal of this lab is to introduce you to 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.

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

You will…

Access help documentation with ?
Create scatterplots
Calculate correlation coefficients
Tweak the YAML

As the labs progress, you are encouraged to explore beyond what the labs dictate; a willingness to experiment will make you a much better data scientist. 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.

For this lab, you will collaborate in your (randomly assigned) groups (see Blackboard), but you will each submit your own lab.

Getting Started - RStudio

You should have already downloaded R & RStudio and created an R Project called “STAT_4380”. If you haven’t, touch base with the professor to get set up. To get started on this lab, do the following:

Open our class GitHub repo
Navigate to LABS > lab-01.qmd
Click to “Download raw file”
Place the downloaded .qmd document in your STAT_4380 folder on your computer
From inside your STAT_4380 RStudio Project, open and render the Quarto document

The document you are currently reading has all the instructions for Lab 01, but you will complete the lab by adding code and text responses to the lab-01.qmd document in RStudio.

Warm up

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

YAML

Important

In the Quarto (qmd) file in your project, change the author name to your name, and render the document.

The top portion of your Quarto 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.

Packages

In this lab we will work with two packages: datasauRus which contains the dataset we’ll be using and tidyverse which is a collection of packages for doing data analysis in a “tidy” way. These packages should already be installed if you ran the code in the install_packages.R file previously. You can load the packages by running the following in the Console.

library(tidyverse) 
library(datasauRus)

Note that the packages are also loaded with the same commands in your Quarto document.

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 visualisation is important and how summary statistics alone can be misleading. The different datasets are marked by the dataset variable.

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?

Exercises

Exercise 1:

To find out more about the dataset, type the following in your Console: ?datasaurus_dozen
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.

A question mark before the name of an object will always bring up its help file. This command must be run in the Console.

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)

# 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

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.

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.

Exercise 2:

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 .qmd document and successfully render 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()

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

For the second part of these exercises, 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.

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

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

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

⏸ ➡️ ✅ This is a good place to pause and Render to make sure there are no errors in your .qmd

Exercise 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 a good place to pause and Render to make sure there are no errors in your .qmd

Exercise 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?

⏸ ➡️ ✅ This is a good place to pause and Render to make sure there are no errors in your .qmd

Exercise 5

Finally, let’s plot all datasets at once and calculate the correlation coefficient for each (code below).
Comment on your findings - articulate why this is a cautionary tale about the correlation coefficient! (Google “correlation coefficient” if you’re unsure what it measures).

In order to plot all 12 datasets 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")

facet_wrap line facets by the dataset variable, placing the plots in a 3 column grid. theme line removes the legend.

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))

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

Exercise 6: Resize your figures:

In the YAML under the html: settings, add fig-width: 3 and fig-height: 3 This will make each plot 3 inches by 3 inches.

You can also use different figure sizes for different figures. To do so, add #| fig-width: and #| fig-height: as two new lines to the top of the code chunk for the figure you want to change. For example, change the figure in Exercise 5 to be 15 x 9.

Exercise 7: Change the look of your report

In the YAML, this lab is currently set to have theme: "cosmo". See a list of all available themes below and on the Quarto website. Play around with these until you’re happy with the look.

➡️ ✅ ⬆️ Yay, you’re done! Render one last time, and verify that everything is displayed correctly in your lab-01.html file. To view the .html, click on the file name in the Files quadrant, and select “View in Web Browser”. If everything looks good, you’re ready to submit!

Submit

Important

Unfortunately, Blackboard does not accept html file submissions. So, you will first need to turn it into a .zip file.

Outside of RStudio, locate the .html file (e.g., lab_01.html) on your computer. It should be in the STAT_4380 folder you created.
Right click on the file name, then
- On Windows, select “Send to –> Compressed (zipped) folder”
- On Mac, select “Compress”
A .zip file will be created in the same folder
Upload the .zip file to the Lab 01 assignment in Blackboard and submit

Grading (50 pts)

Component	Points
Ex 1	6
Ex 2	6
Ex 3	6
Ex 4	6
Ex 5	6
Ex 6	6
Ex 7	6
Reflection prompts	4
Workflow & formatting	4

Grading notes:

The “Workflow & formatting” grade is to assess the reproducible workflow. This includes having readable code (e.g. adequate use of spacing and line breaks), labeled code chunks, informative headers and sub-headers, and an overall organized and uncluttered report (e.g. suppress messages & warnings, no extraneous output).