Getting started

R and R Studio

These are the general components of a R Markdown File

These are the general components of a R Project

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.

The packages we are using should already be installed and only need to be loaded.

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.

?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.

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

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 the same summary statistics to the Datasaurus but have very different distributions.

Data visualization and summary

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 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 × 1
##         r
##     <dbl>
## 1 -0.0645

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?

⊕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)) 

4. 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.

Your code that created the figure

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.

Once your work is finalized in your GitHub repo, you will submit it to Gradescope.

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

To submit your assignment:

• Go to http://www.gradescope.com and click Log in in the top right corner.
• Click School Credentials \(\rightarrow\) Duke NetID and log in using your NetID credentials.
• Click on your STA 199 course.
• Click on the assignment, and you’ll be prompted to submit it.
• Mark the pages associated with each exercise, 1 - 4. All of the papers of your lab should be associated with at least one question (i.e., should be “checked”).
• Select the first page of your .pdf submission to be associated with the “Workflow and formatting” section.

Grading (50 pts)

Grading notes:

• Workflow and formatting includes the number of commits made, naming chunks, and updating the name on the assignment to your name.

• Note: the points for resizing are part of the total for the other exercises.