Getting Started

Each of your assignments will begin with similar steps below. You saw these once in class, and they’re outlined in detail here again. Going forward each lab will start with a “Getting started” section but details will be a bit more sparse than this. You can always refer back to this lab for a detailed list of the steps involved for getting started with an assignment.

library(usethis)
use_git_config(user.name="your name", user.email="your email")

library(usethis)
use_git_config(user.name="Shawn Santo", user.email="shawn.santo@duke.edu")

Packages

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

At any point, you can Knit your document and see the results.

Are both tidyverse and datasauRus installed? If not, install them in the console (refer to Tuesday’s slides).

library(tidyverse) 
library(datasauRus)

Warm up

Before we introduce the data, we’re going to go through our first stage, commit, and push version control cycle.

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.

1. Based on the help file, how many rows and how many columns does datasaurus_dozen 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) 

## # 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

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 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.

For illustrative purposes, let’s calculate 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, commit changes with the commit message “Added answer for Ex 2”, and push.

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, commit changes with the commit message “Added answer for Ex 3”, and push.

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.

⊕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.

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)

5. Include the faceted plot and summary of the correlation coefficients in your lab write-up. To do this, add code chunks to your Rmd document and give the chunks an appropriate name. Below the code chunks, briefly comment on what you observe about the plots and correlation values. One to two sentences is sufficient.

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

Submission

To facilitate and expedite grading in this class, we’ll be submitting .pdf documents to Gradescope. Once you are 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.

Once you’ve created this .pdf file, you’re done! Stage and commit all remaining changes, use the commit message “Done with Lab 1!” and push. 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.

Please only upload your PDF document to Gradescope. Before you submit the uploaded document, mark where each answer is to the exercises. If any answer spans multiple pages, then mark all pages.