Lab 2 - Data wrangling

Lab
Important

This lab is due Friday, September 22 at 11:59pm.

Learning goals

In this lab, you will…

  • use data wrangling to extract meaning from data
  • continue developing a workflow for reproducible data analysis
  • continue working with data visualization tools

Getting started

  • Go to the sta199-f23-2 organization on GitHub. Click on the repo with the prefix lab-02. It contains the starter documents you need to complete the lab.
  • Clone the repo and start a new project in RStudio. See the Lab 1 instructions for details on cloning a repo and starting a new R project.
  • First, open the Quarto document lab-02.qmd and Render it.
  • Make sure it compiles without errors.

Warm up

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

  • Update the YAML, changing the author name to your name and the date to the date, and render the document.
  • Commit your changes with a meaningful commit message. Commit ALL files that you see in the Git window.
  • Push your changes to GitHub.
  • Go to your repo on GitHub and confirm that your changes are visible in your `.qmd and .pdf files. If anything is missing, render, commit, and push again.

Packages

We’ll use the tidyverse package for much of the data wrangling. This package is already installed for you. You can load it by running the following in your Console:

read_csv

Before we get started, I want to introduce read_csv.

read_csv reads comma delimited files. The first argument to read_csv() is the most important: it’s the path to the file to read. This function uses the first line of the data for the column names, which is a very common convention. The data might not have column names. You can use col_names = FALSE to tell read_csv() not to treat the first row as headings, and instead label them: read_csv(path, col_names = FALSE).

Another option that commonly needs tweaking is na: this specifies the value (or values) that are used to represent missing values in your file: read_csv(path, na = "NA")

Data

The dataset for this assignment can be found as a CSV (comma separated values) file in the data folder of your repository. You can read it in using the following.

nobel <- read_csv("data/nobel.csv")

The descriptions of the variables are as follows:

  1. id: ID number
  2. firstname: First name of laureate
  3. surname: Surname
  4. year: Year prize won
  5. category: Category of prize
  6. affiliation: Affiliation of laureate
  7. city: City of laureate in prize year
  8. country: Country of laureate in prize year
  9. born_date: Birth date of laureate
  10. died_date: Death date of laureate
  11. gender: Gender of laureate
  12. born_city: City where laureate was born
  13. born_country: Country where laureate was born
  14. born_country_code: Code of country where laureate was born
  15. died_city: City where laureate died
  16. died_country: Country where laureate died
  17. died_country_code: Code of country where laureate died
  18. overall_motivation: Overall motivation for recognition
  19. share: Number of other winners award is shared with
  20. motivation: Motivation for recognition

In a few cases the name of the city/country changed after laureate was given (e.g. in 1975 Bosnia and Herzegovina was called the Socialist Federative Republic of Yugoslavia). In these cases the variables below reflect a different name than their counterparts without the suffix _original.

  1. born_country_original: Original country where laureate was born
  2. born_city_original: Original city where laureate was born
  3. died_country_original: Original country where laureate died
  4. died_city_original: Original city where laureate died
  5. city_original: Original city where laureate lived at the time of winning the award
  6. country_original: Original country where laureate lived at the time of winning the award

Get to know your data

  1. How many observations and how many variables are in the dataset? Use inline code to answer this question. What does each row represent? (Hint: Check AE-04 for discussion on inline code.)

There are some observations in this dataset that we will exclude from our analysis to match the Buzzfeed results.

  1. Create a new data frame called nobel_living that filters for
  • laureates for whom country is available
  • laureates who are people as opposed to organizations (organizations are denoted with "org" as their gender)
  • laureates who are still alive (their died_date is NA)

Hint: you can use the function is.na to check for NAs

Confirm that once you have filtered for these characteristics you are left with a data frame with 228 observations, once again using inline code.

Now is a good time to render, commit, and push. Make sure that you commit and push all changed documents and your Git pane is completely empty before proceeding.


Most living Nobel laureates were based in the US when they won their prizes

… says the Buzzfeed article. Let’s see if that’s true.

First, we’ll create a new variable to identify whether the laureate was in the US when they won their prize. We’ll use the mutate() function for this. The following pipeline mutates the nobel_living data frame by adding a new variable called country_us. We use an if statement to create this variable. The first argument in the if_else() function we’re using to write this if statement is the condition we’re testing for. If country is equal to "USA", we set country_us to "USA". If not, we set the country_us to "Other".

nobel_living <- nobel_living |>
  mutate(
    country_us = if_else(country == "USA", "USA", "Other")
  )

Next, we will limit our analysis to only the following categories: Physics, Medicine, Chemistry, and Economics.

nobel_living_science <- nobel_living |>
  filter(category %in% c("Physics", "Medicine", "Chemistry", "Economics"))

For the following exercises, work with the nobel_living_science data frame you created above. This means you’ll need to define this data frame in your Quarto document, even though the next exercise doesn’t explicitly ask you to do so.

  1. Create a faceted bar plot visualizing the relationship between the category of prize and whether the laureate was in the US when they won the nobel prize. Interpret your visualization, and say a few words about whether the Buzzfeed headline is supported by the data.

    • Your visualization should be faceted by category.
    • For each facet you should have two bars, one for winners in the US and one for Other.
    • Flip the coordinates so the bars are horizontal, not vertical.

Now is a good time to render, commit, and push. Make sure that you commit and push all changed documents and your Git pane is completely empty before proceeding.


But of those US-based Nobel laureates, many were born in other countries

  1. Create a new variable called born_country_us in nobel_living_science that has the value "USA" if the laureate is born in the US, and "Other" otherwise. Then, in a separate code pipeline, how many of the winners are born in the US?
Note

You should be able to cheat borrow from code you used earlier to create the country_us variable.

  1. Add a second variable to your visualization from Exercise 3 based on whether the laureate was born in the US or not. Create two visualizations with this new variable added:

    • Plot 1: Segmented frequency bar plot

    • Plot 2: Segmented relative frequency bar plot (Hint: Add position = "fill" to geom_bar().)

    Here are some instructions that apply to both of these visualizations:

    • Your final visualization should contain a facet for each category.
    • Within each facet, there should be two bars for whether the laureate won the award in the US or not.
    • Each bar should have segments for whether the laureate was born in the US or not.

    Which of these visualizations is a better fit for answering the following question: “Do the data appear to support Buzzfeed’s claim that of those US-based Nobel laureates, most were born in other countries?” First, state which plot you’re using to answer the question. Then, answer the question, explaining your reasoning in 1-2 sentences.

Now is a good time to render, commit, and push. Make sure that you commit and push all changed documents and your Git pane is completely empty before proceeding.


  1. In a single pipeline, filter the nobel_living_science data frame for laureates who:
  • won their prize in the US,
  • but were born outside of the US.

Then create a frequency table (with the count() function) for their birth country (born_country). Arrange the resulting data frame in descending order of number of observations for each country. Which country (or countries) is the most common?

Now is a good time to render, commit, and push. Make sure that you commit and push all changed documents and your Git pane is completely empty before proceeding.


Submission

Once you are finished with the lab, you will your final PDF document to Gradescope.

Warning

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.

You must turn in a PDF file to the Gradescope page by the submission deadline to be considered “on time”.

Make sure your data are tidy! That is, your code should not be running off the pages and spaced properly. See: https://style.tidyverse.org/ggplot2.html.

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 all the pages associated with exercise. All the pages of your lab should be associated with at least one question (i.e., should be “checked”). If you do not do this, you will be subject to lose points on the assignment.
  • Do not select any pages of your .pdf submission to be associated with the “Workflow & formatting” question.

Grading

Component Points
Ex 1 5
Ex 2 7
Ex 3 8
Ex 4 7
Ex 5 10
Ex 6 8
Workflow & formatting 5
Total 50
Note

The “Workflow & formatting” grade is to assess the reproducible workflow. This includes:

  • linking all pages appropriately on Gradescope
  • putting your name in the YAML at the top of the document
  • committing the submitted version of your .qmd to GitHub
  • Are you under the 80 character code limit? (You shouldn’t have to scroll to see all your code). Pipes %>%, |> and ggplot layers + should be followed by a new line
  • You should be consistent with stylistic choices, e.g. only use 1 of = vs <- and %>% vs |>
  • All binary operators should be surrounded by space. For example x + y is appropriate. x+y is not.