Get to know your data

1. How many observations and how many variables are in the dataset? Use inline code to answer this question.

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

2. 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)

Confirm that once you have filtered for these characteristics you are left with a data frame with 228 observations.

⊕You can pull by clicking the blue down arrow in the Git pane in RStudio. Once you click to pull, you will see the updates your team member pushed to GitHub in your RStudio project.

“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/else statement to create this variable. The first argument in the if_else() function 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".

⊕Note that we can achieve the same result using the fct_other() function (i.e. with country_us = fct_other(country, “USA”)).

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"))

You will work with the nobel_living_science data frame you created above for the remainder of the lab. This means you’ll need to define this data frame in your R Markdown document.

⊕Hint: You can change the orientation of the bars using the coord_flip() function in ggplot2. Click here to read more about the function.

3. 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. Note: 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. Interpret your visualization, and say a few words about whether the Buzzfeed headline is supported by the data.

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

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

4. Create a new variable called born_country_us that has the value "USA" if the laureate is born in the US, and "Other" otherwise. Be sure to save the variable to the nobel_living_science data frame.

5. Add a second variable to your visualization from Exercise 3 based on whether the laureate was born in the US or not. Your final visualization should contain a facet for each category, within each facet a bar for whether they won the award in the US or not, and within each bar whether they were born in the US or not. Based on your visualization, do the data appear to support Buzzfeed’s claim? Explain your reasoning in 1-2 sentences.

Here’s where those immigrant Nobelists were born

⊕Note that your bar plot won’t exactly match the one from the Buzzfeed article. This is likely because the data has been updated since the article was published.

6. In a single pipeline, filter for laureates who were living in the US when they won their prize, but were born outside of the US, then create a frequency table (with the count function) for their birth country (born_country), and arrange the resulting data frame in descending order of number of observations for each country.

Wrapping up

Go back through your write up to make sure you followed the coding style guidelines we discussed in class (e.g. no long lines of code).

Also, make sure all of your R chunks are properly labeled, and your figures are reasonably sized.

Team Member 2: Make any edits as needed. Then knit, commit, and push the updated documents to GitHub if you made any changes.

All other team members can click to pull the finalized document.

Submission

Team Member 3: Upload the team’s PDF to Gradescope. Be sure to include every team member’s name in the Gradescope submission Associate the “Overall” graded section with the first page of your PDF, and mark where each answer is to the exercises. If any answer spans multiple pages, then mark all pages.

There should only be one submission per team on Gradescope.

Interested in how Buzzfeed made their visualizations?

The plots in the Buzzfeed article are called waffle plots. You can find the code used for making these plots in Buzzfeed’s GitHub repo (yes, they have one!) here. You’re not expected to recreate them as part of your assignment, but you’re welcomed to do so for fun! © 2020 GitHub, Inc.

This lab was adapted from Data Science in a Box.