In this lab we will work with the data from this article to recreate some of their visualizations as well as explore new questions.

Getting started

Go to the course GitHub organization and locate your Lab 03 repo, which should be named lab-03-nobel-laureates-[GITHUB USERNAME]. Grab the URL of the repo, and clone it in RStudio. Refer to Lab 01 for step-by-step for cloning a repo and creating a new RStudio project.

Configure git

Before we can get started we need to do one more thing. Specifically, we need to configure your git so that RStudio can communicate with GitHub. This requires two pieces of information: your email address and your GitHub username.

To do so, run the following:

usethis::use_git_config(user.name = "github username", user.email = "your email")

Update YAML

Change the author to your name in YAML.

Packages

We’ll use the tidyverse package for this analysis. Run the following code in the Console to load this package.

library(tidyverse)

The data

The dataset for this assignment can be found as a csv file in the data folder of your repository. You can read it in using the following.

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

The variable descriptions are as follows:

id: ID number
firstname: First name of laureate
surname: Surname
year: Year prize won
category: Category of prize
affiliation: Affiliation of laureate
city: City of laureate in prize year
country: Country of laureate in prize year
born_date: Birth date of laureate
died_date: Death date of laureate
gender: Gender of laureate
born_city: City where laureate was born
born_country: Country where laureate was born
born_country_code: Code of country where laureate was born
died_city: City where laureate died
died_country: Country where laureate died
died_country_code: Code of country where laureate died
overall_motivation: Overall motivation for recognition
share: Number of other winners award is shared with
motivation: Motivation for recognition

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

born_country_original: Original country where laureate was born
born_city_original: Original city where laureate was born
died_country_original: Original country where laureate died
died_city_original: Original city where laureate died
city_original: Original city where laureate lived at the time of winning the award
country_original: Original country where laureate lived at the time of winning the award

Exercises

Note that in this lab, the R chunks are not provided for you. Therefore you must create your own code chunks and name them properly. A portion of the lab grade will be based on: - Naming code chunks - Reasonable number of commits to ensure you are tracking your progress - Good coding style

Get to know your data

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.

⊕Hint: The lecture about logical operators could be useful here!

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.

Knit, commit and push your changes to GitHub with an appropriate commit message again. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

“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.

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.

Knit, commit and push your changes to GitHub with an appropriate commit message again. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.*

“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.

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.
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.

Knit, commit and push your changes to GitHub with an appropriate commit message again. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

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.

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.

Knit, commit and push your changes to GitHub with an appropriate commit message again. Make sure to commit and push all changed files so that your Git pane is cleared up afterwards.

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.

Submission

Upload your to Gradescope. 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.

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.

Lab 03 - Nobel laureates

due Tue, June 1 at 11:59p

Getting started

Configure git

Update YAML

Packages

The data

Exercises

Get to know your data

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

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

Here’s where those immigrant Nobelists were born

Wrapping up

Submission

Interested in how Buzzfeed made their visualizations?