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.

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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".

⊕Note that we can achieve the same result using the fct_other() function we’ve seen before (i.e. with country_us = fct_other(country, “USA”)). We decided to use the if_else() here to show you one example of an if statement in R.

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

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.

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

⊕Hint: You should be able to cheat 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.

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.

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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 won their prize in the US, but were born outside of the US, and 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.

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