Lab 3

Describing Distributions

Author

YOUR NAME HERE

How to complete this lab

Fill in each ??? with the correct code. Once all placeholders are filled in, change completed: false to completed: true in the YAML header above and render to HTML. For your final submission, change format: html to format: pdf.

Overview

In this lab, you will practice describing distributions using the V-Dem dataset. You will:

  1. Choose one V-Dem variable you are interested in and create a clean variable name for it
  2. Calculate and interpret measures of central tendency (mean and median)
  3. Calculate and interpret measures of spread (range, IQR, and standard deviation)
  4. Visualize the distribution using a histogram/density plot and a box plot
  5. Render your document to PDF and submit

You are encouraged to have Module 3.1 open while completing this lab.

Getting Started

Load the required packages.

library(tidyverse)
library(vdemdata)
run <- isTRUE(params$completed)
Installing vdemdata locally

If you are working on your own computer and don’t have vdemdata installed, you can install it from GitHub. First install the pak package, then use it to install vdemdata:

install.packages("pak")
pak::pak("vdeminstitute/vdemdata")
Choosing a variable

Pick one V-Dem variable that interests you. You can browse variable definitions in the V-Dem codebook: V-Dem Codebook

Part 1: Build Your Data Frame (20 points)

Filter to year 2022, recode region as in class, and select one variable of your choice.

In the code below, replace your_var_name with a short name you choose and replace ??? with the V-Dem variable code.

vdem2022 <- vdem |>
  filter(year == 2022) |>
  select(
    country = country_name,
    year,
    region = e_regionpol_6C,
    your_var_name = ???
  ) |>
  mutate(
    region = case_match(region,
      1 ~ "Eastern Europe",
      2 ~ "Latin America",
      3 ~ "Middle East",
      4 ~ "Africa",
      5 ~ "The West",
      6 ~ "Asia"
    )
  )

Question: Which variable did you choose, and what does it measure?

YOUR ANSWER HERE

Part 2: Measures of Central Tendency (30 points)

Use your chosen variable name in all code below.

Step 1: Calculate mean and median (15 pts)

vdem2022 |>
  summarize(
    mean_var = ???,
    median_var = ???
  )

Step 2: Visualize center on the distribution (15 pts)

Create either a histogram or a density plot of your chosen variable, and add vertical lines for both mean and median.

# Write your plot code here

Question: Is the mean larger than the median, smaller, or about the same? What does that suggest about skew?

YOUR ANSWER HERE

Part 3: Measures of Dispersion and Box Plot (50 points)

Step 1: Calculate spread statistics (25 pts)

Calculate the following for your chosen variable:

  • minimum and maximum
  • first quartile (Q1) and third quartile (Q3)
  • IQR
  • standard deviation
vdem2022 |>
  summarize(
    min_var = ???,
    max_var = ???,
    q1_var = ???,
    q3_var = ???,
    iqr_var = ???,
    sd_var = ???
  )

Step 2: Create a box plot (15 pts)

Create one overall box plot of your chosen variable.

# Write a single box plot for your variable here

Step 3: Interpret spread and outliers (10 pts)

In 3-4 sentences, describe what you learned from your spread statistics and box plot.

  1. Are values tightly clustered or widely dispersed?
  2. Does the box plot suggest potential outliers?
  3. How does this compare to what you saw in your histogram/density plot?

YOUR INTERPRETATION HERE

Render as PDF and Submit Your Work (20 pts)

  1. Replace “YOUR NAME HERE” at the top with your actual name
  2. Make sure all your code chunks run without errors
  3. Click “Render” to create your PDF
  4. Submit the PDF to Blackboard

Hints

Only look at these if you’re stuck!

Hint 1 - Filtering to one year:

filter(year == 2022)

Hint 2 - Mean and median (replace your_var_name):

summarize(
  mean_var = mean(your_var_name, na.rm = TRUE),
  median_var = median(your_var_name, na.rm = TRUE)
)

Hint 3 - Histogram with reference lines (replace your_var_name):

ggplot(vdem2022, aes(x = your_var_name)) +
  geom_histogram(bins = 30, fill = "steelblue", color = "white") +
  geom_vline(xintercept = mean(vdem2022$your_var_name, na.rm = TRUE), color = "orange") +
  geom_vline(xintercept = median(vdem2022$your_var_name, na.rm = TRUE), color = "darkgreen", linetype = "dashed") +
  theme_minimal()

Hint 4 - Spread statistics (replace your_var_name):

summarize(
  min_var = min(your_var_name, na.rm = TRUE),
  max_var = max(your_var_name, na.rm = TRUE),
  q1_var = quantile(your_var_name, 0.25, na.rm = TRUE),
  q3_var = quantile(your_var_name, 0.75, na.rm = TRUE),
  iqr_var = IQR(your_var_name, na.rm = TRUE),
  sd_var = sd(your_var_name, na.rm = TRUE)
)

Hint 5 - Single box plot (replace your_var_name):

ggplot(vdem2022, aes(x = "", y = your_var_name)) +
  geom_boxplot(fill = "steelblue") +
  theme_minimal()