Exercise 1 (20 minutes)

Do the Setup, if you have not yet done so.
Start a new R script and copy in this starter code (it loads the packages and downloads both data sets — we walked through every line on the slides):

pacman::p_load(
  tidyverse,    # Data manipulation & figures
  wbstats,      # World Bank data via API
  vdemdata,     # Varieties of Democracy data
  estimatr,     # OLS with robust standard errors
  modelsummary  # Nicely formatted regression tables
)

# V-Dem: civil liberties, and state ownership of the economy
Dat_vdem <- vdem %>%
  as_tibble() %>%
  select(country_text_id, year,
         civ_liberties = v2xcl_rol,    # Equality before the law & individual liberty
         state_own_raw   = v2clstown) %>% # State ownership of the economy
  mutate(state_ownership = -state_own_raw)  # Reverse: higher = MORE state ownership

# World Bank: extreme poverty (share below $3.00 a day, 2021 PPP).
# Try the live API; if it is down, fall back to the cached copy.
Dat_poverty <- tryCatch(
  wb_data("SI.POV.DDAY", start_date = 1972, end_date = 2025),
  error = function(e) readRDS("data/wb_poverty_raw.rds")
) %>%
  rename(poverty = SI.POV.DDAY,
         year = date,
         country_text_id = iso3c) %>%
  select(country_text_id, year, country, poverty) %>%
  drop_na(poverty) %>%
  group_by(country) %>%
  filter(year == max(year)) %>% # Keep the most recent estimate per country
  ungroup()

Now write the join yourself: combine Dat_poverty and Dat_vdem into one tibble called Dat. Think first: which join do you want, and which variables form the key?

We want only countries that appear in both tibbles (an inner join), and a country is identified by its ISO code together with the year.

Dat <- inner_join(Dat_poverty, Dat_vdem, by = c("country_text_id", "year"))

Stuck?

Did the pacman::p_load(...) line run without red error messages? If not, fix that first.

Strange results? Clear leftovers with a fresh start: Session → Restart R, then rerun your script from the top.

World Bank API down or very slow? Load our cached copy instead: Dat <- readRDS(url("https://willowy-quokka-e604ee.netlify.app/data/Dat_L2.rds"))

Still stuck? Ask (and answer!) in the t-R-ouble forum on Absalon.

Make a scatter plot with poverty as outcome on the Y-axis and civil liberties as predictor on the X-axis. Ask it the four questions from the lecture: direction, form, spread, outliers.

ggplot(data = Dat, aes(y = poverty, x = civ_liberties)) +
  geom_text(aes(label = country)) +
  labs(y = "% of population below $3.00 a day (2021 PPP)",
       x = "Civil liberties") +
  theme_minimal()

Before you compute anything: judging only from your scatter plot, the correlation between poverty and civil liberties is …
Now check yourself: estimate the correlation with cor() (rounded to two digits; point or comma is fine):

Dat %>%
  select(poverty, civ_liberties) %>%
  cor() %>%
  round(2)
#               poverty civ_liberties
# poverty          1.00         -0.33
# civ_liberties   -0.33          1.00

This is edge ② of our triangle. What does the correlation suggest about a liberty–equality trade-off?
Write one sentence in your R script (as a # comment) interpreting the correlation — as if for your term paper. Then compare with the model answer:

Across 161 countries, civil liberties correlate moderately negatively with extreme poverty (r ≈ -0.33): countries with stronger civil liberties tend to have a smaller share of their population in extreme poverty.

(Yours doesn't need the same words — but it should name the direction, the rough strength, and the units of analysis (countries), and it should not claim causality.)

Bonus (for the fast): Dat has ~160 countries, but V-Dem covers 179 — where did the rest go? Find the countries we lost in the join, using anti_join(). Why are they missing?

Dat_vdem %>%
  filter(year > 2015) %>% # Recent years only
  anti_join(Dat_poverty, by = c("country_text_id", "year")) %>%
  distinct(country_text_id)
# # A tibble: 179 × 1
#    country_text_id
#    <chr>          
#  1 MEX            
#  2 SUR            
#  3 SWE            
#  4 CHE            
#  5 GHA            
#  6 ZAF            
#  7 JPN            
#  8 MMR            
#  9 RUS            
# 10 ALB            
# # ℹ 169 more rows

They are countries (and country-years) with no World Bank poverty estimate — poverty data require household surveys, which some countries run rarely or not at all (e.g. North Korea). An inner_join() silently drops them. Data that are missing are a finding, too: our analysis can only speak about countries that measure poverty.

Exercise 1 (20 minutes)

You practice: APIs, joins, scatter plots, and the correlation coefficient — edge ② of our triangle. When you're done, assist some of the others …