Merging and Summarizing Data

November 3, 2026

Merging Data Frames

• Often we have data from two different sources
• Results in two data frames
• How to make them one so we can analyze?
• Key questions
  • What is the unit of analysis?
  • What is/are the corresponding identifier variables?
  • Are the identifier variables in common?
  • Or do they have to be added/transformed to match?

Merging WB and V-Dem Data

• These are both time-series, country-level data
• Need to merge by country-year
• Year is easy
• But there are many different country codes
• Can use countrycode package to assign country codes

countrycode Example

# Load countrycode
library(countrycode)

# Create new iso3c variable
dem_data <- dem_data |>    
  mutate(iso3c = countrycode(sourcevar = country_id, # what we are converting
        origin = "vdem",         # we are converting from vdem
        destination = "wb"))  |> # and converting to the WB iso3c code 
  relocate(iso3c, .after = country_id) # move iso3c 

# View the data
glimpse(dem_data)

Try it Yourself

• Using your democracy data frame from the last lesson
• Use mutate() and countrycode() to add iso3c country codes
• Use relocate to move your iso3c code to the “front” of your data frame (optional)

Types of Joins in dplyr

• Mutating versus filtering joins
• Four types of mutating joins
  • inner_join()
  • full_join()
  • left_join()
  • right_join()
• For the most part we will use left_join()

left_join() Example

# Perform left join using common iso3c variable and year
dem_women <- left_join(dem_data, women_emp, by = c("iso3c", "year")) |> 
  select(-c(country_text_id, country_id, country)) |> # drop extra country codes/names
  rename(region = e_regionpol_6C, 
         polyarchy = v2x_polyarchy,
         libdem = v2x_libdem) |>
    mutate(
    region = case_match(region, # replace the values in region with country names
                     1 ~ "Eastern Europe", 
                     2 ~ "Latin America",  
                     3 ~ "Middle East",   
                     4 ~ "Africa", 
                     5 ~ "The West", 
                     6 ~ "Asia")
    )
  
# View the data
glimpse(dem_women)  

Try it Yourself

• Take your V-Dem data frame and your World Bank data frame
• Using left_join() to merge on country code and year
• Along the way, use rename() and select() to insure you have just one country name

Group, Summarize and Arrange

• group_by(), summarize(), arrange()
• A very common sequence in data science:
  • Take an average or some other statistic for a group
  • Rank from high to low values of summary value

Example

# group_by(), summarize() and arrange()
dem_women |> # save result as new object
  group_by(region)  |> # group data by region
  summarize(           # summarize following vars (by region)
    polyarchy_mean = mean(polyarchy, na.rm = TRUE), # calculate mean after remove NAs
    libdem_median = median(libdem, na.rm = TRUE), # calculate median after remove NAs
    women_rep_sd = sd(women_rep, na.rm = TRUE), # calculate std. dev after remove NAs
    flfp_min = min(flfp, na.rm = TRUE) # calculate minimum flfp after remove NAs
  ) |> 
  arrange(desc(polyarchy_mean)) # arrange in descending order by polyarchy score

Use across() to Apply Same Function to Multiple Columns

dem_women |>
  group_by(region) |> 
  summarize(
    across(c(polyarchy, libdem, women_rep, flfp), mean, na.rm = TRUE)
  ) |> 
  arrange(desc(polyarchy))

Try it Yourself

• Try changing the parameters in the code to answer these questions:

1. Try summarizing the data with a different function for one or more of the variables.

1. What is the median value of polyarchy for The West?
2. What is the max value of libdem for Eastern Europe?
3. What is the standard deviation of women_rep for Africa?
4. What is the mean of flfp for the Middle East?

2. Now try grouping by country instead of region and filter for years >= 2000.

1. What is the median value of polyarchy for Sweden?
2. What is the max value of libdem New Zealand?
3. What is the standard deviation of women_rep for Norway?
4. What is the min of flfp for Germany?

3. Try sorting countries in ascending order of the selected variable (hint: delete “desc” from the arrange() call). Which country ranks at the “top” of the list? Now change it back to descending order.

From Summary to Chart

• A summary table is a good start, but a chart is easier to read
• Save the summary as an object, then hand it to ggplot()
• geom_col() draws one bar per region at the height of the summary value
• Note: arrange() sorts the table, not the bars
  • Use fct_reorder() to sort the bars

Step 1: Save the Summary

polyarchy_region <- dem_women |> # save result as new object
  group_by(region) |> # group data by region
  summarize(
    polyarchy = mean(polyarchy, na.rm = TRUE) # mean polyarchy score by region
  ) |> 
  arrange(desc(polyarchy)) # arrange in descending order

polyarchy_region

# A tibble: 6 × 2
  region         polyarchy
  <chr>              <dbl>
1 The West           0.871
2 Latin America      0.637
3 Eastern Europe     0.539
4 Asia               0.408
5 Africa             0.393
6 Middle East        0.246

Step 2: Make a Column Chart

ggplot(polyarchy_region, aes(x = fct_reorder(region, -polyarchy), y = polyarchy)) +
  geom_col(fill = "steelblue") + # one bar per region
  labs(
    x = "Region", 
    y = "Avg. Polyarchy Score", 
    title = "Democracy by region, 1990 - present", 
    caption = "Source: V-Dem Institute"
  ) +
  theme_minimal()

Step 2: Make a Column Chart

Try it Yourself

• Using your merged data frame from today:

1. Make a column chart of the median libdem score by region. Which region ranks highest? Lowest?

2. Add filter(year == 2019) before group_by(). Does the ranking change compared to the 1990 - present average?

3. Now chart one of your World Bank variables (e.g. flfp or women_rep) by region. Do the regions line up in the same order as they do for democracy?

4. Change the fill color and the theme, and update the axis labels and title to match your new chart.