Working With Data

September 10, 2026

How Do We Get Tidy/Clean Data?

Wrangle it ourselves
Use a package where it has been wrangled for us
Download via an API

This Lesson

Practice with World Bank and V-Dem data
World Bank data through wbstats
- There is another package called WDI
- Both packages for accessing data through WB API
Varieties of Democracy (V-Dem) through vdemdata
- Downloads the full V-Dem dataset, which we pare down with dplyr

`filter()`, `select()`, `mutate()`

Along the way we will practice some important dplyr verbs:

filter() is used to select observations based on their values
select() is used to select variables
mutate() is used to create new variables or modifying existing ones

As well as some helpful functions from the janitor package.

APIs

API stands for “Application Programming Interface”
Way for two computers to talk to each other
In our case, we will use APIs to download social science data

APIs in R

APIs are accessed through packages in R
Sometimes there can be more than one package for an API
Much easier than reading in data from messy flat file!
We will use a few API packages in this course
- World Bank data through wbstats (or WDI)
- fredr for Federal Reserve Economic Data
- tidycensus for US Census data
But there are many APIs out there (please explore!)

Searching for WB Indicators

flfp_indicators <- wb_search("female labor force") # store the list of indicators

print(flfp_indicators, n=26) # view the indicators

`wbstats` Example

# Load packages
library(wbstats) # for downloading WB data
library(dplyr) # for selecting, renaming and mutating
library(janitor) # for rounding

# Store the list of indicators in an object
indicators <- c("flfp" = "SL.TLF.CACT.FE.ZS", "women_rep" = "SG.GEN.PARL.ZS") 

# Download the data  
women_emp <- wb_data(indicators, mrv = 50) |> # download data for last 50 yrs
  select(!iso2c) |> # drop the iso2c code which we won't be using
  rename(year = date) |> # rename date to year 
  mutate(
    flfp = round_to_fraction(flfp, denominator = 100), # round to nearest 100th
    women_rep = round_to_fraction(women_rep, denominator = 100) 
  )

# View the data
glimpse(women_emp)

Your Turn!

Search for a WB indicator
Download the data

The V-Dem Dataset

V-Dem stands for Varieties of Democracy
It is a dataset that measures democracy around the world
Based on expert assessments of the quality of democracy in each country
We will access V-Dem data through the vdemdata package

Downloading V-Dem Data

The vdem function from vdemdata just downloads all of the data. Try running this code chunk. What do you see in democracy?

library(vdemdata) # load the V-Dem package

democracy <- vdem() # download the V-Dem dataset

`filter()`

Run this code. What do you see?
Try changing the year
For one year, use == instead of >=
Or try <= and see what happens

democracy <- vdem |> # download the V-Dem dataset
  filter(year >= 1990) # filter out years less than 1990
  
glimpse(democracy)

`=` versus `==`

= is used to assign values to variables, just like <-
== is used to test if two values are equal to each other
So filter(year == 1990) will give you just the observations for 1990

`>=` and `<=`

>= is used to test if a value is greater than or equal to another value
<= is used to test if a value is less than or equal to another value
So filter(year >= 1990) will give you the observations for 1990 and later
And filter(year <= 1990) will give you the observations for 1990 and earlier

`select()`

Run this code. What do you see?
Now try v2x_libdem instead of v2x_polyarchy
Choose more from the codebook

democracy <- vdem |> # download the V-Dem dataset
  select(                  # select (and rename) these variables
    country = country_name,     # before the = sign is new name  
    vdem_ctry_id = country_id,  # after the = sign is the old name
    year, 
    polyarchy = v2x_polyarchy
  )
  
glimpse(democracy)

`mutate()`

Modify the code to create new variable that is three times the value of polyarchy
How about polyarchy squared?

democracy <- vdem |> # download the V-Dem dataset
  filter(year == 2015) |> # keep only observations from 2015
  select(                  # select (and rename) these variables
    country = country_name,     # name before the = sign is new name  
    vdem_ctry_id = country_id,  # name after the = sign is old name
    year, 
    polyarchy = v2x_polyarchy 
    ) |>
  mutate(
    polyarchy_dbl = polyarchy * 2 # create variable 2X polyarchy
  )
  
glimpse(democracy)

Some Common Arithmetic Operators

+ addition
- subtraction
* multiplication
/ division
^ exponentiation (also **)

`vdemdata` Example

# Load packages
library(vdemdata) # to download V-Dem data
library(dplyr)

# Download the data
democracy <- vdem |> # download the V-Dem dataset
  filter(year == 2015)  |> # filter year, keep 2015
  select(                  # select (and rename) these variables
    country = country_name,     # the name before the = sign is the new name  
    vdem_ctry_id = country_id,  # the name after the = sign is the old name
    year, 
    polyarchy = v2x_polyarchy, 
    gdp_pc = e_gdppc, 
    region = e_regionpol_6C
    ) |>
  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(democracy)

Use filter() to select years…

# Download the data
democracy <- vdem |> 
  filter(year == 2015)  |> # keep 2015
  select(                 
    country = country_name,       
    vdem_ctry_id = country_id,  
    year, 
    polyarchy = v2x_polyarchy, 
    gdp_pc = e_gdppc, 
    region = e_regionpol_6C
    ) |>
  mutate(
    region = case_match(region,
                     1 ~ "Eastern Europe", 
                     2 ~ "Latin America",  
                     3 ~ "Middle East",   
                     4 ~ "Africa", 
                     5 ~ "The West", 
                     6 ~ "Asia")
  )

Use select() to choose variables…

# Download the data
democracy <- vdem |> 
  filter(year == 2015)  |> 
  select(                  # select (and rename) these variables
    country = country_name,     # the name before the = sign is the new name  
    vdem_ctry_id = country_id,  # the name after the = sign is the old name
    year, 
    polyarchy = v2x_polyarchy, 
    gdp_pc = e_gdppc, 
    region = e_regionpol_6C
    ) |>
  mutate(
    region = case_match(region, 
                     1 ~ "Eastern Europe", 
                     2 ~ "Latin America",  
                     3 ~ "Middle East",   
                     4 ~ "Africa", 
                     5 ~ "The West", 
                     6 ~ "Asia")
  )

Use mutate with case_match() to Recode Region….

# Download the data
democracy <- vdem |>
  filter(year == 2015)  |> 
  select(                  
    country = country_name,     
    vdem_ctry_id = country_id,  
    year, 
    polyarchy = v2x_polyarchy, 
    gdp_pc = e_gdppc, 
    region = e_regionpol_6C
    ) |>
  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")
                    # number on the left of the ~ is the V-Dem region code
                    # we are changing the number to the country name on the right
                    # of the equals sign
  )

Your Turn

Use vdem to download the full V-Dem dataset
Use filter() to keep a year (or years) you are interested in
Use select() to choose country_name, year, and an indicator of interest
- e.g., v2x_polyarchy for electoral democracy
Use mutate() with case_match() to recode the region variable (e_regionpol_6C)
glimpse() your data to check the result