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Tidying the Freedom Index

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< section id="motivation" class="level1">

Motivation

I needed to create a few plots by using the Civil Liberties and Political Rights scores from the Freedom Index. However, the data provided in Excel format was not in an easy to use presentation. This blog post shows how I reshaped the data to make it easier to work with.

There is an excellent post, Cleaning Freedom House indicators, by Marta Kolczynska that I used as reference. The post is four years old, so here I am using some updated functions and I tried to make some steps more general.

< section id="download-and-read-the-data" class="level1">

Download and read the data

Required packages:

# install_github("pachadotdev/tintin")

library(dplyr)
library(tidyr)
library(readxl)
library(countrycode)
library(ggplot2)
library(stringr)
library(tintin)

Download the data:

url <- "https://freedomhouse.org/sites/default/files/2023-02/Country_and_Territory_Ratings_and_Statuses_FIW_1973-2023%20.xlsx"
raw_xlsx <- gsub("%20", "", gsub(".*/", "", url))

if (!file.exists(raw_xlsx)) {
  download.file(url, raw_xlsx)
}

After opening the Excel file with Libre Office I realized I need to read the data from the second sheet and recode “-” to missing:

freedom_house <- read_excel(raw_xlsx, sheet = 2, na = "-")
freedom_house
# A tibble: 207 × 151
   `Survey Edition`    `Jan.-Feb. 1973` ...3  ...4  `Jan.-Feb. 1974` ...6  ...7 
   <chr>               <chr>            <chr> <chr> <chr>            <chr> <chr>
 1 Year(s) Under Revi… 1972             <NA>  <NA>  1973             <NA>  <NA> 
 2 <NA>                PR               CL    Stat… PR               CL    Stat…
 3 Afghanistan         4                5     PF    7                6     NF   
 4 Albania             7                7     NF    7                7     NF   
 5 Algeria             6                6     NF    6                6     NF   
 6 Andorra             4                3     PF    4                4     PF   
 7 Angola              <NA>             <NA>  <NA>  <NA>             <NA>  <NA> 
 8 Antigua and Barbuda <NA>             <NA>  <NA>  <NA>             <NA>  <NA> 
 9 Argentina           6                3     PF    2                2     F    
10 Armenia             <NA>             <NA>  <NA>  <NA>             <NA>  <NA> 
# ℹ 197 more rows
# ℹ 144 more variables: `Jan.-Feb. 1975` <chr>, ...9 <chr>, ...10 <chr>,
#   `Jan.-Feb. 1976` <chr>, ...12 <chr>, ...13 <chr>, `Jan.-Feb. 1977` <chr>,
#   ...15 <chr>, ...16 <chr>, `1978` <chr>, ...18 <chr>, ...19 <chr>,
#   `1979` <chr>, ...21 <chr>, ...22 <chr>, `1980` <chr>, ...24 <chr>,
#   ...25 <chr>, `1981` <chr>, ...27 <chr>, ...28 <chr>, `1982` <chr>,
#   ...30 <chr>, ...31 <chr>, `1983-84` <chr>, ...33 <chr>, ...34 <chr>, …
< section id="tidy-the-data" class="level1">

Tidy the data

Fix the first column name:

names(freedom_house)[1] <- "country"
freedom_house
# A tibble: 207 × 151
   country             `Jan.-Feb. 1973` ...3  ...4  `Jan.-Feb. 1974` ...6  ...7 
   <chr>               <chr>            <chr> <chr> <chr>            <chr> <chr>
 1 Year(s) Under Revi… 1972             <NA>  <NA>  1973             <NA>  <NA> 
 2 <NA>                PR               CL    Stat… PR               CL    Stat…
 3 Afghanistan         4                5     PF    7                6     NF   
 4 Albania             7                7     NF    7                7     NF   
 5 Algeria             6                6     NF    6                6     NF   
 6 Andorra             4                3     PF    4                4     PF   
 7 Angola              <NA>             <NA>  <NA>  <NA>             <NA>  <NA> 
 8 Antigua and Barbuda <NA>             <NA>  <NA>  <NA>             <NA>  <NA> 
 9 Argentina           6                3     PF    2                2     F    
10 Armenia             <NA>             <NA>  <NA>  <NA>             <NA>  <NA> 
# ℹ 197 more rows
# ℹ 144 more variables: `Jan.-Feb. 1975` <chr>, ...9 <chr>, ...10 <chr>,
#   `Jan.-Feb. 1976` <chr>, ...12 <chr>, ...13 <chr>, `Jan.-Feb. 1977` <chr>,
#   ...15 <chr>, ...16 <chr>, `1978` <chr>, ...18 <chr>, ...19 <chr>,
#   `1979` <chr>, ...21 <chr>, ...22 <chr>, `1980` <chr>, ...24 <chr>,
#   ...25 <chr>, `1981` <chr>, ...27 <chr>, ...28 <chr>, `1982` <chr>,
#   ...30 <chr>, ...31 <chr>, `1983-84` <chr>, ...33 <chr>, ...34 <chr>, …

Remove the first and second row of data:

freedom_house <- freedom_house %>%
  filter(
    country != "Year(s) Under Review",
    !is.na(country)
  )

freedom_house
# A tibble: 205 × 151
   country             `Jan.-Feb. 1973` ...3  ...4  `Jan.-Feb. 1974` ...6  ...7 
   <chr>               <chr>            <chr> <chr> <chr>            <chr> <chr>
 1 Afghanistan         4                5     PF    7                6     NF   
 2 Albania             7                7     NF    7                7     NF   
 3 Algeria             6                6     NF    6                6     NF   
 4 Andorra             4                3     PF    4                4     PF   
 5 Angola              <NA>             <NA>  <NA>  <NA>             <NA>  <NA> 
 6 Antigua and Barbuda <NA>             <NA>  <NA>  <NA>             <NA>  <NA> 
 7 Argentina           6                3     PF    2                2     F    
 8 Armenia             <NA>             <NA>  <NA>  <NA>             <NA>  <NA> 
 9 Australia           1                1     F     1                1     F    
10 Austria             1                1     F     1                1     F    
# ℹ 195 more rows
# ℹ 144 more variables: `Jan.-Feb. 1975` <chr>, ...9 <chr>, ...10 <chr>,
#   `Jan.-Feb. 1976` <chr>, ...12 <chr>, ...13 <chr>, `Jan.-Feb. 1977` <chr>,
#   ...15 <chr>, ...16 <chr>, `1978` <chr>, ...18 <chr>, ...19 <chr>,
#   `1979` <chr>, ...21 <chr>, ...22 <chr>, `1980` <chr>, ...24 <chr>,
#   ...25 <chr>, `1981` <chr>, ...27 <chr>, ...28 <chr>, `1982` <chr>,
#   ...30 <chr>, ...31 <chr>, `1983-84` <chr>, ...33 <chr>, ...34 <chr>, …

Convert the dataset to long format:

freedom_house <- freedom_house %>%
  pivot_longer(cols = -country, names_to = "year", values_to = "value")

freedom_house
# A tibble: 30,750 × 3
   country     year           value
   <chr>       <chr>          <chr>
 1 Afghanistan Jan.-Feb. 1973 4    
 2 Afghanistan ...3           5    
 3 Afghanistan ...4           PF   
 4 Afghanistan Jan.-Feb. 1974 7    
 5 Afghanistan ...6           6    
 6 Afghanistan ...7           NF   
 7 Afghanistan Jan.-Feb. 1975 7    
 8 Afghanistan ...9           6    
 9 Afghanistan ...10          NF   
10 Afghanistan Jan.-Feb. 1976 7    
# ℹ 30,740 more rows

Fix the year by removing all non-numeric characters in the year column (i.e., the 2023 survey reflects the scores for 2022):

freedom_house <- freedom_house %>%
  mutate(
    year = as.integer(gsub("[^0-9]", "", year)) - 1L,
    year = case_when(
      year < 1972 ~ NA_integer_,
      TRUE ~ year
    )
  )

freedom_house
# A tibble: 30,750 × 3
   country      year value
   <chr>       <int> <chr>
 1 Afghanistan  1972 4    
 2 Afghanistan    NA 5    
 3 Afghanistan    NA PF   
 4 Afghanistan  1973 7    
 5 Afghanistan    NA 6    
 6 Afghanistan    NA NF   
 7 Afghanistan  1974 7    
 8 Afghanistan    NA 6    
 9 Afghanistan    NA NF   
10 Afghanistan  1975 7    
# ℹ 30,740 more rows

Replace blank years:

freedom_house <- freedom_house %>%
  fill(year)

freedom_house
# A tibble: 30,750 × 3
   country      year value
   <chr>       <int> <chr>
 1 Afghanistan  1972 4    
 2 Afghanistan  1972 5    
 3 Afghanistan  1972 PF   
 4 Afghanistan  1973 7    
 5 Afghanistan  1973 6    
 6 Afghanistan  1973 NF   
 7 Afghanistan  1974 7    
 8 Afghanistan  1974 6    
 9 Afghanistan  1974 NF   
10 Afghanistan  1975 7    
# ℹ 30,740 more rows

Add measurement categories:

freedom_house <- freedom_house %>%
  group_by(country, year) %>%
  mutate(
    n = row_number(),
    category = case_when(
        n == 1 ~ "political_rights",
        n == 2 ~ "civil_liberties",
        n == 3 ~ "status"
    )
  ) %>%
  ungroup()

freedom_house
# A tibble: 30,750 × 5
   country      year value     n category        
   <chr>       <int> <chr> <int> <chr>           
 1 Afghanistan  1972 4         1 political_rights
 2 Afghanistan  1972 5         2 civil_liberties 
 3 Afghanistan  1972 PF        3 status          
 4 Afghanistan  1973 7         1 political_rights
 5 Afghanistan  1973 6         2 civil_liberties 
 6 Afghanistan  1973 NF        3 status          
 7 Afghanistan  1974 7         1 political_rights
 8 Afghanistan  1974 6         2 civil_liberties 
 9 Afghanistan  1974 NF        3 status          
10 Afghanistan  1975 7         1 political_rights
# ℹ 30,740 more rows

Convert the data to wide format because the value column is not tidy (i.e., it contains different units in the same column):

freedom_house <- freedom_house %>%
  select(-n) %>% 
  pivot_wider(names_from = category, values_from = value)

freedom_house
# A tibble: 10,250 × 5
   country      year political_rights civil_liberties status
   <chr>       <int> <chr>            <chr>           <chr> 
 1 Afghanistan  1972 4                5               PF    
 2 Afghanistan  1973 7                6               NF    
 3 Afghanistan  1974 7                6               NF    
 4 Afghanistan  1975 7                6               NF    
 5 Afghanistan  1976 7                6               NF    
 6 Afghanistan  1977 6                6               NF    
 7 Afghanistan  1978 7                7               NF    
 8 Afghanistan  1979 7                7               NF    
 9 Afghanistan  1980 7                7               NF    
10 Afghanistan  1981 7                7               NF    
# ℹ 10,240 more rows

Convert political rights and civil liberties to integer (i.e., for South Africa in 1973 I considered the worst score, which is inside parenthesis, because the index was separated into White and Black populations):

remove_parenthesis <- function(x) {
  y <- str_extract(x, "\\((.*?)\\)")
  y <- str_replace_all(y, "\\(|\\)", "")
  return(y)
}

freedom_house <- freedom_house %>%
  mutate(
    political_rights = case_when(
      country == "South Africa" & year == 1973 ~ remove_parenthesis(political_rights),
      TRUE ~ political_rights
    ),
    civil_liberties = case_when(
      country == "South Africa" & year == 1973 ~ remove_parenthesis(civil_liberties),
      TRUE ~ civil_liberties
    ),
    status = case_when(
      country == "South Africa" & year == 1973 ~ remove_parenthesis(status),
      TRUE ~ status
    ),

    political_rights = as.integer(political_rights),
    civil_liberties = as.integer(civil_liberties)
  )
Warning: There were 2 warnings in `mutate()`.
The first warning was:
ℹ In argument: `political_rights = as.integer(political_rights)`.
Caused by warning:
! NAs introduced by coercion
ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
freedom_house %>%
  filter(country == "South Africa", year == 1973)
# A tibble: 1 × 5
  country       year political_rights civil_liberties status
  <chr>        <int>            <int>           <int> <chr> 
1 South Africa  1973               NA              NA <NA>  
freedom_house
# A tibble: 10,250 × 5
   country      year political_rights civil_liberties status
   <chr>       <int>            <int>           <int> <chr> 
 1 Afghanistan  1972                4               5 PF    
 2 Afghanistan  1973                7               6 NF    
 3 Afghanistan  1974                7               6 NF    
 4 Afghanistan  1975                7               6 NF    
 5 Afghanistan  1976                7               6 NF    
 6 Afghanistan  1977                6               6 NF    
 7 Afghanistan  1978                7               7 NF    
 8 Afghanistan  1979                7               7 NF    
 9 Afghanistan  1980                7               7 NF    
10 Afghanistan  1981                7               7 NF    
# ℹ 10,240 more rows

Recode the status to make it more readable:

freedom_house <- freedom_house %>%
  mutate(
    status = factor(
      case_when(
        status == "F" ~ "Free",
        status == "PF" ~ "Partially Free",
        status == "NF" ~ "Not Free"
      ),
      levels = c("Free", "Partially Free", "Not Free"),
    )
  )

freedom_house
# A tibble: 10,250 × 5
   country      year political_rights civil_liberties status        
   <chr>       <int>            <int>           <int> <fct>         
 1 Afghanistan  1972                4               5 Partially Free
 2 Afghanistan  1973                7               6 Not Free      
 3 Afghanistan  1974                7               6 Not Free      
 4 Afghanistan  1975                7               6 Not Free      
 5 Afghanistan  1976                7               6 Not Free      
 6 Afghanistan  1977                6               6 Not Free      
 7 Afghanistan  1978                7               7 Not Free      
 8 Afghanistan  1979                7               7 Not Free      
 9 Afghanistan  1980                7               7 Not Free      
10 Afghanistan  1981                7               7 Not Free      
# ℹ 10,240 more rows

Do some verifications:

freedom_house %>%
  filter(is.na(country))
# A tibble: 0 × 5
# ℹ 5 variables: country <chr>, year <int>, political_rights <int>,
#   civil_liberties <int>, status <fct>
freedom_house %>%
  filter(is.na(year))
# A tibble: 0 × 5
# ℹ 5 variables: country <chr>, year <int>, political_rights <int>,
#   civil_liberties <int>, status <fct>
freedom_house %>%
  filter(is.na(political_rights))
# A tibble: 1,207 × 5
   country   year political_rights civil_liberties status
   <chr>    <int>            <int>           <int> <fct> 
 1 Andorra   1977               NA              NA <NA>  
 2 Andorra   1978               NA              NA <NA>  
 3 Andorra   1979               NA              NA <NA>  
 4 Andorra   1980               NA              NA <NA>  
 5 Andorra   1981               NA              NA <NA>  
 6 Andorra 198383               NA              NA <NA>  
 7 Andorra 198484               NA              NA <NA>  
 8 Andorra 198585               NA              NA <NA>  
 9 Andorra 198686               NA              NA <NA>  
10 Andorra 198787               NA              NA <NA>  
# ℹ 1,197 more rows

Fix the year variable because some years were formatted as “198384” (i.e, 1983-84):

freedom_house <- freedom_house %>% 
  mutate(
    year = case_when(
      nchar(year) > 4 ~ as.integer(substr(year, 1, 4)),
      TRUE ~ year
    )
  )

freedom_house %>%
  filter(is.na(political_rights))
# A tibble: 1,207 × 5
   country  year political_rights civil_liberties status
   <chr>   <int>            <int>           <int> <fct> 
 1 Andorra  1977               NA              NA <NA>  
 2 Andorra  1978               NA              NA <NA>  
 3 Andorra  1979               NA              NA <NA>  
 4 Andorra  1980               NA              NA <NA>  
 5 Andorra  1981               NA              NA <NA>  
 6 Andorra  1983               NA              NA <NA>  
 7 Andorra  1984               NA              NA <NA>  
 8 Andorra  1985               NA              NA <NA>  
 9 Andorra  1986               NA              NA <NA>  
10 Andorra  1987               NA              NA <NA>  
# ℹ 1,197 more rows

Discard country-year combinations with missing values:

freedom_house <- freedom_house %>% 
  drop_na(political_rights, civil_liberties, status)

freedom_house
# A tibble: 9,043 × 5
   country      year political_rights civil_liberties status        
   <chr>       <int>            <int>           <int> <fct>         
 1 Afghanistan  1972                4               5 Partially Free
 2 Afghanistan  1973                7               6 Not Free      
 3 Afghanistan  1974                7               6 Not Free      
 4 Afghanistan  1975                7               6 Not Free      
 5 Afghanistan  1976                7               6 Not Free      
 6 Afghanistan  1977                6               6 Not Free      
 7 Afghanistan  1978                7               7 Not Free      
 8 Afghanistan  1979                7               7 Not Free      
 9 Afghanistan  1980                7               7 Not Free      
10 Afghanistan  1981                7               7 Not Free      
# ℹ 9,033 more rows
< section id="additional-steps" class="level1">

Additional steps

The data is now tidy, but adding ISO-2 and ISO-3 codes is highly convenient, so I create a table to join it with the data later:

countries <- freedom_house %>% 
  ungroup() %>%
  distinct(country) %>%
  mutate(
    iso2c = countrycode(
      country,
      origin = "country.name",
      destination = "iso2c"
    ),
    iso3c = countrycode(
      country,
      origin = "country.name",
      destination = "iso3c"
    )
  )
Warning: There were 2 warnings in `mutate()`.
The first warning was:
ℹ In argument: `iso2c = countrycode(country, origin = "country.name",
  destination = "iso2c")`.
Caused by warning in `countrycode_convert()`:
! Some values were not matched unambiguously: Czechoslovakia, Kosovo, Micronesia, Serbia and Montenegro, Yugoslavia
ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
countries
# A tibble: 205 × 3
   country             iso2c iso3c
   <chr>               <chr> <chr>
 1 Afghanistan         AF    AFG  
 2 Albania             AL    ALB  
 3 Algeria             DZ    DZA  
 4 Andorra             AD    AND  
 5 Angola              AO    AGO  
 6 Antigua and Barbuda AG    ATG  
 7 Argentina           AR    ARG  
 8 Armenia             AM    ARM  
 9 Australia           AU    AUS  
10 Austria             AT    AUT  
# ℹ 195 more rows

Add continent:

countries <- countries %>%
  mutate(
    continent = countrycode(
      iso3c,
      origin = "iso3c",
      destination = "continent"
    )
  )

countries
# A tibble: 205 × 4
   country             iso2c iso3c continent
   <chr>               <chr> <chr> <chr>    
 1 Afghanistan         AF    AFG   Asia     
 2 Albania             AL    ALB   Europe   
 3 Algeria             DZ    DZA   Africa   
 4 Andorra             AD    AND   Europe   
 5 Angola              AO    AGO   Africa   
 6 Antigua and Barbuda AG    ATG   Americas 
 7 Argentina           AR    ARG   Americas 
 8 Armenia             AM    ARM   Asia     
 9 Australia           AU    AUS   Oceania  
10 Austria             AT    AUT   Europe   
# ℹ 195 more rows

Because some ISO codes could not be matched unambiguously, for some countries I need to add the continent manually:

countries %>%
  filter(is.na(continent))
# A tibble: 5 × 4
  country               iso2c iso3c continent
  <chr>                 <chr> <chr> <chr>    
1 Czechoslovakia        <NA>  <NA>  <NA>     
2 Kosovo                <NA>  <NA>  <NA>     
3 Micronesia            <NA>  <NA>  <NA>     
4 Serbia and Montenegro <NA>  <NA>  <NA>     
5 Yugoslavia            <NA>  <NA>  <NA>     
countries <- countries %>%
  mutate(
    continent = case_when(
      country == "Czechoslovakia" ~ "Europe",
      country == "Kosovo" ~ "Europe",
      country == "Micronesia" ~ "Oceania",
      country == "Serbia and Montenegro" ~ "Europe",
      country == "Yugoslavia" ~ "Europe",
      TRUE ~ continent
    )
  )

Join the two data sets:

freedom_house <- freedom_house %>%
  left_join(countries, by = "country") %>% 
  select(year, country, iso2c, iso3c, continent, political_rights, civil_liberties, status)

freedom_house
# A tibble: 9,043 × 8
    year country   iso2c iso3c continent political_rights civil_liberties status
   <int> <chr>     <chr> <chr> <chr>                <int>           <int> <fct> 
 1  1972 Afghanis… AF    AFG   Asia                     4               5 Parti…
 2  1973 Afghanis… AF    AFG   Asia                     7               6 Not F…
 3  1974 Afghanis… AF    AFG   Asia                     7               6 Not F…
 4  1975 Afghanis… AF    AFG   Asia                     7               6 Not F…
 5  1976 Afghanis… AF    AFG   Asia                     7               6 Not F…
 6  1977 Afghanis… AF    AFG   Asia                     6               6 Not F…
 7  1978 Afghanis… AF    AFG   Asia                     7               7 Not F…
 8  1979 Afghanis… AF    AFG   Asia                     7               7 Not F…
 9  1980 Afghanis… AF    AFG   Asia                     7               7 Not F…
10  1981 Afghanis… AF    AFG   Asia                     7               7 Not F…
# ℹ 9,033 more rows

Add colours for the status (i.e., here I avoided colours such as red and green because they are linked to positive and negative associations):

freedom_house <- freedom_house %>%
  mutate(
    color = case_when(
      status == "Free" ~ "#549f95",
      status == "Partially Free" ~ "#a1aafc",
      status == "Not Free" ~ "#7454a6"
    )
  )
< section id="final-result" class="level1">

Final result

The final result is a tidy dataset that can be used to create plots and perform analysis:

freedom_house
# A tibble: 9,043 × 9
    year country   iso2c iso3c continent political_rights civil_liberties status
   <int> <chr>     <chr> <chr> <chr>                <int>           <int> <fct> 
 1  1972 Afghanis… AF    AFG   Asia                     4               5 Parti…
 2  1973 Afghanis… AF    AFG   Asia                     7               6 Not F…
 3  1974 Afghanis… AF    AFG   Asia                     7               6 Not F…
 4  1975 Afghanis… AF    AFG   Asia                     7               6 Not F…
 5  1976 Afghanis… AF    AFG   Asia                     7               6 Not F…
 6  1977 Afghanis… AF    AFG   Asia                     6               6 Not F…
 7  1978 Afghanis… AF    AFG   Asia                     7               7 Not F…
 8  1979 Afghanis… AF    AFG   Asia                     7               7 Not F…
 9  1980 Afghanis… AF    AFG   Asia                     7               7 Not F…
10  1981 Afghanis… AF    AFG   Asia                     7               7 Not F…
# ℹ 9,033 more rows
# ℹ 1 more variable: color <chr>

As an example, I can create a plot of the political rights and civil liberties scores in my country, Chile, where between 1973 and 1990 the country was under a dictatorship and then transitioned to democracy:

dplot <- freedom_house %>%
  filter(country == "Chile", year >= 1973) %>%
  mutate(
    political_rights = abs(political_rights - 8),
    civil_liberties = abs(civil_liberties - 8)
  ) %>%
  pivot_longer(cols = c("political_rights", "civil_liberties"),
    names_to = "category", values_to = "value") %>%
  mutate(category = str_to_title(str_replace_all(category, "_", " ")))

ggplot(dplot) +
  geom_line(aes(x = year, y = value, color = category),
    linewidth = 1.2) +
  facet_wrap(~category, nrow = 2) +
  theme_minimal(base_size = 13) +
  theme(legend.position = "none") +
  labs(
    title = "Political Rights and Civil Liberties in Chile",
    subtitle = "Source: Freedom House Index.\nNote: Scores reversed for visual clarity (1 = least free, 7 = most free).",
    x = "Year",
    y = "Score"
  ) +
  scale_colour_tintin_d(option = "tintin_in_the_land_of_the_soviets")

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