How to recreate Pew opinion graphs with ggplot2 in R

Packages we will need

library(HH)
library(tidyverse)
library(bbplot)
library(haven)

In this blog post, we are going to recreate Pew Opinion poll graphs.

This is the plot we will try to recreate on gun control opinions of Americans:

To do this, we will download the data from the Pew website by following the link below:

atp <- read.csv(file.choose())

We then select the variables related to gun control opinions

atp %>% 
  select(GUNPRIORITY1_b_W87:GUNPRIORITY2_j_W87) -> gun_df

I want to rename the variables so I don’t forget what they are.

Then, we convert them all to factor variables because haven labelled class variables are sometimes difficult to wrangle…

gun_df %<>%
  select(mental_ill = GUNPRIORITY1_b_W87,
         assault_rifle = GUNPRIORITY1_c_W87, 
         gun_database = GUNPRIORITY1_d_W87,
         high_cap_mag = GUNPRIORITY1_e_W87,
         gunshow_bkgd_check = GUNPRIORITY1_f_W87,
         conceal_gun =GUNPRIORITY2_g_W87,
         conceal_gun_no_permit = GUNPRIORITY2_h_W87,
         teacher_gun = GUNPRIORITY2_i_W87,
         shorter_waiting = GUNPRIORITY2_j_W87) %>% 
  mutate(across(everything()), haven::as_factor(.))

Also we can convert the “Refused” to answer variables to NA if we want, so it’s easier to filter out.

gun_df %<>% 
  mutate(across(where(is.factor), ~na_if(., "Refused")))

Next we will pivot the variables to long format. The new names variable will be survey_question and the responses (Strongly agree, Somewhat agree etc) will go to the new response variable!

gun_df %>% 
  pivot_longer(everything(), names_to = "survey_question", values_to = "response") -> gun_long

And next we calculate counts and frequencies for each variable

gun_long %<>% 
  group_by(survey_question, response) %>% 
  summarise(n = n()) %>%
  mutate(freq = n / sum(n)) %>% 
  ungroup() 

Then we want to reorder the levels of the factors so that they are in the same order as the original Pew graph.

gun_long %>% 
  mutate(survey_question = as.factor(survey_question))   %>% 
   mutate(survey_question_reorder = factor(survey_question, 
          levels =  c( 
           "conceal_gun_no_permit",
           "shorter_waiting",
           "teacher_gun",
           "conceal_gun",
           "assault_rifle",
           "high_cap_mag",
           "gun_database",
           "gunshow_bkgd_check",
           "mental_ill"
           ))) -> gun_reordered

And we use the hex colours from the original graph … very brown… I used this hex color picker website to find the right hex numbers: https://imagecolorpicker.com/en

brown_palette <- c("Strongly oppose" = "#8c834b",
                   "Somewhat oppose" = "#beb88f",
                   "Somewhat favor" = "#dfc86c",
                   "Strongly favor" = "#caa31e")

And last, we use the geom_bar() – with position = "stack" and stat = "identity" arguments – to create the bar chart.

To add the numbers, write geom_text() function with label = frequency within aes() and then position = position_stack() with hjust and vjust to make sure you’re happy with where the numbers are

gun_reordered %>% 
  filter(!is.na(response)) %>% 
  mutate(frequency = round(freq * 100), 0) %>% 
  ggplot(aes(x = survey_question_reorder, 
             y = frequency, fill = response)) +
  geom_bar(position = "stack",
           stat = "identity") + 
  coord_flip() + 
  scale_fill_manual(values = brown_palette) +
  geom_text(aes(label = frequency), size = 10, 
            color = "black", 
            position = position_stack(vjust = 0.5)) +
  bbplot::bbc_style() + 
  labs(title = "Broad support for barring people with mental illnesses 
       \n from obtaining guns, expanded background checks",
       subtitle = "% who", 
       caption = "Note: No answer resposes not shown.\n Source: Survey of U.S. adults conducted April 5-11 2021.") + 
  scale_x_discrete(labels = c(
    "Allowing people to carry conealed \n guns without a person",
    "Shortening waiting periods for people \n who want to buy guns leagally",
    "Allowing reachers and school officials \n to carry guns in K-12 school",
    "Allowing people to carry \n concealed guns in more places",
    "Banning assault-style weapons",
    "Banning high capacity ammunition \n magazines that hold more than 10 rounds",
    "Creating a federal government \n database to track all gun sales",
    "Making private gun sales \n subject to background check",
    "Preventing people with mental \n illnesses from purchasing guns"
    ))
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Unfortunately this does not have diverving stacks from the middle of the graph

We can make a diverging stacked bar chart using function likert() from the HH package.

For this we want to turn the dataset back to wider with a column for each of the responses (strongly agree, somewhat agree etc) and find the frequency of each response for each of the questions on different gun control measures.

Then with the likert() function, we take the survey question variable and with the ~tilda~ make it the product of each response. Because they are the every other variable in the dataset we can use the shorthand of the period / fullstop.

We use positive.order = TRUE because we want them in a nice descending order to response, not in alphabetical order or something like that

gun_reordered %<>%
    filter(!is.na(response)) %>%  
  select(survey_question, response, freq) %>%  
  pivot_wider(names_from = response, values_from = freq ) %>%
  ungroup() %>% 
  HH::likert(survey_question ~., positive.order = TRUE,
            main =  "Broad support for barring people with mental illnesses
            \n from obtaining guns, expanded background checks")

With this function, it is difficult to customise … but it is very quick to make a diverging stacked bar chart.

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If we return to ggplot2, which is more easy to customise … I found a solution on Stack Overflow! Thanks to this answer! The solution is to put two categories on one side of the centre point and two categories on the other!

gun_reordered %>% 
filter(!is.na(response)) %>% 
  mutate(frequency = round(freq * 100), 0) -> gun_final

And graph out

ggplot(data = gun_final, aes(x = survey_question_reorder, 
            fill = response)) +
  geom_bar(data = subset(gun_final, response %in% c("Strongly favor",
           "Somewhat favor")),
           aes(y = -frequency), position="stack", stat="identity") +
  geom_bar(data = subset(gun_final, !response %in% c("Strongly favor",
            "Somewhat favor")), 
           aes(y = frequency), position="stack", stat="identity") +
  coord_flip() + 
  scale_fill_manual(values = brown_palette) +
  geom_text(data = gun_final, aes(y = frequency, label = frequency), size = 10, color = "black", position = position_stack(vjust = 0.5)) +
  bbplot::bbc_style() + 
  labs(title = "Broad support for barring people with mental illnesses 
       \n from obtaining guns, expanded background checks",
       subtitle = "% who", 
       caption = "Note: No answer resposes not shown.\n Source: Survey of U.S. adults conducted April 5-11 2021.") + 
  scale_x_discrete(labels = c(
    "Allowing people to carry conealed \n guns without a person",
    "Shortening waiting periods for people \n who want to buy guns leagally",
    "Allowing reachers and school officials \n to carry guns in K-12 school",
    "Allowing people to carry \n concealed guns in more places",
    "Banning assault-style weapons",
    "Banning high capacity ammunition \n magazines that hold more than 10 rounds",
    "Creating a federal government \n database to track all gun sales",
    "Making private gun sales \n subject to background check",
    "Preventing people with mental \n illnesses from purchasing guns"
  ))
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Next to complete in PART 2 of this graph, I need to figure out how to add lines to graphs and add the frequency in the correct place

Examining Ireland’s foreign policy in pictures with R

Packages we will need:

library(peacesciencer)  
library(forcats)
library(ggflags)
library(tidyverse)
library(magrittr)
library(waffle)
library(bbplot)
library(rvest)

In January 2015, the Irish government published a review of Ireland’s foreign policy. The document, The Global Island: Ireland’s Foreign Policy for a Changing World offers a perspective on Ireland’s place in the world.

In this blog, we will graph out some of the key features of Ireland’ foreign policy and so we can have a quick overview of the key relationships and trends.

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First, we will look at the aid that Ireland gives to foreign countries. This read.csv(file.choose()) will open up the file window and you can navigate to the file and data that you can download from DAC OECD website: https://data.oecd.org/oda/net-oda.htm

dac <- read.csv(file.choose())

We will filter only Ireland and clean the names with the clean_names() function from the janitor package:

dac %<>% 
  filter(Donor == "Ireland") %>% 
  clean_names()

And change the variables, adding the Correlates of War codes and cleaning up some of the countries’ names.

dac %<>% 
  mutate(cown = countrycode(recipient_2, "country.name", "cown"),
         aid_amount = value*1000000) %>%  
  select(country = recipient_2, cown,
         year, time, aid_type, value, aid_amount) %>%
  mutate(cown = ifelse(country == "West Bank and Gaza Strip", 6666,
         ifelse(country == "Serbia", 345, 
         ifelse(country == "Micronesia", 987,cown))))%>%
  filter(!is.na(cown)) 

Next we can convert dataframe to wider format so we have a value column for each aid type

dac %>% 
  distinct(country, cown, year, time, aid_type, value, .keep_all = TRUE)  %>%  
  pivot_wider(names_from = "aid_type", values_from = "aid_amount") %>% 
  mutate(across(where(is.numeric), ~ replace_na(., 0))) %>% 
  clean_names() -> dac_wider

And we graph out the three main types of aid:

dac_wider %>%
  group_by(year) %>% 
  summarise(total_humanitarian = sum(humanitarian_aid, na.rm = TRUE),
  total_technical = sum(technical_cooperation, na.rm = TRUE),
  total_development_food_aid = sum(development_food_aid)) %>% 
  ungroup() %>% 
  pivot_longer(!year, names_to = "aid_type", values_to = "aid_value") %>% 
  ggplot(aes(x = year, y = aid_value, groups = aid_type)) + 
  geom_line(aes(color = aid_type), size = 2, show_guide  = FALSE) +
  geom_point(aes(color = aid_type), fill = "white", shape = 21, size = 3, stroke = 2) +
  bbplot::bbc_style()  +
  scale_y_continuous(labels = scales::comma) + 
  scale_x_discrete(limits = c(2010:2018)) +
  labs(title = "Irish foreign aid by aid type (2010 - 2018)",
       subtitle = ("Source: OECD DAC")) +
  scale_color_discrete(name = "Aid type", 
        labels = c("Development and Food", "Humanitarian", "Technical"))

We will look at total ODA aid:

dac %>% 
  count(aid_type) %>% 
  arrange(desc(n)) %>% 
  knitr::kable(format = "html")
aid_type n
Imputed Multilateral ODA 2298
Memo: ODA Total, excl. Debt 1292
Memo: ODA Total, Gross disbursements 1254
ODA: Total Net 1249
Grants, Total 1203
Technical Cooperation 541
ODA per Capita 532
Humanitarian Aid 518
ODA as % GNI (Recipient) 504
Development Food Aid 9

And get some pretty hex colours:

pal_10 <- c("#001219","#005f73","#0a9396","#94d2bd","#e9d8a6","#ee9b00","#ca6702","#bb3e03","#ae2012","#9b2226")

And download some regime, democracy, region and continent data from the PACL datase with the democracyData() package

pacl <- redownload_pacl() 

pacl %<>% 
  mutate(regime_name = ifelse(regime == 0, "Parliamentary democracies",
         ifelse(regime == 1, "Mixed democracies",
         ifelse(regime == 2, "Presidential democracies",
         ifelse(regime == 3, "Civilian autocracies",
         ifelse(regime == 4, "Military dictatorships",
         ifelse(regime ==  5,"Royal dictatorships", regime))))))) %>%
  mutate(regime = as.factor(regime)) 

pacl %<>% 
  select(year, country = pacl_country, 
         democracy, regime_name,
         region_name = un_region_name, 
         continent_name = un_continent_name)

pacl %<>% 
  mutate(cown = countrycode(country, "country.name", "cown")) %>% 
  select(!country)

Summarise the total aid for each country across the years and choose the top 20 countries

dac %>% 
  filter(aid_type == "Memo: ODA Total, Gross disbursements") %>% 
  group_by(country) %>% 
  summarise(total_country_aid = sum(aid_amount, na.rm = TRUE)) %>% 
  ungroup() %>% 
  top_n(n = 20) %>% 
  mutate(cown = countrycode::countrycode(country, "country.name", "cown")) %>% 
  inner_join(pacl, by = "cown") %>%  
  mutate(region_name = ifelse(country == "West Bank and Gaza Strip", "Western Asia", region_name)) %>% 
  mutate(region_name = ifelse(region_name == "Western Asia", "Middle East", region_name)) %>% 
  mutate(country = ifelse(country == "West Bank and Gaza Strip", "Palestine",
  ifelse(country == "Democratic Republic of the Congo", "DR Congo",
  ifelse(country == "Syrian Arab Republic", "Syria", country)))) %>% 
  mutate(iso2 = tolower(countrycode::countrycode(country, "country.name", "iso2c"))) %>% 
  ggplot(aes(x = forcats::fct_reorder(country, total_country_aid), y = total_country_aid)) + 
  geom_bar(aes(fill = region_name), stat = "identity", width = 0.7) + 
  coord_flip() + bbplot::bbc_style() + 
  geom_flag(aes(x = country, y = -100, country = iso2), size = 12) +
  scale_fill_manual(values = pal_10) +
  labs(title = "Ireland's largest ODA foreign aid recipients, 2010 - 2018",
       subtitle = ("Source: OECD DAC")) + 
  xlab("") + ylab("") + 
  scale_x_continuous(labels = scales::comma)

We can make a waffle plot to look at the different types of regimes to which the Irish government gave aid over the decades

 dac %>% 
  mutate(decade = substr(year, 1, 3)) %>% 
  mutate(decade = paste0(decade, "0s")) %>% 
  group_by(decade) %>% 
  count(regime_name) %>% 
  ggplot(aes(fill = regime_name, values = n)) +
  geom_waffle(color = "white", size = 0.3, n_rows = 10, flip = TRUE) +
  facet_wrap(~decade, nrow = 1, strip.position = "bottom") + 
  bbplot::bbc_style()  +
  scale_fill_manual(values = pal_10) +
   scale_x_discrete(breaks = round(seq(0, 1, by = 0.2),3)) +
  labs(title = "Ireland's ODA foreign aid recipient regime types since 1945",
       subtitle = ("Source: OECD DAC"))  

Next, we will download dyadic foreign policy similarity measures from peacesciencer.

Peacesciencer package has tools and data sets for the study of quantitative peace science. 

Click here to read more about the peacesciencer package by Steven Miller

fp_similar_df <- peacesciencer::create_dyadyears() %>% 
  add_gwcode_to_cow() %>% 
  add_fpsim()	

I am only looking at dyadic foreign policy similarity with Ireland, so filter by Ireland’s Correlates of War code, 205.

Click here to find out all countries’ COW code

fp_similar_df %<>% 
  filter(ccode1 == 205)

Data on alliance portfolios comes from the Correlates of War and is used to calculate similarity of foreign policy positions (see Altfeld & Mesquita, 1979).

The assumption is that similar alliance portfolios are the result of similar foreign policy positions.

With increasing in level of commitment, the strength of alliance commitments can be:

  1. no commitment
  2. entente
  3. neutrality or nonaggression pact
  4. defense pact

We will map out alliance similarity. This will use the measurement calculated with Cohen’s Kappa. Check out Hage’s (2011) article to read more about the different ways to measure alliance similarity.

Next we can look at UN similarity.

The UN voting variable calculates three values:

1 = Yes

2 = Abstain

3 = No

Based on these data, if two countries in a similar way on the same UN resolutions, this is a measure of the degree to which dyad members’ foreign policy positions are similar.

fp_similarity_df %>% 
  mutate(country = countrycode::countrycode(ccode2, "cown", "country.name")) %>% 
  select(country, ccode2, year,
         un_similar = kappavv) %>% 
  filter(year > 1989) %>% 
  filter(!is.na(country)) %>%
  mutate(iso2 = tolower(countrycode::countrycode(country, "country.name", "iso2c"))) %>% 
  group_by(country) %>% 
  mutate(avg_un = mean(un_similar, na.rm = TRUE)) %>%
  distinct(country, avg_un, iso2, .keep_all = FALSE) %>% 
  ungroup() %>% 
  top_n(n = 10)  -> top_un_similar

And graph out the top ten

  top_un_similar %>%
  ggplot(aes(x = forcats::fct_reorder(country, avg_un), 
             y = avg_un)) + 
  geom_bar(stat = "identity",
           width = 0.7, 
           color = "#0a85e5", 
           fill = "#0a85e5") +
  ggflags::geom_flag(aes(x = country, y = 0, country = iso2), size = 15) +
  coord_flip() + bbplot::bbc_style()  +
  ggtitle("UN voting similarity with Ireland since 1990")

If we change the top_n() to negative, we can get the bottom 10

top_n(n = -10)

We can quickly scrape data about the EU countries with the rvest package


eu_members_html <- read_html("https://en.wikipedia.org/wiki/European_Union")
eu_members_tables <- eu_members_html %>% html_table(header = TRUE, fill = TRUE)

eu_member <- eu_members_tables[[6]]

eu_member %<>% 
  janitor::clean_names()

eu_member %>% distinct(state) %>%  pull(state) -> eu_state

Last we are going to look at globalization scores. The data comes from the the KOF Globalisation Index. This measures the economic, social and political dimensions of globalisation. Globalisation in the economic, social and political fields has been on the rise since the 1970s, receiving a particular boost after the end of the Cold War.

Click here for data that you can download comes from the KOF website

kof %>%
  filter(country %in% eu_state) -> kof_eu

And compare Ireland to other EU countries on financial KOF index scores. We will put Ireland in green and the rest of the countries as grey to make it pop.

Ireland appears to follow the general EU trends and is not an outlier for financial globalisation scores.

kof_eu %>% 
  ggplot(aes(x = year,  y = finance, groups = country)) + 
  geom_line(color = ifelse(kof_eu$country == "Ireland",     "#2a9d8f", "#8d99ae"),
  size = ifelse(kof_eu$country == "Ireland", 3, 2), 
  alpha = ifelse(kof_eu$country == "Ireland", 0.9, 0.3)) +
  bbplot::bbc_style() + 
  ggtitle("Financial Globalization in Ireland, 1970 to 2020", 
          subtitle = "Source: KOF")

References

Häge, F. M. (2011). Choice or circumstance? Adjusting measures of foreign policy similarity for chance agreement. Political Analysis19(3), 287-305.

Dreher, Axel (2006): Does Globalization Affect Growth? Evidence from a new Index of Globalizationcall_made, Applied Economics 38, 10: 1091-​1110.

Scraping and wrangling UN peacekeeping data with tidyr package in R

Packages we will need:

library(tidyverse)
library(rvest)
library(magrittr)
library(tidyr)
library(countrycode)
library(democracyData)
library(janitor)
library(waffle)

For this blog post, we will look at UN peacekeeping missions and compare across regions.

Despite the criticisms about some operations, the empirical record for UN peacekeeping records has been robust in the academic literature

“In short, peacekeeping intervenes in the most difficult
cases, dramatically increases the chances that peace will
last, and does so by altering the incentives of the peacekept,
by alleviating their fear and mistrust of each other, by
preventing and controlling accidents and misbehavior by
hard-line factions, and by encouraging political inclusion”
(Goldstone, 2008: 178).

The data on the current and previous PKOs (peacekeeping operations) will come from the Wikipedia page. But the variables do not really lend themselves to analysis as they are.

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Once we have the url, we scrape all the tables on the Wikipedia page in a few lines

pko_members <- read_html("https://en.wikipedia.org/wiki/List_of_United_Nations_peacekeeping_missions")
pko_tables <- pko_members %>% html_table(header = TRUE, fill = TRUE)

Click here to read more about the rvest package for scraping data from websites.

pko_complete_africa <- pko_tables[[1]]
pko_complete_americas <- pko_tables[[2]]
pko_complete_asia <- pko_tables[[3]]
pko_complete_europe <- pko_tables[[4]]
pko_complete_mena <- pko_tables[[5]]

And then we bind them together! It’s very handy that they all have the same variable names in each table.

rbind(pko_complete_africa, pko_complete_americas, pko_complete_asia, pko_complete_europe, pko_complete_mena) -> pko_complete

Next, we will add a variable to indicate that all the tables of these missions are completed.

pko_complete %<>% 
  mutate(complete = ifelse(!is.na(pko_complete$Location), "Complete", "Current"))

We do the same with the current missions that are ongoing:

pko_current_africa <- pko_tables[[6]]
pko_current_asia <- pko_tables[[7]]
pko_current_europe <- pko_tables[[8]]
pko_current_mena <- pko_tables[[9]]

rbind(pko_current_europe, pko_current_mena, pko_current_asia, pko_current_africa) -> pko_current

pko_current %<>% 
  mutate(complete = ifelse(!is.na(pko_current$Location), "Current", "Complete"))

We then bind the completed and current mission data.frames

rbind(pko_complete, pko_current) -> pko

Then we clean the variable names with the function from the janitor package.

pko_df <-  pko %>% 
  janitor::clean_names()

Next we’ll want to create some new variables.

We can make a new row for each country that is receiving a peacekeeping mission. We can paste all the countries together and then use the separate function from the tidyr package to create new variables.

 pko_df %>%
  group_by(conflict) %>%
  mutate(location = paste(location, collapse = ', ')) %>% 
  separate(location,  into = c("country_1", "country_2", "country_3", "country_4", "country_5"), sep = ", ")  %>% 
  ungroup() %>% 
  distinct(conflict, .keep_all = TRUE) %>% 

Next we can create a new variable that only keeps the acroynm for the operation name. I took these regex codes from the following stack overflow link

pko_df %<>% 
  mutate(acronym = str_extract_all(name_of_operation, "\\([^()]+\\)")) %>% 
  mutate(acronym = substring(acronym, 2, nchar(acronym)-1)) %>% 
  separate(dates_of_operation, c("start_date", "end_date"), "–")

I will fill in the end data for the current missions that are still ongoing in 2022

pko_df %<>% 
  mutate(end_date = ifelse(complete == "Current", 2022, end_date)) 

And next we can calculate the duration for each operation

pko_df %<>% 
  mutate(end_date = as.integer(end_date)) %>% 
  mutate(start_date = as.integer(start_date)) %>% 
  mutate(duration = ifelse(!is.na(end_date), end_date - start_date, 1)) 

I want to compare regions and graph out the different operations around the world.

We can download region data with democracyData package (best package ever!)

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pacl <- redownload_pacl()

pacl %>% 
  select(cown = pacl_cowcode,
        un_region_name, un_continent_name) %>% 
  distinct(cown, .keep_all = TRUE) -> pacl_region

We join the datasets together with the inner_join() and add Correlates of War country codes.

pko_df %<>% 
  mutate(cown = countrycode(country_1, "country.name", "cown")) %>%   mutate(cown = ifelse(country_1 == "Western Sahara", 605, 
                       ifelse(country_1 == "Serbia", 345, cown))) %>% 
  inner_join(pacl_region, by = "cown")

Now we can start graphing our duration data:

pko_df %>% 
  ggplot(mapping = aes(x = forcats::fct_reorder(un_region_name, duration), 
                       y = duration, 
                       fill = un_region_name)) +
  geom_boxplot(alpha = 0.4) +
  geom_jitter(aes(color = un_region_name),
              size = 6, alpha = 0.8, width = 0.15) +
  coord_flip() + 
  bbplot::bbc_style() + ggtitle("Duration of Peacekeeping Missions")
Years

We can see that Asian and “Western Asian” – i.e. Middle East – countries have the longest peacekeeping missions in terns of years.

pko_countries %>% 
  filter(un_continent_name == "Asia") %>%
  unite("country_names", country_1:country_5, remove = TRUE,  na.rm = TRUE, sep = ", ") %>% 
  arrange(desc(duration)) %>% 
  knitr::kable("html")
Start End Duration Region Country
1949 2022 73 Southern Asia India, Pakistan
1964 2022 58 Western Asia Cyprus, Northern Cyprus
1974 2022 48 Western Asia Israel, Syria, Lebanon
1978 2022 44 Western Asia Lebanon
1993 2009 16 Western Asia Georgia
1991 2003 12 Western Asia Iraq, Kuwait
1994 2000 6 Central Asia Tajikistan
2006 2012 6 South-Eastern Asia East Timor
1988 1991 3 Southern Asia Iran, Iraq
1988 1990 2 Southern Asia Afghanistan, Pakistan
1965 1966 1 Southern Asia Pakistan, India
1991 1992 1 South-Eastern Asia Cambodia, Cambodia
1999 NA 1 South-Eastern Asia East Timor, Indonesia, East Timor, Indonesia, East Timor
1958 NA 1 Western Asia Lebanon
1963 1964 1 Western Asia North Yemen
2012 NA 1 Western Asia Syria

Next we can compare the decades

pko_countries %<>% 
  mutate(decade = substr(start_date, 1, 3)) %>% 
  mutate(decade = paste0(decade, "0s")) 

And graph it out:

pko_countries %>% 
  ggplot(mapping = aes(x = decade, 
                       y = duration, 
                       fill = decade)) +
  geom_boxplot(alpha = 0.4) +
  geom_jitter(aes(color = decade),
              size = 6, alpha = 0.8, width = 0.15) +
   coord_flip() + 
  geom_curve(aes(x = "1950s", y = 60, xend = "1940s", yend = 72),
  arrow = arrow(length = unit(0.1, "inch")), size = 0.8, color = "black",
   curvature = -0.4) +
  annotate("text", label = "First Mission to Kashmir",
           x = "1950s", y = 49, size = 8, color = "black") +
  geom_curve(aes(x = "1990s", y = 46, xend = "1990s", yend = 32),
             arrow = arrow(length = unit(0.1, "inch")), size = 0.8, color = "black",curvature = 0.3) +
  annotate("text", label = "Most Missions after the Cold War",
           x = "1990s", y = 60, size = 8, color = "black") +

  bbplot::bbc_style() + ggtitle("Duration of Peacekeeping Missions")
Years

Following the end of the Cold War, there were renewed calls for the UN to become the agency for achieving world peace, and the agency’s peacekeeping dramatically increased, authorizing more missions between 1991 and 1994 than in the previous 45 years combined.

We can use a waffle plot to see which decade had the most operation missions. Waffle plots are often seen as more clear than pie charts.

Click here to read more about waffle charts in R

To get the data ready for a waffle chart, we just need to count the number of peacekeeping missions (i.e. the number of rows) in each decade. Then we fill the groups (i.e. decade) and enter the n variable we created as the value.

pko_countries %>% 
  group_by(decade) %>% 
  count() %>%  
  ggplot(aes(fill = decade, values = n)) + 
  waffle::geom_waffle(color = "white", size= 3, n_rows = 8) +
  scale_x_discrete(expand=c(0,0)) +
  scale_y_discrete(expand=c(0,0)) +
  coord_equal() +
  labs(title = "Number of Peacekeeper Missions") + bbplot::bbc_style() 
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If we want to add more information, we can go to the UN Peacekeeping website and download more data on peacekeeping troops and operations.

We can graph the number of peacekeepers per country

Click here to learn more about adding flags to graphs!

le_palette <- c("#5f0f40", "#9a031e", "#94d2bd", "#e36414", "#0f4c5c")

pkt %>%
  mutate(contributing_country = ifelse(contributing_country == "United Republic of Tanzania", "Tanzania",ifelse(contributing_country == "Côte d’Ivoire", "Cote d'Ivoire", contributing_country))) %>% 
  mutate(iso2 = tolower(countrycode::countrycode(contributing_country, "country.name", "iso2c"))) %>% 
  mutate(cown = countrycode::countrycode(contributing_country, "country.name", "cown")) %>% 
  inner_join(pacl_region, by = "cown") %>% 
  mutate(un_region_name = case_when(grepl("Africa", un_region_name) ~ "Africa",grepl("Eastern Asia", un_region_name) ~ "South-East Asia",
 un_region_name == "Western Africa" ~ "Middle East",TRUE ~ as.character(un_region_name))) %>% 
  filter(total_uniformed_personnel > 700) %>% 
  ggplot(aes(x = reorder(contributing_country, total_uniformed_personnel),
             y = total_uniformed_personnel)) + 
  geom_bar(stat = "identity", width = 0.7, aes(fill = un_region_name), color = "white") +
  coord_flip() +
  ggflags::geom_flag(aes(x = contributing_country, y = -1, country = iso2), size = 8) +
  # geom_text(aes(label= values), position = position_dodge(width = 0.9), hjust = -0.5, size = 5, color = "#000500") + 
  scale_fill_manual(values = le_palette) +
  labs(title = "Total troops serving as peacekeepers",
       subtitle = ("Across countries"),
       caption = "         Source: UN ") +
  xlab("") + 
  ylab("") + bbplot::bbc_style()

We can see that Bangladesh, Nepal and India have the most peacekeeper troops!

Download democracy data with democracyData package in R

Packages we will need:

library(democracyData)
library(tidyverse)
library(magrittr)       # for pipes
library(ggstream)       # proportion plots
library(ggthemes)       # nice ggplot themes
library(forcats)        # reorder factor variables
library(ggflags)        # add flags
library(peacesciencer)  # more great polisci data
library(countrycode)    # add ISO codes to countries

This blog will highlight some quick datasets that we can download with this nifty package.

To install the democracyData package, it is best to do this via the github of Xavier Marquez:

remotes::install_github("xmarquez/democracyData", force = TRUE)
library(democracyData)

We can download the dataset from the Democracy and Dictatorship Revisited paper by Cheibub Gandhi and Vreeland (2010) with the redownload_pacl() function. It’s all very simple!

pacl <- redownload_pacl()
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This gives us over 80 variables, with information on things such as regime type, geographical data, the name and age of the leaders, and various democracy variables.

We are going to focus on the different regimes across the years.

The six-fold regime classification Cheibub et al (2010) present is rooted in the dichotomous classification of regimes as democracy and dictatorship introduced in Przeworski et al. (2000). They classify according to various metrics, primarily by examining the way in which governments are removed from power and what constitutes the “inner sanctum” of power for a given regime. Dictatorships can be distinguished according to the characteristics of these inner sanctums. Monarchs rely on family and kin networks along with consultative councils; military rulers confine key potential rivals from the armed forces within juntas; and, civilian dictators usually create a smaller body within a regime party—a political bureau—to coopt potential rivals. Democracies highlight their category, depending on how the power of a given leadership ends

We can change the regime variable from numbers to a factor variables, describing the type of regime that the codebook indicates:

pacl %<>% 
  mutate(regime_name = ifelse(regime == 0, "Parliamentary democracies",
       ifelse(regime == 1, "Mixed democracies",
       ifelse(regime == 2, "Presidential democracies",
       ifelse(regime == 3, "Civilian autocracies",
       ifelse(regime == 4, "Military dictatorships",
       ifelse(regime ==  5,"Royal dictatorships", regime))))))) %>%
  mutate(regime = as.factor(regime)) 

Before we make the graph, we can give traffic light hex colours to the types of democracy. This goes from green (full democracy) to more oranges / reds (autocracies):

regime_palette <- c("Military dictatorships" = "#f94144", 
                    "Civilian autocracies" = "#f3722c", 
                    "Royal dictatorships" =  "#f8961e", 
                    "Mixed democracies" = "#f9c74f", 
                    "Presidential democracies" = "#90be6d", 
                    "Parliamentary democracies" = "#43aa8b")

We will use count() to count the number of countries in each regime type and create a variable n

pacl %>% 
  mutate(regime_name = as.factor(regime_name)) %>% 
  mutate(regime_name = fct_relevel(regime_name, 
 levels = c("Parliamentary democracies", 
           "Presidential democracies",
           "Mixed democracies",
           "Royal dictatorships",
           "Civilian autocracies",
           "Military dictatorships"))) %>% 
  group_by(year, un_continent_name) %>% 
  filter(!is.na(regime_name)) %>% 
  count(regime_name) %>% 
  ungroup() %>%  
  filter(un_continent_name != "") %>%
  filter(un_continent_name != "Oceania") -> pacl_count
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We have all the variables we need.

We can now graph the count variables across different regions.

pacl_count %>% 
  ggplot(aes(x = year, y = n, 
             groups = regime_name, 
             fill = regime_name)) +
  ggstream::geom_stream(type = "proportion") + 
  facet_wrap(~un_continent_name) + 
  scale_fill_manual(values = regime_palette) + 
  ggthemes::theme_fivethirtyeight() + 
  theme(legend.title = element_blank(),
        text = element_text(size = 30)) 

I added the title and source header / footer section on canva.com to finish the graph.

Of course, the Cheibub et al (2010) dataset is not the only one that covers types of regimes.

Curtis Bell in 2016 developed the Rulers, Elections, and Irregular Governance Dataset (REIGN) dataset.

This describes political conditions in every country (including tenures and personal characteristics of world leaders, the types of political institutions and political regimes in effect, election outcomes and election announcements, and irregular events like coups)

Again, to download this dataset with the democracyData package, it is very simple:

reign <- download_reign()
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I want to compare North and South Korea since their independence from Japan and see the changes in regimes and democracy scores over the years.

Next, we can easily download Freedom House or Polity 5 scores.

The Freedom House Scores default dataset ranges from 1972 to 2020, covering around 195 countries (depending on the year)

fh <- download_fh()

Alternatively, we can look at Polity Scores. This default dataset countains around 190 ish countries (again depending on the year and the number of countries in existance at that time) and covers a far longer range of years; from 1880 to 2018.

polityiv <- redownload_polityIV()

Alternatively, to download democracy scores, we can also use the peacesciencer dataset. Click here to read more about this package:

democracy_scores <- peacesciencer::create_stateyears() %>% 
  add_gwcode_to_cow() %>%
  add_democracy() 

With inner_join() we can merge these two datasets together:

reign %>% 
  select(ccode = cown, everything()) %>% 
  inner_join(democracy_scores, by = c("year", "ccode")) -> reign_demo

We next choose the years and countries for our plot.

Also, for the geom_flag() we will need the country name to be lower case ISO code. Click here to read more about the ggflags package.

reign_demo %>% 
    filter(year > 1945) %>% 
    mutate(gwf_regimetype = str_to_title(gwf_regimetype)) %>% 
    mutate(iso2c_lower = tolower(countrycode::countrycode(reign_country, "country.name", "iso2c"))) %>% 
filter(reign_country == "Korea North" | reign_country == "Korea South") -> korea_reign

We may to use specific hex colours for our graphs. I always prefer these deeper colours, rather than the pastel defaults that ggplot uses. I take them from coolors.co website!

korea_palette <- c("Military" = "#5f0f40",
                   "Party-Personal" = "#9a031e",
                   "Personal" = "#fb8b24",
                   "Presidential" = "#2a9d8f",
                   "Parliamentary" = "#1e6091")

We will add a flag to the start of the graph, so we create a mini dataset that only has the democracy scores for the first year in the dataset.

  korea_start <- korea_reign %>%
    group_by(reign_country) %>% 
    slice(which.min(year)) %>% 
    ungroup() 

Next we plot the graph

korea_reign %>% 
 ggplot(aes(x = year, y = v2x_polyarchy, groups = reign_country))  +
    geom_line(aes(color = gwf_regimetype), 
         size = 7, alpha = 0.7, show.legend = FALSE) +
    geom_point(aes(color = gwf_regimetype), size = 7, alpha = 0.7) +
    ggflags::geom_flag(data = korea_start, 
       aes(y = v2x_polyarchy, x = 1945, country = iso2c_lower), 
           size = 20) -> korea_plot

And then work on the aesthetics of the plot:

korea_plot + ggthemes::theme_fivethirtyeight() + 
    ggtitle("Electoral democracy on Korean Peninsula") +
    labs(subtitle = "Sources: Teorell et al. (2019) and Curtis (2016)") +
    xlab("Year") + 
    ylab("Democracy Scores") + 
    theme(plot.title = element_text(face = "bold"),
      axis.ticks = element_blank(),
      legend.box.background = element_blank(),
      legend.title = element_blank(),
      legend.text = element_text(size = 40),
      text = element_text(size = 30)) +
    scale_color_manual(values = korea_palette) + 
    scale_x_continuous(breaks = round(seq(min(korea_reign$year), max(korea_reign$year), by = 5),1))

While North Korea has been consistently ruled by the Kim dynasty, South Korea has gone through various types of government and varying levels of democracy!

References

Cheibub, J. A., Gandhi, J., & Vreeland, J. R. (2010). . Public choice143(1), 67-101.

Przeworski, A., Alvarez, R. M., Alvarez, M. E., Cheibub, J. A., Limongi, F., & Neto, F. P. L. (2000). Democracy and development: Political institutions and well-being in the world, 1950-1990 (No. 3). Cambridge University Press.

Scrape and graph election polling data from Wikipedia

Packages we will need:

library(rvest)
library(tidyverse)
library(magrittr)
library(forcats)
library(janitor)

With the Korean Presidential elections coming up, I wanted to graph the polling data since the beginning of this year.

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The data we can use is all collated together on Wikipedia.

Click here to read more about using the rvest package for scraping data from websites and click here to read the CRAN PDF for the package.

poll_html <- read_html("https://en.wikipedia.org/wiki/2022_South_Korean_presidential_election")

poll_tables <- poll_html %>% html_table(header = TRUE, fill = TRUE)

There are 22 tables on the page in total.

I count on the page that the polling data is the 16th table on the page, so extract index [[16]] from the list

feb_poll <- poll_tables[[16]]
View(feb_poll)

It is a bit messy, so we will need to do a bit of data cleaning before we can graph.

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First the names of many variables are missing or on row 2 / 3 of the table, due to pictures and split cells in Wikipedia.

 [1] "Polling firm / Client" "Polling firm / Client" "Fieldwork  date"       "Sample  size" "Margin of  error"     
 [6] ""       ""      ""     ""      ""                     
[11] ""  "Others/Undecided"   "Lead"   

The clean_names() function from the janitor package does a lot of the brute force variable name cleaning!

feb_poll %<>% clean_names()

We now have variable names rather than empty column names, at least.

 [1] "polling_firm_client" "polling_firm_client_2" "fieldwork_date"        "sample_size"  "margin_of_error"      
 [6] "x"  "x_2"  "x_3"  "x_4"  "x_5"                  
[11] "x_6"  "others_undecided"   "lead"

We can choose the variables we want and rename the x variables with the names of each candidate, according to Wikipedia.

feb_poll %<>% 
  select(fieldwork_date, 
         Lee = x, 
         Yoon = x_2,
         Shim = x_3,
         Ahn = x_4, 
         Kim = x_5, 
         Heo = x_6,
         others_undecided)

We then delete the rows that contain text not related to the poll number values.

feb_poll = feb_poll[-25,]
feb_poll = feb_poll[-81,]
feb_poll = feb_poll[-1,]

I want to clean up the fieldwork_date variable and convert it from character to Date class.

First I found that very handy function on Stack Overflow that extracts the last n characters from a string variable.

substrRight <- function(x, n){
  substr(x, nchar(x)-n+1, nchar(x))
}

If we look at the table, some of the surveys started in Feb but ended in March. We want to extract the final section (i.e. the March section) and use that.

So we use grepl() to find rows that have both Feb AND March, and just extract the March section. If it only has one of those months, we leave it as it is.

feb_poll %<>% 
  mutate(clean_date = ifelse(grepl("Feb", fieldwork_date) & grepl("Mar", fieldwork_date), substrRight(fieldwork_date, 5), fieldwork_date))

Next want to extract the three letter date from this variables and create a new month variable

feb_poll %<>%
  mutate(month = substrRight(clean_date, 3)) 

Following that, we use the parse_number() function from tidyr package to extract the first number found in the string and create a day_number varible (with integer class now)

 feb_poll %<>%
   mutate(day_number = parse_number(clean_date))   

We want to take these two variables we created and combine them together with the unite() function from tidyr again! We want to delete the variables after we unite them. But often I want to keep the original variables, so usually I change the argument remove to FALSE.

We indicate we want to have nothing separating the vales with the sep = "" argument

 feb_poll %<>%
     unite("date", day_number:month, sep = "", remove = TRUE)

And we convert this new date into Date class with as.Date() function.

Here is a handy cheat sheet to help choose the appropriate % key so the format recognises the dates. I will never memorise these values, so I always need to refer to this site.

We have days as numbers (1, 2, 3) and abbreviated 3 character month (Jan, Feb, Mar), so we choose %d and %b

feb_poll %<>%
  mutate(dates_format = as.Date(date, "%d%b")) %>% 
  select(dates_format, Lee:others_undecided) 

Next, we will use the pivot_longer() function to combine all the poll number values into one column. This will make it far easier to plot later.

feb_poll %<>%
  pivot_longer(!dates_format, names_to = "candidate", values_to = "favour") 

After than, we need to clean the actual numbers, remove the percentage signs and convert from character to number class. We use the str_extract() and the regex code to extract the number and not keep the percentage sign.

feb_poll %<>%
    mutate(candidate = as.factor(candidate),
 favour_percent = str_extract(favour, "\\d+\\.*\\d*")) %>% 
   mutate(favour_percent = as.integer(favour_percent)) 

Some of the different polls took place on the same day. So we will take the average poll favourability value for each candidate on each day with the group_by() function

feb_poll %<>%
  group_by(dates_format, candidate) %>% 
  mutate(favour_percent_mean = mean(favour_percent, na.rm = TRUE)) %>% 
  ungroup() %>% 
  select(candidate, dates_format, favour_percent_mean) 

And this is how the cleaned up data should look!

We repeat for the 17th and 16th tables, which contain data going back to the beginning of January 2022

early_feb_poll <- poll_tables[[17]]
early_feb_poll = early_feb_poll[-37,]
early_feb_poll = early_feb_poll[-1,]

We repeat the steps from above with early Feb in one chunk

early_feb_poll %<>%
  clean_names() %>% 
  mutate(month = substrRight(fieldwork_date, 3))  %>% 
  mutate(day_number = parse_number(fieldwork_date)) %>%
  unite("date", day_number:month, sep = "", remove = FALSE) %>% 
  mutate(dates_format = as.Date(date, "%d%b")) %>% 
  select(dates_format, 
         Lee = lee_jae_myung, 
         Yoon = yoon_seok_youl,
         Shim = sim_sang_jung,
         Ahn = ahn_cheol_soo, 
         Kim = kim_dong_yeon, 
         Heo = huh_kyung_young,
         others_undecided) %>% 
  pivot_longer(!dates_format, names_to = "candidate", values_to = "favour") %>% 
  mutate(candidate = as.factor(candidate),
         favour_percent = str_extract(favour, "\\d+\\.*\\d*")) %>% 
  mutate(favour_percent = as.integer(favour_percent)) %>% 
  group_by(dates_format, candidate) %>% 
  mutate(favour_percent_mean = mean(favour_percent, na.rm = TRUE)) %>% 
  ungroup() %>% 
  select(candidate, dates_format, favour_percent_mean)

And we use rbind() to combine the two data.frames

polls <- rbind(feb_poll, early_feb_poll)

Next we repeat with January data:

jan_poll <- poll_tables[[18]]

jan_poll = jan_poll[-34,]
jan_poll = jan_poll[-1,]

jan_poll %<>% 
  clean_names() %>% 
  mutate(month = substrRight(fieldwork_date, 3))  %>% 
  mutate(day_number = parse_number(fieldwork_date)) %>%   # drops any non-numeric characters before or after the first number. 
  unite("date", day_number:month, sep = "", remove = FALSE) %>% 
  mutate(dates_format = as.Date(date, "%d%b")) %>% 
  select(dates_format, 
         Lee = lee_jae_myung, 
         Yoon = yoon_seok_youl,
         Shim = sim_sang_jung,
         Ahn = ahn_cheol_soo, 
         Kim = kim_dong_yeon, 
         Heo = huh_kyung_young,
         others_undecided) %>% 
  pivot_longer(!dates_format, names_to = "candidate", values_to = "favour") %>% 
  mutate(candidate = as.factor(candidate),
         favour_percent = str_extract(favour, "\\d+\\.*\\d*")) %>% 
  mutate(favour_percent = as.integer(favour_percent)) %>% 
  group_by(dates_format, candidate) %>% 
  mutate(favour_percent_mean = mean(favour_percent, na.rm = TRUE)) %>% 
  ungroup() %>% 
  select(candidate, dates_format, favour_percent_mean)

And bind to our combined data.frame:

polls <- rbind(polls, jan_poll)

We can create variables to help us filter different groups of candidates. If we want to only look at the largest candidates, we can makes an important variable and then filter

We can lump the candidates that do not have data from every poll (i.e. the smaller candidate) and add them into the “other_undecided” category with the fct_lump_min() function from the forcats package

polls %>% 
  mutate(important = ifelse(candidate %in% c("Ahn", "Yoon", "Lee", "Shim"), 1, 0)) %>% 
  mutate(few_candidate = fct_lump_min(candidate, min = 110, other_level = "others_undecided")) %>% 
  group_by(few_candidate, dates_format) %>% 
  filter(important == 1) -> poll_data

I want to only look at the main two candidates from the main parties that have been polling in the 40% range – Lee and Yoon – as well as the data for Ahn (who recently dropped out and endorsed Yoon).

poll_data %>% 
  filter(candidate %in% c("Lee", "Yoon", "Ahn")) -> lee_yoon_data

We take the official party hex colors for the graph and create a vector to use later with the scale_color_manual() function below:

party_palette <- c(
  "Ahn" = "#df550a",
  "Lee" = "#00a0e2",
  "Yoon" = "#e7001f")

And we plot the variables.

lee_yoon_data %>% 
  ggplot(aes(x = dates_format, y = favour_percent_mean,
             groups = candidate, color = candidate)) + 
  geom_line( size = 2, alpha = 0.8) +
  geom_point(fill = "#5e6472", shape = 21, size = 4, stroke = 3) + 
  labs(title = "Polling data for Korean Presidential Election", subtitle = "Source: various polling companies, via Wikipedia") -> poll_graph

The bulk of aesthetics for changing the graph appearance in the theme()

poll_graph + theme(panel.border = element_blank(),
        legend.position = "bottom",        
        text = element_text(size = 15, color = "white"),
        plot.title = element_text(size = 40),
        legend.title = element_blank(),
        legend.text = element_text(size = 50, color = "white"),
        axis.text.y = element_text(size = 20), 
        axis.text.x = element_text(size = 20),
        legend.background = element_rect(fill = "#5e6472"),
        axis.title = element_blank(),
        axis.text = element_text(color = "white", size = 20),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        legend.key = element_rect(fill = "#5e6472"),
        plot.background = element_rect(fill = "#5e6472"),
        panel.background = element_rect(fill = "#5e6472")) +
  scale_color_manual(values = party_palette) 

Last, with the annotate() functions, we can also add an annotation arrow and text to add some more information about Ahn Cheol-su the candidate dropping out.

  annotate("text", x = as.Date("2022-02-11"), y = 13, label = "Ahn dropped out just as the polling blackout began", size = 10, color = "white") +
  annotate(geom = "curve", x = as.Date("2022-02-25"), y = 13, xend = as.Date("2022-03-01"), yend = 10, 
    curvature = -.3, arrow = arrow(length = unit(2, "mm")), size = 1, color = "white")

We will just have to wait until next Wednesday / Thursday to see who is the winner ~

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Exploratory Data Analysis and Descriptive Statistics for Political Science Research in R

Packages we will use:

library(tidyverse)      # of course
library(ggridges)       # density plots
library(GGally)         # correlation matrics
library(stargazer)      # tables
library(knitr)          # more tables stuff
library(kableExtra)     # more and more tables
library(ggrepel)        # spread out labels
library(ggstream)       # streamplots
library(bbplot)         # pretty themes
library(ggthemes)       # more pretty themes
library(ggside)         # stack plots side by side
library(forcats)        # reorder factor levels

Before jumping into any inferentional statistical analysis, it is helpful for us to get to know our data. For me, that always means plotting and visualising the data and looking at the spread, the mean, distribution and outliers in the dataset.

Before we plot anything, a simple package that creates tables in the stargazer package. We can examine descriptive statistics of the variables in one table.

Click here to read this practically exhaustive cheat sheet for the stargazer package by Jake Russ. I refer to it at least once a week.

I want to summarise a few of the stats, so I write into the summary.stat() argument the number of observations, the mean, median and standard deviation.

The kbl() and kable_classic() will change the look of the table in R (or if you want to copy and paste the code into latex with the type = "latex" argument).

In HTML, they do not appear.

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To find out more about the knitr kable tables, click here to read the cheatsheet by Hao Zhu.

Choose the variables you want, put them into a data.frame and feed them into the stargazer() function

stargazer(my_df_summary, 
          covariate.labels = c("Corruption index",
                               "Civil society strength", 
                               'Rule of Law score',
                               "Physical Integerity Score",
                               "GDP growth"),
          summary.stat = c("n", "mean", "median", "sd"), 
          type = "html") %>% 
  kbl() %>% 
  kable_classic(full_width = F, html_font = "Times", font_size = 25)
StatisticNMeanMedianSt. Dev.
Corruption index1790.4770.5190.304
Civil society strength1790.6700.8050.287
Rule of Law score1737.4517.0004.745
Physical Integerity Score1790.6960.8070.284
GDP growth1630.0190.0200.032

Next, we can create a barchart to look at the different levels of variables across categories. We can look at the different regime types (from complete autocracy to liberal democracy) across the six geographical regions in 2018 with the geom_bar().

my_df %>% 
  filter(year == 2018) %>%
  ggplot() +
  geom_bar(aes(as.factor(region),
               fill = as.factor(regime)),
           color = "white", size = 2.5) -> my_barplot

And we can add more theme changes

my_barplot + bbplot::bbc_style() + 
  theme(legend.key.size = unit(2.5, 'cm'),
        legend.text = element_text(size = 15),
        text = element_text(size = 15)) +
  scale_fill_manual(values = c("#9a031e","#00a896","#e36414","#0f4c5c")) + 
  scale_color_manual(values = c("#9a031e","#00a896","#e36414","#0f4c5c")) 

This type of graph also tells us that Sub-Saharan Africa has the highest number of countries and the Middle East and North African (MENA) has the fewest countries.

However, if we want to look at each group and their absolute percentages, we change one line: we add geom_bar(position = "fill"). For example we can see more clearly that over 50% of Post-Soviet countries are democracies ( orange = electoral and blue = liberal democracy) as of 2018.

We can also check out the density plot of democracy levels (as a numeric level) across the six regions in 2018.

With these types of graphs, we can examine characteristics of the variables, such as whether there is a large spread or normal distribution of democracy across each region.

my_df %>% 
  filter(year == 2018) %>%
  ggplot(aes(x = democracy_score, y = region, fill = regime)) +
  geom_density_ridges(color = "white", size = 2, alpha = 0.9, scale = 2) -> my_density_plot

And change the graph theme:

my_density_plot + bbplot::bbc_style() + 
  theme(legend.key.size = unit(2.5, 'cm')) +
  scale_fill_manual(values = c("#9a031e","#00a896","#e36414","#0f4c5c")) + 
  scale_color_manual(values = c("#9a031e","#00a896","#e36414","#0f4c5c")) 

Click here to read more about the ggridges package and click here to read their CRAN PDF.

Next, we can also check out Pearson’s correlations of some of the variables in our dataset. We can make these plots with the GGally package.

The ggpairs() argument shows a scatterplot, a density plot and correlation matrix.

my_df %>%
  filter(year == 2018) %>%
  select(regime, 
         corruption, 
         civ_soc, 
         rule_law, 
         physical, 
         gdp_growth) %>% 
  ggpairs(columns = 2:5, 
          ggplot2::aes(colour = as.factor(regime), 
          alpha = 0.9)) + 
  bbplot::bbc_style() +
  scale_fill_manual(values = c("#9a031e","#00a896","#e36414","#0f4c5c")) + 
  scale_color_manual(values = c("#9a031e","#00a896","#e36414","#0f4c5c"))

Click here to read more about the GGally package and click here to read their CRAN PDF.

We can use the ggside package to stack graphs together into one plot.

There are a few arguments to add when we choose where we want to place each graph.

For example, geom_xsideboxplot(aes(y = freedom_house), orientation = "y") places a boxplot for the three Freedom House democracy levels on the top of the graph, running across the x axis. If we wanted the boxplot along the y axis we would write geom_ysideboxplot(). We add orientation = "y" to indicate the direction of the boxplots.

Next we indiciate how big we want each graph to be in the panel with theme(ggside.panel.scale = .5) argument. This makes the scatterplot take up half and the boxplot the other half. If we write .3, the scatterplot takes up 70% and the boxplot takes up the remainning 30%. Last we indicade scale_xsidey_discrete() so the graph doesn’t think it is a continuous variable.

We add Darjeeling Limited color palette from the Wes Anderson movie.

Click here to learn about adding Wes Anderson theme colour palettes to graphs and plots.

my_df %>%
 filter(year == 2018) %>% 
 filter(!is.na(fh_number)) %>% 
  mutate(freedom_house = ifelse(fh_number == 1, "Free", 
         ifelse(fh_number == 2, "Partly Free", "Not Free"))) %>%
  mutate(freedom_house = forcats::fct_relevel(freedom_house, "Not Free", "Partly Free", "Free")) %>% 
ggplot(aes(x = freedom_from_torture, y = corruption_level, colour = as.factor(freedom_house))) + 
  geom_point(size = 4.5, alpha = 0.9) +
  geom_smooth(method = "lm", color ="#1d3557", alpha = 0.4) +  
  geom_xsideboxplot(aes(y = freedom_house), orientation = "y", size = 2) +
  theme(ggside.panel.scale = .3) +
  scale_xsidey_discrete() +
  bbplot::bbc_style() + 
  facet_wrap(~region) + 
  scale_color_manual(values= wes_palette("Darjeeling1", n = 3))

The next plot will look how variables change over time.

We can check out if there are changes in the volume and proportion of a variable across time with the geom_stream(type = "ridge") from the ggstream package.

In this instance, we will compare urban populations across regions from 1800s to today.

my_df %>% 
  group_by(region, year) %>% 
  summarise(mean_urbanization = mean(urban_population_percentage, na.rm = TRUE)) %>% 
  ggplot(aes(x = year, y = mean_urbanization, fill = region)) +
  geom_stream(type = "ridge") -> my_streamplot

And add the theme changes

  my_streamplot + ggthemes::theme_pander() + 
  theme(
legend.title = element_blank(),
        legend.position = "bottom",
        legend.text = element_text(size = 25),
        axis.text.x = element_text(size = 25),
        axis.title.y = element_blank(),
        axis.title.x = element_blank()) +
  scale_fill_manual(values = c("#001219",
                               "#0a9396",
                               "#e9d8a6",
                               "#ee9b00", 
                               "#ca6702",
                               "#ae2012")) 

Click here to read more about the ggstream package and click here to read their CRAN PDF.

We can also look at interquartile ranges and spread across variables.

We will look at the urbanization rate across the different regions. The variable is calculated as the ratio of urban population to total country population.

Before, we will create a hex color vector so we are not copying and pasting the colours too many times.

my_palette <- c("#1d3557",
                "#0a9396",
                "#e9d8a6",
                "#ee9b00", 
                "#ca6702",
                "#ae2012")

We use the facet_wrap(~year) so we can separate the three years and compare them.

my_df %>% 
  filter(year == 1980 | year == 1990 | year == 2000)  %>% 
  ggplot(mapping = aes(x = region, 
                       y = urban_population_percentage, 
                       fill = region)) +
  geom_jitter(aes(color = region),
              size = 3, alpha = 0.5, width = 0.15) +
  geom_boxplot(alpha = 0.5) + facet_wrap(~year) + 
  scale_fill_manual(values = my_palette) +
  scale_color_manual(values = my_palette) + 
  coord_flip() + 
  bbplot::bbc_style()

If we want to look more closely at one year and print out the country names for the countries that are outliers in the graph, we can run the following function and find the outliers int he dataset for the year 1990:

is_outlier <- function(x) {
  return(x < quantile(x, 0.25) - 1.5 * IQR(x) | x > quantile(x, 0.75) + 1.5 * IQR(x))
}

We can then choose one year and create a binary variable with the function

my_df_90 <- my_df %>% 
  filter(year == 1990) %>% 
  filter(!is.na(urban_population_percentage))

my_df_90$my_outliers <- is_outlier(my_df_90$urban_population_percentage)

And we plot the graph:

my_df_90 %>% 
  ggplot(mapping = aes(x = region, y = urban_population_percentage, fill = region)) +
  geom_jitter(aes(color = region), size = 3, alpha = 0.5, width = 0.15) +
  geom_boxplot(alpha = 0.5) +
  geom_text_repel(data = my_df_90[which(my_df_90$my_outliers == TRUE),],
            aes(label = country_name), size = 5) + 
  scale_fill_manual(values = my_palette) +
  scale_color_manual(values = my_palette) + 
  coord_flip() + 
  bbplot::bbc_style() 

In the next blog post, we will look at t-tests, ANOVAs (and their non-parametric alternatives) to see if the difference in means / medians is statistically significant and meaningful for the underlying population.

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Comparing proportions across time with ggstream in R

Packages we need:

library(tidyverse)
library(ggstream)
library(magrittr)
library(bbplot)
library(janitor)

We can look at proportions of energy sources across 10 years in Ireland. Data source comes from: https://www.seai.ie/data-and-insights/seai-statistics/monthly-energy-data/electricity/

Before we graph the energy sources, we can tidy up the variable names with the janitor package. We next select column 2 to 12 which looks at the sources for electricity generation. Other rows are aggregates and not the energy-related categories we want to look at.

Next we pivot the dataset longer to make it more suitable for graphing.

We can extract the last two digits from the month dataset to add the year variable.

elec %<>% 
  janitor::clean_names()

elec[2:12,] -> elec

el <- elec %>% 
  pivot_longer(!electricity_generation_g_wh, 
               names_to = "month", values_to = "value") %>% 

substrRight <- function(x, n){
  substr(x, nchar(x) - n + 1, nchar(x))}

el$year <- substrRight(el$month, 2)

el %<>% select(year, month, elec_type = electricity_generation_g_wh, elec_value = value) 

First we can use the geom_stream from the ggstream package. There are three types of plots: mirror, ridge and proportion.

First we will plot the proportion graph.

Select the different types of energy we want to compare, we can take the annual values, rather than monthly with the tried and trusted group_by() and summarise().

Optionally, we can add the bbc_style() theme for the plot and different hex colors with scale_fill_manual() and feed a vector of hex values into the values argument.

el %>% 
  filter(elec_type == "Oil" | 
           elec_type == "Coal" |
           elec_type == "Peat" | 
           elec_type == "Hydro" |
           elec_type == "Wind" |
           elec_type == "Natural Gas") %>% 
  group_by(year, elec_type) %>%
  summarise(annual_value = sum(elec_value, na.rm = TRUE)) %>% 
  ggplot(aes(x = year, 
             y = annual_value,
             group = elec_type,
             fill = elec_type)) +
  ggstream::geom_stream(type = "proportion") + 
  bbplot::bbc_style() +
  labs(title = "Comparing energy proportions in Ireland") +
  scale_fill_manual(values = c("#f94144",
                               "#277da1",
                               "#f9c74f",
                               "#f9844a",
                               "#90be6d",
                               "#577590"))

With ggstream::geom_stream(type = "mirror")

With ggstream::geom_stream(type = "ridge")

Without the ggstream package, we can recreate the proportion graph with slightly different looks

https://giphy.com/gifs/filmeditor-clueless-movie-l0ErMA0xAS1Urd4e4

el %>% 
  filter(elec_type == "Oil" | 
           elec_type == "Coal" |
           elec_type == "Peat" | 
           elec_type == "Hydro" |
           elec_type == "Wind" |
           elec_type == "Natural Gas") %>% 
  group_by(year, elec_type) %>%
  summarise(annual_value = sum(elec_value, na.rm = TRUE)) %>% 
  ggplot(aes(x = year, 
             y = annual_value,
             group = elec_type,
             fill = elec_type)) +
  geom_area(alpha=0.8 , size=1.5, colour="white") +
  bbplot::bbc_style() +
  labs(title = "Comparing energy proportions in Ireland") +
  theme(legend.key.size = unit(2, "cm")) + 
  scale_fill_manual(values = c("#f94144",
                               "#277da1",
                               "#f9c74f",
                               "#f9844a",
                               "#90be6d",
                               "#577590"))

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Create density plots with ggridges package in R

Packages we will need:

library(tidyverse)
library(ggridges)
library(ggimage)  # to add png images
library(bbplot)   # for pretty graph themes

We will plot out the favourability opinion polls for the three main political parties in Ireland from 2016 to 2020. Data comes from Louwerse and Müller (2020)

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Before we dive into the ggridges plotting, we have a little data cleaning to do. First, we extract the last four “characters” from the date string to create a year variable.

I took this quick function from a StackOverflow response:

substrRight <- function(x, n){
  substr(x, nchar(x)-n+1, nchar(x))}

polls_csv$year <- substrRight(polls_csv$Date, 4)

Next, pivot the data from wide to long format.

More information of pivoting data with dplyr can be found here. I tend to check it at least once a month as the arguments refuse to stay in my head.

I only want to take the main parties in Ireland to compare in the plot.

polls <- polls_csv %>%
  select(year, FG:SF) %>% 
  pivot_longer(!year, names_to = "party", values_to = "opinion_poll")

I went online and found the logos for the three main parties (sorry, Labour) and saved them in the working directory I have for my RStudio. That way I can call the file with the prefix “~/**.png” rather than find the exact location they are saved on the computer.

polls %>% 
  filter(party == "FF" | party == "FG" | party == "SF" ) %>% 
  mutate(image = ifelse(party=="FF","~/ff.png",
 ifelse(party=="FG","~/fg.png", "~/sf.png"))) -> polls_three

Now we are ready to plot out the density plots for each party with the geom_density_ridges() function from the ggridges package.

We will add a few arguments into this function.

We add an alpha = 0.8 to make each density plot a little transparent and we can see the plots behind.

The scale = 2 argument pushes all three plots togheter so they are slightly overlapping. If scale =1, they would be totally separate and 3 would have them overlapping far more.

The rel_min_height = 0.01 argument removes the trailing tails from the plots that are under 0.01 density. This is again for aesthetics and just makes the plot look slightly less busy for relatively normally distributed densities

The geom_image takes the images and we place them at the beginning of the x axis beside the labels for each party.

Last, we use the bbplot package BBC style ggplot theme, which I really like as it makes the overall graph look streamlined with large font defaults.

polls_three %>% 
  ggplot(aes(x = opinion_poll, y = as.factor(party))) +  
  geom_density_ridges(aes(fill = party), 
                      alpha = 0.8, 
                      scale = 2,
                      rel_min_height = 0.01) + 
  ggimage::geom_image(aes(y = party, x= 1, image = image), asp = 0.9, size = 0.12) + 
  facet_wrap(~year) + 
  bbplot::bbc_style() +
  scale_fill_manual(values = c("#f2542d", "#edf6f9", "#0e9594")) +
  theme(legend.position = "none") + 
  labs(title = "Favourability Polls for the Three Main Parties in Ireland", subtitle = "Data from Irish Polling Indicator (Louwerse & Müller, 2020)")
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Comparing mean values across OECD countries with ggplot

Packages we will need:

library(tidyverse)
library(magrittr) # for pipes
library(ggrepel) # to stop overlapping labels
library(ggflags)
library(countrycode) # if you want create the ISO2C variable

I came across code for this graph by Tanya Shapiro on her github for #TidyTuesday.

Her graph compares Dr. Who actors and their average audience rating across their run as the Doctor on the show. So I have very liberally copied her code for my plot on OECD countries.

That is the beauty of TidyTuesday and the ability to be inspired and taught by other people’s code.

I originally was going to write a blog about how to download data from the OECD R package. However, my attempts to download the data leads to an unpleasant looking error and ends the donwload request.

I will try to work again on that blog in the future when the package is more established.

So, instead, I went to the OECD data website and just directly downloaded data on level of trust that citizens in each of the OECD countries feel about their governments.

Then I cleaned up the data in excel and used countrycode() to add ISO2 and country name data.

Click here to read more about the countrycode() package.

First I will only look at EU countries. I tried with all the countries from the OECD but it was quite crowded and hard to read.

I add region data from another dataset I have. This step is not necessary but I like to colour my graphs according to categories. This time I am choosing geographic regions.

my_df %<>%
  mutate(region = case_when(
    e_regiongeo == 1 ~ "Western",
    e_regiongeo == 2  ~ "Northern",
    e_regiongeo == 3  ~ "Southern", 
    e_regiongeo == 4  ~ "Eastern"))

To make the graph, we need two averages:

  1. The overall average trust level for all countries (avg_trust) and
  2. The average for each country across the years (country_avg_trust),
my_df %<>% 
  mutate(avg_trust = mean(trust, na.rm = TRUE)) %>% 
  group_by(country_name) %>% 
  mutate(country_avg_trust = mean(trust, na.rm = TRUE)) %>%
  ungroup()

When we plot the graph, we need a few geom arguments.

Along the x axis we have all the countries, and reorder them from most trusting of their goverments to least trusting.

We will color the points with one of the four geographic regions.

We use geom_jitter() rather than geom_point() for the different yearly trust values to make the graph a little more interesting.

I also make the sizes scaled to the year in the aes() argument. Again, I did this more to look interesting, rather than to convey too much information about the different values for trust across each country. But smaller circles are earlier years and grow larger for each susequent year.

The geom_hline() plots a vertical line to indicate the average trust level for all countries.

We then use the geom_segment() to horizontally connect the country’s individual average (the yend argument) to the total average (the y arguement). We can then easily see which countries are above or below the total average. The x and xend argument, we supply the country_name variable twice.

Next we use the geom_flag(), which comes from the ggflags package. In order to use this package, we need the ISO 2 character code for each country in lower case!

Click here to read more about the ggflags package.

my_df %>%
ggplot(aes(x = reorder(country_name, trust_score), y = trust_score, color = as.factor(region))) +
geom_jitter(aes(color = as.factor(region), size = year), alpha = 0.7, width = 0.15) +
geom_hline(aes(yintercept = avg_trust), color = "white", size = 2)+
geom_segment(aes(x = country_name, xend = country_name, y = country_avg_trust, yend = avg_trust), size = 2, color = "white") +
ggflags::geom_flag(aes(x = country_name, y = country_avg_trust, country = iso2), size = 10) + 
  coord_flip() + 
  scale_color_manual(values = c("#9a031e","#fb8b24","#5f0f40","#0f4c5c")) -> my_plot

Last we change the aesthetics of the graph with all the theme arguments!

my_plot +
 theme(panel.border = element_blank(),
        legend.position = "right",
        legend.title = element_blank(),
        legend.text = element_text(size = 20),
        legend.background = element_rect(fill = "#5e6472"),
        axis.title = element_blank(),
        axis.text = element_text(color = "white", size = 20),
        text= element_text(size = 15, color = "white"),
        panel.grid.major.y = element_blank(),
        panel.grid.minor.y = element_blank(),
        panel.grid.major.x = element_blank(),
        panel.grid.minor.x = element_blank(),
        legend.key = element_rect(fill = "#5e6472"),
        plot.background = element_rect(fill = "#5e6472"),
        panel.background = element_rect(fill = "#5e6472")) +
  guides(colour = guide_legend(override.aes = list(size=10))) 

And that is the graph.

We can see that countries in southern Europe are less trusting of their governments than in other regions. Western countries seem to occupy the higher parts of the graph, with France being the least trusting of their government in the West.

There is a large variation in Northern countries. However, if we look at the countries, we can see that the Scandinavian countries are more trusting and the Baltic countries are among the least trusting. This shows they are more similar in their trust levels to other Post-Soviet countries.

Next we can look into see if there is a relationship between democracy scores and level of trust in the goverment with a geom_point() scatterplot

The geom_smooth() argument plots a linear regression OLS line, with a standard error bar around.

We want the labels for the country to not overlap so we use the geom_label_repel() from the ggrepel package. We don’t want an a in the legend, so we add show.legend = FALSE to the arguments


my_df %>% 
  filter(!is.na(trust_score)) %>% 
  ggplot(aes(x = democracy_score, y = trust_score)) +
  geom_smooth(method = "lm", color = "#a0001c", size = 3) +
  geom_point(aes(color = as.factor(region)), size = 20, alpha = 0.6) +
 geom_label_repel(aes(label = country_name, color = as.factor(region)), show.legend = FALSE, size = 5) + 
scale_color_manual(values = c("#9a031e","#fb8b24","#5f0f40","#0f4c5c")) -> scatter_plot

And we change the theme and add labels to the plot.

scatter_plot + theme(panel.border = element_blank(),
        legend.position = "bottom",
        legend.title = element_blank(),
        legend.text = element_text(size = 20),
        legend.background = element_rect(fill = "#5e6472"),
        text= element_text(size = 15, color = "white"),

        legend.key = element_rect(fill = "#5e6472"),
        plot.background = element_rect(fill = "#5e6472"),
        panel.background = element_rect(fill = "#5e6472")) +
  guides(colour = guide_legend(override.aes = list(size=10)))  +
  labs(title = "Democracy and trust levels", 
       y = "Democracy score",
       x = "Trust level of respondents",
       caption="Data from OECD") 

We can filter out the two countries with low democracy and examining the consolidated democracies.

Thank you for reading!

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Graphing Pew survey responses with ggplot in R

Packages we will need:

library(tidyverse)
library(forcats)
library(ggthemes)

We are going to look at a few questions from the 2019 US Pew survey on relations with foreign countries.

Data can be found by following this link:

We are going to make bar charts to plot out responses to the question asked to American participaints: Should the US cooperate more or less with some key countries? The countries asked were China, Russia, Germany, France, Japan and the UK.

Before we dive in, we can find some nice hex colors for the bar chart. There are four possible responses that the participants could give: cooperate more, cooperate less, cooperate the same as before and refuse to answer / don’t know.

pal <- c("Cooperate more" = "#0a9396",
         "Same as before" = "#ee9b00",
         "Don't know" = "#005f73",
         "Cooperate less" ="#ae2012")

We first select the questions we want from the full survey and pivot the dataframe to long form with pivot_longer(). This way we have a single column with all the different survey responses. that we can manipulate more easily with dplyr functions.

Then we summarise the data to count all the survey reponses for each of the four countries and then calculate the frequency of each response as a percentage of all answers.

Then we mutate the variables so that we can add flags. The geom_flag() function from the ggflags packages only recognises ISO2 country codes in lower cases.

After that we change the factors level for the four responses so they from positive to negative views of cooperation

pew %>% 
  select(id = case_id, Q2a:Q2f) %>% 
  pivot_longer(!id, names_to = "survey_question", values_to = "response")  %>% 
  group_by(survey_question, response) %>% 
  summarise(n = n()) %>%
  mutate(freq = n / sum(n)) %>% 
  ungroup() %>% 
  mutate(response_factor = as.factor(response)) %>% 
  mutate(country_question = ifelse(survey_question == "Q2a", "fr",
ifelse(survey_question == "Q2b", "gb",
ifelse(survey_question == "Q2c", "ru",
ifelse(survey_question == "Q2d", "cn",
ifelse(survey_question == "Q2e", "de",
ifelse(survey_question == "Q2f", "jp", survey_question))))))) %>% 
  mutate(response_string = ifelse(response_factor == 1, "Cooperate more",
ifelse(response_factor == 2, "Cooperate less",
ifelse(response_factor == 3, "Same as before",
ifelse(response_factor == 9, "Don't know", response_factor))))) %>% 
  mutate(response_string = fct_relevel(response_string, c("Cooperate less","Same as before","Cooperate more", "Don't know"))) -> pew_clean

We next use ggplot to plot out the responses.

We use the position = "stack" to make all the responses “stack” onto each other for each country. We use stat = "identity" because we are not counting each reponses. Rather we are using the freq variable we calculated above.

pew_clean %>%
  ggplot() +
  geom_bar(aes(x = forcats::fct_reorder(country_question, freq), y = freq, fill = response_string), color = "#e5e5e5", size = 3, position = "stack", stat = "identity") +
  geom_flag(aes(x = country_question, y = -0.05 , country = country_question), color = "black", size = 20) -> pew_graph

And last we change the appearance of the plot with the theme function

pew_graph + 
coord_flip() + 
  scale_fill_manual(values = pal) +
  ggthemes::theme_fivethirtyeight() + 
  ggtitle("Should the US cooperate more or less with the following country?") +
  theme(legend.title = element_blank(),
        legend.position = "top",
        legend.key.size = unit(2, "cm"),
        text = element_text(size = 25),
        legend.text = element_text(size = 20),
        axis.text = element_blank())