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@resulumit
Last active January 18, 2023 22:15
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# r code for the graph at
# https://twitter.com/ResulUmit/status/1352218275699154945?s=20
# load the packages -------------------------------------------------------
library(dataverse)
library(rio)
library(tidyverse)
# specify which installation ----------------------------------------------
Sys.setenv("DATAVERSE_SERVER" = "dataverse.harvard.edu")
# get the data from harvard dataverse -------------------------------------
cabs <- get_file("view_cabinet.tab", "doi:10.7910/DVN/Q6CVHX")
tmp <- tempfile(fileext = ".csv")
writeBin(as.vector(cabs), tmp)
df_cabs <- import(tmp)
# tidy the data -----------------------------------------------------------
df_ends <- df_cabs %>%
filter(start_date > as.Date("1948-12-31") & start_date < as.Date("2020-01-01")) %>%
mutate(end_year = as.numeric(format(start_date,'%Y'))) %>%
select(previous_cabinet_id, end_year) %>%
distinct()
df <- df_cabs %>%
filter(start_date > as.Date("1948-12-31") & start_date < as.Date("2020-01-01")) %>%
mutate(start_year = as.numeric(format(start_date,'%Y'))) %>%
group_by(cabinet_id, start_year) %>%
summarise(parties = sum(cabinet_party)) %>%
ungroup() %>%
left_join(., df_ends, by = c("cabinet_id" = "previous_cabinet_id")) %>%
mutate(end_year = replace_na(end_year, 2019)) %>%
pivot_longer(cols = c("start_year", "end_year"), values_to = "year") %>%
select(-name) %>%
group_by(cabinet_id) %>%
complete(parties, year = full_seq(year, 1)) %>%
group_by(year) %>%
summarise(cabinet_size = mean(parties))
# plot the data -----------------------------------------------------------
ggplot(df, aes(x = year, y = cabinet_size)) +
geom_line(size = 1.5) +
theme_minimal() +
theme(axis.title = element_text(size = 14),
axis.text = element_text(size = 14)) +
labs(y = "Average number of parties in government, 1949 - 2019\n", x = "",
caption = "Data: ParlGov")
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