POLI 144AB Coding Workshop 2
1 minute read
V-Dem Dataset (v9) V-Dem Dataset Codebook
Dataset Citation: Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I. Lindberg, Jan Teorell, David Altman, Michael Bernhard, M. Steven Fish, Adam Glynn, Allen Hicken, Anna Lührmann, Kyle L. Marquardt, Kelly McMann, Pamela Paxton, Daniel Pemstein, Brigitte Seim, Rachel Sigman, Svend-Erik Skaaning, Jeffrey Staton, Agnes Cornell, Lisa Gastaldi, Haakon Gjerløw, Valeriya Mechkova, Johannes von Römer, Aksel Sundtröm, Eitan Tzelgov, Luca Uberti, Yi-tingWang, Tore Wig, and Daniel Ziblatt. 2019. “V-Dem Codebook v9” Varieties of Democracy (V-Dem) Project.
Load data
library(here)
library(readr)
library(tidyverse)
library(haven)
vdem<- read_csv("~/Dropbox/Teaching/SS2_2024_POLI144AB/Data/V-Dem Dataset.csv")
Select our variables
vdem_cleaned <- vdem %>%
select(country_name, year, v2x_polyarchy, v2mecenefm)
Histogram
vdem_cleaned %>%
ggplot(aes(x = v2x_polyarchy)) +
geom_histogram()
vdem_cleaned %>%
ggplot(aes(x = year, y = v2x_polyarchy, color = country_name)) +
geom_line() +
theme(legend = "none") +
guides(color="none")
# quite messy here!
# let's take a summary across all countries for each year
vdem_cleaned %>%
group_by(year) %>%
summarise(v2x_polyarchy_average = mean(v2x_polyarchy, na.rm = TRUE)) %>%
ggplot(aes(x = year, y = v2x_polyarchy_average)) +
geom_line()
# that's better!
Scatterplot
vdem_cleaned %>%
ggplot(aes(x = v2x_polyarchy, y = v2mecenefm)) +
geom_point() +
geom_smooth(method = "lm")
lm(v2mecenefm ~ v2x_polyarchy,
data = vdem_cleaned)
##
## Call:
## lm(formula = v2mecenefm ~ v2x_polyarchy, data = vdem_cleaned)
##
## Coefficients:
## (Intercept) v2x_polyarchy
## -1.575 4.479
-1.575 + 4.479
## [1] 2.904
vdem_global_mean <- vdem_cleaned %>%
group_by(year) %>%
summarise(v2x_polyarchy = mean(v2x_polyarchy, na.rm = TRUE)) %>%
mutate(country_name = "Global")
vdem_cleaned %>%
filter(country_name == "United States of America") %>%
select(-v2mecenefm) %>%
bind_rows(vdem_global_mean) %>%
ggplot(aes(x = year, y = v2x_polyarchy, color = country_name)) +
geom_line() +
labs(x = "Year",
y = "V-Dem Polyarchy",
color = "Country",
title = "Polyarchy Trends over Time (US vs Global)")
vdem_cleaned %>%
filter(country_name %in% c("United States of America", "Mexico", "Canada")) %>%
select(-v2mecenefm) %>%
ggplot(aes(x = year, y = v2x_polyarchy, fill = country_name)) +
geom_col(position = "dodge") +
scale_fill_viridis_d()
library(plotly)
piechart_data = vdem_cleaned %>%
filter(country_name %in% c("United States of America", "Mexico", "Canada") & year == 2000)
plot_ly(piechart_data, labels = ~country_name, values = ~v2x_polyarchy, type = "pie", marker = list(colors = c("#F23030", "#267365", "#F28705")) )