Show the code
war_support_tracker <- readxl::read_xlsx(here::here("./data/UkraineTracker_v2_02052022.xlsx"), sheet="Aggregate Aid", range = "A1:S33")EU Country Military Donations
Inspiration
This post inspired me to look at how distance from the war in Ukraine correlates with amount of money sent to Russia.
Import
Ukraine War Tracker data from the IFW-Kiel Institute for the World Economy (Retrieved 2022-05-10)
You can add options to executable code like this
Warning: Geocoding "EU (Commission an..." failed with error:# A tibble: 6 x 3
Country `Direct Military Aid` `GDP (EUR)`
<chr> <dbl> <dbl>
1 Estonia 0.22 28153109213.
2 Latvia 0.22 30961072839.
3 Poland 1.47 548015394507.
4 Luxembourg 0.00303 67376807201.
5 Slovakia 0.195 96603802700.
6 Lithuania 0.039 51939891614.Show the code
war_support_tracker %>%
filter(!str_detect(Country,"Commission")) %>%
ggplot(aes(`Total aid (bilateral + EU) in % of GDP`)) +
geom_histogram(fill = ukraine_palette$ukraine_blue) +
labs(title = "Total Aid in % GDP of Selected Countries") +
ukraine_plot_theme()`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Show the code
war_support_tracker %>%
filter(!str_detect(Country,"Commission")) %>%
ggplot(
aes(fct_reorder(Country, `Total aid (bilateral + EU) in % of GDP`),
`Total aid (bilateral + EU) in % of GDP`)
) +
geom_col(fill = ukraine_palette$ukraine_blue) +
labs(title = "Total Aid in % GDP of Selected Countries", x = "Country") +
ukraine_plot_theme() +
coord_flip()
Contribution vs Distance to Moscow
Show the code
moscow_geolocation = ggmap::geocode("Moscow")
google_map_geo_to_vector <- function(geo){ c(geo$lon, geo$lat) }
war_support_tracker <-
war_support_tracker %>%
mutate(
country_geolocation = purrr::map(
country_geolocation,
google_map_geo_to_vector
),
moscow_distance_km = purrr::map_dbl(
country_geolocation,
geosphere::distVincentyEllipsoid,
p2=google_map_geo_to_vector(moscow_geolocation)
) / 1000
)Show the code
war_support_tracker %>%
ggplot(
aes(
moscow_distance_km,
`Total aid (bilateral + EU) in % of GDP`,
size = `GDP (EUR)`
)
) +
geom_point(colour = ukraine_palette$ukraine_blue, alpha=0.7) +
ukraine_plot_theme() +
scale_size(range = c(0.1, 15),
breaks = c(1e12, 5e12, 10e12, 15e12),
labels = c("1", "5", "10", "15"),
name = "GDP (Trillion EUR)") +
labs(title = "Country Distance to Moscow vs GDP Aid to Ukraine",
x = "Distance from Country Centre to Moscow")Warning: Removed 1 rows containing missing values (geom_point).
Show the code
war_support_tracker %>%
ggplot(
aes(
moscow_distance_km,
`Total aid (bilateral + EU) in % of GDP`,
size = `GDP (EUR)`
)
) +
geom_point(colour = ukraine_palette$ukraine_blue, alpha=0.7) +
ukraine_plot_theme() +
scale_size(range = c(0.1, 15),
breaks = c(1e12, 5e12, 10e12, 15e12),
labels = c("1", "5", "10", "15"),
name = "GDP (Trillion EUR)") +
labs(title = "Country Distance to Moscow vs GDP Aid to Ukraine",
x = "Distance from Country Centre to Moscow")
Show the code
aid_vs_distance_eu_lm <-
war_support_tracker %>%
filter(EU == 1) %>%
select(
moscow_distance_km, `Total aid (bilateral + EU) in % of GDP`, `GDP (EUR)`
) %>%
mutate(across(c(moscow_distance_km), .fns=log10)) %>%
lm(formula = `Total aid (bilateral + EU) in % of GDP` ~ moscow_distance_km,
data=.)
aid_vs_distance_eu_lm_summary = summary(aid_vs_distance_eu_lm)
aid_vs_distance_eu_lm_summary
Call:
lm(formula = `Total aid (bilateral + EU) in % of GDP` ~ moscow_distance_km,
data = .)
Residuals:
Min 1Q Median 3Q Max
-0.29911 -0.10970 -0.03176 0.03689 0.42300
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.4922 0.6160 4.046 0.000440 ***
moscow_distance_km -0.7159 0.1891 -3.785 0.000859 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1732 on 25 degrees of freedom
Multiple R-squared: 0.3643, Adjusted R-squared: 0.3388
F-statistic: 14.32 on 1 and 25 DF, p-value: 0.0008594Show the code
war_support_tracker %>%
filter(EU == 1) %>%
select(
Country,
moscow_distance_km,
`Total aid (bilateral + EU) in % of GDP`,
`GDP (EUR)`
) %>%
mutate(
across(
c(
moscow_distance_km,
# `Total aid (bilateral + EU) in % of GDP`
),
.fns=log10)
) %>%
mutate(`GDP (EUR) ` = `GDP (EUR)`) %>%
ggplot(
aes(
moscow_distance_km,
`Total aid (bilateral + EU) in % of GDP`,
size = `GDP (EUR)`
)
) +
geom_point(aes(
moscow_distance_km,
`Total aid (bilateral + EU) in % of GDP`,
size = `GDP (EUR) `
),
colour = ukraine_palette$ukraine_blue, alpha=0.6) +
ukraine_plot_theme() +
scale_size(range = c(0.5, 15),
breaks = c(0.1e12, 1e12, 3e12),
labels = c("0.1", "1", "3"),
name = "GDP (Trillion EUR)") +
labs(title = "Log of EU Country Distance to Moscow vs GDP % Aid to Ukraine",
x = "Log10 Distance from Country to Moscow",
y = "Total Aid as % of GDP") +
geom_smooth(method = "lm",
show.legend = FALSE,
se = FALSE,
formula = y ~ x,
colour = ukraine_palette$ukraine_yellow_darkened) +
annotate("text",
x=Inf,
y = Inf,
label = paste0(
"R² = ", round(aid_vs_distance_eu_lm_summary$r.squared, 2)
),
vjust=1.2,
hjust=1.1,
colour = ukraine_palette$text_colour,
size=6) +
geom_text(mapping = aes(label = Country),
check_overlap = TRUE,
size = 5,
colour = ukraine_palette$text_colour,
hjust = -0.2) +
xlim(2.9, 3.7) +
ylim(0, 1)
Correlations
Show the code
war_support_tracker %>%
select(
moscow_distance_km, `Total aid (bilateral + EU) in % of GDP`, `GDP (EUR)`
) %>%
# mutate(across(.fns=log10)) %>%
cor() %>%
corrplot::corrplot(method="number")