Scatterplots and barcharts
Paula
8/19/2021
Global Terrorism Dataset
First, I had a look at the dataset that Dr. Martinez Machain suggested in her email, the Global Terrorism Dataset.
If we sum the number of total terrorist acts per country per year, we see that the top three countries are Iraq, Pakistan and Afghanistan.
Source: https://www.start.umd.edu/data-tools/global-terrorism-database-gtd
The Global Terrorism Database (GTD) is an open-source database including information on terrorist events around the world since 1970 (currently updated through 2018). Unlike many other event databases, the GTD includes systematic data on international as well as domestic terrorist incidents that have occurred during this time period and now includes over 190,000 cases.
LaFree, G., & Dugan, L. (2007). Introducing the global terrorism database. Terrorism and political violence, 19(2), 181-204.
Total terrorist attacks
Total terrorist attacks between 1970 and 2019
gtd %>%
group_by(country_txt, region_txt) %>%
summarise(sum = n()) %>%
ungroup() %>%
filter(sum > 4000) %>%
ggplot(aes(x = reorder(country_txt, sum),
y = sum,
fill = as.factor(region_txt))) +
scale_fill_manual(values = c("#001219","#005f73","#0a9396","#94d2bd",
"#ee9b00","#ca6702","#bb3e03","#ae2012","#9b2226")) +
coord_flip() +
geom_bar(stat = "identity") +
ggthemes::theme_pander() +
theme(legend.position = "bottom",
legend.title = element_blank()) +
ggtitle("Total terrorist attacks 1970 - 2019")## `summarise()` has grouped output by 'country_txt'. You can override using the `.groups` argument.
Suicide attacks
If we look only at suicide attack, the countries are still Iraq, Afghanistan and Pakistan.
There does not appear to be a variable to indicate if the terrorist attack was related explicitly to Islamic Fundamentalism.
Suicide terrorist attacks between 1970 and 2019
gtd %>%
filter(suicide == 1 ) %>%
group_by(country_txt, region_txt) %>%
summarise(suicide_sum = n()) %>%
ungroup() %>%
filter(suicide_sum > 50) %>%
ggplot(aes(x = reorder(country_txt, suicide_sum),
y = suicide_sum,
fill = as.factor(region_txt))) +
scale_fill_manual(values = c("#001219", "#0a9396", "#ee9b00", "#ca6702","#bb3e03",
"#005f73", "#94d2bd", "#ae2012", "#9b2226")) +
coord_flip() +
geom_bar(stat = "identity") +
ggthemes::theme_pander() +
theme(legend.position = "bottom", legend.title = element_blank()) +
ggtitle("Suicide terrorist attacks 1970 - 2019")## `summarise()` has grouped output by 'country_txt'. You can override using the `.groups` argument.
Look only at 2019
gtd %>%
filter(suicide == 1 ) %>%
filter(iyear == 2019) %>%
group_by(country_txt, iyear, region_txt) %>%
select(year = iyear, everything()) %>%
summarise(suicide_sum = n()) %>%
arrange(desc(suicide_sum)) %>%
head(20) %>%
ungroup() %>%
ggplot(aes(x = reorder(country_txt, suicide_sum),
y = suicide_sum,
fill = as.factor(region_txt))) +
scale_fill_manual(values = c("#001219", "#0a9396",
"#ca6702","#9b2226",
"#94d2bd","#ee9b00")) +
coord_flip() +
geom_bar(stat = "identity") + facet_wrap(~year) +
ggthemes::theme_pander() +
theme(legend.position = "bottom", legend.title = element_blank()) +
ggtitle("Total suicide terrorist attacks per country in 2019")## `summarise()` has grouped output by 'country_txt', 'year'. You can override using the `.groups` argument.
So for the dataset, we can add a variable with number of terrorist attacks per country per year or we can create a binary variable with a cut-off point.
We can also look at number of terrorist attacks per capita per year and run a scatter plot with PD budgets to see if there is a linear relationship
## `summarise()` has grouped output by 'cow_code', 'year'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'cow_code'. You can override using the `.groups` argument.
## `summarise()` has grouped output by 'cow_code'. You can override using the `.groups` argument.## `summarise()` has grouped output by 'cow_code', 'year'. You can override using the `.groups` argument.A tibble: 173 x 2
country avg_suicide
5 Pakistan 0.833 6 Egypt 0.5
7 Niger 0.444 8 Sri Lanka 0.389 9 Philippines 0.333 10 Mali 0.278 # … with 163 more rows
Scatterplot comparing all types of terrorist attacks per capita vs PD budgets per capita
the_df %>%
mutate(attacks_pc = sum_attacks / pop, na.rm = TRUE) %>%
ggplot(aes(y = log(budget_pc), x = log(attacks_pc))) +
geom_smooth(method = "lm", color = "#9d0208") +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~year) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
xlab("Logged number of terrorist attacks per capita") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander() +
ggtitle("Total terrorist attacks per capita vs PD budgets per capita")## `geom_smooth()` using formula 'y ~ x'
Scatterplot comparing suicide terrorist attacks per capita vs PD budgets per capita
the_df %>%
mutate(attacks_pc = sum_suicides / pop, na.rm = TRUE) %>%
ggplot(aes(y = log(budget_pc), x = log(attacks_pc))) +
geom_smooth(method = "lm", color = "#9d0208") +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~year) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b",
"#6d597a", "#e63946", "#f4a261"), name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
xlab("Logged number of terrorist attacks per capita") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander() +
ggtitle("Suicide attacks per capita vs PD budgets per capita")## `geom_smooth()` using formula 'y ~ x'
the_df %>%
ggplot(aes(y = log(budget_pc), x = log(sum_suicides))) +
geom_smooth(method = "lm", color = "#9d0208") +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~year) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b",
"#6d597a", "#e63946", "#f4a261"), name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
xlab("Logged number of terrorist attacks per capita") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander() +
ggtitle("Suicide attacks per capita vs PD budgets per capita")## `geom_smooth()` using formula 'y ~ x'
Only looking at terrorist attacks in 2019
the_df %>%
filter(year == 2019) %>%
mutate(attacks_pc = sum_attacks / pop, na.rm = TRUE) %>%
ggplot(aes(y = log(budget), x = log(sum_attacks), group = as.factor(region_6))) +
geom_smooth(method = "lm", color = "#9d0208", alpha = 0.5) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~region_6) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
xlab("Logged number of terrorist attacks per capita") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander() +
ggtitle("Total terrorist attacks per capita vs PD budgets per capita in 2019")## `geom_smooth()` using formula 'y ~ x'
Suicide attacks per capita vs PD budgets per capita since 2013
the_df %>%
filter(year > 2012) %>%
mutate(attacks_pc = sum_suicides / pop, na.rm = TRUE) %>%
ggplot(aes(y = log(budget_pc), x = log(attacks_pc))) +
geom_smooth(method = "lm", color = "#9d0208") +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~region_6) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
xlab("Logged number of terrorist attacks per capita") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander() +
ggtitle("Suicide attacks per capita vs PD budgets per capita since 2013")## `geom_smooth()` using formula 'y ~ x'
Chinese aid dataset
Next we can decide which countries are in China’s sphere of influence.
Source: https://www.aiddata.org/data/chinese-global-official-finance-dataset
This dataset tracks the known universe of overseas Chinese official finance between 2000-2014, capturing 4,373 records totaling $354.4 billion. The data includes both Chinese aid and non-concessional official financing.
Bluhm, R., Dreher, A., Fuchs, A., Parks, B. C., Strange, A., & Tierney, M.J. (2018). Connective Financing: Chinese Infrastructure Projects and the Diffusion of Economic Activity in Developing Countries. AidData Working Paper
Looking at the total aid from China between 2000 to 2014
china_aid %>%
filter(!is.na(region_name)) %>%
group_by(country, region_name) %>%
filter(!grepl("regional", country)) %>%
summarise(sum_aid = sum(chn_aid, na.rm = TRUE)) %>%
ungroup() %>%
top_n(30) %>%
ggplot(aes(x= reorder(country, sum_aid),
y = sum_aid,
fill = as.factor(region_name))) +
geom_bar(stat = "identity") +
coord_flip() +
scale_fill_manual(values = c("#005f73","#0a9396","#94d2bd","#ee9b00",
"#ca6702","#bb3e03","#ae2012","#9b2226"),
name = NULL) +
theme(axis.text.y = element_text(size = 10),
axis.title.y = element_blank(),
legend.position = "bottom") +
xlab("country") +
ylab("Total aid from China") +
ggthemes::theme_pander() +
ggtitle("Total aid from China betweem 2000 - 2014") ## `summarise()` has grouped output by 'country'. You can override using the `.groups` argument.## Selecting by sum_aid
china_aid %>%
filter(!is.na(region_name)) %>%
group_by(country, region_name) %>%
filter(!grepl("regional", country)) %>%
summarise(sum_aid = sum(chn_aid, na.rm = TRUE)) %>%
ungroup() %>%
top_n(20) %>% pull(country) -> top_china_aid_vector## `summarise()` has grouped output by 'country'. You can override using the `.groups` argument.
## Selecting by sum_aidtop_china_aid_vector[1] “Angola” “Argentina” “Belarus” “Brazil” “Ecuador”
[6] “Ethiopia” “Ghana” “India” “Indonesia” “Iran”
[11] “Kazakhstan” “Laos” “Nigeria” “Pakistan” “Philippines” [16] “Russia” “Sri Lanka” “Sudan” “Venezuela” “Viet Nam”
china_aid %>%
filter(!is.na(region_name)) %>%
group_by(country, region_name) %>%
filter(!grepl("regional", country)) %>%
mutate(chn_aid_pc = chn_aid / population, na.rm = TRUE) %>%
summarise(sum_aid_pc = sum(chn_aid_pc, na.rm = TRUE)) %>%
ungroup() %>%
top_n(20) %>% pull(country) -> top_china_aid_pc_vector## `summarise()` has grouped output by 'country'. You can override using the `.groups` argument.## Selecting by sum_aid_pctop_china_aid_pc_vector[1] “Albania” “Antigua & Barbuda” “Bahamas”
[4] “Belarus” “Bosnia-Herzegovina” “Cyprus”
[7] “Dominica” “Equatorial Guinea” “Fiji”
[10] “Grenada” “Laos” “Maldives”
[13] “Mauritius” “Mongolia” “Samoa”
[16] “Sao Tome & Principe” “Seychelles” “Tonga”
[19] “Turkmenistan” “Venezuela”
Chinese FDI in Africa
Source: https://www.aiddata.org/data/aiddatas-chinese-official-finance-to-africa-dataset-version-1-1-1
This is a dataset of Chinese official finance activities that spans 50 African countries over the 2000-2012 period. It includes 1,952 Chinese development finance projects across 3,545 physical locations.
Dreher, A., Fuchs, A., Hodler, R., Parks, B. C., Raschky, P. A., & Tierney, M.J. (2019). African Leaders and the Geography of China’s Foreign Assistance. Journal of Development Economics, 140, 44-71.
Total development finance investment to Africa from China 2000 to 2012
fdi_af %>%
top_n(25) %>%
ggplot(aes(x = reorder(country, sum_fdi), y = sum_fdi, fill = country)) +
geom_bar(stat = "identity") +
coord_flip() + ggthemes::theme_pander() +
theme(axis.text.y = element_text(size = 10),
axis.title.y = element_blank(),
legend.position = "none") + ggthemes::theme_pander() +
xlab("Total development finance costs") +
ylab("Country") +
ggthemes::theme_pander() + theme(legend.position = "none") +
ggtitle("Total development finance investment from China 2000 to 2012 ") ## Selecting by cow_code
Looking at the financial development investment per capita to Africa 2000 to 2102
the_df <-merge(the_df, fdi_af, by = c("cow_code"), all.x = TRUE)
the_df %>%
filter(sum_fdi > 10000) %>%
mutate(fdi_pc = sum_fdi / pop, na.rm = TRUE) %>%
ggplot(aes(x = reorder(country.x, fdi_pc), y = fdi_pc, fill = country.x)) +
geom_bar(stat = "identity") +
coord_flip() + ggthemes::theme_pander() +
theme(axis.text.y = element_text(size = 10),
axis.title.y = element_blank(),
legend.position = "none") + ggthemes::theme_pander() +
xlab("Total finance costs per capita") +
ylab("Country") +
ggthemes::theme_pander() + theme(legend.position = "none") +
ggtitle("Total finance per capita from China 2000 to 2012 ") 
Russia’s Sphere of Influence
Russia’s sphere contains all post-soviet countries and all countries that are contiguous to Russia
China’s Sphere of Influence
China’s spehere contains top 20 aid recipients per capita and top financial recipients per capita from XXX YYY years/
It also contains all countries contiguous to China.
Average voting similarity to China in the UN
Source: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/5GQIVU
Rodrigues Vieira, V. (2018). Who joins counter-hegemonic IGOs? Early and late members of the China-led Asian Infrastructure Investment Bank. Research & Politics, 5(2)
China Alignment correspond to the average ideal point scale (ranging from 0 to 100) of Bailey et al. (2015) for a state’s vote with Chinese positions in the United Nations General Assembly between 1990 and 2009, a period during which US leadership in world affairs seemed robust. This measures the strength of international ties with China in political terms
aiib <- read_dta("C:/Users/Paula/Desktop/AIIB-ForResubmission.dta")
aiib %>%
select(country, chinaalignment) %>%
arrange(desc(chinaalignment)) %>%
filter(chinaalignment > 9000) %>%
kbl() %>%
kable_paper("striped",
full_width = F)| country | chinaalignment |
|---|---|
| Vietnam | 9407.000 |
| Myanmar | 9340.000 |
| Korea, Dem. Rep. | 9259.000 |
| Lao PDR | 9223.000 |
| Cuba | 9175.999 |
| Zimbabwe | 9109.000 |
| Syrian Arab Republic | 9096.000 |
| Libya | 9086.000 |
| Iran, Islamic Rep. | 9086.000 |
| Yemen, Rep. | 9070.000 |
| Sudan | 9061.000 |
| Oman | 9027.000 |
| Egypt, Arab Rep. | 9019.000 |
| Qatar | 9016.000 |
| Iraq | 9008.000 |
aiib %>%
filter(chinaalignment > 8800) %>%
ggplot(aes(x = reorder(country, chinaalignment), y = chinaalignment, fill = country)) +
geom_bar(stat = "identity") +
coord_flip() + ggthemes::theme_pander() +
theme(axis.text.y = element_text(size = 10),
axis.title.y = element_blank(),
legend.position = "none") + ggthemes::theme_pander() +
ylab("Voting similarity to China (average)") +
xlab("Country") +
ggthemes::theme_pander() + theme(legend.position = "none") +
ggtitle("Top aligned to Chinese UN voting 2000 - 2014") 
aiib %>%
filter(chinaalignment < 5500) %>%
ggplot(aes(x = reorder(country, chinaalignment), y = chinaalignment, fill = country)) +
geom_bar(stat = "identity") +
coord_flip() + ggthemes::theme_pander() +
theme(axis.text.y = element_text(size = 10),
axis.title.y = element_blank(),
legend.position = "none") + ggthemes::theme_pander() +
ylab("Voting similarity to China (average)") +
xlab("Country") +
ggthemes::theme_pander() + theme(legend.position = "none") +
ggtitle("Least aligned to Chinese UN voting 2000 - 2014") 
map <- ne_countries(scale = "medium", returnclass = "sf")
aiib$iso3c <- countrycode(aiib$country, "cown","iso3c")
map_aiib <- merge(map, aiib, by.x = "adm0_a3", by.y = "wbcode", all.x = TRUE)
View(map)
coord <- read_html("https://developers.google.com/public-data/docs/canonical/countries_csv")
coord_tables <- coord %>% html_table(header = TRUE, fill = TRUE)
coordy <- coord_tables[[1]]
col_map <- merge(map_aiib, coordy, by.x= "iso_a2", by.y = "country", all.y = TRUE)
map_aiib %>%
# filter(geounit != "Siachen Glacier") %>%
filter(subregion == "Eastern Asia" | subregion == "South-Eastern Asia" | subregion == "Southern Asia") %>%
ggplot() +
geom_sf(aes(fill = chinaalignment),
position = "identity") +
labs(fill='Russian Sphere') +
# geom_label(aes(longitude+1, latitude+1, label = factor(geounit)), size = 3) +
ggthemes::theme_map() +
theme(legend.position = "blank") +
scale_fill_distiller(palette = "RdBu")
col_map %>%
filter(continent == "Africa") %>% ggplot() +
geom_sf(aes(fill = chinaalignment),
position = "identity") +
labs(fill='Russian Sphere') +
geom_label(aes(longitude+1, latitude+1, label = factor(geounit)), size = 3) +
ggthemes::theme_map() +
theme(legend.position = "blank") +
scale_fill_distiller(palette = "RdBu")
plyr::count(map_aiib$subregion) x freq1 Antarctica 1 2 Australia and New Zealand 4 3 Caribbean 25 4 Central America 8 5 Central Asia 5 6 Eastern Africa 19 7 Eastern Asia 8 8 Eastern Europe 10 9 Melanesia 5 10 Micronesia 7 11 Middle Africa 9 12 Northern Africa 7 13 Northern America 5 14 Northern Europe 15 15 Polynesia 8 16 Seven seas (open ocean) 5 17 South-Eastern Asia 11 18 South America 13 19 Southern Africa 5 20 Southern Asia 10 21 Southern Europe 16 22 Western Africa 17 23 Western Asia 19 24 Western Europe 9
europe_cropped <- st_crop(map_aiib, xmin = -20, xmax = 45,
ymin = 30, ymax = 73)## although coordinates are longitude/latitude, st_intersection assumes that they are planareurope_cropped %>%
filter(subregion == "Eastern Europe" | subregion == "Northern Europe" | subregion == "Southern Europe" | subregion == "Western Europe") %>%
ggplot() +
geom_sf(aes(fill = chinaalignment),
position = "identity") +
labs(fill='Russian Sphere') +
# geom_label(aes(longitude+1, latitude+1, label = factor(geounit)), size = 3) +
ggthemes::theme_map() +
theme(legend.position = "blank") +
scale_fill_distiller(palette = "RdBu")
aiib %>%
filter(aiibmember == 1) %>%
select(country) %>%
kbl() %>%
kable_paper("striped",
full_width = F)| country |
|---|
| Brazil |
| United Kingdom |
| Netherlands |
| Luxembourg |
| France |
| Switzerland |
| Spain |
| Portugal |
| Germany |
| Poland |
| Austria |
| Italy |
| Malta |
| Russian Federation |
| Georgia |
| Azerbaijan |
| Finland |
| Sweden |
| Norway |
| Denmark |
| Iceland |
| South Africa |
| Iran, Islamic Rep. |
| Turkey |
| Egypt, Arab Rep. |
| Jordan |
| Israel |
| Saudi Arabia |
| Kuwait |
| Qatar |
| United Arab Emirates |
| Oman |
| Tajikistan |
| Kyrgyz Republic |
| Uzbekistan |
| Kazakhstan |
| Mongolia |
| Korea, Rep. |
| India |
| Pakistan |
| Bangladesh |
| Myanmar |
| Sri Lanka |
| Maldives |
| Nepal |
| Thailand |
| Cambodia |
| Lao PDR |
| Vietnam |
| Malaysia |
| Singapore |
| Philippines |
| Indonesia |
| Australia |
| New Zealand |
Chinese Public Diplomacy Efforts and Activities
soviet_iron_curtain_vector <- c("Albania",
"Bulgaria",
"Czech Republic",
"Poland",
"Kosovo",
"Romania",
"Hungary",
"Slovakia",
"Bosnia and Herzegovina",
"Croatia",
"Macedonia",
"Montenegro",
"Serbia")
soviet_republics_vector <- c("Russia", "Lithuania", "Georgia",
"Estonia" ,
"Latvia" ,
"Ukraine" ,
"Belarus" ,
"Moldova" ,
"Kyrgyzstan",
"Uzbekistan",
"Tajikistan",
"Armenia" ,
"Azerbaijan",
"Turkmenistan",
"Russia" ,
"Kazakhstan")
the_df %<>%
dplyr::mutate(soviet_iron_curtain = ifelse(country.x %in% soviet_iron_curtain_vector, 1, 0))
the_df %<>%
dplyr::mutate(soviet_republics = ifelse(country.x %in% soviet_republics_vector, 1, 0))
the_df %<>% mutate(rus_influence = ifelse(country.x == "Russia", 1,
ifelse(soviet_republics == 1, 1,
ifelse(soviet_iron_curtain == 1, 1, 0))))
eurasian_dev_bank <- c("Russia", "Armenia", "Belarus", "Kazakhstan", "Kyrgystan", "Tajikistan")
brics <- c("Brazil", "Russia", "India", "China", "South Africa")
qccm <- c("China", "Afhganistan", "Pakistan", "Tajikistan")
forum_china_africa_coop <- c("China", "Algeria", "Angola", "Benin", "Botswana", "Burkina Faso")Making variables
Trade openness formula:
total exports and total imports / total GDP
US dependence formula:
exports to US and imports from US / total exports and total imports
the_df$country <- the_df$country.x
the_df$country.y <- NULL
the_df %>%
mutate(budget_pc = budget / pop, na.rm = TRUE) %>%
mutate(exports_pc = exports_bop / pop, na.rm = TRUE) %>%
mutate(balance_trade = exports_bop + imports_bop, na.rm = TRUE) %>%
mutate(trade_openess = balance_trade / gdp_const, na.rm = TRUE) %>%
mutate(us_balance_trade = us_exports_to_country + us_imports_from_country, na.rm = TRUE) %>%
mutate(us_trade_dependence = us_balance_trade / balance_trade, na.rm = TRUE) %>%
mutate(us_exports_pc = us_exports_to_country / pop, na.rm = TRUE) %>%
mutate(muslim_percent = percent_muslim_jitter / pop, na.rm = TRUE) %>%
mutate(us_imports_pc = us_imports_from_country / pop, na.rm = TRUE) -> the_dfUS Trade Dependence
the_df %>%
select(country, year, us_trade_dependence) %>%
filter(us_trade_dependence > 0.1) %>%
arrange(desc(us_trade_dependence)) %>%
filter(year == 2019) %>%
filter(us_trade_dependence > 0.3) %>%
kbl() %>%
kable_paper("striped",
full_width = F)| country | year | us_trade_dependence |
|---|---|---|
| Mexico | 2019 | 0.6193343 |
| Honduras | 2019 | 0.5459561 |
| Canada | 2019 | 0.5366950 |
| Nicaragua | 2019 | 0.4553335 |
| Bahamas, The | 2019 | 0.3743550 |
| Trinidad and Tobago | 2019 | 0.3496008 |
| Haiti | 2019 | 0.3317885 |
| Dominican Republic | 2019 | 0.3269815 |
| Guatemala | 2019 | 0.3067713 |
the_df %>%
filter(gdp_const > 500000000000) %>%
select(country, year, us_trade_dependence) %>%
arrange(desc(us_trade_dependence)) %>%
# filter(us_trade_dependence > 0.2) %>%
ggplot(aes(y = us_trade_dependence,
x = reorder(country, us_trade_dependence), fill = country)) +
coord_flip() +
geom_histogram(stat = "identity") + ggthemes::theme_pander() +
facet_grid(~year, scales = "free") + theme(legend.position = "none") +
ggtitle("Level of trade dependence in countries over 500 million GDP") + xlab("") + ylab("Percentage of total trade that is with US")
Divide budget by cost of living
url <- "https://www.numbeo.com/cost-of-living/rankings_by_country.jsp?title=2019"
# Countries with no INDEX
col_url <- read_html(url)
col_table <- col_url %>% html_table(header = TRUE, fill = TRUE)
col_19 <- col_table[[2]]
col_19$year <- replicate(119, 2019)
col_19$rank <- NULL
col_19 %<>%
clean_names()
col_19$cow_code <- countrycode(col_19$country, "country.name", "cown")
col_19 %<>% dplyr::mutate(cow_code = ifelse(country == "Palestine", 6666,
ifelse(country == "Serbia", 345,
ifelse(country == "Hong Kong", 997, cow_code))))
col_19 %>%
arrange(desc(cost_of_living_index)) %>%
filter(cost_of_living_index > 65) %>%
head() %>%
kbl() %>%
kable_paper("striped",
full_width = F)| rank | country | cost_of_living_index | rent_index | cost_of_living_plus_rent_index | groceries_index | restaurant_price_index | local_purchasing_power_index | year | cow_code |
|---|---|---|---|---|---|---|---|---|---|
| NA | Switzerland | 121.16 | 50.25 | 87.11 | 120.81 | 123.09 | 129.70 | 2019 | 225 |
| NA | Iceland | 101.86 | 48.28 | 76.13 | 92.10 | 111.85 | 91.80 | 2019 | 395 |
| NA | Norway | 100.99 | 37.28 | 70.40 | 92.67 | 111.53 | 103.61 | 2019 | 385 |
| NA | Bahamas | 92.40 | 33.49 | 64.11 | 76.97 | 99.92 | 57.01 | 2019 | 31 |
| NA | Luxembourg | 86.09 | 55.64 | 71.47 | 75.10 | 96.08 | 106.26 | 2019 | 212 |
| NA | Japan | 83.33 | 27.97 | 56.75 | 83.58 | 48.75 | 103.12 | 2019 | 740 |
Examining 2019 cost of living adjustment
# df_19 <- the_df %>%
# filter(year == 2019)
#
#
# df_19 <- merge(df_19, col_19, by = c("cow_code"), all.x = TRUE)
#
# options(scipen = 999)
#
#
#
# df_19 %>%
# # mutate(cost_living_percent = cost_of_living_index / 100, na.rm = TRUE) %>%
# mutate(adj_budget = budget / cost_of_living_index) %>%
# select(country = country.x, adj_budget, budget) %>%
# arrange(desc(adj_budget)) -> t1
#
# df_19 %>%
# # mutate(cost_living_percent = cost_of_living_index / 100, na.rm = TRUE) %>%
# mutate(adj_budget = budget / cost_of_living_index) %>%
# select(country = country.x, budget, adj_budget) %>%
# arrange(desc(budget)) -> t2
#
# kbl(list(t1, t2), digits = 0, caption = "Arranged By Adjusted Budget LEFT and By Raw Budget RIGHT") %>%
# kable_paper("striped",
# full_width = T)Purchase Power Parity
Source: https://data.worldbank.org/indicator/PA.NUS.PPPC.RF
Price level ratio is the ratio of a purchasing power parity (PPP) conversion factor to an exchange rate. It provides a measure of the differences in price levels between countries by indicating the number of units of the common currency needed to buy the same volume of the aggregation level in each country.
Indicator source: International Comparison Program, World Bank
#
# library(WDI)
#
# ppp = WDI(indicator='PA.NUS.PPPC.RF', country="all", start=2019, end=2019)
# ppp$ppp <- ppp$PA.NUS.PPPC.RF
# ppp$PA.NUS.PPPC.RF <- NULL
#
# View(ppp)
#
# ppp$cow_code <- countrycode(ppp$iso2c, "iso2c", "cown")
#
# ppp %<>%
# dplyr::mutate(cow_code = ifelse(country == "West Bank and Gaza", 6666,
# ifelse(country == "Serbia", 345,
# ifelse(country == "Hong Kong SAR, China", 997, cow_code))))
#
# ppp$country <- NULL
#
#
# try %<>%
# filter(!is.na(cow_code)) %>%
# mutate(ppp_budget_div = budget / ppp) %>%
# mutate(ppp_budget_mul = budget * ppp) %>%
# filter(country.x != "Kosovo") %>%
# select(country.x, budget, ppp, cow_code, ppp_budget_div,ppp_budget_mul ) -> hi
#
# View(hi)
#
# try %>%
# group_by(country.x) %>%
# mutate(ppp_budget = budget / ppp) %>%
# select(country = country.x, ppp_budget, raw_budget = budget) %>%
# arrange(desc(ppp_budget)) -> t3
#
# try %>%
# group_by(country.x) %>%
# mutate(ppp_budget = budget / ppp) %>%
# select(country.x, raw_budget = budget, ppp_budget) %>%
# arrange(desc(raw_budget)) -> t4
#
# kbl(list(t3, t4), digits = 0, caption = "Arranged By PPP Adjusted Budget LEFT and By PPP Raw Budget RIGHT") %>%
# kable_paper("striped",
# full_width = T)# col %<>%
# select(cow_code, year, everything())
#
#
# url <- "https://www.numbeo.com/cost-of-living/rankings_by_country.jsp?title=2018"
#
# col_url <- read_html(url)
# col_table <- col_url %>% html_table(header = TRUE, fill = TRUE)
# col_18 <- col_table[[2]]
#
# col_18$year <- replicate(115, 2018)
#
# col_18 %<>%
# clean_names()
#
# col_18$cow_code <- countrycode(col_18$country, "country.name", "cown")
# # Some values were not matched unambiguously: Bermuda, Hong Kong, Jersey, Macao, Palestine, Puerto Rico, Serbia
#
# col_18 %<>% dplyr::mutate(cow_code = ifelse(country == "Palestine", 6666,
# ifelse(country == "Serbia", 345,
# ifelse(country == "Hong Kong", 997, cow_code))))
# col %<>%
# select(cow_code, year, everything()) Adjust prices to 2014 prices
country <- "United States"
countries_dataframe <- show_countries()Generating URL to request all 299 results
inflation_dataframe <- retrieve_inflation_data(country, countries_dataframe)Retrieving inflation data for US Generating URL to request all 61 results
# Provide a World Bank API URL and `url_all_results` will convert it into one with all results for that indicator
original_url <- "http://api.worldbank.org/v2/country"
# "http://api.worldbank.org/v2/country?format=json&per_page=304"
url_all_results(original_url)Generating URL to request all 299 results [1] “http://api.worldbank.org/v2/country?format=json&per_page=299”
the_df$budget_const <- adjust_for_inflation(the_df$budget,
the_df$year,
country,
to_date = 2014,
inflation_dataframe = inflation_dataframe,
countries_dataframe = countries_dataframe)
the_df$budget_pc_const <- adjust_for_inflation(the_df$budget_pc,
the_df$year,
country,
to_date = 2014,
inflation_dataframe = inflation_dataframe,
countries_dataframe = countries_dataframe)Economic factors
PD Budget per capita and US exports per capita to the country
Across the years
the_df %>%
# filter(pop > 1000000) %>%
ggplot(aes(y = log(budget_const), x = log(us_exports_pc))) +
geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + facet_wrap(~year) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("Exports from US per capita vs PD budget per capita") +
xlab("Logged US exports per capita") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") + ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
Only for 2019
the_df %>%
# filter(pop > 1000000) %>%
filter(year == 2018) %>%
ggplot(aes(y = log(budget_const), x = log(us_exports_pc), group = as.factor(region_6))) +
geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~region_6) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("Exports per capita vs PD budget per capita in 2019") +
xlab("Logged US exports per capita") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") + ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
Trade openness
Total export and total imports divided by total GDP
Across the years
the_df %>%
# filter(pop > 1000000) %>%
ggplot(aes(y = log(budget_const), x =log(trade_openess))) +
geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + facet_wrap(~year) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("Trade openess vs PD budget per capita") +
xlab("Trade openess") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
Only for 2019
the_df %>%
filter(year == 2019) %>%
ggplot(aes(y = log(budget_const), x = log(trade_openess))) +
geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
# geom_text(aes(label = country, color = as.factor(region_6)), size = 3) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("Trade openess vs PD budget per capita in 2019") +
facet_wrap(~region_6) +
xlab("Trade openness") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") + ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
Infant mortality
Infant mortality is a proxy for poverty levels: number of infant deaths per thousand live births
Across the years
the_df %>%
# filter(pop > 1000000) %>%
filter(year < 2018) %>%
ggplot(aes(y = log(budget_const), x = log(infant_mort), group = as.factor(region_6))) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + facet_wrap(~year) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("Infant mortality age vs PD budget per capita") +
xlab("Deaths per thousand under 5") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") + ggthemes::theme_pander()
Only for 2019
the_df %>%
filter(year == 2015) %>%
ggplot(aes(y = log(budget_const), x = infant_mort, group = as.factor(region_6))) +
geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~region_6) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("Infant mortality vs PD budget per capita in 2019") +
xlab("Infant mortality") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") + ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
GDP per capita
Across the years
the_df %>%
ggplot(aes(y = log(budget_const), x = log(gdp_pc_const), group = as.factor(region_6))) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + facet_wrap(~year) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("Logged GDP per capita and logged PD budget per capita") +
xlab("Logged GDP per capita") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") + ggthemes::theme_pander()
Only for 2019
the_df %>%
filter(year == 2019) %>%
ggplot(aes(y = log(budget_const), x = log(gdp_pc_const), group = as.factor(region_6))) +
geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~region_6) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("GDP per capita vs PD budget per capita in 2019") +
ylab("Logged GDP per capita") +
xlab("Logged PD budget per capita") +
theme(legend.position = "left") + ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
Polity Index
Across the years
Evidence of a U shaped relationship? More pronounced as years go on.
the_df %>%
filter(pop > 1000000) %>%
ggplot(aes(y = log(budget_pc), x = electoral_demo)) +
geom_smooth(method = "lm", color = "#9d0208") +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~year) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("Polity Score vs PD budget per capita") +
xlab("Polity Score") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") + ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
Only for 2019
the_df %>%
filter(year == 2019) %>%
ggplot(aes(y = log(budget_pc), x = electoral_demo, group = as.factor(region_6))) +
geom_smooth(method = "loess", color = "#9d0208", se = FALSE) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~region_6) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("Polity Score vs PD budget per capita in 2019") +
xlab("Polity Score (-10 to 10)") +
ylab("Logged budget") +
theme(legend.position = "left") + ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
Percentage of population that follows Muslim faith
Across the years
the_df %>%
mutate(muslim_pc = percent_muslim_jitter / pop, na.rm = TRUE) %>%
ggplot(aes(y = log(budget_const), x = muslim_pc)) +
geom_smooth(method = "loess", color = "#9d0208") +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~year) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("Muslim population % vs PD budget per capita") +
xlab("Percentage of population Muslim") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") + ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
Only for 2019
the_df %>%
mutate(muslim_pc = percent_muslim_jitter / pop, na.rm = TRUE) %>%
filter(year == 2019) %>%
ggplot(aes(y = log(budget_const), x = muslim_pc, group = as.factor(region_6))) +
# geom_smooth(method = "loess", color = "#9d0208") +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~region_6) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("Muslim population % vs PD budget per capita in 2019") +
xlab("Percentage of population Muslim") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") + ggthemes::theme_pander()
Government censorship of media
Level of government censorship of the national media (from V-DEM dataset)
Higher scores indicate more freedeom from government censorship in the national media
Across the years
the_df %>%
ggplot(aes(y = log(budget_const), x = gov_censor_media.x, group = as.factor(region_6))) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~year) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),name = NULL) +
ggtitle("Government media censorship score vs PD budget per capita") +
xlab("Government media") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander()
Only for 2019
the_df %>%
filter(year == 2019) %>%
ggplot(aes(y = log(budget_const), x = gov_censor_media.x)) +
geom_smooth(method = "lm", color = "#9d0208") +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~region_6) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
ggtitle("Government media censorship score vs PD budget per capita in 2019") +
xlab("Freedeom from government censorship in the media") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
Similar UN voting
It reflects states’ UN voting positions in relation to the US-led liberal order (calculated on a single dimension).
Source: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LEJUQZ
Bailey, M.A., Strezhnev, A., and Voeten, E (2017). Estimating dynamic state preferences from United Nations voting data. Journal of Conflict Resolution 61.2: 430-456.
Across the years
the_df %>%
filter(year == 2014) %>%
ggplot(aes(y = log(budget), x = ideal_point_distance)) +
geom_smooth(method = "lm", color = "#9d0208", alpha = 0.5) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
# facet_wrap(~year) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
ggtitle("UN voting similarity vs PD budget per capita") +
xlab("Distance from US ideal score") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
We can see two clusters appear!
Only for 2019
the_df %>%
filter(year == 2019) %>%
ggplot(aes(y = log(budget_const), x = ideal_point_distance, group = as.factor(region_6))) +
geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~region_6) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("UN voting similarity vs PD budget per capita") +
xlab("Distance from US ideal score") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
Important US votes
Source: Erik Voeten “Data and Analyses of Voting in the UN General Assembly” Routledge Handbook of International Organization, edited by Bob Reinalda (published May 27, 2013)
Important votes determined by the US State Department: Whether the vote was classified as important by the U.S. State Department report “Voting Practices in the United Nations”. These classifications began with session 39
# library(unvotes)
# library(lubridate)
#
# count(un_roll_call_issues, issue, sort = TRUE)
#
# joined <- un_votes %>%
# inner_join(un_roll_calls, by = "rcid") %>%
# inner_join(un_roll_call_issues, by = "rcid") %>%
# ungroup()
#
# important <- joined %>%
# filter(importantvote == 1) %>%
# select(rcid, country, vote, session, date)
#
# important %>%
# mutate(vote_fac = as.factor(vote)) %>%
# mutate(vote_num = as.numeric(vote_fac)) %>%
# distinct(session, country, rcid, vote_num, .keep_all = "TRUE") -> temp2
#
# temp2 %>%
# select(rcid, country, vote_num, session) %>%
# pivot_wider(names_from = country, values_from = "vote_num" ) -> imp_wide
#
# temp2 %>%
# group_by(country_name)
# mutate(country = if_else(Response == Correct_answer, 1, 0))
# Level of domestic autonomy
Measures the extent to which the country’s govt have full sovereign control over the territory.
Higher scores indicate more control
Across all years
the_df %>%
ggplot(aes(y = log(budget_pc_const), x = intl_autonomy)) +
geom_smooth(method = "lm", color = "#9d0208") +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~year) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
ggtitle("Domestic autonomy vs PD budget per capita") +
xlab("Domestic autonomy") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
Only for 2019
the_df %>%
filter(year == 2013) %>%
ggplot(aes(y = log(budget_pc_const), x = intl_autonomy, group = as.factor(region_6))) +
geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~region_6) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("Domestic autonomy vs PD budget per capita") +
xlab("Domestic autonomy") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") + ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
US diplomatic visits
Number of visits by heads of state to the US
## `summarise()` has grouped output by 'cow_code'. You can override using the `.groups` argument.| From | year | n |
|---|---|---|
| Israel | 2000 | 5 |
| United Kingdom | 1990 | 5 |
| Germany, Federal Republic of | 1990 | 4 |
| Iraq | 2004 | 4 |
| Ireland | 1996 | 4 |
| Israel | 1978 | 4 |
| Israel | 1993 | 4 |
| Israel | 1995 | 4 |
| Israel | 1996 | 4 |
| Israel | 2001 | 4 |
| Israel | 2002 | 4 |
| Israel | 2009 | 4 |
| Italy | 1990 | 4 |
| Japan | 1990 | 4 |
| Jordan | 2002 | 4 |
| Peru | 2006 | 4 |
| Australia | 2009 | 3 |
| Bosnia-Herzegovina | 1994 | 3 |
| Canada | 1983 | 3 |
| Canada | 1989 | 3 |
| Canada | 1990 | 3 |
| Canada | 1993 | 3 |
| Canada | 2008 | 3 |
| Canada | 2011 | 3 |
| Canada | 2012 | 3 |
| Colombia | 1990 | 3 |
| Colombia | 2002 | 3 |
| Ecuador | 1992 | 3 |
| Egypt | 1978 | 3 |
| Germany | 2007 | 3 |
| Germany | 2009 | 3 |
| Germany, Federal Republic of | 1963 | 3 |
| Germany, Federal Republic of | 1982 | 3 |
| Germany, Federal Republic of | 1983 | 3 |
| Iraq | 2007 | 3 |
| Iraq | 2008 | 3 |
| Iraq | 2009 | 3 |
| Israel | 1973 | 3 |
| Israel | 1977 | 3 |
| Israel | 1983 | 3 |
| Israel | 1987 | 3 |
| Israel | 1994 | 3 |
| Israel | 1997 | 3 |
| Israel | 1998 | 3 |
| Israel | 2010 | 3 |
| Israel | 2011 | 3 |
| Israel | 2014 | 3 |
| Italy | 1983 | 3 |
| Italy | 1999 | 3 |
| Italy | 2003 | 3 |
| Italy | 2004 | 3 |
| Italy | 2012 | 3 |
| Italy | 2016 | 3 |
| Japan | 1983 | 3 |
| Japan | 1993 | 3 |
| Japan | 2001 | 3 |
| Japan | 2009 | 3 |
| Japan | 2016 | 3 |
| Jordan | 1994 | 3 |
| Jordan | 1996 | 3 |
| Jordan | 1998 | 3 |
| Jordan | 1999 | 3 |
| Jordan | 2000 | 3 |
| Jordan | 2004 | 3 |
| Jordan | 2014 | 3 |
| Jordan | 2016 | 3 |
| Mexico | 1992 | 3 |
| Mexico | 2008 | 3 |
| Pakistan | 2003 | 3 |
| Pakistan | 2006 | 3 |
| Singapore | 2016 | 3 |
| Slovak Republic | 1999 | 3 |
| Spain | 2003 | 3 |
| Spain | 2004 | 3 |
| United Kingdom | 1960 | 3 |
| United Kingdom | 1970 | 3 |
| United Kingdom | 1978 | 3 |
| United Kingdom | 1983 | 3 |
| United Kingdom | 1992 | 3 |
| United Kingdom | 2001 | 3 |
| United Kingdom | 2003 | 3 |
| United Kingdom | 2004 | 3 |
| United Kingdom | 2006 | 3 |
| United Kingdom | 2007 | 3 |
| United Kingdom | 2008 | 3 |
| United Kingdom | 2009 | 3 |
| United Kingdom | 2012 | 3 |
| Afghanistan | 2003 | 2 |
| Afghanistan | 2004 | 2 |
| Afghanistan | 2007 | 2 |
| Albania | 1999 | 2 |
| Algeria | 2001 | 2 |
| Antigua and Barbuda | 1994 | 2 |
| Argentina | 1961 | 2 |
| Argentina | 1994 | 2 |
| Argentina | 1999 | 2 |
| Argentina | 2001 | 2 |
| Australia | 1952 | 2 |
| Australia | 1956 | 2 |
| Australia | 1962 | 2 |
| Australia | 1965 | 2 |
| Australia | 1966 | 2 |
| Australia | 1993 | 2 |
| Australia | 2003 | 2 |
| Australia | 2008 | 2 |
| Australia | 2011 | 2 |
| Australia | 2016 | 2 |
| Barbados | 1994 | 2 |
| Belgium | 1948 | 2 |
| Belgium | 1969 | 2 |
| Bosnian Federation | 1996 | 2 |
| Brazil | 1990 | 2 |
| Brazil | 1991 | 2 |
| Brazil | 2001 | 2 |
| Brazil | 2009 | 2 |
| Bulgaria | 1998 | 2 |
| Bulgaria | 2012 | 2 |
| Cameroon | 2003 | 2 |
| Canada | 1947 | 2 |
| Canada | 1960 | 2 |
| Canada | 1961 | 2 |
| Canada | 1963 | 2 |
| Canada | 1964 | 2 |
| Canada | 1965 | 2 |
| Canada | 1976 | 2 |
| Canada | 1977 | 2 |
| Canada | 1981 | 2 |
| Canada | 1992 | 2 |
| Canada | 1997 | 2 |
| Canada | 2000 | 2 |
| Canada | 2001 | 2 |
| Canada | 2002 | 2 |
| Canada | 2004 | 2 |
| Canada | 2009 | 2 |
| Canada | 2016 | 2 |
| Chile | 1994 | 2 |
| China, People’s Republic of | 2009 | 2 |
| China, People’s Republic of | 2010 | 2 |
| China, People’s Republic of | 2011 | 2 |
| China, People’s Republic of | 2016 | 2 |
| Colombia | 1998 | 2 |
| Colombia | 2001 | 2 |
| Colombia | 2003 | 2 |
| Colombia | 2006 | 2 |
| Colombia | 2008 | 2 |
| Colombia | 2009 | 2 |
| Costa Rica | 1982 | 2 |
| Croatia | 1995 | 2 |
| Croatia | 1996 | 2 |
| Czech Republic | 1995 | 2 |
| Czech Republic | 1999 | 2 |
| Czechoslovakia | 1990 | 2 |
| Democratic Republic of the Congo (Zaire) | 1989 | 2 |
| Democratic Republic of the Congo (Zaire) | 2003 | 2 |
| Denmark | 1967 | 2 |
| Denmark | 1970 | 2 |
| Denmark | 1978 | 2 |
| Denmark | 2012 | 2 |
| Denmark | 2016 | 2 |
| Djibouti | 2014 | 2 |
| Dominica | 1984 | 2 |
| Dominica | 1994 | 2 |
| Egypt | 1979 | 2 |
| Egypt | 1983 | 2 |
| Egypt | 1985 | 2 |
| Egypt | 1989 | 2 |
| Egypt | 1993 | 2 |
| Egypt | 1995 | 2 |
| Egypt | 2002 | 2 |
| Egypt | 2014 | 2 |
| El Salvador | 1984 | 2 |
| El Salvador | 1985 | 2 |
| El Salvador | 1990 | 2 |
| El Salvador | 1991 | 2 |
| El Salvador | 2004 | 2 |
| El Salvador | 2005 | 2 |
| El Salvador | 2006 | 2 |
| El Salvador | 2007 | 2 |
| El Salvador | 2008 | 2 |
| Ethiopia | 1963 | 2 |
| Ethiopia | 2014 | 2 |
| Finland | 1970 | 2 |
| Finland | 1999 | 2 |
| Finland | 2002 | 2 |
| Finland | 2016 | 2 |
| France | 1951 | 2 |
| France | 1976 | 2 |
| France | 1990 | 2 |
| France | 1993 | 2 |
| France | 1999 | 2 |
| France | 2000 | 2 |
| France | 2001 | 2 |
| France | 2007 | 2 |
| France | 2008 | 2 |
| France | 2010 | 2 |
| France | 2011 | 2 |
| France | 2012 | 2 |
| Gabon | 1977 | 2 |
| Georgia | 1999 | 2 |
| Georgia | 2012 | 2 |
| Germany | 1991 | 2 |
| Germany | 1992 | 2 |
| Germany | 1993 | 2 |
| Germany | 1997 | 2 |
| Germany | 1999 | 2 |
| Germany | 2001 | 2 |
| Germany | 2004 | 2 |
| Germany | 2006 | 2 |
| Germany | 2012 | 2 |
| Germany | 2015 | 2 |
| Germany, Federal Republic of | 1961 | 2 |
| Germany, Federal Republic of | 1965 | 2 |
| Germany, Federal Republic of | 1969 | 2 |
| Germany, Federal Republic of | 1971 | 2 |
| Germany, Federal Republic of | 1973 | 2 |
| Germany, Federal Republic of | 1975 | 2 |
| Germany, Federal Republic of | 1976 | 2 |
| Germany, Federal Republic of | 1980 | 2 |
| Germany, Federal Republic of | 1984 | 2 |
| Germany, Federal Republic of | 1988 | 2 |
| Ghana | 2005 | 2 |
| Greece | 1996 | 2 |
| Guatemala | 1989 | 2 |
| Guatemala | 2008 | 2 |
| Haiti | 1993 | 2 |
| Haiti | 1994 | 2 |
| Honduras | 2006 | 2 |
| Hungary | 1990 | 2 |
| Hungary | 1991 | 2 |
| Hungary | 1998 | 2 |
| Hungary | 1999 | 2 |
| Iceland | 1988 | 2 |
| Iceland | 1991 | 2 |
| India | 1985 | 2 |
| India | 2008 | 2 |
| India | 2009 | 2 |
| India | 2016 | 2 |
| Indonesia | 1961 | 2 |
| Iran | 1968 | 2 |
| Iran | 1969 | 2 |
| Iraq | 2005 | 2 |
| Iraq | 2006 | 2 |
| Iraq | 2015 | 2 |
| Ireland | 1963 | 2 |
| Ireland | 1993 | 2 |
| Ireland | 1997 | 2 |
| Ireland | 1998 | 2 |
| Ireland | 2000 | 2 |
| Ireland | 2001 | 2 |
| Ireland | 2004 | 2 |
| Ireland | 2008 | 2 |
| Israel | 1970 | 2 |
| Israel | 1975 | 2 |
| Israel | 1979 | 2 |
| Israel | 1980 | 2 |
| Israel | 1982 | 2 |
| Israel | 1986 | 2 |
| Israel | 1989 | 2 |
| Israel | 1992 | 2 |
| Israel | 1999 | 2 |
| Israel | 2005 | 2 |
| Israel | 2006 | 2 |
| Israel | 2007 | 2 |
| Israel | 2008 | 2 |
| Israel | 2012 | 2 |
| Israel | 2015 | 2 |
| Italy | 1967 | 2 |
| Italy | 1976 | 2 |
| Italy | 1982 | 2 |
| Italy | 1985 | 2 |
| Italy | 1987 | 2 |
| Italy | 1988 | 2 |
| Italy | 1996 | 2 |
| Italy | 1998 | 2 |
| Italy | 2008 | 2 |
| Italy | 2009 | 2 |
| Italy | 2010 | 2 |
| Jamaica | 1970 | 2 |
| Jamaica | 1983 | 2 |
| Japan | 1972 | 2 |
| Japan | 1975 | 2 |
| Japan | 1976 | 2 |
| Japan | 1985 | 2 |
| Japan | 1989 | 2 |
| Japan | 1991 | 2 |
| Japan | 1992 | 2 |
| Japan | 1994 | 2 |
| Japan | 1996 | 2 |
| Japan | 1997 | 2 |
| Japan | 2004 | 2 |
| Japan | 2007 | 2 |
| Japan | 2010 | 2 |
| Japan | 2011 | 2 |
| Japan | 2012 | 2 |
| Jordan | 1967 | 2 |
| Jordan | 1974 | 2 |
| Jordan | 1985 | 2 |
| Jordan | 1995 | 2 |
| Jordan | 1997 | 2 |
| Jordan | 2001 | 2 |
| Jordan | 2003 | 2 |
| Jordan | 2005 | 2 |
| Jordan | 2006 | 2 |
| Jordan | 2007 | 2 |
| Jordan | 2008 | 2 |
| Jordan | 2010 | 2 |
| Kazakhstan | 1999 | 2 |
| Korea | 1969 | 2 |
| Korea | 2009 | 2 |
| Kuwait | 1988 | 2 |
| Lebanon | 1983 | 2 |
| Lebanon | 1996 | 2 |
| Lebanon | 2002 | 2 |
| Liberia | 2007 | 2 |
| Liberia | 2015 | 2 |
| Luxembourg | 2005 | 2 |
| Macedonia, Former Yugoslav Republic of | 1999 | 2 |
| Marshall Islands | 1990 | 2 |
| Mexico | 1964 | 2 |
| Mexico | 1970 | 2 |
| Mexico | 1981 | 2 |
| Mexico | 1990 | 2 |
| Mexico | 1991 | 2 |
| Mexico | 1994 | 2 |
| Mexico | 2000 | 2 |
| Mexico | 2001 | 2 |
| Mexico | 2009 | 2 |
| Mexico | 2010 | 2 |
| Mexico | 2012 | 2 |
| Mexico | 2016 | 2 |
| Micronesia | 1990 | 2 |
| Morocco | 1970 | 2 |
| Morocco | 1982 | 2 |
| Nepal | 1960 | 2 |
| Netherlands | 1952 | 2 |
| Netherlands | 1995 | 2 |
| Netherlands | 1999 | 2 |
| Netherlands | 2008 | 2 |
| Netherlands | 2009 | 2 |
| New Zealand | 2014 | 2 |
| Nigeria | 1999 | 2 |
| Nigeria | 2001 | 2 |
| Nigeria | 2004 | 2 |
| Norway | 1979 | 2 |
| Norway | 1989 | 2 |
| Norway | 1995 | 2 |
| Norway | 1999 | 2 |
| Norway | 2016 | 2 |
| Pakistan | 1998 | 2 |
| Pakistan | 2002 | 2 |
| Pakistan | 2004 | 2 |
| Pakistan | 2008 | 2 |
| Pakistan | 2009 | 2 |
| Panama | 1977 | 2 |
| Panama | 2008 | 2 |
| Papua New Guinea | 1990 | 2 |
| Paraguay | 2008 | 2 |
| Peru | 2011 | 2 |
| Poland | 1990 | 2 |
| Poland | 1991 | 2 |
| Poland | 2002 | 2 |
| Poland | 2003 | 2 |
| Poland | 2004 | 2 |
| Poland | 2005 | 2 |
| Portugal | 1990 | 2 |
| Portugal | 1992 | 2 |
| Qatar | 2015 | 2 |
| Romania | 2004 | 2 |
| Russia | 1992 | 2 |
| Russia | 1997 | 2 |
| Russia | 2009 | 2 |
| Russia | 2010 | 2 |
| Senegal | 2004 | 2 |
| South Africa | 2001 | 2 |
| South Korea | 2008 | 2 |
| South Korea | 2011 | 2 |
| Spain | 1980 | 2 |
| Spain | 1993 | 2 |
| Spain | 1999 | 2 |
| Spain | 2001 | 2 |
| Spain | 2002 | 2 |
| Spain | 2009 | 2 |
| Spain | 2010 | 2 |
| Sweden | 2009 | 2 |
| Tanzania | 2008 | 2 |
| Thailand | 2016 | 2 |
| Tunisia | 2014 | 2 |
| Turkey | 1954 | 2 |
| Turkey | 1992 | 2 |
| Turkey | 1993 | 2 |
| Turkey | 1995 | 2 |
| Turkey | 1999 | 2 |
| Turkey | 2004 | 2 |
| Turkey | 2008 | 2 |
| Turkey | 2009 | 2 |
| Turkey | 2012 | 2 |
| Uganda | 2003 | 2 |
| Ukraine | 1994 | 2 |
| Ukraine | 1999 | 2 |
| Ukraine | 2014 | 2 |
| United Kingdom | 1957 | 2 |
| United Kingdom | 1959 | 2 |
| United Kingdom | 1961 | 2 |
| United Kingdom | 1964 | 2 |
| United Kingdom | 1975 | 2 |
| United Kingdom | 1976 | 2 |
| United Kingdom | 1985 | 2 |
| United Kingdom | 1991 | 2 |
| United Kingdom | 1998 | 2 |
| United Kingdom | 2002 | 2 |
| United Kingdom | 2005 | 2 |
| Uruguay | 1990 | 2 |
| Uruguay | 1994 | 2 |
| Venezuela | 1977 | 2 |
| Venezuela | 1989 | 2 |
| Venezuela | 1990 | 2 |
| Venezuela | 1991 | 2 |
| Venezuela | 1999 | 2 |
| From | n |
|---|---|
| Israel | 113 |
| United Kingdom | 99 |
| Japan | 80 |
| Canada | 76 |
| Jordan | 76 |
| Italy | 75 |
| Australia | 55 |
| Mexico | 55 |
| Germany, Federal Republic of | 53 |
| France | 52 |
| Ireland | 50 |
| Pakistan | 41 |
| Turkey | 41 |
US trips
Trips by US President / Secretaries of State abroad
travels %>%
group_by(cow_code, year) %>%
filter(Pres == 1) %>%
summarise(sum_pres_travels = n()) %>%
ungroup() -> pres_travel## `summarise()` has grouped output by 'cow_code'. You can override using the `.groups` argument.travels %>%
group_by(cow_code, year) %>%
summarise(sum_all_travels = n()) %>%
ungroup() -> all_travel## `summarise()` has grouped output by 'cow_code'. You can override using the `.groups` argument.travels %>%
group_by(cow_code, year) %>%
summarise(sum_index_travels = sum(dashindex)) %>%
ungroup() -> score_travel## `summarise()` has grouped output by 'cow_code'. You can override using the `.groups` argument.the_df <- merge(the_df, pres_travel, by = c("cow_code", "year"), all.x = TRUE)
the_df <- merge(the_df, head_visits, by = c("cow_code", "year"), all.x = TRUE)
the_df <- merge(the_df, all_travel, by = c("cow_code", "year"), all.x = TRUE)
the_df <- merge(the_df, score_travel, by = c("cow_code", "year"), all.x = TRUE) ### Across the years
library(tidyr)
the_df %>%
filter(year < 2017) %>%
mutate(all_travel = coalesce(sum_all_travels, 0)) %>%
ggplot(aes(y = log(budget_pc_const), x = all_travel)) +
geom_smooth(method = "lm", color = "#9d0208", alpha = 0.5) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~year)+
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
ggtitle("UN voting similarity vs PD budget per capita") +
xlab("Distance from US ideal score") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
the_df %>%
filter(year < 2017) %>%
# mutate(score_travel = coalesce(sum_index_travels, 0)) %>%
ggplot(aes(y = log(budget_pc_const), x = sum_index_travels)) +
geom_smooth(method = "lm", color = "#9d0208", alpha = 0.5) +
geom_text(aes(label = country, color = as.factor(region_6))) +
# geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~year)+
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
ggtitle("High level visits DASH score vs PD budget per capita") +
xlab("High level visits DASH score") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
Only for 2016
the_df %>%
filter(year == 2016) %>%
mutate(all_travel = coalesce(sum_all_travels, 0)) %>%
ggplot(aes(y = log(budget_pc_const), x = all_travel)) +
geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
facet_wrap(~region_6) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("high level visits vs PD budget per capita") +
xlab("Number of high level visits") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") +
ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
travels %>%
filter(Pres == 1) %>%
group_by(Country, year) %>%
count() %>%
arrange(desc(n)) %>%
kbl() %>%
kable_paper("striped",
full_width = F) | Country | year | n |
|---|---|---|
| Germany | 2015 | 5 |
| Germany | 2016 | 3 |
| Italy | 1999 | 3 |
| Japan | 2016 | 3 |
| Afghanistan | 2010 | 2 |
| Belgium | 1989 | 2 |
| Belgium | 2014 | 2 |
| Canada | 1981 | 2 |
| Canada | 1991 | 2 |
| Canada | 1995 | 2 |
| Denmark | 2009 | 2 |
| Egypt | 1979 | 2 |
| Egypt | 2000 | 2 |
| Egypt | 2008 | 2 |
| France | 1959 | 2 |
| France | 1974 | 2 |
| France | 1982 | 2 |
| France | 1989 | 2 |
| France | 1991 | 2 |
| France | 2009 | 2 |
| France | 2011 | 2 |
| Germany | 1999 | 2 |
| Germany | 2009 | 2 |
| Germany | 2014 | 2 |
| Germany, Federal Republic of | 1978 | 2 |
| Germany, Federal Republic of | 1985 | 2 |
| Israel | 2008 | 2 |
| Italy | 1994 | 2 |
| Japan | 2000 | 2 |
| Mexico | 1966 | 2 |
| Mexico | 1981 | 2 |
| Mexico | 2002 | 2 |
| Mexico | 2009 | 2 |
| Russia | 2002 | 2 |
| Russia | 2006 | 2 |
| Saudi Arabia | 2008 | 2 |
| Senegal | 2013 | 2 |
| South Africa | 2013 | 2 |
| Switzerland | 2000 | 2 |
| U.S.S.R. | 1974 | 2 |
| United Kingdom | 1959 | 2 |
| United Kingdom | 1961 | 2 |
| United Kingdom | 1969 | 2 |
| United Kingdom | 1990 | 2 |
| United Kingdom | 1991 | 2 |
| United Kingdom | 1994 | 2 |
| United Kingdom | 1998 | 2 |
| United Kingdom | 2003 | 2 |
| Afghanistan | 1959 | 1 |
| Afghanistan | 2006 | 1 |
| Afghanistan | 2008 | 1 |
| Afghanistan | 2012 | 1 |
| Afghanistan | 2014 | 1 |
| Albania | 2007 | 1 |
| Argentina | 1960 | 1 |
| Argentina | 1990 | 1 |
| Argentina | 1997 | 1 |
| Argentina | 2005 | 1 |
| Argentina | 2016 | 1 |
| Australia | 1966 | 1 |
| Australia | 1967 | 1 |
| Australia | 1992 | 1 |
| Australia | 1996 | 1 |
| Australia | 2003 | 1 |
| Australia | 2007 | 1 |
| Australia | 2011 | 1 |
| Australia | 2014 | 1 |
| Austria | 1961 | 1 |
| Austria | 1972 | 1 |
| Austria | 1974 | 1 |
| Austria | 1975 | 1 |
| Austria | 1979 | 1 |
| Austria | 2006 | 1 |
| Bahrain | 2008 | 1 |
| Bangladesh | 2000 | 1 |
| Barbados | 1982 | 1 |
| Barbados | 1997 | 1 |
| Belarus | 1994 | 1 |
| Belgium | 1945 | 1 |
| Belgium | 1969 | 1 |
| Belgium | 1974 | 1 |
| Belgium | 1975 | 1 |
| Belgium | 1978 | 1 |
| Belgium | 1985 | 1 |
| Belgium | 1988 | 1 |
| Belgium | 1994 | 1 |
| Belgium | 1999 | 1 |
| Belgium | 2001 | 1 |
| Belgium | 2005 | 1 |
| Benin | 2008 | 1 |
| Bosnia-Herzegovina | 1996 | 1 |
| Bosnia-Herzegovina | 1997 | 1 |
| Bosnia-Herzegovina | 1999 | 1 |
| Botswana | 1998 | 1 |
| Botswana | 2003 | 1 |
| Brazil | 1947 | 1 |
| Brazil | 1960 | 1 |
| Brazil | 1978 | 1 |
| Brazil | 1982 | 1 |
| Brazil | 1990 | 1 |
| Brazil | 1992 | 1 |
| Brazil | 1997 | 1 |
| Brazil | 2005 | 1 |
| Brazil | 2007 | 1 |
| Brazil | 2011 | 1 |
| Brunei Darussalam | 2000 | 1 |
| Bulgaria | 1999 | 1 |
| Bulgaria | 2007 | 1 |
| Burma | 2012 | 1 |
| Burma | 2014 | 1 |
| Cambodia | 2012 | 1 |
| Canada | 1947 | 1 |
| Canada | 1953 | 1 |
| Canada | 1958 | 1 |
| Canada | 1959 | 1 |
| Canada | 1961 | 1 |
| Canada | 1964 | 1 |
| Canada | 1966 | 1 |
| Canada | 1967 | 1 |
| Canada | 1972 | 1 |
| Canada | 1985 | 1 |
| Canada | 1987 | 1 |
| Canada | 1988 | 1 |
| Canada | 1989 | 1 |
| Canada | 1990 | 1 |
| Canada | 1993 | 1 |
| Canada | 1997 | 1 |
| Canada | 1999 | 1 |
| Canada | 2001 | 1 |
| Canada | 2002 | 1 |
| Canada | 2004 | 1 |
| Canada | 2007 | 1 |
| Canada | 2009 | 1 |
| Canada | 2010 | 1 |
| Canada | 2016 | 1 |
| Chile | 1960 | 1 |
| Chile | 1990 | 1 |
| Chile | 1998 | 1 |
| Chile | 2004 | 1 |
| Chile | 2011 | 1 |
| China | 2001 | 1 |
| China | 2002 | 1 |
| China | 2005 | 1 |
| China | 2008 | 1 |
| China, People’s Republic of | 1972 | 1 |
| China, People’s Republic of | 1975 | 1 |
| China, People�s Republic of | 1984 | 1 |
| China, People�s Republic of | 1989 | 1 |
| China, People�s Republic of | 1998 | 1 |
| China, People�s Republic of | 2009 | 1 |
| China, People�s Republic of | 2014 | 1 |
| China, People�s Republic of | 2016 | 1 |
| Colombia | 1961 | 1 |
| Colombia | 1982 | 1 |
| Colombia | 1990 | 1 |
| Colombia | 2000 | 1 |
| Colombia | 2004 | 1 |
| Colombia | 2007 | 1 |
| Colombia | 2012 | 1 |
| Costa Rica | 1963 | 1 |
| Costa Rica | 1968 | 1 |
| Costa Rica | 1982 | 1 |
| Costa Rica | 1989 | 1 |
| Costa Rica | 1997 | 1 |
| Costa Rica | 2013 | 1 |
| Croatia | 1996 | 1 |
| Croatia | 2008 | 1 |
| Cuba | 2016 | 1 |
| Czech Republic | 1994 | 1 |
| Czech Republic | 2002 | 1 |
| Czech Republic | 2007 | 1 |
| Czech Republic | 2009 | 1 |
| Czech Republic | 2010 | 1 |
| Czechoslovakia | 1990 | 1 |
| Denmark | 1997 | 1 |
| Denmark | 2005 | 1 |
| Egypt | 1974 | 1 |
| Egypt | 1978 | 1 |
| Egypt | 1990 | 1 |
| Egypt | 1994 | 1 |
| Egypt | 1996 | 1 |
| Egypt | 2003 | 1 |
| Egypt | 2009 | 1 |
| El Salvador | 1968 | 1 |
| El Salvador | 1999 | 1 |
| El Salvador | 2002 | 1 |
| El Salvador | 2011 | 1 |
| Estonia | 2006 | 1 |
| Estonia | 2014 | 1 |
| Ethiopia | 2015 | 1 |
| Finland | 1975 | 1 |
| Finland | 1988 | 1 |
| Finland | 1990 | 1 |
| Finland | 1992 | 1 |
| Finland | 1997 | 1 |
| France | 1957 | 1 |
| France | 1960 | 1 |
| France | 1961 | 1 |
| France | 1969 | 1 |
| France | 1970 | 1 |
| France | 1975 | 1 |
| France | 1978 | 1 |
| France | 1979 | 1 |
| France | 1984 | 1 |
| France | 1985 | 1 |
| France | 1990 | 1 |
| France | 1993 | 1 |
| France | 1994 | 1 |
| France | 1995 | 1 |
| France | 1996 | 1 |
| France | 1997 | 1 |
| France | 1999 | 1 |
| France | 2002 | 1 |
| France | 2003 | 1 |
| France | 2004 | 1 |
| France | 2008 | 1 |
| France | 2014 | 1 |
| France | 2015 | 1 |
| Georgia | 2005 | 1 |
| Germany | 1945 | 1 |
| Germany | 1963 | 1 |
| Germany | 1967 | 1 |
| Germany | 1969 | 1 |
| Germany | 1978 | 1 |
| Germany | 1982 | 1 |
| Germany | 1987 | 1 |
| Germany | 1990 | 1 |
| Germany | 1992 | 1 |
| Germany | 1994 | 1 |
| Germany | 1995 | 1 |
| Germany | 1998 | 1 |
| Germany | 2000 | 1 |
| Germany | 2002 | 1 |
| Germany | 2005 | 1 |
| Germany | 2006 | 1 |
| Germany | 2007 | 1 |
| Germany | 2008 | 1 |
| Germany | 2013 | 1 |
| Germany, Federal Republic of | 1959 | 1 |
| Germany, Federal Republic of | 1963 | 1 |
| Germany, Federal Republic of | 1975 | 1 |
| Germany, Federal Republic of | 1982 | 1 |
| Germany, Federal Republic of | 1987 | 1 |
| Germany, Federal Republic of | 1989 | 1 |
| Ghana | 1998 | 1 |
| Ghana | 2008 | 1 |
| Ghana | 2009 | 1 |
| Greece | 1959 | 1 |
| Greece | 1991 | 1 |
| Greece | 1999 | 1 |
| Greece | 2016 | 1 |
| Grenada | 1986 | 1 |
| Guatemala | 1968 | 1 |
| Guatemala | 1999 | 1 |
| Guatemala | 2007 | 1 |
| Haiti | 1995 | 1 |
| Honduras | 1968 | 1 |
| Honduras | 1982 | 1 |
| Honduras | 1999 | 1 |
| Hungary | 1989 | 1 |
| Hungary | 1994 | 1 |
| Hungary | 1996 | 1 |
| Hungary | 2006 | 1 |
| Iceland | 1973 | 1 |
| Iceland | 1986 | 1 |
| India | 1959 | 1 |
| India | 1969 | 1 |
| India | 1978 | 1 |
| India | 2000 | 1 |
| India | 2006 | 1 |
| India | 2010 | 1 |
| India | 2015 | 1 |
| Indonesia | 1969 | 1 |
| Indonesia | 1975 | 1 |
| Indonesia | 1986 | 1 |
| Indonesia | 1994 | 1 |
| Indonesia | 2003 | 1 |
| Indonesia | 2006 | 1 |
| Indonesia | 2010 | 1 |
| Indonesia | 2011 | 1 |
| Iran | 1959 | 1 |
| Iran | 1972 | 1 |
| Iran | 1978 | 1 |
| Iraq | 2003 | 1 |
| Iraq | 2006 | 1 |
| Iraq | 2007 | 1 |
| Iraq | 2008 | 1 |
| Iraq | 2009 | 1 |
| Ireland | 1963 | 1 |
| Ireland | 1970 | 1 |
| Ireland | 1984 | 1 |
| Ireland | 1995 | 1 |
| Ireland | 1998 | 1 |
| Ireland | 2000 | 1 |
| Ireland | 2004 | 1 |
| Ireland | 2006 | 1 |
| Ireland | 2011 | 1 |
| Israel | 1974 | 1 |
| Israel | 1979 | 1 |
| Israel | 1994 | 1 |
| Israel | 1995 | 1 |
| Israel | 1996 | 1 |
| Israel | 1998 | 1 |
| Israel | 2013 | 1 |
| Israel | 2016 | 1 |
| Italy | 1959 | 1 |
| Italy | 1963 | 1 |
| Italy | 1967 | 1 |
| Italy | 1969 | 1 |
| Italy | 1970 | 1 |
| Italy | 1975 | 1 |
| Italy | 1980 | 1 |
| Italy | 1982 | 1 |
| Italy | 1987 | 1 |
| Italy | 1989 | 1 |
| Italy | 1991 | 1 |
| Italy | 1996 | 1 |
| Italy | 1997 | 1 |
| Italy | 2000 | 1 |
| Italy | 2001 | 1 |
| Italy | 2002 | 1 |
| Italy | 2004 | 1 |
| Italy | 2005 | 1 |
| Italy | 2007 | 1 |
| Italy | 2008 | 1 |
| Italy | 2009 | 1 |
| Italy | 2014 | 1 |
| Jamaica | 1982 | 1 |
| Jamaica | 2015 | 1 |
| Japan | 1974 | 1 |
| Japan | 1979 | 1 |
| Japan | 1980 | 1 |
| Japan | 1983 | 1 |
| Japan | 1986 | 1 |
| Japan | 1989 | 1 |
| Japan | 1992 | 1 |
| Japan | 1993 | 1 |
| Japan | 1996 | 1 |
| Japan | 1998 | 1 |
| Japan | 2002 | 1 |
| Japan | 2003 | 1 |
| Japan | 2005 | 1 |
| Japan | 2008 | 1 |
| Japan | 2009 | 1 |
| Japan | 2010 | 1 |
| Japan | 2014 | 1 |
| Jordan | 1974 | 1 |
| Jordan | 1994 | 1 |
| Jordan | 1999 | 1 |
| Jordan | 2003 | 1 |
| Jordan | 2006 | 1 |
| Jordan | 2013 | 1 |
| Kenya | 2015 | 1 |
| Korea, Republic of | 1952 | 1 |
| Korea, Republic of | 1960 | 1 |
| Korea, Republic of | 1966 | 1 |
| Korea, Republic of | 1974 | 1 |
| Korea, Republic of | 1979 | 1 |
| Korea, Republic of | 1983 | 1 |
| Korea, Republic of | 1989 | 1 |
| Korea, Republic of | 1992 | 1 |
| Korea, Republic of | 1993 | 1 |
| Korea, Republic of | 1996 | 1 |
| Korea, Republic of | 1998 | 1 |
| Korea, Republic of | 2002 | 1 |
| Korea, Republic of | 2005 | 1 |
| Korea, Republic of | 2008 | 1 |
| Korea, Republic of | 2009 | 1 |
| Korea, Republic of | 2010 | 1 |
| Korea, Republic of | 2012 | 1 |
| Korea, Republic of | 2014 | 1 |
| Kuwait | 1994 | 1 |
| Kuwait | 2008 | 1 |
| Laos | 2016 | 1 |
| Latvia | 1994 | 1 |
| Latvia | 2005 | 1 |
| Latvia | 2006 | 1 |
| Liberia | 1978 | 1 |
| Liberia | 2008 | 1 |
| Lithuania | 2002 | 1 |
| Macedonia, Former Yugoslav Republic of | 1999 | 1 |
| Malaysia | 1966 | 1 |
| Malaysia | 2014 | 1 |
| Malaysia | 2015 | 1 |
| Malta | 1989 | 1 |
| Mexico | 1947 | 1 |
| Mexico | 1953 | 1 |
| Mexico | 1959 | 1 |
| Mexico | 1960 | 1 |
| Mexico | 1962 | 1 |
| Mexico | 1967 | 1 |
| Mexico | 1969 | 1 |
| Mexico | 1970 | 1 |
| Mexico | 1974 | 1 |
| Mexico | 1979 | 1 |
| Mexico | 1982 | 1 |
| Mexico | 1983 | 1 |
| Mexico | 1986 | 1 |
| Mexico | 1988 | 1 |
| Mexico | 1990 | 1 |
| Mexico | 1997 | 1 |
| Mexico | 1999 | 1 |
| Mexico | 2001 | 1 |
| Mexico | 2004 | 1 |
| Mexico | 2006 | 1 |
| Mexico | 2007 | 1 |
| Mexico | 2012 | 1 |
| Mexico | 2013 | 1 |
| Mexico | 2014 | 1 |
| Mongolia | 2005 | 1 |
| Morocco | 1959 | 1 |
| Morocco | 1999 | 1 |
| Netherlands | 1989 | 1 |
| Netherlands | 1991 | 1 |
| Netherlands | 1997 | 1 |
| Netherlands | 2005 | 1 |
| Netherlands | 2014 | 1 |
| New Zealand | 1966 | 1 |
| New Zealand | 1999 | 1 |
| Nicaragua | 1968 | 1 |
| Nicaragua | 1999 | 1 |
| Nigeria | 1978 | 1 |
| Nigeria | 2000 | 1 |
| Nigeria | 2003 | 1 |
| Norway | 1999 | 1 |
| Norway | 2009 | 1 |
| Oman | 2000 | 1 |
| Pakistan | 1959 | 1 |
| Pakistan | 1967 | 1 |
| Pakistan | 1969 | 1 |
| Pakistan | 2000 | 1 |
| Pakistan | 2006 | 1 |
| Palestinian Authority | 1998 | 1 |
| Palestinian Authority | 2008 | 1 |
| Palestinian Authority | 2013 | 1 |
| Panama | 1956 | 1 |
| Panama | 1978 | 1 |
| Panama | 1992 | 1 |
| Panama | 2005 | 1 |
| Panama | 2015 | 1 |
| Peru | 2002 | 1 |
| Peru | 2008 | 1 |
| Peru | 2016 | 1 |
| Philippines | 1960 | 1 |
| Philippines | 1966 | 1 |
| Philippines | 1969 | 1 |
| Philippines | 1975 | 1 |
| Philippines | 1994 | 1 |
| Philippines | 1996 | 1 |
| Philippines | 2003 | 1 |
| Philippines | 2014 | 1 |
| Philippines | 2015 | 1 |
| Poland | 1972 | 1 |
| Poland | 1975 | 1 |
| Poland | 1977 | 1 |
| Poland | 1989 | 1 |
| Poland | 1992 | 1 |
| Poland | 1994 | 1 |
| Poland | 1997 | 1 |
| Poland | 2001 | 1 |
| Poland | 2003 | 1 |
| Poland | 2007 | 1 |
| Poland | 2011 | 1 |
| Poland | 2014 | 1 |
| Poland | 2016 | 1 |
| Portugal | 1960 | 1 |
| Portugal | 1971 | 1 |
| Portugal | 1974 | 1 |
| Portugal | 1980 | 1 |
| Portugal | 1985 | 1 |
| Portugal | 2000 | 1 |
| Portugal | 2003 | 1 |
| Portugal | 2010 | 1 |
| Qatar | 2003 | 1 |
| Republic of China | 1960 | 1 |
| Romania | 1969 | 1 |
| Romania | 1975 | 1 |
| Romania | 1997 | 1 |
| Romania | 2002 | 1 |
| Romania | 2008 | 1 |
| Russia | 1993 | 1 |
| Russia | 1994 | 1 |
| Russia | 1995 | 1 |
| Russia | 1996 | 1 |
| Russia | 1998 | 1 |
| Russia | 2000 | 1 |
| Russia | 2003 | 1 |
| Russia | 2005 | 1 |
| Russia | 2008 | 1 |
| Russia | 2009 | 1 |
| Russia | 2013 | 1 |
| Rwanda | 1998 | 1 |
| Rwanda | 2008 | 1 |
| Saudi Arabia | 1974 | 1 |
| Saudi Arabia | 1978 | 1 |
| Saudi Arabia | 1990 | 1 |
| Saudi Arabia | 1992 | 1 |
| Saudi Arabia | 1994 | 1 |
| Saudi Arabia | 2009 | 1 |
| Saudi Arabia | 2014 | 1 |
| Saudi Arabia | 2015 | 1 |
| Saudi Arabia | 2016 | 1 |
| Senegal | 1998 | 1 |
| Senegal | 2003 | 1 |
| Serbia-Montenegro (Kosovo) | 1999 | 1 |
| Singapore | 1992 | 1 |
| Singapore | 2003 | 1 |
| Singapore | 2006 | 1 |
| Singapore | 2009 | 1 |
| Slovakia | 2005 | 1 |
| Slovenia | 1999 | 1 |
| Slovenia | 2001 | 1 |
| Slovenia | 2008 | 1 |
| Somalia | 1993 | 1 |
| South Africa | 1998 | 1 |
| South Africa | 2003 | 1 |
| Spain | 1959 | 1 |
| Spain | 1970 | 1 |
| Spain | 1975 | 1 |
| Spain | 1980 | 1 |
| Spain | 1985 | 1 |
| Spain | 1991 | 1 |
| Spain | 1995 | 1 |
| Spain | 1997 | 1 |
| Spain | 2001 | 1 |
| Spain | 2016 | 1 |
| Suriname | 1967 | 1 |
| Sweden | 2001 | 1 |
| Sweden | 2013 | 1 |
| Switzerland | 1955 | 1 |
| Switzerland | 1977 | 1 |
| Switzerland | 1985 | 1 |
| Switzerland | 1990 | 1 |
| Switzerland | 1994 | 1 |
| Switzerland | 1998 | 1 |
| Switzerland | 1999 | 1 |
| Syria | 1974 | 1 |
| Syria | 1994 | 1 |
| Tanzania | 2000 | 1 |
| Tanzania | 2008 | 1 |
| Tanzania | 2013 | 1 |
| Thailand | 1966 | 1 |
| Thailand | 1967 | 1 |
| Thailand | 1969 | 1 |
| Thailand | 1996 | 1 |
| Thailand | 2003 | 1 |
| Thailand | 2008 | 1 |
| Thailand | 2012 | 1 |
| Trinidad and Tobago | 2009 | 1 |
| Tunisia | 1959 | 1 |
| Turkey | 1959 | 1 |
| Turkey | 1991 | 1 |
| Turkey | 1999 | 1 |
| Turkey | 2004 | 1 |
| Turkey | 2009 | 1 |
| Turkey | 2015 | 1 |
| U.S.S.R. | 1972 | 1 |
| U.S.S.R. | 1988 | 1 |
| U.S.S.R. | 1991 | 1 |
| Uganda | 1998 | 1 |
| Uganda | 2003 | 1 |
| Ukraine | 1994 | 1 |
| Ukraine | 1995 | 1 |
| Ukraine | 2000 | 1 |
| Ukraine | 2008 | 1 |
| United Arab Emirates | 2008 | 1 |
| United Kingdom | 1945 | 1 |
| United Kingdom | 1946 | 1 |
| United Kingdom | 1953 | 1 |
| United Kingdom | 1957 | 1 |
| United Kingdom | 1962 | 1 |
| United Kingdom | 1963 | 1 |
| United Kingdom | 1970 | 1 |
| United Kingdom | 1971 | 1 |
| United Kingdom | 1977 | 1 |
| United Kingdom | 1982 | 1 |
| United Kingdom | 1984 | 1 |
| United Kingdom | 1988 | 1 |
| United Kingdom | 1989 | 1 |
| United Kingdom | 1995 | 1 |
| United Kingdom | 1997 | 1 |
| United Kingdom | 2000 | 1 |
| United Kingdom | 2001 | 1 |
| United Kingdom | 2005 | 1 |
| United Kingdom | 2008 | 1 |
| United Kingdom | 2009 | 1 |
| United Kingdom | 2011 | 1 |
| United Kingdom | 2016 | 1 |
| United Kingdom (Northern Ireland) | 2013 | 1 |
| United Kingdom (Wales) | 2014 | 1 |
| Uruguay | 1960 | 1 |
| Uruguay | 1967 | 1 |
| Uruguay | 1990 | 1 |
| Uruguay | 2007 | 1 |
| Vatican City | 1959 | 1 |
| Vatican City | 1963 | 1 |
| Vatican City | 1967 | 1 |
| Vatican City | 1969 | 1 |
| Vatican City | 1970 | 1 |
| Vatican City | 1975 | 1 |
| Vatican City | 1980 | 1 |
| Vatican City | 1982 | 1 |
| Vatican City | 1987 | 1 |
| Vatican City | 1989 | 1 |
| Vatican City | 1991 | 1 |
| Vatican City | 1994 | 1 |
| Vatican City | 2002 | 1 |
| Vatican City | 2004 | 1 |
| Vatican City | 2005 | 1 |
| Vatican City | 2007 | 1 |
| Vatican City | 2008 | 1 |
| Vatican City | 2009 | 1 |
| Vatican City | 2014 | 1 |
| Venezuela | 1961 | 1 |
| Venezuela | 1978 | 1 |
| Venezuela | 1990 | 1 |
| Venezuela | 1997 | 1 |
| Vietnam | 1966 | 1 |
| Vietnam | 1967 | 1 |
| Vietnam | 1969 | 1 |
| Vietnam | 2000 | 1 |
| Vietnam | 2006 | 1 |
| Vietnam | 2016 | 1 |
| Yugoslavia | 1970 | 1 |
| Yugoslavia | 1975 | 1 |
| Yugoslavia | 1980 | 1 |
| Yugoslavia (Kosovo) | 2001 | 1 |
Trips by US presidents and Secretaries of State
Total number of trips by US presidents and Secretaries of State between 1945 to 2017
travels %>%
group_by(Country) %>%
count() %>%
filter(n > 50) %>%
arrange(desc(n)) %>%
ggplot(aes(y = n,
x = reorder(Country, n), fill = Country)) +
coord_flip() +
geom_histogram(stat = "identity") + ggthemes::theme_pander() +
theme(legend.position = "none") +
ggtitle("Trips by US Presidents & State Secretaries 1945-2017") +
xlab("Country") +
ylab("Number Trips by US President & Secretaries of State")
| Country | n |
|---|---|
| United Kingdom | 221 |
| France | 210 |
| Israel | 158 |
| Belgium | 135 |
| Egypt | 126 |
| Germany | 106 |
| Italy | 94 |
| Canada | 86 |
| Switzerland | 86 |
| Mexico | 85 |
| Saudi Arabia | 84 |
| Japan | 81 |
| Jordan | 81 |
| Syria | 75 |
| Palestinian Authority | 63 |
| Korea, Republic of | 60 |
| Germany, Federal Republic of | 58 |
| Russia | 54 |
US troop levels
troops <- read.csv("C:/Users/Paula/Desktop/troops and military spending.csv", header = TRUE)
troops %>%
filter(year > 1950) %>%
group_by(country) %>%
summarise(sum_troops = sum(troops)) %>%
filter(sum_troops > 50000) %>%
arrange(desc(sum_troops)) %>%
kbl() %>%
kable_paper("striped",
full_width = F) | country | sum_troops |
|---|---|
| Germany | 9854626 |
| Japan | 3827418 |
| Korea, Republic of | 3528080 |
| South Vietnam | 2636065 |
| United Kingdom | 1331832 |
| France | 684654 |
| Philippines | 663660 |
| Italy | 645229 |
| Panama | 474807 |
| Iraq | 420463 |
| Thailand | 381483 |
| Spain | 374901 |
| Turkey | 293246 |
| Canada | 251386 |
| Cuba / Guantanamo | 172792 |
| Iceland | 160254 |
| Kuwait | 154330 |
| Taiwan | 152322 |
| Greece | 144344 |
| Saudi Arabia | 101200 |
| Portugal | 94018 |
| Morocco | 92359 |
| Netherlands | 84441 |
| Belgium | 83230 |
| Libya | 76543 |
| Afghanistan | 56699 |
| Bosnia and Herzegovina | 52056 |
troops %>%
group_by(ccode) %>%
summarise(sum_troops = sum(troops)) %>%
ungroup() -> troop_levels
the_df <- merge(the_df, troop_levels, by.x = "cow_code", by.y = "ccode", all.x = TRUE)library(stargazer)
the_df$sum_pres_travels[is.na(the_df$sum_pres_travels)] <- 0
the_df$sum_head_vists[is.na(the_df$sum_head_vists)] <- 0
the_df$sum_troops[is.na(the_df$sum_troops)] <- 0the_df %>%
filter(sum_troops > 1) %>%
filter(year == 2019) %>%
ggplot(aes(y = log(budget_pc), x = log(sum_troops + 1), group = as.factor(region_6))) +
# geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
geom_smooth(method = "lm", se = FALSE, aes(color = as.factor(region_6))) +
geom_text(aes(label = country, color = region_6), size = 4) +
facet_wrap(~region_6) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("UN voting similarity vs PD budget per capita") +
xlab("Distance from US ideal score") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") + ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
the_df %>%
# filter(sum_troops > 1) %>%
ggplot(aes(y = log(budget_pc), x = log(sum_troops + 1), group = as.factor(region_6)), alpha = 0.5) +
geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
geom_smooth(method = "lm", se = FALSE, aes(color = as.factor(region_6))) +
# geom_text(aes(label = country, color = region_6), size = 4) +
facet_wrap(~year) +
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
name = NULL) +
scale_shape_manual(values = c(15,16,17,18,19,10),
name = NULL) +
ggtitle("UN voting similarity vs PD budget per capita") +
xlab("Distance from US ideal score") +
ylab("Logged PD budget per capita") +
theme(legend.position = "left") + ggthemes::theme_pander()## `geom_smooth()` using formula 'y ~ x'
Overseas Military Bases in China, Russia and the US
Source: https://en.wikipedia.org/wiki/List_of_countries_with_overseas_military_bases
US bases and “lily pads”
Source: https://github.com/meflynn/troopdata
Troop deployment data were initially compiled by Tim Kane using information obtained from the U.S. Department of Defense’s Defense Manpower Data Center (DMDC). The original data ended in 2005 and we have updated it to run through 2020
library(troopdata)
troops_us <- get_troopdata(startyear = 2013, endyear = 2020)us_base_df <-read.csv("C:/Users/Paula/Desktop/PD_original_datasets/Bases.csv")
us_base_df %>%
filter(base == 1) %>%
group_by(Country.Name) %>%
count() %>%
arrange(desc(n)) %>%
filter(n > 2) %>%
kbl() %>%
kable_paper("striped",
full_width = F)| Country.Name | n |
|---|---|
| Japan | 42 |
| Germany | 40 |
| Korea, South | 30 |
| Puerto Rico | 19 |
| United Kingdom | 16 |
| Italy | 14 |
| Afghanistan | 9 |
| Kuwait | 9 |
| Marshall Islands | 6 |
| Oman | 6 |
| Turkey | 6 |
| Belgium | 5 |
| Qatar | 5 |
| Bahrain | 4 |
| Australia | 3 |
| Guam | 3 |
| Northern Mariana Islands | 3 |
| Romania | 3 |
| United Arab Emirates | 3 |
us_base_df %>%
filter(lilypad == 1) %>%
group_by(Country.Name) %>%
count() %>%
arrange(desc(n)) %>%
filter(n > 2) %>%
kbl() %>%
kable_paper("striped",
full_width = F)| Country.Name | n |
|---|---|
| Saudi Arabia | 9 |
| Syria | 8 |
| Israel | 6 |
| Pakistan | 6 |
| Philippines | 6 |
| Colombia | 4 |
| Germany | 4 |
| Netherlands | 4 |
| Bulgaria | 3 |
| Cameroon | 3 |
| Peru | 3 |
us_base_df %>%
group_by(Country.Name) %>%
count() %>%
arrange(desc(n)) %>%
filter(n > 5) %>%
kbl() %>%
kable_paper("striped",
full_width = F)| Country.Name | n |
|---|---|
| Germany | 44 |
| Japan | 42 |
| Korea, South | 32 |
| Puerto Rico | 19 |
| United Kingdom | 16 |
| Italy | 15 |
| Syria | 10 |
| Afghanistan | 9 |
| Honduras | 9 |
| Kuwait | 9 |
| Saudi Arabia | 9 |
| Belize | 8 |
| Guatemala | 7 |
| Belgium | 6 |
| Israel | 6 |
| Marshall Islands | 6 |
| Netherlands | 6 |
| Oman | 6 |
| Pakistan | 6 |
| Panama | 6 |
| Philippines | 6 |
| Turkey | 6 |
# war_on_terror <- c("Afghanistan", "Yemen", "Syria", "Iraq", "Pakistan")
#
# my_pd9 %<>%
# dplyr::mutate(war_on_terror = ifelse(country_name %in% war_on_terror, 1, 0)) UN VOTING TWO CLUSTERS
# the_df %>%
# filter(year == 2014) %>%
# select(country, ideal_point_distance) %>%
# arrange(desc(ideal_point_distance))
#
#
#
# names(the_df)
#
# low_model <- plm(lead(log(budget_pc)) ~ log(gdp_pc_const) +
# log(pop) +
# log(us_exports_pc) +
# civ_lib_fh +
# relig_frac +
# rule_law_fh,
# data = low_un,
# model = "within", effect = "time",
# index = c("cow_code", "year"))
#
#
# high_model <- plm(lead(log(budget_pc)) ~ log(gdp_pc_const) +
# log(pop) +
# log(us_exports_pc) +
# civ_lib_fh +
# relig_frac +
# rule_law_fh,
# data = high_un,
# model = "within", effect = "time",
# index = c("cow_code", "year"))
#
# stargazer(low_model, high_model, column.labels = c("Low", "High"),
# type = "text")Five year averages of dependent variables
# the_df$period <- cut(the_df$year, seq(2013, 2018, 1))
#
# pwt.ma <- ddply(pwt, .(country, period), numcolwise(mean))
#
# pwt.ma
#
# excluding_countries <- c("Cuba", "Syria", "Yemen", "Libya")
the_df %<>%
mutate(muslim_percent = percent_muslim_jitter / pop, na.rm = TRUE) %>%
filter(!is.na(cow_code))
asia_df <- the_df %>%
filter(region_6 == "Asia")
africa_df <- the_df %>%
filter(region_6 == "Africa")
mena_df <- the_df %>%
filter(region_6 == "MENA")
west_df <- the_df %>%
filter(region_6 == "West")
latin_df <- the_df %>%
filter(region_6 == "Latin")
soviet_df <- the_df %>%
filter(region_6 == "Post Soviet")
dv <- "log(budget)"
ivs <- c("electoral_demo",
"log(gdp_pc_const)",
"log(pop)",
"muslim_percent")
form_1 <- as.formula(paste(dv, paste(ivs, collapse = " + "), sep = " ~ "))---
title: "Scatterplots and barcharts"
author: "Paula"
date: "8/19/2021"
output:
  html_document:
    theme: flatly
    toc: true
    toc_float: true
    code_download: true
    toc_depth: 5
    
---

```{css, echo = FALSE}

tbody tr:nth-child(odd) {background: #eee;}
    
h1, h2, h3 {text-align: center;}

```

```{r setup, include = FALSE}

library(plm)
library(stargazer)
library(lmtest)
library(sandwich)
library(tidyverse)
library(magrittr)
library(wesanderson)
library(countrycode)
library(skimr)
library(priceR)
library(knitr) 
library(kableExtra)
library(scales)
library(reshape2)
library(janitor)
library(rnaturalearth)
library(sf)
library(rvest)
library(haven)
library(RColorBrewer)
library(jsonlite)
library(priceR)


the_df <- read.csv("C:/Users/Paula/Desktop/PD_original_datasets/proto2.csv")


the_df$X.1 <- NULL

the_df %<>% 
  mutate(country = ifelse(country == "Trinidad And Tobago", "Trinidad and Tobago",
                          ifelse(country == "Bosnia And Herzegovina", "Bosnia and Herzegovina", country))) %>% 
  mutate(cow_code = ifelse(country == "Taiwan", 713, cow_code))

gtd <- read.csv("C:/Users/Paula/Desktop/PD_original_datasets/gtd.csv")


the_df$region_6 <- recode_factor(the_df$region_6, "Asia and Pacific" = "Asia",
                "Latin America Caribbean" = "Latin",
                "Sub-Sahara" = "Africa")

knitr::opts_chunk$set(echo = TRUE, warning = FALSE, results = "asis", out.width = "400%")

```


# Global Terrorism Dataset

First, I had a look at the dataset that Dr. Martinez Machain suggested in her email, the Global Terrorism Dataset.

If we sum the number of total terrorist acts per country per year, we see that the top three countries are Iraq, Pakistan and Afghanistan.

Source: https://www.start.umd.edu/data-tools/global-terrorism-database-gtd

The Global Terrorism Database (GTD) is an open-source database including information on terrorist events around the world since 1970 (currently updated through 2018). Unlike many other event databases, the GTD includes systematic data on international as well as domestic terrorist incidents that have occurred during this time period and now includes over 190,000 cases.

LaFree, G., & Dugan, L. (2007). Introducing the global terrorism database. Terrorism and political violence, 19(2), 181-204.

# Total terrorist attacks 

### Total terrorist attacks between 1970 and 2019

```{r gtd_attacks}

gtd %>% 
  group_by(country_txt, region_txt) %>% 
  summarise(sum = n()) %>% 
  ungroup() %>% 
  filter(sum > 4000) %>% 
  ggplot(aes(x = reorder(country_txt, sum), 
             y = sum, 
             fill = as.factor(region_txt))) + 
    scale_fill_manual(values = c("#001219","#005f73","#0a9396","#94d2bd",
                                 "#ee9b00","#ca6702","#bb3e03","#ae2012","#9b2226")) + 
  coord_flip() +
  geom_bar(stat = "identity") + 
  ggthemes::theme_pander() +
  theme(legend.position = "bottom", 
        legend.title = element_blank()) + 
  ggtitle("Total terrorist attacks 1970 - 2019")

```

# Suicide attacks 

If we look only at suicide attack, the countries are still Iraq, Afghanistan and Pakistan.

There does not appear to be a variable to indicate if the terrorist attack was related explicitly to Islamic Fundamentalism. 

### Suicide terrorist attacks between 1970 and 2019

```{r gtd_suicide}

gtd %>% 
  filter(suicide == 1 ) %>% 
  group_by(country_txt, region_txt) %>% 
  summarise(suicide_sum = n()) %>% 
  ungroup() %>% 
  filter(suicide_sum > 50) %>% 
  ggplot(aes(x = reorder(country_txt, suicide_sum), 
             y = suicide_sum, 
             fill = as.factor(region_txt))) + 
  scale_fill_manual(values = c("#001219", "#0a9396", "#ee9b00", "#ca6702","#bb3e03",
                               "#005f73", "#94d2bd", "#ae2012", "#9b2226")) +
  coord_flip() +
  geom_bar(stat = "identity") + 
  ggthemes::theme_pander() +
  theme(legend.position = "bottom", legend.title = element_blank()) +
  ggtitle("Suicide terrorist attacks 1970 - 2019")

```

### Look only at 2019

```{r gtd_suicide_2019}

gtd %>% 
  filter(suicide == 1 ) %>% 
    filter(iyear == 2019) %>% 
  group_by(country_txt, iyear, region_txt) %>% 
  select(year = iyear, everything()) %>% 
  summarise(suicide_sum = n()) %>% 
  arrange(desc(suicide_sum)) %>% 
  head(20) %>% 
  ungroup()  %>% 
  ggplot(aes(x = reorder(country_txt, suicide_sum), 
             y = suicide_sum, 
             fill = as.factor(region_txt))) + 
  scale_fill_manual(values = c("#001219", "#0a9396",
                               "#ca6702","#9b2226",
                               "#94d2bd","#ee9b00")) +
  coord_flip() +
  geom_bar(stat = "identity") + facet_wrap(~year) +
  ggthemes::theme_pander() +
  theme(legend.position = "bottom", legend.title = element_blank()) +
  ggtitle("Total suicide terrorist attacks per country in 2019")


```


So for the dataset, we can add a variable with number of terrorist attacks per country per year or we can create a binary variable with a cut-off point. 

We can also look at number of terrorist attacks per capita per year and run a scatter plot with PD budgets to see if there is a linear relationship

```{r, echo = FALSE}

gtd$cow_code <- countrycode(gtd$country_txt, "country.name", "cown")

gtd %<>% 
   mutate(cow_code = ifelse(country_txt == "West Bank and Gaza Strip", 6666,
                            ifelse(country_txt == "Serbia", 345, 
                                    ifelse(country_txt == "Hong Kong", 997, cow_code))))

gtd %>% 
  group_by(cow_code, iyear, region_txt) %>% 
  select(year = iyear, everything()) %>% 
  summarise(sum_attacks = n()) %>% 
  ungroup() -> gtd_annual_sum

gtd %>% 
  group_by(cow_code, iyear) %>% 
  select(year = iyear, everything()) %>% 
  filter(suicide == 1) %>% 
  summarise(sum_suicides = n()) %>% 
  ungroup() -> gtd_annual_suicide

gtd %>% 
  group_by(cow_code, iyear) %>% 
  select(year = iyear, everything()) %>% 
  filter(suicide == 1) %>% 
  summarise(sum_all_year_suicides = n()) %>% 
  ungroup() -> gtd_total_suicide

gtd %>% 
  group_by(cow_code, iyear, region_txt) %>% 
  select(year = iyear, everything()) %>% 
  summarise(sum_all_year_attacks = n()) %>% 
  ungroup() -> gtd_total_sum

the_df <- merge(the_df, gtd_annual_sum, by = c("cow_code", "year"), all.x = TRUE)
the_df <- merge(the_df, gtd_annual_suicide, by = c("cow_code", "year"), all.x = TRUE)

the_df <- merge(the_df, gtd_total_suicide, by = c("cow_code", "year"), all.x = TRUE)
the_df <- merge(the_df, gtd_total_sum, by = c("cow_code", "year"), all.x = TRUE)

the_df %>% 
  group_by(cow_code) %>% 
  mutate(avg_suicide = sum_all_year_suicides / 18) %>%
  ungroup() %>% 
  filter(year == 2019) %>% 
  select(country, avg_suicide) %>% 
  arrange(desc(avg_suicide))

the_df %>% 
  group_by(cow_code) %>% 
  mutate(avg_attacks = round(mean(sum_all_year_attacks, na.rm = TRUE)), 0) %>%
  ungroup() %>% 
  filter(year == 2019) %>% 
  select(country, avg_attacks) %>% 
  arrange(desc(avg_attacks)) -> ho

View(ho)

```


Scatterplot comparing all types of terrorist attacks per capita vs PD budgets per capita

```{r  attack_scatter}

the_df %>%
  mutate(attacks_pc = sum_attacks / pop, na.rm = TRUE) %>% 
  ggplot(aes(y = log(budget_pc), x = log(attacks_pc))) +
      geom_smooth(method = "lm", color = "#9d0208") +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~year) +
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
  xlab("Logged number of terrorist attacks per capita") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") +
  ggthemes::theme_pander() + 
  ggtitle("Total terrorist attacks per capita vs PD budgets per capita")

```

Scatterplot comparing suicide terrorist attacks per capita vs PD budgets per capita


```{r suicide_bar}

the_df %>%
  mutate(attacks_pc = sum_suicides / pop, na.rm = TRUE) %>% 
  ggplot(aes(y = log(budget_pc), x = log(attacks_pc))) +
  geom_smooth(method = "lm", color = "#9d0208") +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~year) +
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", 
                                "#6d597a", "#e63946", "#f4a261"), name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
  xlab("Logged number of terrorist attacks per capita") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") +
  ggthemes::theme_pander() + 
  ggtitle("Suicide attacks per capita vs PD budgets per capita")

```

```{r suicide_total_scatter}

the_df %>%
  
  
  ggplot(aes(y = log(budget_pc), x = log(sum_suicides))) +
  geom_smooth(method = "lm", color = "#9d0208") +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~year) +
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", 
                                "#6d597a", "#e63946", "#f4a261"), name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
  xlab("Logged number of terrorist attacks per capita") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") +
  ggthemes::theme_pander() + 
  ggtitle("Suicide attacks per capita vs PD budgets per capita")

```

## Only looking at terrorist attacks in 2019

```{r suicide_bar_19}

the_df %>%
  filter(year == 2019) %>% 
  mutate(attacks_pc = sum_attacks / pop, na.rm = TRUE) %>% 
  ggplot(aes(y = log(budget), x = log(sum_attacks), group = as.factor(region_6))) + 
  geom_smooth(method = "lm", color = "#9d0208", alpha = 0.5) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + 
  facet_wrap(~region_6) + 
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  xlab("Logged number of terrorist attacks per capita") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") +
  ggthemes::theme_pander() + 
  ggtitle("Total terrorist attacks per capita vs PD budgets per capita in 2019")

```


## Suicide attacks per capita vs PD budgets per capita since 2013

```{r suicide_scatter}

the_df %>%
  filter(year > 2012) %>% 
  mutate(attacks_pc = sum_suicides / pop, na.rm = TRUE) %>% 
  ggplot(aes(y = log(budget_pc), x = log(attacks_pc))) +
  geom_smooth(method = "lm", color = "#9d0208") +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~region_6) +
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
  xlab("Logged number of terrorist attacks per capita") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") +
  ggthemes::theme_pander() + 
  ggtitle("Suicide attacks per capita vs PD budgets per capita since 2013")

```




# Chinese aid dataset

Next we can decide which countries are in China's sphere of influence.


```{r china_aid_install, echo = FALSE}

library(foreign)
library(readxl)

master <-  read.csv("C:/Users/Paula/Desktop/master_copy_1948_2019.csv")

china_orig <- read_excel("C:/Users/Paula/Desktop/GlobalChineseOfficialFinanceDataset_v1.0.xlsx")


china_orig %>% 
  dplyr::select(country = recipient_condensed, year, 
                chn_aid = usd_defl_2014) -> china_aid

china_aid$cow_code <- countrycode(china_aid$country, "country.name", "cown")

china_aid <- merge(china_aid, master, by = c("cow_code", "year"), all.x = TRUE)

```

Source:  https://www.aiddata.org/data/chinese-global-official-finance-dataset

This dataset tracks the known universe of overseas Chinese official finance between 2000-2014, capturing 4,373 records totaling $354.4 billion. The data includes both Chinese aid and non-concessional official financing.

Bluhm, R., Dreher, A., Fuchs, A., Parks, B. C., Strange, A., & Tierney, M.J. (2018). Connective Financing: Chinese Infrastructure Projects and the Diffusion of Economic Activity in Developing Countries. AidData Working Paper

### Looking at the total aid from China between 2000 to 2014

```{r aid_china_00_14}

china_aid %>% 
  filter(!is.na(region_name)) %>% 
  group_by(country, region_name) %>% 
  filter(!grepl("regional", country)) %>% 
  summarise(sum_aid = sum(chn_aid, na.rm = TRUE)) %>% 
  ungroup() %>% 
  top_n(30) %>% 
  ggplot(aes(x= reorder(country, sum_aid), 
             y = sum_aid, 
             fill = as.factor(region_name))) + 
  geom_bar(stat = "identity") + 
  coord_flip() + 
  scale_fill_manual(values = c("#005f73","#0a9396","#94d2bd","#ee9b00",
                               "#ca6702","#bb3e03","#ae2012","#9b2226"), 
                    name = NULL) +
  theme(axis.text.y = element_text(size = 10),
        axis.title.y = element_blank(), 
        legend.position = "bottom") + 
  xlab("country") +
  ylab("Total aid from China") +
  ggthemes::theme_pander() + 
  ggtitle("Total aid from China betweem 2000 - 2014") 

china_aid %>% 
  filter(!is.na(region_name)) %>% 
  group_by(country, region_name) %>% 
  filter(!grepl("regional", country)) %>% 
  summarise(sum_aid = sum(chn_aid, na.rm = TRUE)) %>% 
  ungroup() %>% 
  top_n(20) %>% pull(country) -> top_china_aid_vector

top_china_aid_vector


china_aid %>% 
  filter(!is.na(region_name)) %>% 
  group_by(country, region_name) %>% 
  filter(!grepl("regional", country)) %>% 
  mutate(chn_aid_pc = chn_aid / population, na.rm = TRUE) %>% 
  summarise(sum_aid_pc = sum(chn_aid_pc, na.rm = TRUE)) %>% 
  ungroup() %>% 
  top_n(20) %>% pull(country) -> top_china_aid_pc_vector

top_china_aid_pc_vector
  

```

## Chinese FDI in Africa

Source: https://www.aiddata.org/data/aiddatas-chinese-official-finance-to-africa-dataset-version-1-1-1

This is a dataset of Chinese official finance activities that spans 50 African countries over the 2000-2012 period. It includes 1,952 Chinese development finance projects across 3,545 physical locations.

Dreher, A., Fuchs, A., Hodler, R., Parks, B. C., Raschky, P. A., & Tierney, M.J. (2019). African Leaders and the Geography of China's Foreign Assistance. Journal of Development Economics, 140, 44-71.

```{r china_fdi, echo = FALSE}

africa_fdi <- read.csv("C:/Users/Paula/Desktop/china_fdi.csv")
names(africa_fdi) <- africa_fdi[1,]
africa_fdi <- africa_fdi[-1,]

africa_fdi %<>% 
  select(year = "US$ mn, unadjusted", everything()) 

africa_fdi2 <- africa_fdi[-c(1:13), ] 
africa_fdi3 <- africa_fdi2[-c(18:22), ]  


wide_fdi <- melt(africa_fdi3,
                   id.vars = c("year"),
                   variable.name = "country",
                   value.name = "fdi")

wide_fdi %>%
  filter(country != "total_us_mn") %>% 
  mutate(fdi_amount = as.numeric(fdi)) %>% 
  filter(!grepl("Total", country)) %>% 
  filter(!is.na(country)) %>% 
  group_by(country) %>% 
  summarise(sum_fdi = sum(fdi_amount, na.rm = TRUE)) %>%
  ungroup() -> fdi_af 

fdi_af$cow_code <- countrycode(fdi_af$country, "country.name", "cown")

```


## Total development finance investment to Africa from China 2000 to 2012

```{r china_dev_fin}

fdi_af %>% 
    top_n(25) %>% 
  ggplot(aes(x = reorder(country, sum_fdi), y = sum_fdi, fill = country)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + ggthemes::theme_pander() +
  theme(axis.text.y = element_text(size = 10),
        axis.title.y = element_blank(), 
        legend.position = "none") + ggthemes::theme_pander() +
  xlab("Total development finance costs") +
  ylab("Country") +
  ggthemes::theme_pander() + theme(legend.position = "none") +
  ggtitle("Total development finance investment from China 2000 to 2012 ") 


```

## Looking at the financial development investment per capita to Africa 2000 to 2102

```{r china_dev_fin_pc}

the_df <-merge(the_df, fdi_af, by = c("cow_code"), all.x = TRUE)

the_df %>% 
  filter(sum_fdi > 10000) %>% 
  mutate(fdi_pc = sum_fdi / pop, na.rm = TRUE) %>% 
  ggplot(aes(x = reorder(country.x, fdi_pc), y = fdi_pc, fill = country.x)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + ggthemes::theme_pander() +
  theme(axis.text.y = element_text(size = 10),
        axis.title.y = element_blank(), 
        legend.position = "none") + ggthemes::theme_pander() +
  xlab("Total finance costs per capita") +
  ylab("Country") +
  ggthemes::theme_pander() + theme(legend.position = "none") +
  ggtitle("Total finance per capita from China 2000 to 2012 ") 

```


# Russia's Sphere of Influence 

Russia's sphere contains all post-soviet countries and all countries that are contiguous to Russia

```{r, echo = FALSE}

russia_dyad <- read.csv("C:/Users/Paula/Desktop/PD_original_datasets/russia_dyad.csv")

the_df <- merge(the_df, russia_dyad, by = c("cow_code", "year"), all.x = TRUE)

```


# China's Sphere of Influence

China's spehere contains top 20 aid recipients per capita and top financial recipients per capita from XXX YYY years/

It also contains all countries contiguous to China.

# Average voting similarity to China in the UN

Source: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/5GQIVU

Rodrigues Vieira, V. (2018). Who joins counter-hegemonic IGOs? Early and late members of the China-led Asian Infrastructure Investment Bank. Research & Politics, 5(2)

China Alignment correspond to the average ideal point scale (ranging from 0 to 100) of Bailey et al. (2015) for a state’s vote with Chinese positions in the United Nations General Assembly between 1990 and 2009, a period during which US leadership in world affairs seemed robust. This measures the strength of international ties with China in political terms

```{r aiib}

aiib <- read_dta("C:/Users/Paula/Desktop/AIIB-ForResubmission.dta")
  
aiib %>% 
  select(country, chinaalignment) %>%
  arrange(desc(chinaalignment)) %>% 
  filter(chinaalignment > 9000) %>% 
  kbl() %>%
  kable_paper("striped",
              full_width = F)

aiib %>% 
    filter(chinaalignment > 8800) %>% 
  ggplot(aes(x = reorder(country, chinaalignment), y = chinaalignment, fill = country)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + ggthemes::theme_pander() +
  theme(axis.text.y = element_text(size = 10),
        axis.title.y = element_blank(), 
        legend.position = "none") + ggthemes::theme_pander() +
  ylab("Voting similarity to China (average)") +
  xlab("Country") +
  ggthemes::theme_pander() + theme(legend.position = "none") +
  ggtitle("Top aligned to Chinese UN voting 2000 - 2014")  

aiib %>% 
    filter(chinaalignment < 5500) %>% 
  ggplot(aes(x = reorder(country, chinaalignment), y = chinaalignment, fill = country)) + 
  geom_bar(stat = "identity") + 
  coord_flip() + ggthemes::theme_pander() +
  theme(axis.text.y = element_text(size = 10),
        axis.title.y = element_blank(), 
        legend.position = "none") + ggthemes::theme_pander() +
  ylab("Voting similarity to China (average)") +
  xlab("Country") +
  ggthemes::theme_pander() + theme(legend.position = "none") +
  ggtitle("Least aligned to Chinese UN voting 2000 - 2014") 

```

```{r}

map <- ne_countries(scale = "medium", returnclass = "sf")

aiib$iso3c <- countrycode(aiib$country, "cown","iso3c")

map_aiib <- merge(map, aiib, by.x = "adm0_a3", by.y = "wbcode", all.x = TRUE)

View(map)

coord <- read_html("https://developers.google.com/public-data/docs/canonical/countries_csv")
coord_tables <- coord %>% html_table(header = TRUE, fill = TRUE)
coordy <- coord_tables[[1]]
col_map <- merge(map_aiib, coordy, by.x= "iso_a2", by.y = "country", all.y = TRUE)

map_aiib %>%
  # filter(geounit != "Siachen Glacier") %>%
  filter(subregion == "Eastern Asia" | subregion == "South-Eastern Asia" | subregion == "Southern Asia") %>%
  ggplot() +
  geom_sf(aes(fill = chinaalignment),
           position = "identity") +
  labs(fill='Russian Sphere') +
  # geom_label(aes(longitude+1, latitude+1, label = factor(geounit)), size = 3) +
  ggthemes::theme_map() +
  theme(legend.position = "blank") +
  scale_fill_distiller(palette = "RdBu")

col_map %>%
filter(continent == "Africa") %>%   ggplot() +
  geom_sf(aes(fill = chinaalignment),
           position = "identity") +
  labs(fill='Russian Sphere') +
  geom_label(aes(longitude+1, latitude+1, label = factor(geounit)), size = 3) +
  ggthemes::theme_map() +
  theme(legend.position = "blank") +
  scale_fill_distiller(palette = "RdBu")

plyr::count(map_aiib$subregion)

europe_cropped <- st_crop(map_aiib, xmin = -20, xmax = 45,
                                    ymin = 30, ymax = 73)

europe_cropped %>%
  filter(subregion == "Eastern Europe" | subregion == "Northern Europe" | subregion == "Southern Europe" | subregion == "Western Europe") %>%
  ggplot() +
  geom_sf(aes(fill = chinaalignment),
           position = "identity") +
  labs(fill='Russian Sphere') +
  # geom_label(aes(longitude+1, latitude+1, label = factor(geounit)), size = 3) +
  ggthemes::theme_map() +
  theme(legend.position = "blank") +
  scale_fill_distiller(palette = "RdBu")


```


```{r}

aiib %>% 
  filter(aiibmember == 1) %>% 
  select(country) %>% 
  kbl() %>%
  kable_paper("striped",
              full_width = F)

```


# Chinese Public Diplomacy Efforts and Activities


```{r echo = FALSE}

china_pd <- read.csv("C:/Users/Paula/Desktop/ChinesePublicDiplomacy.csv")

```


```{r}

soviet_iron_curtain_vector <- c("Albania",
                                "Bulgaria",
                                "Czech Republic", 
                                "Poland", 
                                "Kosovo", 
                                "Romania", 
                                "Hungary",
                                "Slovakia", 
                                "Bosnia and Herzegovina",
                                "Croatia",
                                "Macedonia", 
                                "Montenegro", 
                                "Serbia")

soviet_republics_vector <- c("Russia", "Lithuania", "Georgia",
                             "Estonia"	,
                             "Latvia"	,
                             "Ukraine"	,
                             "Belarus"	,
                             "Moldova"	,
                             "Kyrgyzstan",	
                             "Uzbekistan",	
                             "Tajikistan",	
                             "Armenia"	,
                             "Azerbaijan",	
                             "Turkmenistan",	
                             "Russia"	,
                             "Kazakhstan")

the_df %<>%
  dplyr::mutate(soviet_iron_curtain = ifelse(country.x %in% soviet_iron_curtain_vector, 1, 0)) 

the_df %<>%
  dplyr::mutate(soviet_republics = ifelse(country.x %in% soviet_republics_vector, 1, 0)) 


the_df %<>% mutate(rus_influence = ifelse(country.x == "Russia", 1, 
                                              ifelse(soviet_republics == 1, 1,
                                                ifelse(soviet_iron_curtain == 1, 1, 0))))

eurasian_dev_bank <- c("Russia", "Armenia", "Belarus", "Kazakhstan", "Kyrgystan", "Tajikistan")

brics <- c("Brazil", "Russia", "India", "China", "South Africa")

qccm <- c("China", "Afhganistan", "Pakistan", "Tajikistan")

forum_china_africa_coop <- c("China", "Algeria", "Angola", "Benin", "Botswana", "Burkina Faso")


```


```{r, echo = FALSE}
# 
# map <- ne_countries(scale = "medium", returnclass = "sf")
# 
# mini15 <- the_df[which(the_df$year == 2015),]
# 
# map$cow_code <- countrycode(map$name_long, "country.name", "cown")
#  
# map15 <- merge(map, mini15, by = "cow_code", all.x = TRUE)
# 
# russia_cropped <- st_crop(map15, xmin = -20, xmax = 200,
#                                     ymin = 10, ymax = 150)
# 
# russia_cropped %>% 
#   ggplot() +
#   geom_sf(aes(fill = as.factor(rus_influence)), 
#            position = "identity") + 
#    labs(fill='Russian Sphere') + ggthemes::theme_map() + 
#   theme(legend.position = "blank")

map %>% 
  select(name, iso3c = brk_a3, continent, region_wb, region_un, subunit, income_grp, geometry) -> my_map



```

## Making variables

#### Trade openness formula:

total exports and total imports / total GDP


#### US dependence formula:

exports to US and imports from US / total exports and total imports


```{r per_capita_variables}

the_df$country <- the_df$country.x
the_df$country.y <- NULL

the_df %>% 
  mutate(budget_pc = budget / pop, na.rm = TRUE) %>%
  mutate(exports_pc = exports_bop / pop, na.rm = TRUE) %>% 
  mutate(balance_trade = exports_bop + imports_bop, na.rm = TRUE) %>% 
  mutate(trade_openess = balance_trade / gdp_const, na.rm = TRUE) %>% 
  mutate(us_balance_trade = us_exports_to_country + us_imports_from_country, na.rm = TRUE) %>% 
  mutate(us_trade_dependence = us_balance_trade / balance_trade, na.rm = TRUE) %>% 
  mutate(us_exports_pc = us_exports_to_country / pop, na.rm = TRUE) %>% 
  mutate(muslim_percent = percent_muslim_jitter / pop, na.rm = TRUE) %>% 
  mutate(us_imports_pc = us_imports_from_country / pop, na.rm = TRUE) -> the_df


```


## US Trade Dependence

```{r}

the_df %>% 
  select(country, year, us_trade_dependence) %>% 
  filter(us_trade_dependence > 0.1) %>% 
  arrange(desc(us_trade_dependence)) %>%
  filter(year == 2019) %>% 
  filter(us_trade_dependence > 0.3) %>% 
  kbl() %>%
  kable_paper("striped",
              full_width = F)

the_df %>% 
  filter(gdp_const > 500000000000) %>% 
  select(country, year, us_trade_dependence) %>% 
  arrange(desc(us_trade_dependence)) %>%
  # filter(us_trade_dependence > 0.2) %>% 
  ggplot(aes(y = us_trade_dependence,
             x = reorder(country, us_trade_dependence), fill = country)) +
  coord_flip() +
  geom_histogram(stat = "identity") + ggthemes::theme_pander() +
  facet_grid(~year, scales = "free") + theme(legend.position = "none") + 
  ggtitle("Level of trade dependence in countries over 500 million GDP") + xlab("") + ylab("Percentage of total trade that is with US")

```

## Divide budget by cost of living

```{r}
url <- "https://www.numbeo.com/cost-of-living/rankings_by_country.jsp?title=2019"


# Countries with no INDEX



col_url <- read_html(url)
col_table <- col_url %>% html_table(header = TRUE, fill = TRUE)
col_19 <- col_table[[2]]

col_19$year <- replicate(119, 2019)
col_19$rank <- NULL
col_19 %<>% 
  clean_names()

col_19$cow_code <- countrycode(col_19$country, "country.name", "cown")

col_19 %<>% dplyr::mutate(cow_code = ifelse(country == "Palestine", 6666, 
                                              ifelse(country == "Serbia", 345, 
                                                ifelse(country == "Hong Kong", 997, cow_code))))

col_19 %>% 
  arrange(desc(cost_of_living_index)) %>% 
  filter(cost_of_living_index > 65) %>% 
  head() %>% 
  kbl() %>%
  kable_paper("striped",
              full_width = F)


```


# Examining 2019 cost of living adjustment

```{r}

# df_19 <- the_df %>% 
#   filter(year == 2019)
# 
# 
# df_19 <- merge(df_19, col_19, by = c("cow_code"), all.x = TRUE)
# 
# options(scipen = 999)
# 
# 
# 
# df_19 %>%  
#   # mutate(cost_living_percent = cost_of_living_index / 100, na.rm = TRUE) %>% 
#   mutate(adj_budget = budget / cost_of_living_index) %>% 
#   select(country = country.x, adj_budget, budget) %>% 
#   arrange(desc(adj_budget)) -> t1
# 
# df_19 %>%  
#   # mutate(cost_living_percent = cost_of_living_index / 100, na.rm = TRUE) %>% 
#   mutate(adj_budget = budget / cost_of_living_index) %>% 
#   select(country = country.x, budget, adj_budget) %>% 
#   arrange(desc(budget)) -> t2
# 
# kbl(list(t1, t2), digits = 0, caption = "Arranged By Adjusted Budget LEFT and By Raw Budget RIGHT") %>%
#    kable_paper("striped",
#                full_width = T)

```

# Purchase Power Parity
 
Source: https://data.worldbank.org/indicator/PA.NUS.PPPC.RF

Price level ratio is the ratio of a purchasing power parity (PPP) conversion factor to an exchange rate. It provides a measure of the differences in price levels between countries by indicating the number of units of the common currency needed to buy the same volume of the aggregation level in each country.

Indicator source: 
International Comparison Program, World Bank


```{r}
# 
# library(WDI)
# 
# ppp = WDI(indicator='PA.NUS.PPPC.RF', country="all", start=2019, end=2019)
# ppp$ppp <- ppp$PA.NUS.PPPC.RF
# ppp$PA.NUS.PPPC.RF <- NULL
# 
# View(ppp)
# 
# ppp$cow_code <- countrycode(ppp$iso2c, "iso2c", "cown")
# 
# ppp %<>% 
# dplyr::mutate(cow_code = ifelse(country == "West Bank and Gaza", 6666, 
#                                 ifelse(country == "Serbia", 345, 
#                                        ifelse(country == "Hong Kong SAR, China", 997, cow_code))))
# 
# ppp$country <- NULL
# 
# 
# try %<>%  
#   filter(!is.na(cow_code)) %>% 
#   mutate(ppp_budget_div = budget / ppp) %>% 
#   mutate(ppp_budget_mul = budget * ppp) %>% 
#   filter(country.x != "Kosovo") %>% 
#   select(country.x, budget, ppp, cow_code, ppp_budget_div,ppp_budget_mul ) -> hi
# 
# View(hi)
# 
# try %>% 
#   group_by(country.x) %>% 
#   mutate(ppp_budget = budget / ppp) %>% 
#   select(country = country.x, ppp_budget, raw_budget = budget) %>% 
#   arrange(desc(ppp_budget)) -> t3 
# 
# try %>% 
#   group_by(country.x) %>% 
#   mutate(ppp_budget = budget / ppp) %>% 
#   select(country.x, raw_budget = budget, ppp_budget) %>% 
#   arrange(desc(raw_budget)) -> t4
# 
# kbl(list(t3, t4), digits = 0, caption = "Arranged By PPP Adjusted Budget LEFT and By PPP Raw Budget RIGHT") %>%
#    kable_paper("striped",
#                full_width = T)

```

```{r}

# col %<>% 
#   select(cow_code, year, everything()) 
# 
# 
# url <- "https://www.numbeo.com/cost-of-living/rankings_by_country.jsp?title=2018"
# 
# col_url <- read_html(url)
# col_table <- col_url %>% html_table(header = TRUE, fill = TRUE)
# col_18 <- col_table[[2]]
# 
# col_18$year <- replicate(115, 2018)
# 
# col_18 %<>% 
#   clean_names()
# 
# col_18$cow_code <- countrycode(col_18$country, "country.name", "cown")
# # Some values were not matched unambiguously: Bermuda, Hong Kong, Jersey, Macao, Palestine, Puerto Rico, Serbia
# 
# col_18 %<>% dplyr::mutate(cow_code = ifelse(country == "Palestine", 6666, 
#                                               ifelse(country == "Serbia", 345, 
#                                                 ifelse(country == "Hong Kong", 997, cow_code))))
# col %<>% 
#   select(cow_code, year, everything()) 

```



## Adjust prices to 2014 prices

```{r message = FALSE, warning = FALSE}

country <- "United States"
countries_dataframe <- show_countries()
inflation_dataframe <- retrieve_inflation_data(country, countries_dataframe)

# Provide a World Bank API URL and `url_all_results` will convert it into one with all results for that indicator
original_url <- "http://api.worldbank.org/v2/country" 

# "http://api.worldbank.org/v2/country?format=json&per_page=304"
url_all_results(original_url)

the_df$budget_const <- adjust_for_inflation(the_df$budget, 
                                                the_df$year, 
                                                country, 
                                                to_date = 2014,
                                                inflation_dataframe = inflation_dataframe,
                                                countries_dataframe = countries_dataframe)

the_df$budget_pc_const <- adjust_for_inflation(the_df$budget_pc, 
                                              the_df$year, 
                                                   country, 
                                                   to_date = 2014,
                                                   inflation_dataframe = inflation_dataframe,
                                                   countries_dataframe = countries_dataframe)


```

## Economic factors 

### PD Budget per capita and US exports per capita to the country

#### Across the years

```{r}

the_df %>%
  # filter(pop > 1000000) %>% 
  ggplot(aes(y = log(budget_const), x = log(us_exports_pc))) + 
    geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + facet_wrap(~year) + 
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("Exports from US per capita vs PD budget per capita") + 
  xlab("Logged US exports per capita") +
  ylab("Logged PD budget per capita") + 
  theme(legend.position = "left") + ggthemes::theme_pander()

```



#### Only for 2019

```{r}

the_df %>%
  # filter(pop > 1000000) %>% 
  filter(year == 2018) %>% 
  ggplot(aes(y = log(budget_const), x = log(us_exports_pc), group = as.factor(region_6))) + 
  geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + 
  facet_wrap(~region_6) + 
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("Exports per capita vs PD budget per capita in 2019") + 
  xlab("Logged US exports per capita") +
  ylab("Logged PD budget per capita") + 
  theme(legend.position = "left") + ggthemes::theme_pander()

```

# Trade openness

Total export and total imports divided by total GDP

## Across the years

```{r}

the_df %>%
  # filter(pop > 1000000) %>% 
  ggplot(aes(y = log(budget_const), x =log(trade_openess))) + 
  geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + facet_wrap(~year) + 
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("Trade openess vs PD budget per capita") + 
  xlab("Trade openess") +
  ylab("Logged PD budget per capita") + 
  theme(legend.position = "left") + 
  ggthemes::theme_pander()

```

### Only for 2019

```{r}

the_df %>%
  filter(year == 2019) %>% 
  ggplot(aes(y = log(budget_const), x = log(trade_openess))) +
  geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
  # geom_text(aes(label = country, color = as.factor(region_6)), size = 3) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + 
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("Trade openess vs PD budget per capita in 2019") +
  facet_wrap(~region_6) + 
  xlab("Trade openness") +
  ylab("Logged PD budget per capita") + 
  theme(legend.position = "left") + ggthemes::theme_pander()

```


# Infant mortality

Infant mortality is a proxy for poverty levels: number of infant deaths per thousand live births

### Across the years

```{r}

the_df %>%
  # filter(pop > 1000000) %>% 
  filter(year < 2018) %>% 
  ggplot(aes(y = log(budget_const), x = log(infant_mort), group = as.factor(region_6))) + 
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + facet_wrap(~year) + 
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("Infant mortality age vs PD budget per capita") + 
  xlab("Deaths per thousand under 5") +
  ylab("Logged PD budget per capita") + 
  theme(legend.position = "left") + ggthemes::theme_pander()

```

### Only for 2019

```{r}

the_df %>%
  filter(year == 2015) %>% 
  ggplot(aes(y = log(budget_const), x = infant_mort, group = as.factor(region_6))) + 
  geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~region_6) + 
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("Infant mortality vs PD budget per capita in 2019") + 
  xlab("Infant mortality") +
  ylab("Logged PD budget per capita") + 
  theme(legend.position = "left") + ggthemes::theme_pander()

```



# GDP per capita

### Across the years

```{r}

the_df %>%
  ggplot(aes(y = log(budget_const), x = log(gdp_pc_const), group = as.factor(region_6))) + 
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + facet_wrap(~year) + 
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("Logged GDP per capita and logged PD budget per capita") + 
  xlab("Logged GDP per capita") +
  ylab("Logged PD budget per capita") + 
  theme(legend.position = "left") + ggthemes::theme_pander()

```

### Only for 2019

```{r}

the_df %>%
  filter(year == 2019) %>% 
  ggplot(aes(y = log(budget_const), x = log(gdp_pc_const), group = as.factor(region_6))) + 
  geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + 
  facet_wrap(~region_6) + 
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("GDP per capita vs PD budget per capita in 2019") + 
  ylab("Logged GDP per capita") +
  xlab("Logged PD budget per capita") + 
  theme(legend.position = "left") + ggthemes::theme_pander()

```


# Polity Index

### Across the years

Evidence of a U shaped relationship? More pronounced as years go on.

```{r}

the_df %>%
  filter(pop > 1000000) %>% 
  ggplot(aes(y = log(budget_pc), x = electoral_demo)) + 
    geom_smooth(method = "lm", color = "#9d0208") +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~year) + 
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("Polity Score vs PD budget per capita") + 
  xlab("Polity Score") +
  ylab("Logged PD budget per capita") + 
  theme(legend.position = "left") + ggthemes::theme_pander()

```

### Only for 2019

```{r}

the_df %>%
  filter(year == 2019) %>% 
  ggplot(aes(y = log(budget_pc), x = electoral_demo, group = as.factor(region_6))) + 
  geom_smooth(method = "loess", color = "#9d0208", se = FALSE) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + 
  facet_wrap(~region_6) + 
scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("Polity Score vs PD budget per capita in 2019") + 
  xlab("Polity Score (-10 to 10)") +
  ylab("Logged budget") +
  theme(legend.position = "left") + ggthemes::theme_pander()

```


# Percentage of population that follows Muslim faith

### Across the years

```{r}

the_df %>%
  mutate(muslim_pc = percent_muslim_jitter / pop, na.rm = TRUE) %>% 
  ggplot(aes(y = log(budget_const), x = muslim_pc)) + 
  geom_smooth(method = "loess", color = "#9d0208") +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~year) + 
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("Muslim population % vs PD budget per capita") + 
  xlab("Percentage of population Muslim") +
  ylab("Logged PD budget per capita") + 
  theme(legend.position = "left") + ggthemes::theme_pander()

```

### Only for 2019

```{r}

the_df %>%
  mutate(muslim_pc = percent_muslim_jitter / pop, na.rm = TRUE) %>% 
  filter(year == 2019) %>% 
  ggplot(aes(y = log(budget_const), x = muslim_pc, group = as.factor(region_6))) + 
  # geom_smooth(method = "loess", color = "#9d0208") +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + 
  facet_wrap(~region_6) + 
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("Muslim population % vs PD budget per capita in 2019") + 
  xlab("Percentage of population Muslim") +
  ylab("Logged PD budget per capita") + 
  theme(legend.position = "left") + ggthemes::theme_pander()

```


# Government censorship of media

Level of government censorship of the national media (from V-DEM dataset)

Higher scores indicate more freedeom from government censorship in the national media

### Across the years

```{r}

the_df %>%
  ggplot(aes(y = log(budget_const), x = gov_censor_media.x, group = as.factor(region_6))) + 
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) + 
  facet_wrap(~year) + 
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),name = NULL) +
  ggtitle("Government media censorship score vs PD budget per capita") + 
  xlab("Government media") +
  ylab("Logged PD budget per capita") + 
  theme(legend.position = "left") +
  ggthemes::theme_pander()

```

### Only for 2019

```{r}

the_df %>%
  filter(year == 2019) %>% 
  ggplot(aes(y = log(budget_const), x = gov_censor_media.x)) + 
  geom_smooth(method = "lm", color = "#9d0208") +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~region_6) +
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
  ggtitle("Government media censorship score vs PD budget per capita in 2019") + 
  xlab("Freedeom from government censorship in the media") +
  ylab("Logged PD budget per capita") + 
  theme(legend.position = "left") +
  ggthemes::theme_pander()

```


# Similar UN voting

It reflects states' UN voting positions in relation to the US-led liberal order (calculated on a single dimension).

Source: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LEJUQZ 

Bailey, M.A., Strezhnev, A., and Voeten, E (2017). Estimating dynamic state preferences from United Nations voting data. Journal of Conflict Resolution 61.2: 430-456. 

### Across the years

```{r}

the_df %>%
  filter(year == 2014) %>% 
  ggplot(aes(y = log(budget), x = ideal_point_distance)) +
  geom_smooth(method = "lm", color = "#9d0208", alpha = 0.5) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  # facet_wrap(~year) +
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
  ggtitle("UN voting similarity vs PD budget per capita") +
  xlab("Distance from US ideal score") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") +
  ggthemes::theme_pander()

```

We can see two clusters appear!

#### Only for 2019

```{r}
the_df %>%
  filter(year == 2019) %>%
  ggplot(aes(y = log(budget_const), x = ideal_point_distance, group = as.factor(region_6))) +
  geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~region_6) +
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("UN voting similarity vs PD budget per capita") +
  xlab("Distance from US ideal score") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") + 
  ggthemes::theme_pander()

```

# Important US votes

Source: Erik Voeten "Data and Analyses of Voting in the UN General Assembly" Routledge Handbook of International Organization, edited by Bob Reinalda (published May 27, 2013)

Important votes determined by the US State Department: Whether the vote was classified as important by the U.S. State Department report
"Voting Practices in the United Nations". These classifications began with session 39


```{r}

# library(unvotes)
# library(lubridate)
# 
# count(un_roll_call_issues, issue, sort = TRUE)
# 
# joined <- un_votes %>%
#   inner_join(un_roll_calls, by = "rcid") %>% 
#   inner_join(un_roll_call_issues, by = "rcid") %>% 
#   ungroup()
# 
# important <- joined %>% 
#   filter(importantvote == 1) %>% 
#   select(rcid, country, vote, session, date)
# 
# important %>%
#   mutate(vote_fac = as.factor(vote)) %>% 
#   mutate(vote_num = as.numeric(vote_fac)) %>% 
#   distinct(session, country, rcid, vote_num, .keep_all = "TRUE") -> temp2
# 
# temp2 %>% 
#   select(rcid, country, vote_num, session) %>% 
#   pivot_wider(names_from = country, values_from = "vote_num" ) -> imp_wide
# 
# temp2 %>% 
#   group_by(country_name)
#   mutate(country = if_else(Response == Correct_answer, 1, 0))
# 


```



# Level of domestic autonomy 

Measures the extent to which the country's govt have full sovereign control over the territory.

Higher scores indicate more control

#### Across all years

```{r}

the_df %>%
  ggplot(aes(y = log(budget_pc_const), x = intl_autonomy)) +
  geom_smooth(method = "lm", color = "#9d0208") +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~year) +
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
  ggtitle("Domestic autonomy vs PD budget per capita") +
  xlab("Domestic autonomy") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") +
  ggthemes::theme_pander()

```

#### Only for 2019

```{r}

the_df %>%
  filter(year == 2013) %>%
  ggplot(aes(y = log(budget_pc_const), x = intl_autonomy, group = as.factor(region_6))) +
  geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~region_6) +
 scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("Domestic autonomy vs PD budget per capita") +
  xlab("Domestic autonomy") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") + ggthemes::theme_pander()

```


# US diplomatic visits 

Number of visits by heads of state to the US 

```{r visits, echo = FALSE}

visits <- read.csv("C:/Users/Paula/Desktop/diplomatic_visits.csv")

visits$year <- as.integer(str_sub(visits$Date, -4, -1))

visits$cow_code <- countrycode(visits$From, "country.name", "cown")

visits %<>% 
  select(cow_code, From, everything())

visits %<>% 
  dplyr::mutate(cow_code = ifelse(From == "U.S.S.R.", 365,
                                ifelse(From == "Palestinian Authority", 6666,
                                       ifelse(From == "Serbia and Montenegro", 345,
                                              ifelse(From == "Serbia",345,
                                                     ifelse(From == "Yugoslavia (Kosovo)", 347,
                                                            ifelse(From == "Micronesia", 987, cow_code)))))))

visits %>% 
  group_by(cow_code, year) %>% 
  summarise(sum_head_vists = n()) %>% 
  ungroup() -> head_visits

visits %>% 
  group_by(From, year) %>% 
  count() %>% 
  filter(n > 1) %>% 
  arrange(desc(n)) %>% 
  kbl() %>%
  kable_paper("striped",
              full_width = F) 

visits %>% 
  group_by(From) %>% 
  count() %>% 
  filter(n > 40) %>% 
  arrange(desc(n)) %>% 
  kbl() %>%
  kable_paper("striped",
              full_width = F) 

```

# US trips

Trips by US President / Secretaries of State abroad

```{r trips, echo = FALSE}

travels <- read.csv("C:/Users/Paula/Desktop/diplomatic_travels.csv")

travels$year <- as.integer(str_sub(travels$Date, -4, -1))

travels$cow_code <- countrycode(travels$Country, "country.name", "cown")

travels %<>% 
  select(cow_code, Country, everything())

travels %<>% 
  dplyr::mutate(cow_code = ifelse(Country == "U.S.S.R.", 365,
                                ifelse(Country == "Palestinian Authority", 6666,
                                       ifelse(Country == "Serbia and Montenegro", 345,
                                              ifelse(Country == "Serbia",345,
                                                     ifelse(Country == "Yugoslavia (Kosovo)", 347, cow_code))))))

```


```{r}
travels %>% 
  group_by(cow_code, year) %>% 
  filter(Pres == 1) %>% 
  summarise(sum_pres_travels = n()) %>% 
  ungroup() -> pres_travel

travels %>% 
  group_by(cow_code, year) %>% 
  summarise(sum_all_travels = n()) %>% 
  ungroup() -> all_travel

travels %>% 
  group_by(cow_code, year) %>% 
  summarise(sum_index_travels = sum(dashindex)) %>% 
  ungroup() -> score_travel

the_df <- merge(the_df, pres_travel, by = c("cow_code", "year"), all.x = TRUE) 
the_df <- merge(the_df, head_visits, by = c("cow_code", "year"), all.x = TRUE) 
the_df <- merge(the_df, all_travel, by = c("cow_code", "year"), all.x = TRUE) 
the_df <- merge(the_df, score_travel, by = c("cow_code", "year"), all.x = TRUE) 

```

```{r}
### Across the years

library(tidyr)


the_df %>%
  filter(year < 2017) %>% 
  mutate(all_travel = coalesce(sum_all_travels, 0)) %>% 
  ggplot(aes(y = log(budget_pc_const), x = all_travel)) +
  geom_smooth(method = "lm", color = "#9d0208", alpha = 0.5) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~year)+
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
  ggtitle("UN voting similarity vs PD budget per capita") +
  xlab("Distance from US ideal score") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") +
  ggthemes::theme_pander()


```

```{r}


the_df %>%
  filter(year < 2017) %>% 
  # mutate(score_travel = coalesce(sum_index_travels, 0)) %>% 
  ggplot(aes(y = log(budget_pc_const), x = sum_index_travels)) +
  geom_smooth(method = "lm", color = "#9d0208", alpha = 0.5) +
  geom_text(aes(label = country, color = as.factor(region_6))) +
  # geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~year)+
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"), name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10), name = NULL) +
  ggtitle("High level visits DASH score vs PD budget per capita") +
  xlab("High level visits DASH score") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") +
  ggthemes::theme_pander()

```


#### Only for 2016

```{r}
the_df %>%
  filter(year == 2016) %>%
  mutate(all_travel = coalesce(sum_all_travels, 0)) %>% 
  ggplot(aes(y = log(budget_pc_const), x = all_travel)) +
  geom_smooth(method = "lm", color = "#9d0208", se = FALSE) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  facet_wrap(~region_6) +
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("high level visits vs PD budget per capita") +
  xlab("Number of high level visits") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") + 
  ggthemes::theme_pander()

```



```{r}

travels %>% 
  filter(Pres == 1) %>% 
  group_by(Country, year) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  kbl() %>%
  kable_paper("striped",
              full_width = F) 

```

# Trips by US presidents and Secretaries of State 

### Total number of trips by US presidents and Secretaries of State between 1945 to 2017

```{r}

travels %>% 
  group_by(Country) %>% 
  count() %>% 
  filter(n > 50) %>% 
  arrange(desc(n)) %>% 
  ggplot(aes(y = n,
             x = reorder(Country, n), fill = Country)) +
  coord_flip() +
  geom_histogram(stat = "identity") + ggthemes::theme_pander() +
  theme(legend.position = "none") + 
  ggtitle("Trips by US Presidents & State Secretaries 1945-2017") + 
  xlab("Country") +
  ylab("Number Trips by US President & Secretaries of State")

```

```{r, echo = FALSE}

travels %>% 
  group_by(Country) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  filter(n > 50) %>% 
  kbl() %>%
  kable_paper("striped",
              full_width = F) 

```

# US troop levels

```{r}

troops <- read.csv("C:/Users/Paula/Desktop/troops and military spending.csv", header = TRUE)

troops %>% 
  filter(year > 1950) %>%  
  group_by(country) %>% 
  summarise(sum_troops = sum(troops)) %>%
  filter(sum_troops > 50000) %>% 
  arrange(desc(sum_troops)) %>% 
  kbl() %>%
  kable_paper("striped",
              full_width = F) 

```

```{r}

troops %>% 
  group_by(ccode) %>% 
  summarise(sum_troops = sum(troops)) %>% 
  ungroup() -> troop_levels

the_df <- merge(the_df, troop_levels, by.x = "cow_code", by.y = "ccode", all.x = TRUE)

```


```{r}

library(stargazer)

the_df$sum_pres_travels[is.na(the_df$sum_pres_travels)] <- 0
the_df$sum_head_vists[is.na(the_df$sum_head_vists)] <- 0
the_df$sum_troops[is.na(the_df$sum_troops)] <- 0

```

```{r}

the_df %>%
  filter(sum_troops > 1) %>% 
  filter(year == 2019) %>% 
  ggplot(aes(y = log(budget_pc), x = log(sum_troops + 1), group = as.factor(region_6))) +
  # geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  geom_smooth(method = "lm", se = FALSE, aes(color = as.factor(region_6))) + 
  geom_text(aes(label = country, color = region_6), size = 4) +
  facet_wrap(~region_6) +
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("UN voting similarity vs PD budget per capita") +
  xlab("Distance from US ideal score") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") + ggthemes::theme_pander()

```


```{r}

the_df %>%
  # filter(sum_troops > 1) %>% 
  ggplot(aes(y = log(budget_pc), x = log(sum_troops + 1), group = as.factor(region_6)), alpha = 0.5) +
  geom_point(aes(color = as.factor(region_6), shape = as.factor(region_6))) +
  geom_smooth(method = "lm", se = FALSE, aes(color = as.factor(region_6))) + 
  # geom_text(aes(label = country, color = region_6), size = 4) +
  facet_wrap(~year) + 
  scale_color_manual(values = c("#219ebc", "#264653", "#43aa8b", "#6d597a", "#e63946", "#f4a261"),
                   name = NULL) +
  scale_shape_manual(values = c(15,16,17,18,19,10),
                     name = NULL) +
  ggtitle("UN voting similarity vs PD budget per capita") +
  xlab("Distance from US ideal score") +
  ylab("Logged PD budget per capita") +
  theme(legend.position = "left") + ggthemes::theme_pander()

```


# Overseas Military Bases in China, Russia and the US

Source: https://en.wikipedia.org/wiki/List_of_countries_with_overseas_military_bases



```{r}
  

```

# US bases and "lily pads"

Source:  https://github.com/meflynn/troopdata

Troop deployment data were initially compiled by Tim Kane using information obtained from the U.S. Department of Defense’s Defense Manpower Data Center (DMDC). The original data ended in 2005 and we have updated it to run through 2020

```{r}
library(troopdata)

troops_us <- get_troopdata(startyear = 2013, endyear = 2020)

```



```{r}

us_base_df <-read.csv("C:/Users/Paula/Desktop/PD_original_datasets/Bases.csv") 
  
us_base_df %>% 
  filter(base == 1) %>%
  group_by(Country.Name) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  filter(n > 2) %>% 
  kbl() %>%
  kable_paper("striped",
              full_width = F)

  us_base_df %>% 
  filter(lilypad == 1) %>%
  group_by(Country.Name) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  filter(n > 2) %>% 
  kbl() %>%
  kable_paper("striped",
              full_width = F)

  us_base_df %>% 
  group_by(Country.Name) %>% 
  count() %>% 
  arrange(desc(n)) %>% 
  filter(n > 5) %>% 
  kbl() %>%
  kable_paper("striped",
              full_width = F)
  
# war_on_terror <- c("Afghanistan", "Yemen", "Syria", "Iraq", "Pakistan")
# 
# my_pd9 %<>%
#   dplyr::mutate(war_on_terror = ifelse(country_name %in% war_on_terror, 1, 0)) 

  
```




# UN VOTING TWO CLUSTERS 


```{r un_clusters}

# the_df %>% 
#    filter(year == 2014) %>% 
#    select(country, ideal_point_distance) %>% 
#    arrange(desc(ideal_point_distance))
# 

# 
# 
# names(the_df)
# 
# low_model  <- plm(lead(log(budget_pc)) ~ log(gdp_pc_const) + 
#                               log(pop) + 
#                               log(us_exports_pc) +
#                               civ_lib_fh +
#                               relig_frac +
#                               rule_law_fh,
#                               data = low_un,
#                               model = "within", effect = "time",
#                               index = c("cow_code", "year"))
# 
# 
# high_model  <- plm(lead(log(budget_pc)) ~ log(gdp_pc_const) + 
#                               log(pop) + 
#                               log(us_exports_pc) +
#                               civ_lib_fh +
#                               relig_frac +
#                               rule_law_fh,
#                               data = high_un,
#                               model = "within", effect = "time",
#                               index = c("cow_code", "year"))
# 
# stargazer(low_model, high_model, column.labels = c("Low", "High"),
#           type = "text")


```
 


# Five year averages of dependent variables

```{r}

# the_df$period <- cut(the_df$year, seq(2013, 2018, 1)) 
# 
# pwt.ma <- ddply(pwt, .(country, period), numcolwise(mean))
# 
# pwt.ma
# 
# 
```


```{r}


excluding_countries <- c("Cuba", "Syria", "Yemen", "Libya")


the_df %<>%
  mutate(muslim_percent = percent_muslim_jitter / pop, na.rm = TRUE) %>%
  filter(!is.na(cow_code))

asia_df <- the_df %>%
  filter(region_6 == "Asia")
africa_df <- the_df %>%
  filter(region_6 == "Africa")
mena_df <- the_df %>%
  filter(region_6 == "MENA")
west_df <- the_df %>%
  filter(region_6 == "West")
latin_df <- the_df %>%
  filter(region_6 == "Latin")
soviet_df <- the_df %>%
  filter(region_6 == "Post Soviet")

dv <- "log(budget)"



ivs <- c("electoral_demo",
         "log(gdp_pc_const)",
         "log(pop)",
         "muslim_percent")




form_1 <- as.formula(paste(dv, paste(ivs, collapse = " + "), sep = " ~ "))


```
