democracy.score.vdem = scale(VDem),
democracy.score.polity = scale(polity),
democracy.score.eiu = scale(eiu)
)
Dataframe <- Dataframe %>%
mutate(
freedom.house.z.score = scale(democracy.score.fh),
vdem.z.score = scale(democracy.score.vdem),
polity.z.score = scale(democracy.score.polity),
eiu.z.score = scale(democracy.score.eiu)
)
View(Dataframe)
Dataframe <- Dataframe %>%
mutate(
democracy.scaled = rowMeans(select(., democracy.score.fh, democracy.score.vdem, democracy.score.polity, democracy.score.eiu), na.rm = TRUE)
)
Dataframe <- Dataframe %>%
mutate(
z.scores = rowMeans(select(., democracy.score.fh, democracy.score.vdem, democracy.score.polity, democracy.score.eiu), na.rm = TRUE)
)
View(Dataframe)
rm(Dataframe$democracy.scaled)
Dataframe <- Dataframe %>%
select(-democracy.scaled)
Dataframe <- Dataframe %>%
mutate(
democracy.scaled = 10 * (z.scores - min(z.scores, na.rm = TRUE)) /
(max(z.scores, na.rm = TRUE) - min(z.scores, na.rm = TRUE))
)
Dataframe <- Dataframe %>%
mutate(
freedom.house.z.score = as.numeric(scale(democracy.score.fh)),
vdem.z.score = as.numeric(scale(democracy.score.vdem)),
polity.z.score = as.numeric(scale(democracy.score.polity)),
eiu.z.score = as.numeric(scale(democracy.score.eiu))
)
Dataframe <- Dataframe %>%
mutate(
z.scores = rowMeans(
select(., democracy.score.fh, democracy.score.vdem, democracy.score.polity, democracy.score.eiu),
na.rm = TRUE
)
)
z.score.min <- min(Dataframe$z.scores, na.rm = TRUE)
z.score.max <- max(Dataframe$z.scores, na.rm = TRUE)
Dataframe <- Dataframe %>%
mutate(
z.scores = 10 * (z.score - z.score.min) / (z.score.max - z.score.min)
)
Dataframe <- Dataframe %>%
mutate(
z.scores = 10 * (z.scores - z.scores.min) / (z.scores.max - z.scores.min)
)
z.scores.min <- min(Dataframe$z.scores, na.rm = TRUE)
z.scores.max <- max(Dataframe$z.scores, na.rm = TRUE)
rm(z.score.max, z.score.min)
Dataframe <- Dataframe %>%
mutate(
z.scores = 10 * (z.scores - z.scores.min) / (z.scores.max - z.scores.min)
)
-10 Scale
Dataframe <- Dataframe %>%
mutate(
z.scores = rowMeans(
select(., democracy.score.fh, democracy.score.vdem, democracy.score.polity, democracy.score.eiu),
na.rm = TRUE
)
)
rm(z.scores.max, z.scores.min)
Dataframe <- Dataframe %>%
mutate(
-z.scores
)
Dataframe <- Dataframe %>%
select(-z.scores, --z.scores, -democracy.scaled)
View(Dataframe)
Dataframe <- read.csv("comparative Democracy Dataset.CSV")
Dataframe <- Dataframe %>%
mutate(
freedom.house.z.score = as.numeric(scale(democracy.score.fh)),
vdem.z.score = as.numeric(scale(democracy.score.vdem)),
polity.z.score = as.numeric(scale(democracy.score.polity)),
eiu.z.score = as.numeric(scale(democracy.score.eiu))
)
View(Dataframe)
rescaled.democracy <- function(x) {
10 * (x - min(x, na.rm = TUE)) /
(max(x, na.rm = TUE) - min(x, na.rm = TUE))
}
Dataframe <- Dataframe %>%
mutate(
fh.scaled.score = rescaled.democracy(democracy.score.fh),
vdem.Scaled.Score = rescaled.democracy(democracy.score.vdem),
polity.scaled.score = rescaled.democracy(democracy.score.polity),
eiu.scaled.score = rescaled.democracy(democracy.score.eiu)
)
Dataframe <- Dataframe %>%
mutate(
fh.scaled.score = rescaled.democracy(democracy.score.fh),
vdem.Scaled.Score = rescaled.democracy(democracy.score.vdem),
polity.scaled.score = rescaled.democracy(democracy.score.polity),
eiu.scaled.score = rescaled.democracy(democracy.score.eiu)
)
Dataframe <- Dataframe %>%
mutate(
fh.scaled.score = rescaled.democracy(democracy.score.fh),
vdem.Scaled.Score = rescaled.democracy(democracy.score.vdem),
polity.scaled.score = rescaled.democracy(democracy.score.polity),
eiu.scaled.score = rescaled.democracy(democracy.score.eiu)
)
Dataframe <- Dataframe %>%
mutate(
fh.scaled.score = rescaled.democracy(democracy.score.fh),
vdem.Scaled.Score = rescaled.democracy(democracy.score.vdem),
polity.scaled.score = rescaled.democracy(democracy.score.polity),
eiu.scaled.score = rescaled.democracy(democracy.score.eiu))
rescaled.democracy <- function(x) {
10 * (x - min(x, na.rm = TrUE)) /
(max(x, na.rm = TrUE) - min(x, na.rm = TrUE))
}
Dataframe <- Dataframe %>%
mutate(
fh.scaled.score = rescaled.democracy(democracy.score.fh),
vdem.Scaled.Score = rescaled.democracy(democracy.score.vdem),
polity.scaled.score = rescaled.democracy(democracy.score.polity),
eiu.scaled.score = rescaled.democracy(democracy.score.eiu))
rescaled.democracy <- function(x) {
10 * (x - min(x, na.rm = TRUE)) /
(max(x, na.rm = TRUE) - min(x, na.rm = TRUE))
}
Dataframe <- Dataframe %>%
mutate(
fh.scaled.score = rescaled.democracy(democracy.score.fh),
vdem.Scaled.Score = rescaled.democracy(democracy.score.vdem),
polity.scaled.score = rescaled.democracy(democracy.score.polity),
eiu.scaled.score = rescaled.democracy(democracy.score.eiu))
View(Dataframe)
Dataframe <- Dataframe %>%
select(-freedom.house.z.score, -vdem.z.score, -polity.z.score, -eiu.z.score)
Dataframe <- Dataframe %>%
mutate(
rescaled.democracy = rowMeans(
select(., fh.scaled.score, vdem.Scaled.Score, polity.scaled.score, eiu.scaled.score),
na.rm = TRUE
)
)
Dataframe <- Dataframe %>%
mutate(
scaled.democracy.avg = rowMeans(
select(., fh.scaled.score, vdem.Scaled.Score, polity.scaled.score, eiu.scaled.score),
na.rm = TRUE
)
)
Dataframe <- Dataframe %>%
select(-rescaled.democracy)
library(dplyr)
library(tidyr)
Dataframe <- Dataframe %>%
arrange(ccode, year)
Dataframe <- Dataframe %>%
group_by(ccode) %>%
mutate(
yearly.change = rescaled.democracy - lag(rescaled.democracy)
) %>%
ungroup()
Dataframe <- Dataframe %>%
group_by(ccode) %>%
mutate(
yearly.change = rescaled.democracy - lag(scaled.democracy.avg)
) %>%
ungroup()
Dataframe <- Dataframe %>%
group_by(ccode) %>%
mutate(
yearly.change = scaled.democracy.avg - lag(scaled.democracy.avg)
) %>%
ungroup()
Dataframe <- Dataframe %>%
mutate(
freedom.house.z.score = as.numeric(scale(democracy.score.fh)),
vdem.z.score = as.numeric(scale(democracy.score.vdem)),
polity.z.score = as.numeric(scale(democracy.score.polity)),
eiu.z.score = as.numeric(scale(democracy.score.eiu))
)
median(Dataframe$scaled.democracy.avg)
median(Dataframe$scaled.democracy.avg, na.rm = FALSE)
median(Dataframe$scaled.democracy.avg, na.rm = TRUE)
View(Dataframe)
library(readr)
write_csv(Dataframe, "Comparative Democratic Analysis")
write_csv(Dataframe, "Comparative Democratic Analysis.csv")
View(Dataframe)
Dataframe <- Dataframe %>%
select(-freedom.house.z.score, -vdem.z.score, -polity.z.score, -eiu.z.score)
library(tidyr)
library(dplyr)
Dataframe <- Dataframe %>%
select(-freedom.house.z.score, -vdem.z.score, -polity.z.score, -eiu.z.score)
Dataframe <- Dataframe %>%
mutate(
freedom.status = case_when(
scaled.democracy.avg >= 7 ~"Free",
scaled.democracy.avg >= 3.5 ~"Partly Free",
scaled.democracy.avg < 3.5 ~ "Not Free",
TRUE ~ NA_character_
)
)
View(Dataframe)
Dataframe <- Dataframe %>%
mutate(
freedom.house.z.score = as.numeric(scale(democracy.score.fh)),
vdem.z.score = as.numeric(scale(democracy.score.vdem)),
polity.z.score = as.numeric(scale(democracy.score.polity)),
eiu.z.score = as.numeric(scale(democracy.score.eiu))
)
## Saving the DF
library(readr)
write_csv(Dataframe, "Comparative Democratic Analysis.csv")
Dataframe <- Dataframe %>%
mutate(
freedom.status = case_when(
scaled.democracy.avg >= 7 ~"f",
scaled.democracy.avg >= 3.5 ~"pf",
scaled.democracy.avg < 3.5 ~ "nf",
TRUE ~ NA_character_
)
)
# z-scores
Dataframe <- Dataframe %>%
mutate(
freedom.house.z.score = as.numeric(scale(democracy.score.fh)),
vdem.z.score = as.numeric(scale(democracy.score.vdem)),
polity.z.score = as.numeric(scale(democracy.score.polity)),
eiu.z.score = as.numeric(scale(democracy.score.eiu))
)
## Saving the DF
library(readr)
write_csv(Dataframe, "Comparative Democratic Analysis.csv")
View(Dataframe)
head(Dataframe$region)
unique(Dataframe$region)
View(Dataframe)
View(Dataframe)
unique(Dataframe$cname)
library(countrycode)
install.packages(countrycode)
install.packages("countrycode")
library(countrycode)
Dataframe <- Dataframe %>%
mutate(
country = countrycode(iso3, "ccode", "cname")
)
library(dplyr)
Dataframe <- Dataframe %>%
mutate(
country = countrycode(iso3, "ccode", "cname")
)
Dataframe <- Dataframe %>%
mutate(
country = countrycode(ccode, "ccode", "cname")
)
View(Dataframe)
Dataframe <- Dataframe %>%
mutate(
country = countrycode(ccode, "iso3c", "country.name")
)
unique(Dataframe$cname)
country_map <- c(
"USA" = "United States of America",
"VNM" = "Vietnam",
"COG" = "Republic of the Congo",
"COD" = "Democratic Republic of Congo"
)
Dataframe <- Dataframe %>%
mutate(
country = country_map[ccode]
)
unique(Dataframe$cname)
str(Dataframe$ccode)
head(Dataframe$ccode, 20)
country_map <- c(
"USA " = "United States of America",
"VNM " = "Vietnam",
"COG " = "Republic of the Congo",
"COD " = "Democratic Republic of Congo"
)
Dataframe <- Dataframe %>%
mutate(
country = country_map[ccode]
)
unique(Dataframe$cname)
countrycode(unique(Dataframe$ccode), "iso3c", "country.name")
countrycode(unique(Dataframe$ccode), "iso3c", "country.name")
Dataframe <- Dataframe %>%
mutate(
ccode = trimws(ccode),
ccode = gsub("Viet nam", "Vietnam", ccode)
)
unique(Dataframe$cname)
rm(country_map)
Dataframe <- Dataframe %>%
mutate(
country = case_when(
ccode == "USA" ~ "United States of America",
ccode == "VNM" ~ "Vietnam",
ccode == "COD" ~ "Democratic Republic of the Congo",
ccode == "COG" ~ "Republic of the Congo",
ccode == "PRK" ~ "North Korea",
ccode == "LAO" ~ "Laos",
ccode == "KOR" ~ "South Korea",
ccode == "GMB" ~ "Gambia",
ccode == "CIV" ~ "Ivory Coast",
TRUE ~ country
)
)
View(Dataframe)
country_map <- c(
"USA" = "United States of America",
"VNM" = "Vietnam",
"COD" = "Democratic Republic of the Congo",
"COG" = "Republic of the Congo",
"PRK" = "North Korea",
"LAO" = "Laos",
"KOR" = "South Korea",
"GMB" = "Gambia",
"CIV" = "Ivory Coast"
)
Dataframe <- Dataframe %>%
mutate(
country = ifelse(ccode %in% names(country_map),
country_map[ccode],
country)
)
Dataframe <- Dataframe %>%
mutate(
ccode = trimws(as.character(ccode)),
ccode = toupper(ccode)
)
Dataframe %>%
filter(ccode == "USA")
country_map <- c(
"USA" = "United States of America",
"VNM" = "Vietnam",
"COD" = "Democratic Republic of the Congo",
"COG" = "Republic of the Congo",
"PRK" = "North Korea",
"LAO" = "Laos",
"KOR" = "South Korea",
"GMB" = "Gambia",
"CIV" = "Ivory Coast"
)
Dataframe <- Dataframe %>%
mutate(
country = ifelse(ccode %in% names(country_map),
country_map[ccode],
country)
)
Dataframe <- Dataframe %>%
mutate(
ccode = trimws(as.character(ccode)),
ccode = toupper(ccode)
)
Dataframe %>%
filter(ccode == "USA")
Dataframe <- Dataframe %>%
mutate(
country = case_when(
ccode == "USA" ~ "United States of America",
ccode == "VNM" ~ "Vietnam",
ccode == "COD" ~ "Democratic Republic of the Congo",
ccode == "COG" ~ "Republic of the Congo",
ccode == "PRK" ~ "North Korea",
ccode == "LAO" ~ "Laos",
ccode == "KOR" ~ "South Korea",
ccode == "GMB" ~ "Gambia",
ccode == "CIV" ~ "Ivory Coast",
TRUE ~ ccode
)
)
Dataframe %>%
mutate(ccode_clean = toupper(trimws(as.character(ccode)))) %>%
count(ccode_clean)
names(Dataframe)
head(Dataframe$ccode, 30)
names(Dataframe)
Dataframe <- Dataframe %>%
select(-country)
Dataframe <- Dataframe %>%
mutate(
country = countrycode(ccode, "iso3c", "country.name")
)
country_ref <- data.frame(
ccode = c("USA", "VNM", "COD", "COG", "PRK", "LAO", "KOR", "GMB", "CIV"),
cname = c(
"United States of America",
"Vietnam",
"Democratic Republic of the Congo",
"Republic of the Congo",
"North Korea",
"Laos",
"South Korea",
"Gambia",
"Ivory Coast"
)
)
Dataframe <- Dataframe %>%
left_join(country_ref, by = "ccode")
Dataframe <- Dataframe %>%
relocate(cname.y, .before = ccode)
Dataframe(
select(-cname.y)
Dataframe(
Dataframe <- Dataframe %>%
select(-cname.y)
Dataframe <- Dataframe %>%
relocate(country, .before = ccode)
Dataframe <- Dataframe %>%
rename(country = cname)
Dataframe <- Dataframe %>%
rename(cname = country)
Dataframe <- Dataframe %>%
select(-cname)
Dataframe <- Dataframe %>%
select(-cname)
Dataframe <- Dataframe %>%
rename(cname = cname.x
View(Dataframe)
View(Dataframe)
Dataframe <- Dataframe %>%
rename(cname = cname.x)
df <- data.frame(
iso_code = c("COD", "COG", "PRK", "LAO", "KOR", "GMB", "CIV", "USA", "VNM"),
stringsAsFactors = FALSE
)
# Create a new column with standardized names
df$standard_country_name <- countrycode(df$iso_code,
origin = "iso3c",
destination = "country.name")
print(df)
View(df)
df <- df %>%
mutate(country_name = countrycode(iso_code, "iso3c", "country.name"))
View(df)
Dataframe <- df %>%
mutate(country_name = countrycode(iso_code, "iso3c", "country.name"))
View(Dataframe)
unique(Dataframe$cname)
View(Dataframe)
rm(Dataframe)
rm(rescaled.democracy())
rm(rescaled.democracy)
Dataframe <- read.csv("CDI v1.0.csv")
View(Dataframe)
rm(Dataframe)
Dataframe <- read.csv("CDI v1.0.csv")
unqiue(Dataframe$cname)
unique(Dataframe$cname)
View(Dataframe)
rm(Dataframe)
Dataframe <- read.csv("CDI v1.0.csv")
unique(Dataframe$cname)
rm(Dataframe)
Dataframe <- read.csv("CDI v1.0.csv")
View(Dataframe)
rm(Dataframe)
Dataframe <- read.csv("CDI v1.0.csv")
unique(Dataframe$cname)
View(Dataframe)
View(Dataframe)
View(Dataframe)
Dataframe %>%
count(ccode, country) %>%
filter(n > 0) %>%
arrange(ccode, country)
library(dyplr)
library(dplyr)
Dataframe %>%
count(ccode, country) %>%
filter(n > 0) %>%
arrange(ccode, country)
Dataframe %>%
count(ccode, country) %>%
filter(n > 0) %>%
arrange(ccode, cname)
Dataframe %>%
count(ccode, cname) %>%
filter(n > 0) %>%
arrange(ccode, cname)
rm(Dataframe)
Dataframe <- read.csv(CDI v1.0)
Dataframe <- read.csv("CDI v1.0.csv")
Dataframe %>%
count(ccode, cname) %>%
filter(n > 0) %>%
arrange(ccode, cname)
View(Dataframe)
Dataframe %>%
count(ccode, year) %>%
filter(n > 1)
Polity.Data <- read.csv("Polity_CSV.csv")
Freedom.House.Data <- read.csv("freedom-score-fh.csv")
VDem.Data <- read.csv("V-Dem-CD-v16.csv")
EIU.Data <- read.csv("democracy-index-eiu.csv")
View(Freedom.House.Data)
View(EIU.Data)
View(Polity.Data)
View(Polity.Data)
View(VDem.Data)
Freedom.House.Data <- read.csv("freedom-score-fh.csv")
Freedom.House.Data <- read.csv("freedom-score-fh.csv")
Freedom.House.Data <- read.csv("freedom-score-fh.csv")
View(VDem.Data)
