Classifications
- Classification function
- Classification with two variables each on the health and gender dimensions
- This is the fuzzy version of the above classification (only for illustration, not for analyses)
- Merging the classifications into the dataset
- Subset of 1990 classifications data for which the “floor” rule was applied
- Which countries move more than 2 cells in the classifications?
- Removing countries with untrustworthy statistics
- Trends by classification group
- Cumulative death rate trends by 1995 classification groups
- Cumulative repression trends by 1995 classification groups
- Conflict incidence by 1995 classification groups
- Mean growth rates by classification with t-test (alt H: mean different from global)
- Preparing cross-sectional dataset
- Performance Rankings in classification groups
IsTrue <- function(x) { !is.na(x) & x }
load("_data/LSCMWG_working_data.RData", verbose = TRUE)## Loading objects:
## data
## codebook
## categories
## variablesClassification function
Classification <- function(health, gender,
df = data,
year_to_show = 1990,
min_to_include = 1
){
qs <- paste("q", 1:5, sep = "")
vars = c(health, gender)
df <- df[, c("country", "year", "period", vars)]
df <- df %>% group_by(country, period) %>%
mutate(across(all_of(vars), ~mean(.x, na.rm = TRUE), .names = "{col}_avg"), .keep = "all")
df <- df[df$year %in% c(seq(1965, 2015, 5), 2018), ]
df <- df %>% group_by(year)
df <- df %>% mutate(across(paste(vars, "avg", sep = "_"),
~quantile(.x, probs = seq(0, 1, 0.2), na.rm = TRUE)[2], .names = "{col}_q20"))
df <- df %>% mutate(across(paste(vars, "avg", sep = "_"),
~quantile(.x, probs = seq(0, 1, 0.2), na.rm = TRUE)[3], .names = "{col}_q40"))
df <- df %>% mutate(across(paste(vars, "avg", sep = "_"),
~quantile(.x, probs = seq(0, 1, 0.2), na.rm = TRUE)[4], .names = "{col}_q60"))
df <- df %>% mutate(across(paste(vars, "avg", sep = "_"),
~quantile(.x, probs = seq(0, 1, 0.2), na.rm = TRUE)[5], .names = "{col}_q80"))
df <- ungroup(df)
df[, paste(vars, "quintile", sep = "_")] <- parallel::mclapply(vars, function(var) {
col <- rep(NA, nrow(df))
col[df[, paste(var, "avg", sep = "_")] < df[, paste(var, "avg_q20", sep = "_")]] <- 1
col[df[, paste(var, "avg", sep = "_")] >= df[, paste(var, "avg_q20", sep = "_")] &
df[, paste(var, "avg", sep = "_")] < df[, paste(var, "avg_q40", sep = "_")]] <- 2
col[df[, paste(var, "avg", sep = "_")] >= df[, paste(var, "avg_q40", sep = "_")] &
df[, paste(var, "avg", sep = "_")] < df[, paste(var, "avg_q60", sep = "_")]] <- 3
col[df[, paste(var, "avg", sep = "_")] >= df[, paste(var, "avg_q60", sep = "_")] &
df[, paste(var, "avg", sep = "_")] < df[, paste(var, "avg_q80", sep = "_")]] <- 4
col[df[, paste(var, "avg", sep = "_")] >= df[, paste(var, "avg_q80", sep = "_")]] <- 5
return(col)
})
df <- df[, c("country", "year", "period", names(df)[str_detect(names(df), fixed("quintile"))])]
# combis <- expand.grid(health = health, gender = gender, stringsAsFactors = FALSE)
# combis <- split(combis, seq(nrow(combis)))
df$health_q1 <- rowSums(df[, paste(health, "quintile", sep = "_")] == 1, na.rm = TRUE)
df$health_q2 <- rowSums(df[, paste(health, "quintile", sep = "_")] == 2, na.rm = TRUE)
df$health_q3 <- rowSums(df[, paste(health, "quintile", sep = "_")] == 3, na.rm = TRUE)
df$health_q4 <- rowSums(df[, paste(health, "quintile", sep = "_")] == 4, na.rm = TRUE)
df$health_q5 <- rowSums(df[, paste(health, "quintile", sep = "_")] == 5, na.rm = TRUE)
df$gender_q1 <- rowSums(df[, paste(gender, "quintile", sep = "_")] == 1, na.rm = TRUE)
df$gender_q2 <- rowSums(df[, paste(gender, "quintile", sep = "_")] == 2, na.rm = TRUE)
df$gender_q3 <- rowSums(df[, paste(gender, "quintile", sep = "_")] == 3, na.rm = TRUE)
df$gender_q4 <- rowSums(df[, paste(gender, "quintile", sep = "_")] == 4, na.rm = TRUE)
df$gender_q5 <- rowSums(df[, paste(gender, "quintile", sep = "_")] == 5, na.rm = TRUE)
health_vars <- paste("health", qs, sep = "_")
gender_vars <- paste("gender", qs, sep = "_")
df$health_valid <- rowSums(df[, health_vars])
df$gender_valid <- rowSums(df[, gender_vars])
df$valid <- df$health_valid * df$gender_valid
to_keep <- c("country", "year", "period", "valid", "health_valid", "gender_valid", health_vars, gender_vars)
df <- df[df$valid > 0, to_keep]
health_true <- df[, health_vars] == apply(df[, health_vars], MARGIN = 1, FUN = max)
gender_true <- df[, gender_vars] == apply(df[, gender_vars], MARGIN = 1, FUN = max)
health_help <- health_true * matrix(1:5, nrow = nrow(health_true), ncol = 5, byrow = TRUE)
gender_help <- gender_true * matrix(1:5, nrow = nrow(gender_true), ncol = 5, byrow = TRUE)
help_floor <- function(set) { return(floor(mean(set[set != 0]))) }
help_ceiling <- function(set) { return(ceiling(mean(set[set != 0]))) }
df$health_class <- apply(health_help, MARGIN = 1, help_floor)
df$gender_class <- apply(gender_help, MARGIN = 1, help_floor)
df$health_class_alt <- apply(health_help, MARGIN = 1, help_ceiling)
df$gender_class_alt <- apply(gender_help, MARGIN = 1, help_ceiling)
df$health_flag <- ifelse(df$health_class != df$health_class_alt, 1, 0)
df$gender_flag <- ifelse(df$gender_class != df$gender_class_alt, 1, 0)
health_index <- paste("health_q", df$health_class, sep = "")
gender_index <- paste("gender_q", df$gender_class, sep = "")
df$health_n <- unlist(lapply(1:length(health_index), function(index) {
as.integer(df[index, health_index[index]])
}))
df$gender_n <- unlist(lapply(1:length(gender_index), function(index) {
as.integer(df[index, gender_index[index]])
}))
df$combi <- df$health_n * df$gender_n
df$class <- paste("H", df$health_class, "G", df$gender_class, sep = "")
df$class_num <-df$health_class * df$gender_class
test <- table(df[df$year == year_to_show & df$valid >= min_to_include, c("health_class", "gender_class")])
print(test)
number_of_countries <- sum(test)
df$support <- paste(df$country, " (", df$combi, "/", df$valid, ")", sep = "")
dat <- df[df$year == year_to_show, ]
table_to_return <- tapply(dat$support,
INDEX = list(health = dat$health_class, gender = dat$gender_class),
paste, collapse = "; ")
table_to_return[is.na(table_to_return)] <- ""
dimnames(table_to_return) <- lapply(dimnames(table_to_return), function(name) { paste("Q", name, sep = "") })
## from here, this is the old way, to create the fuzzy table, not needed anymore but useful for comparison
df$H1G1 <- df$health_q1 * df$gender_q1
df$H2G1 <- df$health_q2 * df$gender_q1
df$H3G1 <- df$health_q3 * df$gender_q1
df$H4G1 <- df$health_q4 * df$gender_q1
df$H5G1 <- df$health_q5 * df$gender_q1
df$H1G2 <- df$health_q1 * df$gender_q2
df$H2G2 <- df$health_q2 * df$gender_q2
df$H3G2 <- df$health_q3 * df$gender_q2
df$H4G2 <- df$health_q4 * df$gender_q2
df$H5G2 <- df$health_q5 * df$gender_q2
df$H1G3 <- df$health_q1 * df$gender_q3
df$H2G3 <- df$health_q2 * df$gender_q3
df$H3G3 <- df$health_q3 * df$gender_q3
df$H4G3 <- df$health_q4 * df$gender_q3
df$H5G3 <- df$health_q5 * df$gender_q3
df$H1G4 <- df$health_q1 * df$gender_q4
df$H2G4 <- df$health_q2 * df$gender_q4
df$H3G4 <- df$health_q3 * df$gender_q4
df$H4G4 <- df$health_q4 * df$gender_q4
df$H5G4 <- df$health_q5 * df$gender_q4
df$H1G5 <- df$health_q1 * df$gender_q5
df$H2G5 <- df$health_q2 * df$gender_q5
df$H3G5 <- df$health_q3 * df$gender_q5
df$H4G5 <- df$health_q4 * df$gender_q5
df$H5G5 <- df$health_q5 * df$gender_q5
vars <- c("H1G1", "H2G1", "H3G1", "H4G1", "H5G1",
"H1G2", "H2G2", "H3G2", "H4G2", "H5G2",
"H1G3", "H2G3", "H3G3", "H4G3", "H5G3",
"H1G4", "H2G4", "H3G4", "H4G4", "H5G4",
"H1G5", "H2G5", "H3G5", "H4G5", "H5G5")
names(vars) <- vars
dat <- as.data.frame(df[df$year == year_to_show, c("country", "valid", vars)])
classifications <- parallel::mclapply(vars, function(var_name) {
new <- dat[dat[, var_name] >= min_to_include, c("country", var_name, "valid")]
countries <- new$country
new <- paste(new[, "country"], " (", new[, var_name], "/", new[, "valid"], ")", sep = "")
new <- new[new != " (/)"]
return(list(class = new, ctry = countries))
})
countries <- sort(unique(unlist(lapply(classifications, function(x) { x$ctry }))))
classifications <- lapply(classifications, function(class) { class$class })
classifications <- lapply(classifications, paste, collapse = "; ")
# print(matrix(names(classifications), 5, 5, dimnames = list(health = qs, gender = qs)))
fuzzy_table <- matrix(classifications, 5, 5, dimnames = list(health = qs, gender = qs))
cat("Number of countries classified: ", number_of_countries, "\n")
if(number_of_countries != length(countries)) {
cat("\nNot classifying the same number of countries as the old way!")
}
df <- df[, !names(df) %in% vars]
return(list(df = df, n = number_of_countries, table = table_to_return, fuzzy = fuzzy_table))
}Classification with two variables each on the health and gender dimensions
result <- Classification(health = variables$health, gender = variables$gender)## gender_class
## health_class 1 2 3 4 5
## 1 26 7 1 0 0
## 2 8 15 7 4 0
## 3 0 8 14 10 1
## 4 0 2 10 15 0
## 5 0 0 1 20 7
## Number of countries classified: 156kableExtra::kable(result$table, format = "html") %>%
kableExtra::kable_styling("striped") %>%
kableExtra::add_header_above(c(" " = 1, "Gender" = 5)) %>%
kableExtra::group_rows("Health", 1, 5)| Q1 | Q2 | Q3 | Q4 | Q5 | |
|---|---|---|---|---|---|
| Health | |||||
| Q1 | Afghanistan (4/4); Angola (2/2); Bangladesh (1/4); Benin (2/4); Burkina Faso (2/2); Cameroon (1/4); Central African Republic (4/4); Chad (2/2); Congo, DRC (4/4); Cote d’Ivoire (4/4); Equatorial Guinea (2/2); Gambia (2/4); Guinea (2/2); Laos (2/4); Liberia (4/4); Madagascar (2/2); Malawi (4/4); Mali (4/4); Mozambique (4/4); Nepal (2/4); Niger (4/4); Nigeria (2/2); Sierra Leone (4/4); Tanzania (2/4); Uganda (2/4); Zambia (2/4) | Bhutan (1/2); Burundi (0/4); Cambodia (0/4); Guinea-Bissau (2/2); Haiti (0/4); Pakistan (0/4); Rwanda (2/4) | Somalia (2/2) | ||
| Q2 | Egypt (1/4); India (2/4); Kenya (2/4); Nicaragua (1/4); Senegal (2/4); Sudan (2/4); Togo (2/4); Yemen (4/4) | Bolivia (2/4); Comoros (2/2); Congo (2/4); El Salvador (2/4); Ghana (4/4); Guatemala (2/4); Indonesia (2/4); Iran (2/4); Maldives (0/4); Mauritania (4/4); Namibia (1/4); Papua New Guinea (2/4); Sao Tome & Principe (2/2); Turkey (1/4); Zimbabwe (2/4) | Botswana (0/4); Dominican Republic (0/4); Eswatini (0/4); Gabon (0/4); Morocco (0/4); Myanmar (2/4); Peru (2/4) | Djibouti (2/2); Kiribati (2/2); Lesotho (0/4); Mongolia (4/4) | |
| Q3 | Algeria (0/4); Cape Verde (2/2); Grenada (1/2); Honduras (0/4); Iraq (0/4); Saudi Arabia (2/4); Solomon Islands (2/2); Syria (1/4) | Belize (0/4); Ecuador (2/4); Jordan (0/4); Libya (0/4); Mauritius (0/4); Mexico (2/4); Oman (2/2); Paraguay (2/4); South Africa (0/4); Suriname (2/2); Thailand (1/4); Trinidad & Tobago (1/4); Tunisia (0/4); Vanuatu (2/2) | Albania (1/4); Brazil (0/4); China (1/4); Fiji (2/4); Guyana (0/4); Micronesia (2/2); Philippines (2/4); Samoa (1/2); Tonga (1/4); Viet Nam (0/4) | North Korea (1/2) | |
| Q4 | Panama (2/2); Saint Lucia (2/2) | Antigua & Barbuda (2/2); Bahamas (2/2); Brunei (1/4); Colombia (2/4); Costa Rica (0/4); Hungary (2/4); Jamaica (0/4); Romania (2/4); Saint Vincent & the Grenadines (2/2); Venezuela (0/4) | Argentina (0/4); Bahrain (4/4); Barbados (1/4); Bulgaria (0/4); Chile (2/4); Kuwait (4/4); Lebanon (2/2); Malaysia (0/4); Poland (2/4); Qatar (1/4); Seychelles (2/2); South Korea (0/4); Sri Lanka (4/4); United Arab Emirates (0/4); Uruguay (2/4) | ||
| Q5 | Cyprus (2/4) | Australia (2/4); Austria (0/4); Belgium (2/4); Cuba (0/4); Denmark (0/4); Finland (0/4); France (2/4); Greece (0/4); Israel (2/4); Italy (0/4); Japan (2/4); Luxembourg (0/4); Netherlands (2/4); New Zealand (4/4); Portugal (0/4); Singapore (0/4); Spain (0/4); Switzerland (0/4); United Kingdom (2/4); United States of America (2/4) | Canada (4/4); Iceland (4/4); Ireland (4/4); Malta (4/4); Norway (4/4); Sweden (4/4); Taiwan (2/2) | ||
This is the fuzzy version of the above classification (only for illustration, not for analyses)
kableExtra::kable(result$fuzzy, format = "html") %>%
kableExtra::kable_styling("striped") %>%
kableExtra::add_header_above(c(" " = 1, "Gender" = 5)) %>%
kableExtra::group_rows("Health", 1, 5)| q1 | q2 | q3 | q4 | q5 | |
|---|---|---|---|---|---|
| Health | |||||
| q1 | Afghanistan (4/4); Angola (2/2); Bangladesh (1/4); Benin (2/4); Burkina Faso (2/2); Burundi (2/4); Cambodia (1/4); Cameroon (1/4); Central African Republic (4/4); Chad (2/2); Congo, DRC (4/4); Cote d’Ivoire (4/4); Equatorial Guinea (2/2); Gambia (2/4); Guinea (2/2); Haiti (2/4); Laos (2/4); Liberia (4/4); Madagascar (2/2); Malawi (4/4); Mali (4/4); Mozambique (4/4); Nepal (2/4); Niger (4/4); Nigeria (2/2); Pakistan (1/4); Sierra Leone (4/4); Tanzania (2/4); Uganda (2/4); Zambia (2/4) | Bangladesh (1/4); Benin (2/4); Bhutan (1/2); Cameroon (1/4); Guinea-Bissau (2/2); Laos (2/4); Nepal (2/4); Rwanda (2/4); Tanzania (2/4); Uganda (2/4); Zambia (2/4) | Burundi (2/4); Cambodia (1/4); Haiti (2/4); Pakistan (1/4); Rwanda (2/4); Somalia (2/2) | ||
| q2 | Bangladesh (1/4); Cambodia (1/4); Cameroon (1/4); Egypt (1/4); Eswatini (1/4); Gabon (2/4); Gambia (2/4); India (2/4); Kenya (2/4); Maldives (2/4); Nicaragua (1/4); Pakistan (1/4); Senegal (2/4); Sudan (2/4); Togo (2/4); Yemen (4/4) | Bangladesh (1/4); Bhutan (1/2); Bolivia (2/4); Botswana (1/4); Cameroon (1/4); Comoros (2/2); Congo (2/4); Dominican Republic (1/4); Egypt (1/4); El Salvador (2/4); Ghana (4/4); Guatemala (2/4); India (2/4); Indonesia (2/4); Iran (2/4); Kenya (2/4); Mauritania (4/4); Morocco (1/4); Namibia (1/4); Nicaragua (1/4); Papua New Guinea (2/4); Sao Tome & Principe (2/2); Senegal (2/4); Sudan (2/4); Togo (2/4); Turkey (1/4); Zimbabwe (2/4) | Bolivia (2/4); Cambodia (1/4); El Salvador (2/4); Guatemala (2/4); Indonesia (2/4); Lesotho (2/4); Maldives (2/4); Myanmar (2/4); Namibia (1/4); Pakistan (1/4); Papua New Guinea (2/4); Peru (2/4); Turkey (1/4) | Botswana (1/4); Djibouti (2/2); Dominican Republic (1/4); Kiribati (2/2); Mongolia (4/4); Morocco (1/4); Myanmar (2/4) | Eswatini (1/4); Gabon (2/4); Lesotho (2/4) |
| q3 | Algeria (2/4); Egypt (1/4); Eswatini (1/4); Honduras (2/4); Iraq (2/4); Nicaragua (1/4) | Belize (1/4); Botswana (1/4); Cape Verde (2/2); Congo (2/4); Dominican Republic (1/4); Egypt (1/4); Grenada (1/2); Iran (2/4); Jordan (1/4); Libya (2/4); Mauritius (1/4); Morocco (1/4); Namibia (1/4); Nicaragua (1/4); Saudi Arabia (2/4); Solomon Islands (2/2); South Africa (2/4); Syria (1/4); Tunisia (2/4); Turkey (1/4); Zimbabwe (2/4) | Brazil (2/4); Ecuador (2/4); Guyana (2/4); Iraq (2/4); Mexico (2/4); Namibia (1/4); Oman (2/2); Paraguay (2/4); Peru (2/4); Saudi Arabia (2/4); Suriname (2/2); Syria (1/4); Thailand (1/4); Trinidad & Tobago (1/4); Turkey (1/4); Vanuatu (2/2); Viet Nam (1/4) | Albania (1/4); Algeria (2/4); Botswana (1/4); China (1/4); Dominican Republic (1/4); Ecuador (2/4); Fiji (2/4); Honduras (2/4); Jordan (1/4); Mauritius (1/4); Micronesia (2/2); Morocco (1/4); Paraguay (2/4); Philippines (2/4); Samoa (1/2); South Africa (2/4); Thailand (1/4); Tonga (1/4); Trinidad & Tobago (1/4) | Albania (1/4); Belize (1/4); Brazil (2/4); China (1/4); Eswatini (1/4); Guyana (2/4); Libya (2/4); North Korea (1/2); Philippines (2/4); Tonga (1/4); Tunisia (2/4); Viet Nam (1/4) |
| q4 | Belize (1/4); Costa Rica (1/4); Grenada (1/2); Jamaica (2/4); Jordan (1/4); Mauritius (1/4); Panama (2/2); Saint Lucia (2/2); Syria (1/4); Venezuela (2/4) | Antigua & Barbuda (2/2); Argentina (2/4); Bahamas (2/2); Brunei (1/4); Bulgaria (1/4); Colombia (2/4); Hungary (2/4); Malaysia (1/4); Mexico (2/4); Romania (2/4); Saint Vincent & the Grenadines (2/2); South Korea (1/4); Syria (1/4); Thailand (1/4); Trinidad & Tobago (1/4); United Arab Emirates (2/4); Viet Nam (1/4) | Albania (1/4); Bahrain (4/4); Barbados (1/4); Brunei (1/4); Chile (2/4); China (1/4); Colombia (2/4); Fiji (2/4); Hungary (2/4); Jordan (1/4); Kuwait (4/4); Lebanon (2/2); Mauritius (1/4); Poland (2/4); Qatar (1/4); Romania (2/4); Samoa (1/2); Seychelles (2/2); Sri Lanka (4/4); Thailand (1/4); Tonga (1/4); Trinidad & Tobago (1/4); Uruguay (2/4); Venezuela (2/4) | Albania (1/4); Argentina (2/4); Barbados (1/4); Belize (1/4); Bulgaria (1/4); China (1/4); Costa Rica (1/4); Jamaica (2/4); Malaysia (1/4); North Korea (1/2); Poland (2/4); Qatar (1/4); South Korea (1/4); Tonga (1/4); United Arab Emirates (2/4); Uruguay (2/4); Viet Nam (1/4) | |
| q5 | Costa Rica (1/4) | Austria (2/4); Brunei (1/4); Bulgaria (1/4); Cuba (2/4); Cyprus (2/4); Denmark (2/4); Finland (2/4); Greece (2/4); Italy (2/4); Luxembourg (2/4); Malaysia (1/4); Portugal (2/4); Singapore (2/4); South Korea (1/4); Spain (2/4); Switzerland (2/4) | Australia (2/4); Barbados (1/4); Belgium (2/4); Brunei (1/4); Chile (2/4); Cyprus (2/4); France (2/4); Israel (2/4); Japan (2/4); Netherlands (2/4); New Zealand (4/4); Qatar (1/4); United Kingdom (2/4); United States of America (2/4) | Australia (2/4); Austria (2/4); Barbados (1/4); Belgium (2/4); Bulgaria (1/4); Canada (4/4); Costa Rica (1/4); Cuba (2/4); Denmark (2/4); Finland (2/4); France (2/4); Greece (2/4); Iceland (4/4); Ireland (4/4); Israel (2/4); Italy (2/4); Japan (2/4); Luxembourg (2/4); Malaysia (1/4); Malta (4/4); Netherlands (2/4); Norway (4/4); Portugal (2/4); Qatar (1/4); Singapore (2/4); South Korea (1/4); Spain (2/4); Sweden (4/4); Switzerland (2/4); Taiwan (2/2); United Kingdom (2/4); United States of America (2/4) | |
Merging the classifications into the dataset
variables$class <- c("health_valid", "gender_valid", "health_class", "gender_class",
"health_class_alt", "gender_class_alt", "health_flag", "gender_flag",
"health_n", "gender_n", "class", "support")
df <- as.data.frame(result$df[, c("country", "year", "period", "class_num", variables$class)])
flagged <- df[df$year == 1990 & (df$health_flag == 1 | df$gender_flag == 1),
c("country", "class_num", variables$class)]
df$class_vv <- NA
df$class_vv[df$health_class < 3 & df$gender_class < 3] <- "low"
df$class_vv[df$health_class > 3 & df$gender_class > 3] <- "upp"
df$class_vv[!(df$health_class < 3 & df$gender_class < 3) & !(df$health_class > 3 & df$gender_class > 3)] <- "mid"
df$class_vv[df$class_vv == "mid" & df$health_class > df$gender_class] <- "H > G"
df$class_vv[df$class_vv == "mid" & df$health_class < df$gender_class] <- "G > H"
# addmargins(table(df$class_vv, df$class))
df$class_low <- ifelse(df$class_vv == "low", 1, 0)
df$class_upp <- ifelse(df$class_vv == "upp", 1, 0)
variables$class_core <- c("country", "class", "health_class", "gender_class", "class_vv")
class1990 <- df[df$year == 1990, variables$class_core]
names(class1990) <- c("country", paste(c("class", "health", "gender", "class_vv"), 1990, sep = ""))
class1995 <- df[df$year == 1995, variables$class_core]
names(class1995) <- c("country", paste(c("class", "health", "gender", "class_vv"), 1995, sep = ""))
df <- merge(data, df[, names(df) != "year"], by = c("country", "period"), all = TRUE)
df <- merge(df, class1990, by = "country", all.x = TRUE)
df <- merge(df, class1995, by = "country", all.x = TRUE)
df <- df[order(df$country, df$year),
c("country", "year", "period", names(df)[!names(df) %in% c("country", "year", "period")])]
df <- df[df$year != 1960 & df$year != 2019, ]
filepath <- paste("~/Dropbox/Lancet-SIGHT Commission/Working Groups/Metrics/Datasets/dataset_cy_class_",
lubridate::today(), ".csv", sep = "" )
write_csv(df, file = filepath)Subset of 1990 classifications data for which the “floor” rule was applied
flaggedWhich countries move more than 2 cells in the classifications?
names(years) <- years <- c(1990, 1995, 2015)
dat <- lapply(years, function(year) {
df <- as.data.frame(df[df$year == year, c("country", "class_num")])
names(df)[2] <- paste("class", year, sep = "")
return(df)
})
dat <- Reduce(f = function(...) merge(..., by = "country", all = TRUE), x = dat)
dat$diff <- dat$class2015 - dat$class1990
dat$diff_alt <- dat$class2015 - dat$class1995
better <- dat[IsTrue(dat$diff > 2 | dat$diff_alt > 2), ]
worse <- dat[IsTrue(dat$diff < -2 | dat$diff_alt < -2), ]
better worseRemoving countries with untrustworthy statistics
All data is subject to measurement error. This is particularly problematic if measurement is systematically biased. Without other data to validate a given measure, this is a very difficult to problem to overcome. We only use data from reputable sources, such as academic centre and international organizations, but some self-reported data are still suspect. The World Bank and some NGOs have attempted to rate the capacity of National Statistical System, and some countries are not inlcuded in their rankings because assessments could be not be made based on available information. For instance, the ODIN rankings from 2015 to 2018 do not include the Central African Republic, Eritrea, Equatorial Guinea, and North Korea. Interestingly, among these, only North Korea is relatively highly ranked on the gender and health dimensions according to these data, which is simply not believable. Therefore, we remove only North Korea from the analyses.
df <- df[df$country != "North Korea", ]Trends by classification group
summary(as.factor(df$class_vv1990))
summary(as.factor(df$class_vv1995))
lapply(c(variables$health_full, variables$gender_full), function(var) {
dat <- df[!is.na(df$class_vv1995) & df$class_vv1995 != "mid", ]
ggplot(dat, aes(x = year, y = !!sym(var), group = class_vv1995, color = class_vv1995)) +
stat_summary(fun="mean", geom="line", na.rm = TRUE, size = 1) +
xlim(1980, 2018) +
theme_bw() +
ggtitle(paste(var, "average by 1995 classification over time"))
})
Cumulative death rate trends by 1995 classification groups
lapply(variables$death_rates, function(var) {
dat <- df[!is.na(df$class_vv1995) & df$class_vv1995 != "mid", ]
ggplot(dat, aes(x = year, y = log(!!sym(paste(var, "cumulative1991", sep = "_")) + 1),
group = class_vv1995, color = class_vv1995)) +
stat_summary(fun="mean", geom="line", na.rm = TRUE, size = 1) +
xlim(1990, 2018) +
theme_bw() +
ggtitle(paste(var, "annual cumulative average (logged) since 1991 by 1995 classification"))
})
Cumulative repression trends by 1995 classification groups
lapply(variables$measurement_models, function(var) {
dat <- df[!is.na(df$class_vv1995) & df$class_vv1995 != "mid", ]
ggplot(dat, aes(x = year, y = !!sym(paste(var, "cumulative1991", sep = "_")),
group = class_vv1995, color = class_vv1995)) +
stat_summary(fun="mean", geom="line", na.rm = TRUE, size = 1) +
xlim(1990, 2018) +
theme_bw() +
ggtitle(paste(var, "annual cumulative average since 1991 by 1995 classification"))
})
Conflict incidence by 1995 classification groups
lapply(variables$conflict_incidence, function(var) {
dat <- df[df$year > 1988, ]
tbl <- tapply(dat[, var], list(health = dat$health1995, gender = dat$gender1995), mean, na.rm = TRUE)
dat <- dat[!is.na(dat$class_vv1995) & dat$class_vv1995 != "mid", ]
plot <- ggplot(dat, aes(x = year, y = !!sym(var), group = class_vv1995, color = class_vv1995)) +
stat_summary(fun="mean", geom="line", na.rm = TRUE, size = 1) +
xlim(1989, 2018) +
theme_bw() +
ggtitle(paste(var, " incidence by classification in 1995"))
return(list(tbl, plot))
})
Mean growth rates by classification with t-test (alt H: mean different from global)
GrowthRatesTtest <- function(var, min_obs = 2) {
dat1995 <- df[df$year == 1995, c("country", "class", "health_class", "gender_class", var)]
dat2015 <- df[df$year == 2015, c("country", var)]
names(dat1995)[names(dat1995) != "country"] <- paste(names(dat1995)[names(dat1995) != "country"] ,
1995, sep = "_")
names(dat2015)[names(dat2015) != "country"] <- paste(names(dat2015)[names(dat2015) != "country"] ,
2015, sep = "_")
dat <- merge(dat1995, dat2015, by = c("country"), all = TRUE)
dat$growth <- dat[, paste(var, 2015, sep ="_")] - dat[, paste(var, 1995, sep ="_")]
tbl <- tapply(dat$growth, list(health = dat$health_class_1995, gender = dat$gender_class_1995), function(df) {
if(sum(!is.na(df)) >= min_obs) {
result <- t.test(df, mu = mean(dat$growth, na.rm = TRUE))
mean <- round(result[["estimate"]][["mean of x"]], 2)
pvalue <- round(result[["p.value"]], 2)
df <- result[["parameter"]][["df"]]
cell <- paste(mean, " (p-value ", pvalue, "; df ", df, ")", sep = "")
} else { cell <- "" }
return(cell)
})
tbl[is.na(tbl)] <- ""
return(list(global_mean = mean(dat$growth, na.rm = TRUE), ttests = noquote(tbl)))
}
variables$outcomes <- c(variables$health_full, variables$gender_full, variables$death_rates, variables$measurement_models)
parallel::mclapply(variables$outcomes, GrowthRatesTtest)## $imr_wpp
## $imr_wpp$global_mean
## [1] 22.86067
##
## $imr_wpp$ttests
## gender
## health 1 2 3 4 5
## 1 50.66 (p-value 0; df 28) 54.21 (p-value 0.02; df 10)
## 2 30.62 (p-value 0.2; df 6) 30.81 (p-value 0.08; df 11) 23.66 (p-value 0.87; df 11) 21.77 (p-value 0.75; df 4)
## 3 20.05 (p-value 0.22; df 7) 14.82 (p-value 0; df 18) 17.03 (p-value 0.03; df 11)
## 4 5.58 (p-value 0; df 3) 6.77 (p-value 0; df 14) 9.16 (p-value 0; df 11)
## 5 4.34 (p-value 0.03; df 1) 3.39 (p-value 0; df 22) 3.04 (p-value 0; df 4)
##
##
## $ufmr_wpp
## $ufmr_wpp$global_mean
## [1] 37.07946
##
## $ufmr_wpp$ttests
## gender
## health 1 2 3 4 5
## 1 93.01 (p-value 0; df 28) 104.83 (p-value 0.06; df 10)
## 2 54.14 (p-value 0.12; df 6) 45.28 (p-value 0.2; df 11) 31.56 (p-value 0.39; df 11) 32.84 (p-value 0.42; df 4)
## 3 26.61 (p-value 0.01; df 7) 18.68 (p-value 0; df 18) 20.58 (p-value 0; df 11)
## 4 7.63 (p-value 0; df 3) 8.37 (p-value 0; df 14) 10.74 (p-value 0; df 11)
## 5 6.17 (p-value 0.04; df 1) 4.18 (p-value 0; df 22) 4 (p-value 0; df 4)
##
##
## $life_exp_wpp
## $life_exp_wpp$global_mean
## [1] 5.835333
##
## $life_exp_wpp$ttests
## gender
## health 1 2 3 4 5
## 1 9.14 (p-value 0; df 28) 13.11 (p-value 0.06; df 10)
## 2 5.25 (p-value 0.83; df 6) 7.3 (p-value 0.13; df 11) 2.8 (p-value 0.07; df 11) 5 (p-value 0.29; df 4)
## 3 4.99 (p-value 0.25; df 7) 4.13 (p-value 0; df 18) 5.35 (p-value 0.33; df 11)
## 4 1.75 (p-value 0.04; df 3) 3.59 (p-value 0; df 14) 4.66 (p-value 0.02; df 11)
## 5 4.26 (p-value 0.22; df 1) 4.54 (p-value 0; df 22) 4.58 (p-value 0.01; df 4)
##
##
## $daly_ihme
## $daly_ihme$global_mean
## [1] 14551.19
##
## $daly_ihme$ttests
## gender
## health 1 2 3 4 5
## 1 47988.28 (p-value 0; df 28) 44097.91 (p-value 0; df 10)
## 2 21531.1 (p-value 0.26; df 6) 15662.09 (p-value 0.64; df 11) 6441.71 (p-value 0.01; df 11) 15452.03 (p-value 0.87; df 4)
## 3 7955.44 (p-value 0.01; df 7) 3119.08 (p-value 0; df 18) 4205.14 (p-value 0; df 11)
## 4 -3915.65 (p-value 0.01; df 3) 181.93 (p-value 0; df 14) 3659.28 (p-value 0; df 11)
## 5 961.87 (p-value 0.1; df 1) 2197.38 (p-value 0; df 22) 3045.73 (p-value 0; df 4)
##
##
## $labor_mod_ratio15_ilo
## $labor_mod_ratio15_ilo$global_mean
## [1] 0.05810865
##
## $labor_mod_ratio15_ilo$ttests
## gender
## health 1 2 3 4 5
## 1 0.01 (p-value 0; df 28) 0.04 (p-value 0.26; df 10)
## 2 0.04 (p-value 0.64; df 6) 0.04 (p-value 0.5; df 11) 0.07 (p-value 0.74; df 11) 0.02 (p-value 0.64; df 2)
## 3 0.1 (p-value 0.06; df 7) 0.03 (p-value 0.11; df 18) 0.03 (p-value 0.11; df 11)
## 4 0.06 (p-value 0.94; df 3) 0.08 (p-value 0.5; df 11) 0.05 (p-value 0.83; df 11)
## 5 0.1 (p-value 0.37; df 1) 0.13 (p-value 0; df 22) 0.12 (p-value 0.11; df 4)
##
##
## $wmn_parl_wdi
## $wmn_parl_wdi$global_mean
## [1] NaN
##
## $wmn_parl_wdi$ttests
## gender
## health 1 2 3 4 5
## 1
## 2
## 3
## 4
## 5
##
##
## $tfr_wpp
## $tfr_wpp$global_mean
## [1] 0.9533444
##
## $tfr_wpp$ttests
## gender
## health 1 2 3 4 5
## 1 1.36 (p-value 0.01; df 28) 1.8 (p-value 0; df 10)
## 2 1.42 (p-value 0.01; df 6) 1.2 (p-value 0.23; df 11) 0.83 (p-value 0.39; df 11) 1.39 (p-value 0.29; df 4)
## 3 1.92 (p-value 0.01; df 7) 1 (p-value 0.79; df 18) 0.51 (p-value 0; df 11)
## 4 1.13 (p-value 0.64; df 3) 0.65 (p-value 0.02; df 14) 0.73 (p-value 0.26; df 11)
## 5 -0.04 (p-value 0.05; df 1) 0.08 (p-value 0; df 22) 0.14 (p-value 0; df 4)
##
##
## $asfr_adol_wpp
## $asfr_adol_wpp$global_mean
## [1] 22.53888
##
## $asfr_adol_wpp$ttests
## gender
## health 1 2 3 4 5
## 1 34.17 (p-value 0; df 28) 26.25 (p-value 0.61; df 10)
## 2 38.11 (p-value 0.05; df 6) 26.3 (p-value 0.58; df 11) 21.17 (p-value 0.84; df 11) 18.17 (p-value 0.46; df 4)
## 3 32.47 (p-value 0.2; df 7) 20.92 (p-value 0.64; df 18) 19.61 (p-value 0.56; df 11)
## 4 29.92 (p-value 0.44; df 3) 19.68 (p-value 0.45; df 14) 17.93 (p-value 0.14; df 11)
## 5 12.29 (p-value 0.02; df 1) 8.02 (p-value 0; df 22) 8.36 (p-value 0; df 4)
##
##
## $daly_ratio_ihme
## $daly_ratio_ihme$global_mean
## [1] 0.00972674
##
## $daly_ratio_ihme$ttests
## gender
## health 1 2 3 4 5
## 1 0 (p-value 0.29; df 28) -0.03 (p-value 0.05; df 10)
## 2 -0.02 (p-value 0.07; df 6) -0.01 (p-value 0.21; df 11) 0.03 (p-value 0.48; df 11) -0.02 (p-value 0.01; df 4)
## 3 -0.05 (p-value 0.01; df 7) 0 (p-value 0.41; df 18) 0.05 (p-value 0.08; df 11)
## 4 -0.06 (p-value 0.17; df 3) 0.04 (p-value 0.11; df 14) 0.04 (p-value 0.24; df 11)
## 5 0 (p-value 0.7; df 1) 0.04 (p-value 0; df 22) 0.05 (p-value 0.02; df 4)
##
##
## $mys_ratio_hdr
## $mys_ratio_hdr$global_mean
## [1] 0.09543494
##
## $mys_ratio_hdr$ttests
## gender
## health 1 2 3 4 5
## 1 0.19 (p-value 0; df 20) 0.15 (p-value 0.14; df 6)
## 2 0.12 (p-value 0.76; df 5) 0.13 (p-value 0.24; df 9) 0.03 (p-value 0.19; df 10)
## 3 0.19 (p-value 0.19; df 4) 0.13 (p-value 0.34; df 15) 0.03 (p-value 0; df 9)
## 4 0.07 (p-value 0.19; df 10) 0.04 (p-value 0.07; df 9)
## 5 0.08 (p-value 0.83; df 1) 0.05 (p-value 0; df 22) 0.02 (p-value 0; df 3)
##
##
## $deaths_all_int_rate
## $deaths_all_int_rate$global_mean
## [1] 1.082219
##
## $deaths_all_int_rate$ttests
## gender
## health 1 2 3 4 5
## 1 0.57 (p-value 0.76; df 28) 0.17 (p-value 0.35; df 10)
## 2 -0.69 (p-value 0.01; df 6) -0.4 (p-value 0; df 11) 0 (p-value 0; df 11) 0.11 (p-value 0; df 4)
## 3 3.18 (p-value 0.53; df 7) -0.32 (p-value 0.04; df 18) 0.03 (p-value 0; df 11)
## 4 66.08 (p-value 0.4; df 3) -2.93 (p-value 0.07; df 14) -3.03 (p-value 0.19; df 11)
## 5 0 (p-value 0; df 1) 0 (p-value 0; df 22) -0.19 (p-value 0; df 4)
##
##
## $deaths_civilians_int_rate
## $deaths_civilians_int_rate$global_mean
## [1] 0.2311096
##
## $deaths_civilians_int_rate$ttests
## gender
## health 1 2 3 4 5
## 1 0.07 (p-value 0.51; df 28) 0.05 (p-value 0.04; df 10)
## 2 0.07 (p-value 0.08; df 6) 0 (p-value 0; df 11) -0.01 (p-value 0; df 11) 0.01 (p-value 0; df 4)
## 3 0.3 (p-value 0.83; df 7) 0.06 (p-value 0; df 18) 0.01 (p-value 0; df 11)
## 4 11.64 (p-value 0.4; df 3) -0.17 (p-value 0.01; df 14) -0.54 (p-value 0.17; df 11)
## 5 0 (p-value 0; df 1) 0 (p-value 0; df 22) 0 (p-value 0; df 4)
##
##
## $deaths_civilians_osv_rate
## $deaths_civilians_osv_rate$global_mean
## [1] -2.319773
##
## $deaths_civilians_osv_rate$ttests
## gender
## health 1 2 3 4 5
## 1 -5.45 (p-value 0.45; df 28) -6.38 (p-value 0.54; df 10)
## 2 -0.26 (p-value 0; df 6) 0.01 (p-value 0; df 11) 0 (p-value 0; df 11) -0.05 (p-value 0; df 4)
## 3 0.32 (p-value 0; df 7) 0.05 (p-value 0; df 18) 0.02 (p-value 0; df 11)
## 4 1.91 (p-value 0.11; df 3) -0.21 (p-value 0; df 14) -18.22 (p-value 0.4; df 11)
## 5 0 (p-value 0; df 1) 0.01 (p-value 0; df 22) -0.06 (p-value 0; df 4)
##
##
## $deaths_all_nsc_rate
## $deaths_all_nsc_rate$global_mean
## [1] 0.2496955
##
## $deaths_all_nsc_rate$ttests
## gender
## health 1 2 3 4 5
## 1 0.21 (p-value 0.85; df 28) -0.56 (p-value 0.09; df 10)
## 2 0.29 (p-value 0.88; df 6) -0.08 (p-value 0; df 11) -0.03 (p-value 0; df 11) -1.02 (p-value 0.11; df 4)
## 3 -0.41 (p-value 0.15; df 7) 1.05 (p-value 0.43; df 18) 0 (p-value 0; df 11)
## 4 8.62 (p-value 0.4; df 3) 0 (p-value 0; df 14) 0.07 (p-value 0.02; df 11)
## 5 0 (p-value 0; df 1) 0 (p-value 0; df 22) 0 (p-value 0; df 4)
##
##
## $deaths_civilians_nsc_rate
## $deaths_civilians_nsc_rate$global_mean
## [1] 0.01816283
##
## $deaths_civilians_nsc_rate$ttests
## gender
## health 1 2 3 4 5
## 1 -0.02 (p-value 0.15; df 28) 0 (p-value 0; df 10)
## 2 0 (p-value 0; df 6) 0 (p-value 0; df 11) 0 (p-value 0; df 11) 0 (p-value 0; df 4)
## 3 -0.01 (p-value 0.01; df 7) 0.11 (p-value 0.41; df 18) 0 (p-value 0; df 11)
## 4 0.5 (p-value 0.41; df 3) 0 (p-value 0; df 14) 0 (p-value 0; df 11)
## 5 0 (p-value 0; df 1) 0 (p-value 0; df 22) 0 (p-value 0; df 4)
##
##
## $deaths_all_ucdp_rate
## $deaths_all_ucdp_rate$global_mean
## [1] -0.9926033
##
## $deaths_all_ucdp_rate$ttests
## gender
## health 1 2 3 4 5
## 1 -4.66 (p-value 0.46; df 28) -6.77 (p-value 0.39; df 10)
## 2 -0.65 (p-value 0.55; df 6) -0.5 (p-value 0.18; df 11) -0.03 (p-value 0; df 11) -0.95 (p-value 0.95; df 4)
## 3 3.11 (p-value 0.22; df 7) 0.77 (p-value 0.24; df 18) 0.05 (p-value 0; df 11)
## 4 76.7 (p-value 0.39; df 3) -3.22 (p-value 0.33; df 14) -21.21 (p-value 0.36; df 11)
## 5 0 (p-value 0; df 1) 0 (p-value 0; df 22) -0.26 (p-value 0.05; df 4)
##
##
## $hom_odcwho_rate
## $hom_odcwho_rate$global_mean
## [1] -0.6334615
##
## $hom_odcwho_rate$ttests
## gender
## health 1 2 3 4 5
## 1
## 2 -1.67 (p-value 0.15; df 1) 2.48 (p-value 0.31; df 1) -8.34 (p-value 0.19; df 5)
## 3 -2.29 (p-value 0.94; df 2) -1.31 (p-value 0.91; df 9) -3.8 (p-value 0.12; df 9)
## 4 1.42 (p-value 0.65; df 1) 5.96 (p-value 0.1; df 10) 0.22 (p-value 0.49; df 6)
## 5 -1.4 (p-value 0.57; df 1) -0.54 (p-value 0.61; df 21) -0.4 (p-value 0.33; df 3)
##
##
## $latentmean_fariss
## $latentmean_fariss$global_mean
## [1] -0.5256115
##
## $latentmean_fariss$ttests
## gender
## health 1 2 3 4 5
## 1 -0.4 (p-value 0.5; df 28) -0.3 (p-value 0.58; df 10)
## 2 -1.28 (p-value 0.05; df 6) -0.96 (p-value 0.09; df 11) -0.05 (p-value 0.04; df 11) -0.16 (p-value 0.38; df 3)
## 3 -0.28 (p-value 0.13; df 6) -0.46 (p-value 0.77; df 18) -0.33 (p-value 0.3; df 11)
## 4 0 (p-value 0.51; df 3) -0.71 (p-value 0.54; df 14) -0.64 (p-value 0.79; df 11)
## 5 -0.63 (p-value 0.39; df 1) -0.45 (p-value 0.57; df 22) -0.66 (p-value 0.65; df 4)
##
##
## $v2cltort
## $v2cltort$global_mean
## [1] -0.2589878
##
## $v2cltort$ttests
## gender
## health 1 2 3 4 5
## 1 -0.18 (p-value 0.72; df 28) -0.08 (p-value 0.51; df 10)
## 2 -0.71 (p-value 0.2; df 6) -0.97 (p-value 0.13; df 10) -0.16 (p-value 0.68; df 11) -0.13 (p-value 0.89; df 2)
## 3 -0.37 (p-value 0.54; df 7) -0.61 (p-value 0.16; df 17) -0.54 (p-value 0.15; df 10)
## 4 0.24 (p-value 0.52; df 1) 0.01 (p-value 0.43; df 10) 0.11 (p-value 0.17; df 10)
## 5 -0.26 (p-value 0.99; df 1) -0.02 (p-value 0.06; df 20) -0.12 (p-value 0.44; df 4)
##
##
## $v2clkill
## $v2clkill$global_mean
## [1] -0.2952073
##
## $v2clkill$ttests
## gender
## health 1 2 3 4 5
## 1 -0.42 (p-value 0.58; df 28) -0.37 (p-value 0.72; df 10)
## 2 -0.72 (p-value 0.29; df 6) -0.86 (p-value 0.24; df 10) -0.07 (p-value 0.33; df 11) 0.1 (p-value 0.63; df 2)
## 3 -0.17 (p-value 0.58; df 7) -0.28 (p-value 0.94; df 17) -0.38 (p-value 0.74; df 10)
## 4 0.62 (p-value 0.49; df 1) -0.23 (p-value 0.79; df 10) -0.18 (p-value 0.71; df 10)
## 5 -0.58 (p-value 0.35; df 1) -0.04 (p-value 0.02; df 20) -0.23 (p-value 0.78; df 4)
##
##
## $v2caviol
## $v2caviol$global_mean
## [1] -0.1098632
##
## $v2caviol$ttests
## gender
## health 1 2 3 4 5
## 1 0.17 (p-value 0.47; df 14) 0.36 (p-value 0.11; df 5)
## 2 -1.04 (p-value 0.19; df 4) 0.12 (p-value 0.65; df 7) 0.09 (p-value 0.51; df 5) -0.33 (p-value 0.32; df 1)
## 3 0.15 (p-value 0.75; df 2) 0.33 (p-value 0.47; df 11) -0.35 (p-value 0.42; df 6)
## 4 0.05 (p-value 0.82; df 7) -0.6 (p-value 0.39; df 5)
## 5 -0.69 (p-value 0.18; df 1) -0.72 (p-value 0.2; df 8) -0.33 (p-value 0.24; df 4)Preparing cross-sectional dataset
covariates <- c("pc_rgdpe_avg", "life_exp_wpp", "imr_wpp", "mys_ratio_hdr", "asfr_adol_wpp")
vars <- unlist(lapply(covariates, function(var) { names(df)[str_detect(names(df), var)] }))
vars <- vars[vars != "pasfr_adol_wpp"]
include <- c("country", "class", "class_vv", "class_low", "class_upp", vars)
new1990 <- df[df$year == 1990, c(include, variables$death_rates, variables$measurement_models)]
new1995 <- df[df$year == 1995, c(include, variables$death_rates, variables$measurement_models,
paste(variables$conflict_incidence, "cumulative1989", sep = "_"),
paste(variables$political, "cumulative1991", sep = "_"))]
new2015 <- df[df$year == 2015, c(include,
paste(c(variables$death_rates, variables$measurement_models), "cumulative1991", sep = "_"),
paste(c(variables$death_rates, variables$measurement_models), "cumulative1996", sep = "_"),
paste(variables$conflict_incidence, "cumulative1991", sep = "_"),
paste(variables$conflict_incidence, "cumulative1996", sep = "_"),
paste(variables$political, "cumulative1991", sep = "_"),
paste(variables$political, "cumulative1996", sep = "_"))]
names(new1990)[names(new1990) != "country"] <- paste(names(new1990)[names(new1990) != "country"], "1990", sep = "_")
names(new1995)[names(new1995) != "country"] <- paste(names(new1995)[names(new1995) != "country"], "1995", sep = "_")
names(new2015)[names(new2015) != "country"] <- paste(names(new2015)[names(new2015) != "country"], "2015", sep = "_")
new <- merge(new1990, new1995, by = c("country"), all = TRUE)
new <- merge(new, new2015, by = c("country"), all = TRUE)
## growth vars
new$pc_rgdpe_avg_growth1990_2015 <- new$pc_rgdpe_avg_2015 - new$pc_rgdpe_avg_1990
new$life_exp_wpp_growth1990_2015 <- new$life_exp_wpp_2015 - new$life_exp_wpp_1990
new$imr_wpp_growth1990_2015 <- new$imr_wpp_2015 - new$imr_wpp_1990
new$mys_ratio_hdr_growth1990_2015 <- new$mys_ratio_hdr_2015 - new$mys_ratio_hdr_1990
new$asfr_adol_wpp_growth1990_2015 <- new$asfr_adol_wpp_2015 - new$asfr_adol_wpp_1990
new$pc_rgdpe_avg_growth1995_2015 <- new$pc_rgdpe_avg_2015 - new$pc_rgdpe_avg_1995
new$life_exp_wpp_growth1995_2015 <- new$life_exp_wpp_2015 - new$life_exp_wpp_1995
new$imr_wpp_growth1995_2015 <- new$imr_wpp_2015 - new$imr_wpp_1995
new$mys_ratio_hdr_growth1995_2015 <- new$mys_ratio_hdr_2015 - new$mys_ratio_hdr_1995
new$asfr_adol_wpp_growth1995_2015 <- new$asfr_adol_wpp_2015 - new$asfr_adol_wpp_1995
## logged version of pcGDP growth vars; need to account for negative values
new$lg_pc_rgdpe_avg_growth1990_2015[IsTrue(new$pc_rgdpe_avg_growth1990_2015 < 0)] <- log(-new$pc_rgdpe_avg_growth1990_2015[IsTrue(new$pc_rgdpe_avg_growth1990_2015 < 0)])
new$lg_pc_rgdpe_avg_growth1990_2015[IsTrue(new$pc_rgdpe_avg_growth1990_2015 >= 0)] <- log(new$pc_rgdpe_avg_growth1990_2015[IsTrue(new$pc_rgdpe_avg_growth1990_2015 >= 0)])
new$lg_pc_rgdpe_avg_growth1995_2015[IsTrue(new$pc_rgdpe_avg_growth1995_2015 < 0)] <- log(-new$pc_rgdpe_avg_growth1995_2015[IsTrue(new$pc_rgdpe_avg_growth1995_2015 < 0)])
new$lg_pc_rgdpe_avg_growth1995_2015[IsTrue(new$pc_rgdpe_avg_growth1995_2015 >= 0)] <- log(new$pc_rgdpe_avg_growth1995_2015[IsTrue(new$pc_rgdpe_avg_growth1995_2015 >= 0)])
CodePerformance <- function(y_var, x_vars, show = FALSE, prefix = "perf") {
x_vars <- paste(x_vars, collapse = " + ")
equation <- paste(y_var, " ~ ", x_vars, sep = "")
df <- na.omit(get_all_vars(formula = equation, data = new, country = country))
mod <- lm(formula = equation, data = df)
df$predicted <- predict(mod)
df[, paste(prefix, y_var, sep = "_")] <- df[, y_var] - df$predicted
if(show) print(df)
return(invisible(df[, c("country", paste(prefix, y_var, sep = "_"))]))
}
performance_measures <- unlist(lapply(c(1990, 1995), function(year) {
unlist(lapply(covariates[2:5], function(var) {
return(list(
CodePerformance(y_var = paste(var, "_growth", year, "_2015", sep = ""),
x_vars = paste(c(paste("lg", var, sep = "_"), "lg_pc_rgdpe_avg"), year, sep = "_")),
CodePerformance(y_var = paste(var, "_growth", year, "_2015", sep = ""),
x_vars = c(paste(c(paste("lg", var, sep = "_"), "lg_pc_rgdpe_avg"), year, sep = "_"),
paste("lg_pc_rgdpe_avg_growth", year, "_2015", sep = "")), prefix = "perfv2")
))
}), recursive = FALSE)
}), recursive = FALSE)
performance_measures <- Reduce(f = function(...) merge(..., by = "country", all = TRUE), x = performance_measures)
new <- merge(new, performance_measures, by = "country", all = TRUE)
# filepath <- paste("~/Dropbox/Lancet-SIGHT Commission/Working Groups/Metrics/Datasets/dataset_crosssectional_", lubridate::today(), ".csv", sep = "" )
# write.csv(new, file = filepath)Performance Rankings in classification groups
Performance <- function(category, year, vars) {
countries <- unique(df$country[df$year == year & df$class_vv %in% category])
countries <- countries[!is.na(countries)]
lapply(vars, function(var) {
var <- paste(var, "_growth", year, "_2015", sep = "")
# other <- paste(c("deaths_all_ucdp_rate_cumulative"), year + 1, "_2015", sep = "")
other <- NULL
select_vars <- c("country", var, paste("perf", var, sep = "_"), other)
# select_vars[!select_vars %in% names(new)]
dat <- new[new$country %in% countries, select_vars]
dat[order(dat[, var], decreasing = TRUE, na.last = NA), ]
})
}Low classification
Performance(category = "low", year = 1995, vars = c(variables$health, variables$gender))## $life_exp_wpp
## country life_exp_wpp_growth1995_2015 perf_life_exp_wpp_growth1995_2015
## 146 Rwanda 44.00 15.6370726
## 60 Ethiopia 15.63 5.6604691
## 187 Uganda 14.94 2.7052494
## 20 Bhutan 14.60 5.8882904
## 99 Liberia 14.53 3.9591625
## 129 Niger 14.15 2.3535015
## 158 Sierra Leone 13.86 -2.8115245
## 106 Malawi 13.42 1.8763556
## 29 Cambodia 13.34 5.3515806
## 108 Maldives 13.05 7.0621836
## 200 Zambia 12.93 1.4358004
## 57 Eritrea 12.65 NA
## 5 Angola 12.50 -0.4680210
## 125 Nepal 12.05 5.0213484
## 28 Burundi 11.89 1.0646711
## 105 Madagascar 11.84 3.1958062
## 21 Bolivia 11.60 4.1372284
## 177 Tanzania 11.24 1.4725698
## 95 Laos 11.06 3.0239962
## 14 Bangladesh 10.89 4.8788380
## 1 Afghanistan 10.36 NA
## 152 Sao Tome & Principe 10.24 3.5484075
## 184 Turkey 10.10 3.9775386
## 163 Somalia 9.90 NA
## 109 Mali 9.66 -1.3164510
## 27 Burkina Faso 9.38 -0.5357478
## 120 Morocco 9.06 4.0079560
## 71 Guatemala 8.85 3.1101402
## 79 India 8.65 2.3663202
## 40 Congo, DRC 8.51 -1.3812439
## 154 Senegal 8.15 0.7260010
## 73 Guinea-Bissau 8.03 -3.0481911
## 39 Congo 7.94 -0.8249882
## 121 Mozambique 7.92 -2.7344273
## 198 Yemen 7.81 2.0166379
## 170 Sudan 7.76 0.2364652
## 139 Peru 7.48 3.1433549
## 65 Gambia 7.42 -1.9827570
## 90 Kenya 7.26 -0.8033107
## 80 Indonesia 6.70 0.8808579
## 72 Guinea 6.69 -3.6054338
## 162 Solomon Islands 6.64 NA
## 56 Equatorial Guinea 6.55 -3.8326236
## 75 Haiti 6.38 -1.2937167
## 130 Nigeria 6.08 -4.8908092
## 137 Papua New Guinea 5.42 NA
## 54 Egypt 5.39 0.6098402
## 133 Pakistan 5.20 -1.0735387
## 34 Chad 5.09 -6.0253812
## 38 Comoros 5.01 -2.6493475
## 19 Benin 4.79 -2.8871802
## 68 Ghana 4.04 -3.4379136
## 30 Cameroon 3.68 -5.7480138
## 180 Togo 3.30 -4.2557120
## 112 Mauritania 3.06 -3.2304180
## 201 Zimbabwe 2.27 -7.1765434
## 42 Cote d'Ivoire 1.97 -7.4877519
## 33 Central African Republic 0.79 -9.8151182
## 59 Eswatini -10.23 -17.5566949
##
## $imr_wpp
## country imr_wpp_growth1995_2015 perf_imr_wpp_growth1995_2015
## 146 Rwanda 156.846 101.3413533
## 99 Liberia 95.964 42.3460134
## 121 Mozambique 75.594 24.1016319
## 106 Malawi 74.605 26.1920456
## 129 Niger 73.804 24.4098564
## 60 Ethiopia 68.205 20.0570134
## 5 Angola 66.809 18.5152058
## 158 Sierra Leone 65.676 14.4822822
## 200 Zambia 63.350 16.3859298
## 105 Madagascar 63.279 18.7583253
## 72 Guinea 62.458 16.4746785
## 177 Tanzania 58.400 12.6566076
## 28 Burundi 58.220 11.3285023
## 73 Guinea-Bissau 56.778 9.4914976
## 29 Cambodia 55.983 13.8484742
## 14 Bangladesh 55.101 13.1352285
## 125 Nepal 53.220 10.8521199
## 108 Maldives 51.451 18.3523536
## 130 Nigeria 50.889 1.0891746
## 95 Laos 50.837 6.9736949
## 21 Bolivia 50.737 10.9705565
## 1 Afghanistan 50.233 NA
## 163 Somalia 49.977 NA
## 57 Eritrea 49.419 NA
## 109 Mali 48.481 -0.6866388
## 152 Sao Tome & Principe 47.294 7.0566717
## 187 Uganda 46.314 0.5329932
## 20 Bhutan 45.121 7.0300063
## 184 Turkey 44.359 13.9817225
## 79 India 43.026 2.1123658
## 56 Equatorial Guinea 42.222 -3.4075185
## 54 Egypt 40.630 6.3901490
## 198 Yemen 40.258 -2.7797131
## 27 Burkina Faso 39.229 -6.3409520
## 40 Congo, DRC 36.507 -9.6719667
## 139 Peru 36.046 5.1078794
## 34 Chad 34.977 -12.2552822
## 19 Benin 34.516 -10.1592491
## 154 Senegal 33.307 -4.3098911
## 42 Cote d'Ivoire 32.958 -9.8410415
## 133 Pakistan 32.943 -9.7668121
## 90 Kenya 32.477 -5.9502655
## 30 Cameroon 32.255 -11.0410226
## 170 Sudan 32.087 -8.3896849
## 75 Haiti 31.413 -11.8225649
## 80 Indonesia 30.908 -1.7416743
## 120 Morocco 28.816 -2.6391880
## 71 Guatemala 28.370 -4.0619019
## 180 Togo 27.726 -13.5287278
## 68 Ghana 27.665 -9.8669485
## 65 Gambia 26.241 -12.1945376
## 38 Comoros 22.888 -15.9025083
## 39 Congo 22.552 -13.8615527
## 33 Central African Republic 20.741 -25.5608883
## 137 Papua New Guinea 15.575 NA
## 112 Mauritania 11.458 -26.2089456
## 201 Zimbabwe 10.623 -23.1217832
## 162 Solomon Islands 9.230 NA
## 59 Eswatini 2.495 -28.2730190
##
## $mys_ratio_hdr
## country mys_ratio_hdr_growth1995_2015 perf_mys_ratio_hdr_growth1995_2015
## 54 Egypt 0.2916080638 0.122339021
## 19 Benin 0.2875264271 0.050584543
## 95 Laos 0.2827380952 0.119705726
## 106 Malawi 0.2800000000 0.109914974
## 170 Sudan 0.2674216028 0.079863141
## 125 Nepal 0.2626728111 -0.023718296
## 29 Cambodia 0.2439746300 0.058207622
## 129 Niger 0.2342857143 -0.049720864
## 42 Cote d'Ivoire 0.2275795564 0.012097660
## 137 Papua New Guinea 0.2231132075 NA
## 198 Yemen 0.2229437229 -0.153498062
## 28 Burundi 0.2222222222 0.053776853
## 71 Guatemala 0.2153846154 0.125884306
## 90 Kenya 0.2137931034 0.072132923
## 177 Tanzania 0.2113095238 0.094737206
## 79 India 0.2053658537 -0.023555356
## 133 Pakistan 0.1978021978 -0.039479678
## 39 Congo 0.1960652806 0.071503754
## 158 Sierra Leone 0.1904761905 -0.014129419
## 14 Bangladesh 0.1893772894 0.053103240
## 184 Turkey 0.1877705628 0.047314248
## 109 Mali 0.1820512821 -0.040225847
## 99 Liberia 0.1808510638 -0.076627595
## 146 Rwanda 0.1608695652 0.029638956
## 68 Ghana 0.1588347494 0.011794835
## 59 Eswatini 0.1582515194 0.053247735
## 40 Congo, DRC 0.1423709586 -0.064356031
## 65 Gambia 0.1407867495 -0.044020105
## 33 Central African Republic 0.1303935514 -0.097402003
## 187 Uganda 0.1284313725 -0.027620536
## 75 Haiti 0.1136363636 -0.066800831
## 201 Zimbabwe 0.1100694444 0.009933556
## 120 Morocco 0.1093750000 -0.070031540
## 180 Togo 0.1079365079 -0.110107179
## 121 Mozambique 0.0889328063 -0.096163836
## 1 Afghanistan 0.0883620690 NA
## 108 Maldives 0.0854309687 0.012848684
## 80 Indonesia 0.0728571429 -0.003308769
## 21 Bolivia 0.0561920349 -0.023662447
## 112 Mauritania 0.0522875817 -0.099189748
## 139 Peru 0.0511549002 -0.021239967
## 30 Cameroon 0.0147523709 -0.117335790
## 200 Zambia -0.0008067769 -0.057740368
## 154 Senegal -0.2368421053 -0.331988405
##
## $asfr_adol_wpp
## country asfr_adol_wpp_growth1995_2015 perf_asfr_adol_wpp_growth1995_2015
## 108 Maldives 91.781 59.8200603
## 198 Yemen 78.568 48.5394904
## 20 Bhutan 72.961 43.5443108
## 79 India 65.924 39.8990427
## 1 Afghanistan 63.132 NA
## 65 Gambia 61.531 26.1897721
## 30 Cameroon 56.723 19.3759609
## 125 Nepal 55.789 25.4308254
## 14 Bangladesh 54.862 22.2962156
## 158 Sierra Leone 51.224 15.1493570
## 71 Guatemala 49.724 15.3561099
## 57 Eritrea 49.605 NA
## 5 Angola 45.600 5.0954357
## 187 Uganda 45.266 11.1241668
## 59 Eswatini 43.877 8.1069710
## 34 Chad 39.874 1.9195152
## 60 Ethiopia 39.696 13.4983150
## 154 Senegal 39.530 7.6146333
## 133 Pakistan 38.512 12.8951882
## 95 Laos 37.221 9.9159902
## 72 Guinea 34.428 -4.1402866
## 38 Comoros 33.755 2.7598411
## 68 Ghana 32.062 2.3329430
## 73 Guinea-Bissau 30.469 -1.5326382
## 19 Benin 30.401 0.4124152
## 56 Equatorial Guinea 29.482 -8.6484653
## 27 Burkina Faso 29.414 -1.6782629
## 105 Madagascar 29.174 -3.0543288
## 90 Kenya 29.107 -1.3661345
## 184 Turkey 29.023 2.7953056
## 121 Mozambique 28.210 -4.5363286
## 170 Sudan 28.040 -0.1475028
## 130 Nigeria 26.383 -2.6384502
## 112 Mauritania 24.939 -3.9163510
## 54 Egypt 24.676 -1.5626247
## 28 Burundi 24.075 1.7236570
## 152 Sao Tome & Principe 23.893 -6.6387851
## 42 Cote d'Ivoire 23.647 -11.0948529
## 99 Liberia 22.375 -7.6826402
## 129 Niger 22.023 -15.0031276
## 200 Zambia 20.739 -12.1772835
## 137 Papua New Guinea 19.258 NA
## 21 Bolivia 19.241 -8.4596921
## 146 Rwanda 18.179 1.1620082
## 29 Cambodia 17.965 -2.0759585
## 139 Peru 17.937 -9.2483630
## 177 Tanzania 17.539 -13.1365101
## 109 Mali 17.434 -17.5115791
## 75 Haiti 16.708 -4.9594721
## 33 Central African Republic 16.290 -16.2226275
## 180 Togo 15.749 -12.1741057
## 106 Malawi 15.100 -17.8111240
## 80 Indonesia 12.635 -10.8317346
## 39 Congo 7.054 -24.3730158
## 162 Solomon Islands 6.758 NA
## 120 Morocco 3.478 -12.4133228
## 40 Congo, DRC -2.239 -31.6450446
## 201 Zimbabwe -6.097 -37.6626173
## 163 Somalia -8.646 NAH > G classification
Performance(category = "H > G", year = 1995, vars = c(variables$health, variables$gender))## $life_exp_wpp
## country life_exp_wpp_growth1995_2015 perf_life_exp_wpp_growth1995_2015
## 165 South Korea 8.43 4.2214007
## 81 Iran 8.13 3.4443959
## 128 Nicaragua 7.00 2.4565416
## 76 Honduras 6.22 2.2220818
## 32 Cape Verde 6.03 1.5622962
## 169 Sri Lanka 5.87 2.4782158
## 55 El Salvador 5.74 1.1597323
## 35 Chile 5.07 1.8989982
## 10 Austria 4.84 1.0609495
## 43 Croatia 4.64 1.0876796
## 153 Saudi Arabia 4.27 -0.9025512
## 25 Brunei 4.17 -1.8034918
## 135 Palestinian Territory 4.14 0.4193966
## 6 Antigua & Barbuda 4.03 -0.1625565
## 113 Mauritius 3.86 -0.7785654
## 136 Panama 3.75 0.6459770
## 44 Cuba 3.68 NA
## 148 Saint Lucia 3.49 -0.2187625
## 107 Malaysia 3.49 -0.6208494
## 171 Suriname 3.31 -1.5153235
## 26 Bulgaria 3.13 -0.9514363
## 41 Costa Rica 3.08 0.6169143
## 157 Seychelles 2.33 -2.6278055
## 70 Grenada 2.11 -1.6379999
## 82 Iraq 2.07 -1.7313213
## 195 Venezuela 2.04 -1.9417057
## 12 Bahamas 1.50 -3.6710456
## 149 Saint Vincent & the Grenadines 1.15 -2.7379162
## 86 Jamaica 0.06 -2.5695333
## 174 Syria -1.41 -3.0953525
##
## $imr_wpp
## country imr_wpp_growth1995_2015 perf_imr_wpp_growth1995_2015
## 128 Nicaragua 30.120 -0.8172150
## 32 Cape Verde 25.941 -5.1511974
## 76 Honduras 24.615 -4.6459834
## 81 Iran 24.187 -2.8064882
## 55 El Salvador 23.391 -5.1194119
## 153 Saudi Arabia 21.348 1.8764995
## 171 Suriname 18.017 -7.7247174
## 6 Antigua & Barbuda 13.822 -0.6299222
## 135 Palestinian Territory 13.378 -11.3890169
## 169 Sri Lanka 11.212 -5.9761791
## 136 Panama 10.246 -9.3125900
## 82 Iraq 9.532 -18.5602455
## 12 Bahamas 9.455 -1.1161171
## 86 Jamaica 8.782 -10.0142976
## 174 Syria 8.390 -15.8266195
## 195 Venezuela 8.081 -9.3163286
## 165 South Korea 7.328 4.0461107
## 26 Bulgaria 7.080 -3.8281385
## 35 Chile 6.799 -2.5202355
## 113 Mauritius 6.460 -6.5648696
## 43 Croatia 5.953 1.3439958
## 107 Malaysia 5.922 -1.2888868
## 41 Costa Rica 5.229 -5.0179147
## 44 Cuba 5.138 NA
## 10 Austria 3.546 7.7229816
## 70 Grenada 2.773 -10.3158384
## 149 Saint Vincent & the Grenadines 1.916 -13.1253475
## 25 Brunei 1.764 0.6003328
## 148 Saint Lucia 1.748 -11.7438221
## 157 Seychelles 0.897 -6.0740262
##
## $mys_ratio_hdr
## country mys_ratio_hdr_growth1995_2015 perf_mys_ratio_hdr_growth1995_2015
## 81 Iran 0.339375000 0.211471764
## 128 Nicaragua 0.293269231 0.205600008
## 153 Saudi Arabia 0.278888889 0.132330646
## 82 Iraq 0.192307692 0.017435455
## 43 Croatia 0.166386555 0.064115309
## 113 Mauritius 0.140601504 0.048761372
## 10 Austria 0.137655626 0.046091022
## 174 Syria 0.135154062 0.030489879
## 107 Malaysia 0.129839747 0.050511334
## 165 South Korea 0.090928137 -0.001254859
## 25 Brunei 0.084249084 0.012876261
## 195 Venezuela 0.079081633 0.035772451
## 76 Honduras 0.073924731 0.031679742
## 55 El Salvador 0.042777778 -0.022001923
## 26 Bulgaria 0.041155704 -0.006021852
## 41 Costa Rica 0.037418301 -0.005285118
## 86 Jamaica 0.022513441 -0.008115322
## 44 Cuba 0.021505376 NA
## 35 Chile -0.005316742 -0.050893903
## 169 Sri Lanka -0.022807018 -0.072950267
##
## $asfr_adol_wpp
## country asfr_adol_wpp_growth1995_2015 perf_asfr_adol_wpp_growth1995_2015
## 128 Nicaragua 53.419 17.9640214
## 148 Saint Lucia 50.463 18.4945266
## 153 Saudi Arabia 49.452 21.3973785
## 135 Palestinian Territory 49.295 17.8572712
## 70 Grenada 48.097 19.1792355
## 76 Honduras 47.388 14.1338740
## 86 Jamaica 36.901 5.7247741
## 12 Bahamas 36.846 5.5906029
## 81 Iran 36.039 10.4655784
## 41 Costa Rica 33.819 1.7077213
## 149 Saint Vincent & the Grenadines 33.443 3.0116501
## 32 Cape Verde 33.045 2.7358784
## 171 Suriname 28.907 -2.3264506
## 55 El Salvador 27.685 -2.1281318
## 6 Antigua & Barbuda 24.791 -4.9328592
## 174 Syria 24.632 4.7130102
## 25 Brunei 23.832 -0.7322815
## 26 Bulgaria 22.958 -4.2720931
## 35 Chile 14.682 -13.1339679
## 113 Mauritius 14.190 -7.1100050
## 44 Cuba 12.658 NA
## 10 Austria 11.925 -1.2393054
## 136 Panama 11.135 -21.2608330
## 43 Croatia 9.495 0.1482466
## 169 Sri Lanka 7.446 -3.8410965
## 195 Venezuela 7.119 -25.0833920
## 157 Seychelles 6.788 -23.1967274
## 107 Malaysia 5.943 -2.6367027
## 165 South Korea 2.345 15.5054961
## 82 Iraq -12.038 -34.5125277G > H classification
Performance(category = "G > H", year = 1995, vars = c(variables$health, variables$gender))## $life_exp_wpp
## country life_exp_wpp_growth1995_2015 perf_life_exp_wpp_growth1995_2015
## 176 Tajikistan 11.59 3.8887628
## 58 Estonia 8.28 3.2823350
## 23 Botswana 7.93 -1.1287422
## 118 Mongolia 7.70 1.5594762
## 122 Myanmar 7.03 0.7248560
## 11 Azerbaijan 7.02 1.3627536
## 24 Brazil 7.00 1.9171684
## 96 Latvia 6.47 1.1058001
## 8 Armenia 5.93 1.8833804
## 91 Kiribati 5.85 NA
## 77 Hungary 5.84 0.8566287
## 2 Albania 5.80 3.1027103
## 36 China 5.77 2.2810007
## 150 Samoa 5.49 NA
## 51 Dominican Republic 5.16 0.4473957
## 74 Guyana 5.00 NA
## 185 Turkmenistan 4.60 -2.1867276
## 49 Djibouti 4.53 -3.2197075
## 102 Lithuania 4.26 -0.3154891
## 94 Kyrgyzstan 4.02 -1.0350297
## 193 Uzbekistan 3.96 -0.9080611
## 89 Kazakhstan 3.62 -2.3404482
## 104 Macedonia 3.62 0.1884203
## 16 Belarus 3.00 -1.7445919
## 115 Micronesia 3.00 NA
## 140 Philippines 2.72 -1.6455232
## 64 Gabon 2.69 -4.9771318
## 181 Tonga 1.13 NA
## 123 Namibia -0.80 -7.6495766
## 164 South Africa -2.30 -8.9583696
## 98 Lesotho -10.43 -16.9566239
##
## $imr_wpp
## country imr_wpp_growth1995_2015 perf_imr_wpp_growth1995_2015
## 11 Azerbaijan 50.336 12.4836623
## 176 Tajikistan 50.164 10.8411789
## 118 Mongolia 44.720 7.8597153
## 94 Kyrgyzstan 40.284 7.1847091
## 89 Kazakhstan 36.778 7.5325964
## 193 Uzbekistan 31.685 -1.6891165
## 8 Armenia 30.871 1.5782088
## 122 Myanmar 29.016 -11.2301595
## 36 China 28.998 0.4466554
## 185 Turkmenistan 28.561 -7.0375832
## 24 Brazil 26.953 -0.1234694
## 51 Dominican Republic 22.017 -8.0324827
## 64 Gabon 21.617 -11.0636537
## 2 Albania 21.524 -2.6084387
## 49 Djibouti 20.429 -17.9080291
## 91 Kiribati 18.297 NA
## 104 Macedonia 17.611 -2.7699707
## 58 Estonia 13.135 1.4475069
## 74 Guyana 13.120 NA
## 140 Philippines 12.746 -13.3370769
## 115 Micronesia 12.560 NA
## 96 Latvia 12.398 -0.1824839
## 102 Lithuania 12.110 0.2677959
## 16 Belarus 11.669 1.3327291
## 123 Namibia 11.218 -19.0805007
## 164 South Africa 8.283 -17.9018293
## 77 Hungary 8.214 0.4603962
## 150 Samoa 8.074 NA
## 23 Botswana 6.349 -20.4210048
## 181 Tonga 2.538 NA
## 98 Lesotho -2.540 -39.2772389
##
## $mys_ratio_hdr
## country mys_ratio_hdr_growth1995_2015 perf_mys_ratio_hdr_growth1995_2015
## 118 Mongolia 0.1581632653 0.103094090
## 98 Lesotho 0.1540880503 0.156892357
## 123 Namibia 0.1293350717 0.078201548
## 122 Myanmar 0.1139817629 0.072956712
## 94 Kyrgyzstan 0.0923757993 0.039886366
## 58 Estonia 0.0842653593 0.039706942
## 89 Kazakhstan 0.0824742268 0.024388478
## 2 Albania 0.0812662414 0.021324028
## 74 Guyana 0.0716705295 NA
## 11 Azerbaijan 0.0462962963 -0.012649509
## 140 Philippines 0.0439560440 0.008800213
## 96 Latvia 0.0256598240 -0.010887649
## 164 South Africa 0.0225113122 -0.032465537
## 102 Lithuania 0.0202020202 -0.024607014
## 36 China 0.0172280294 -0.044509577
## 8 Armenia 0.0073179033 -0.023123581
## 16 Belarus 0.0067283431 -0.039117448
## 51 Dominican Republic 0.0016091954 -0.025020736
## 77 Hungary -0.0009182736 -0.051781948
## 181 Tonga -0.0022269460 NA
## 176 Tajikistan -0.0043706294 -0.057162746
## 24 Brazil -0.0184633711 -0.040179006
## 23 Botswana -0.0319148936 -0.070789498
## 64 Gabon -0.3934343434 -0.395144631
##
## $asfr_adol_wpp
## country asfr_adol_wpp_growth1995_2015 perf_asfr_adol_wpp_growth1995_2015
## 64 Gabon 71.596 29.4761310
## 8 Armenia 55.225 28.5484132
## 23 Botswana 46.999 15.1613404
## 193 Uzbekistan 37.056 13.8703486
## 102 Lithuania 31.449 10.1239325
## 58 Estonia 30.958 8.8600675
## 115 Micronesia 29.807 NA
## 96 Latvia 28.216 7.0254010
## 104 Macedonia 25.154 6.2394245
## 94 Kyrgyzstan 24.814 -0.8374210
## 91 Kiribati 24.806 NA
## 49 Djibouti 23.754 6.7289192
## 16 Belarus 22.863 1.5059333
## 89 Kazakhstan 22.423 -1.4837099
## 24 Brazil 19.674 -10.2561609
## 164 South Africa 18.343 -13.2424779
## 77 Hungary 16.222 -2.9451258
## 51 Dominican Republic 15.345 -18.0878566
## 123 Namibia 12.704 -17.2997016
## 122 Myanmar 12.529 0.4521098
## 118 Mongolia 11.837 -2.0850232
## 36 China 8.391 6.9319094
## 181 Tonga 7.922 NA
## 176 Tajikistan 6.931 -14.4826060
## 74 Guyana 5.752 NA
## 150 Samoa 5.418 NA
## 185 Turkmenistan -0.034 -12.6233328
## 98 Lesotho -1.130 -26.2436005
## 2 Albania -1.997 -5.9405149
## 140 Philippines -6.293 -26.5138229
## 11 Azerbaijan -11.708 -28.1384794High classification
Performance(category = "upp", year = 1995, vars = c(variables$health, variables$gender))## $life_exp_wpp
## country life_exp_wpp_growth1995_2015 perf_life_exp_wpp_growth1995_2015
## 97 Lebanon 7.54 4.208014541
## 161 Slovenia 6.40 2.398055630
## 22 Bosnia & Herzegovina 6.39 3.973409283
## 141 Poland 6.00 1.914668431
## 142 Portugal 5.72 2.131493191
## 46 Czech Republic 5.67 1.186405520
## 159 Singapore 5.61 2.142427107
## 103 Luxembourg 5.37 0.885477255
## 83 Ireland 5.26 1.582805656
## 178 Thailand 5.03 0.960831593
## 127 New Zealand 4.97 1.513979442
## 48 Denmark 4.91 0.899166950
## 85 Italy 4.89 1.544748638
## 168 Spain 4.88 1.953203410
## 62 Finland 4.86 1.206067599
## 175 Taiwan 4.84 0.748652146
## 173 Switzerland 4.76 1.185162946
## 84 Israel 4.71 1.356067795
## 9 Australia 4.67 1.331367166
## 160 Slovakia 4.63 0.271752422
## 190 United Kingdom 4.63 1.069920816
## 63 France 4.60 1.244087923
## 67 Germany 4.46 0.608684048
## 131 Norway 4.32 0.874781584
## 189 United Arab Emirates 4.21 -1.881613189
## 17 Belgium 4.12 0.459630160
## 126 Netherlands 4.05 0.597825424
## 31 Canada 4.04 0.638747393
## 192 Uruguay 4.02 0.335029878
## 87 Japan 3.90 1.011350456
## 110 Malta 3.85 1.146780288
## 172 Sweden 3.75 0.604164859
## 69 Greece 3.70 0.763789737
## 78 Iceland 3.66 0.528565654
## 13 Bahrain 3.52 -0.653650118
## 7 Argentina 3.51 -0.230766835
## 15 Barbados 3.43 -0.171597368
## 191 United States of America 3.29 -0.965114082
## 143 Qatar 3.14 -0.354457116
## 45 Cyprus 2.98 0.005218632
## 93 Kuwait 2.37 -2.641210883
##
## $imr_wpp
## country imr_wpp_growth1995_2015 perf_imr_wpp_growth1995_2015
## 178 Thailand 15.988 -3.6988616
## 97 Lebanon 15.523 -4.5409134
## 141 Poland 11.950 0.1537548
## 22 Bosnia & Herzegovina 11.508 -6.4819885
## 7 Argentina 11.173 -6.0614659
## 192 Uruguay 10.003 -4.5048327
## 13 Bahrain 7.950 -0.9897136
## 143 Qatar 7.924 -0.5986872
## 46 Czech Republic 6.457 5.6308813
## 142 Portugal 6.177 4.7791109
## 189 United Arab Emirates 6.159 3.6557103
## 160 Slovakia 6.157 0.5579037
## 69 Greece 5.166 5.1374172
## 161 Slovenia 5.119 7.1682623
## 93 Kuwait 5.108 -0.5395942
## 84 Israel 5.057 5.7612027
## 45 Cyprus 4.989 3.4867553
## 85 Italy 4.390 7.2874830
## 17 Belgium 4.302 6.5323763
## 103 Luxembourg 3.835 8.4969141
## 168 Spain 3.750 7.5789123
## 78 Iceland 3.197 13.0924902
## 83 Ireland 3.191 7.2979840
## 131 Norway 3.067 10.6399414
## 63 France 3.053 7.8277431
## 110 Malta 3.011 2.5125115
## 127 New Zealand 2.992 5.5564138
## 9 Australia 2.884 7.9271383
## 62 Finland 2.836 11.6235560
## 159 Singapore 2.747 12.0637042
## 48 Denmark 2.742 8.2882319
## 126 Netherlands 2.716 8.5490853
## 191 United States of America 2.715 4.0460542
## 172 Sweden 2.706 11.3525502
## 67 Germany 2.631 8.9744356
## 190 United Kingdom 2.625 6.8180660
## 87 Japan 2.216 13.3774091
## 173 Switzerland 2.169 8.9768174
## 15 Barbados 1.973 -5.1488929
## 31 Canada 1.594 7.4614849
## 175 Taiwan 1.160 8.6002349
##
## $mys_ratio_hdr
## country mys_ratio_hdr_growth1995_2015 perf_mys_ratio_hdr_growth1995_2015
## 93 Kuwait 0.201318458 0.138377572
## 189 United Arab Emirates 0.161607143 0.118549606
## 142 Portugal 0.151736111 0.076154021
## 45 Cyprus 0.124747475 0.048998908
## 48 Denmark 0.120347395 0.052192025
## 67 Germany 0.087684729 0.008182759
## 87 Japan 0.082166768 0.020169696
## 69 Greece 0.081317765 0.006259212
## 62 Finland 0.078241431 0.022369713
## 168 Spain 0.072261072 0.004772843
## 15 Barbados 0.070187166 0.023030779
## 85 Italy 0.068307906 -0.002377045
## 31 Canada 0.066239316 0.012367822
## 9 Australia 0.059282051 0.000811882
## 173 Switzerland 0.059145604 -0.017549703
## 192 Uruguay 0.046190476 0.007427191
## 83 Ireland 0.040711430 -0.002831784
## 159 Singapore 0.037296037 -0.037707321
## 141 Poland 0.036234104 -0.010046239
## 17 Belgium 0.025792495 -0.034121154
## 126 Netherlands 0.025081081 -0.039916201
## 63 France 0.025042849 -0.036483448
## 131 Norway 0.024806723 -0.026222876
## 103 Luxembourg 0.023089431 -0.045063814
## 84 Israel 0.017241379 -0.033202069
## 172 Sweden 0.016260163 -0.031020188
## 13 Bahrain 0.013888889 -0.033066865
## 161 Slovenia 0.010294331 -0.041680281
## 127 New Zealand 0.009641026 -0.041719026
## 7 Argentina 0.007369283 -0.030412572
## 46 Czech Republic 0.001512573 -0.050452390
## 190 United Kingdom -0.006731946 -0.054953730
## 191 United States of America -0.008481203 -0.054585453
## 160 Slovakia -0.015037594 -0.059654026
## 110 Malta -0.023188406 -0.073046960
## 178 Thailand -0.030448718 -0.073260887
## 78 Iceland -0.065836941 -0.106703378
## 143 Qatar -0.082306831 -0.080199093
##
## $asfr_adol_wpp
## country asfr_adol_wpp_growth1995_2015 perf_asfr_adol_wpp_growth1995_2015
## 143 Qatar 32.737 8.5625068
## 191 United States of America 31.574 0.9960145
## 46 Czech Republic 30.337 7.8074673
## 97 Lebanon 28.063 9.4482338
## 189 United Arab Emirates 25.502 -2.7527880
## 160 Slovakia 22.897 0.1688794
## 45 Cyprus 19.171 5.1508218
## 78 Iceland 16.141 -0.6033095
## 15 Barbados 15.198 -12.3820446
## 141 Poland 15.030 0.2886285
## 161 Slovenia 14.792 3.9301115
## 31 Canada 13.647 -3.0204085
## 22 Bosnia & Herzegovina 13.529 7.3469200
## 93 Kuwait 13.239 -1.9256931
## 175 Taiwan 12.958 3.2430798
## 190 United Kingdom 12.034 -6.6652161
## 127 New Zealand 10.469 -9.3790489
## 142 Portugal 10.431 -1.9558858
## 7 Argentina 9.988 -18.6961937
## 192 Uruguay 9.948 -18.8091760
## 131 Norway 9.709 0.4781870
## 69 Greece 8.808 -0.3386986
## 9 Australia 8.573 -4.9957003
## 13 Bahrain 8.318 -4.2877142
## 67 Germany 7.845 -1.5992842
## 84 Israel 7.799 -4.1901849
## 103 Luxembourg 6.118 -1.5163718
## 172 Sweden 5.957 1.8379237
## 83 Ireland 5.399 -3.1272447
## 17 Belgium 4.790 1.4458914
## 48 Denmark 4.465 4.0996927
## 159 Singapore 3.980 5.8044345
## 62 Finland 3.957 0.4015450
## 173 Switzerland 3.044 6.1919774
## 126 Netherlands 2.663 5.5793764
## 85 Italy 1.849 3.6117867
## 63 France 1.390 2.6705565
## 168 Spain 0.707 0.2638633
## 87 Japan -0.730 11.7612327
## 110 Malta -1.293 -4.2594156
## 178 Thailand -1.338 -23.1854048save(data, codebook, categories, variables, df, new, file = "_data/LSCMWG_working_class.RData")