Target xeurope_countries: tibble with country names and group from extended europe.

Rather than 5 variables with names of countries for different groups.

src/Covid19MortalityEurope.Rmd

@@ -91,7 +91,7 @@ loadd(
     world_map,
     geo_europe_light,
     roi_europe_light,
-    europe, eu28, efta, candi, noneu,
+    xeurope_countries,
     sdt,
     wgs84,
     merc,
@@ -107,8 +107,8 @@ loadd(
 ## lockdown
 Lock <- do.call(
     "rbind",
-    by(subset(lock, name %in% europe),
-       list(name = subset(lock, name %in% europe)$name),
+    by(subset(lock, name %in% xeurope_countries$name),
+       list(name = subset(lock, name %in% xeurope_countries$name)$name),
        function(x){
            smax <- max(x$stringency, na.rm = T)
            x$T90 <- min(x$date[x$stringency >= .9 * smax], na.rm = T)
@@ -133,13 +133,7 @@ covid$name <- standardise_country_names(covid$name)
 ## group, name, lockdown date, population, cumulated deaths,
 ## and death rate.
 country_data <- 
-  map_dfr(
-    list("europe"),
-    ~tibble(
-      group = .,
-      name = get(.)
-    )
-  ) %>% 
+  xeurope_countries %>% 
   ## filter(name != "Liechtenstein") %>% 
   left_join(
     lockdown_dates,
@@ -364,7 +358,7 @@ sharing similar mortality patterns.
 
 ## Main results {-}
 
-As per `r data_update_date`, in the `r length(europe)` countries
+As per `r data_update_date`, in the `r length(xeurope_countries$name)` countries
 accounted for, the total number of deaths is `r sttnd` for an overall
 population size of `r round(ttpop/1e6)` millions representing a
 cumulative death rate of `r sttcdr` deaths $10^{-5}$ inhabitants.
@@ -422,14 +416,14 @@ implementation.
 
 The geographical scope of the study is:
 
-* the European Union and the UK (28 countries): `r collapse(eu28, cnt = NA)`,
+* the European Union and the UK (28 countries): `r collapse(xeurope_countries %>% filter(group == "EU28") %>% pull(name), cnt = NA)`,
   
 * plus member countries of the European Free Trade Association (EFTA):
-  `r collapse(efta, cnt = NA)`;
+  `r collapse(xeurope_countries %>% filter(group == "EFTA") %>% pull(name), cnt = NA)`;
   
-* plus candidate countries for EU membership: `r collapse(candi, cnt = NA)`;
+* plus candidate countries for EU membership: `r collapse(xeurope_countries %>% filter(group == "Candidates") %>% pull(name), cnt = NA)`;
   
-* plus European, non-EU countries: `r collapse(noneu, cnt = NA)`.
+* plus European, non-EU countries: `r collapse(xeurope_countries %>% filter(group == "Non_EU") %>% pull(name), cnt = NA)`.
 
 Liechtenstein was excluded from the study because of its small population size.
 
@@ -446,7 +440,7 @@ Liechtenstein was excluded from the study because of its small population size.
 
 ## Cumulative death rate
 
-In the `r length(europe)` countries accounted for^[List (number of deaths in braces): `r collapse(nam = levels(Dfr$name), cnt = sond)`.],
+In the `r nrow(xeurope_countries)` countries accounted for^[List (number of deaths in braces): `r collapse(nam = levels(Dfr$name), cnt = sond)`.],
 the total reported deaths is now `r sttnd` for an overall population size of 
 `r round(sum(country_data$pop)/1e3,0)` millions, thus representing a cumulative death rate of 
 `r round(100 * sum(Dfr$deaths) / sum(country_data$pop), 1)` deaths $10^{-5}$ inhabitants (inh.).

src/functions.R

@@ -278,7 +278,7 @@ if ( .Platform$OS.type == "unix") {
 
 ## utility for text output : takes a vector of names (argument nam)
 ## and a vector of counts (cnt), and returns a string with the names
-## and counts within brackets, separated with commas, wwith "and" for
+## and counts within brackets, separated with commas, with "and" for
 ## the last one
 
 collapse <- function(nam = onam, cnt = nd){

src/plan.R

@@ -39,7 +39,19 @@ plan <- drake_plan(## raw_data = read_excel(
             "Georgia", "Israel", "Jordan", "Lebanon", "Libya",
             "Moldova", "Morocco",  "Syria", "Tunisia", "Ukraine"),
 
-    europe = standardise_country_names(sort(c(eu28, efta, candi, noneu))),
+    xeurope_countries =
+        enframe(
+            list(EU28 = eu28, EFTA = efta, Candidates = candi, Non_EU = noneu),
+            name = "group",
+            value = "name"
+        ) %>%
+        unnest(cols = name) %>%
+        mutate(
+            name = standardise_country_names(name),
+            group = fct_inorder(group)
+        ) %>%
+        select(name, group) %>%
+        arrange(name),
 
     wca = c("Cameroon", "Central African Republic", "Chad",
             "Congo DR", "Congo", "Equatorial Guinea", "Gabon",
@@ -87,6 +99,7 @@ plan <- drake_plan(## raw_data = read_excel(
     ## policy responses around the world, rigorously and consistently.
 
     lock = stringency(),
+
     ## CARTOGRAPHY ---------------------------------------------------
 
     world_map = get_world_map(),
@@ -95,8 +108,8 @@ plan <- drake_plan(## raw_data = read_excel(
     ## candidate countries + non-EU countries). Source GADM.org
     ## version 3.6
     geo_europe_light = geosimplify(
-        world_map[world_map$name %in%
-                  standardise_country_names(europe), ]),
+        world_map[world_map$name %in% xeurope_countries$name, ]
+    ),
 
     roi_europe_light = region_of_interest(
         geo_europe_light, world_map),
@@ -119,7 +132,7 @@ plan <- drake_plan(## raw_data = read_excel(
 
     ## Population counts in thousands of people
     pop_countries_2020 = subset(world_population,
-                                subset = name %in% europe),
+                                subset = name %in% xeurope_countries$name),
 
     ## ## today (ANSI)
     ## tod = format(Sys.Date(), "%Y%m%d"),