added longitude and latitude to world annual data

data-raw/long_lat_viz.R

@@ -0,0 +1,62 @@
+library(denguedatahub)
+library(ggplot2)
+library(maps)
+library(magrittr)
+library(viridis)
+
+library(denguedatahub)
+library(ggplot2)
+library(maps)
+library(magrittr)
+library(viridis)
+
+worldMap <- map_data("world")
+worlddata <- dplyr::left_join(worldMap, world_annual, by = c(region = "entity"))
+
+worldMap.entries <- unique(worldMap$region)
+world_annual.entries <- unique(world_annual$entity)
+map.annual <- worldMap.entries %in% world_annual.entries
+annual.map <- world_annual.entries %in% worldMap.entries
+
+map.notincluded <- worldMap.entries[!map.annual]
+annual.notincluded <- world_annual.entries[!annual.map]
+
+world_annual[world_annual == "Antigua and Barbuda"] <- "Antigua"
+world_annual[world_annual == "Congo"] <- "Republic of Congo"
+world_annual[world_annual == "Cote d'Ivoire"] < "Ivory Coast"
+world_annual[world_annual == "Czechia"] <- "Czech Republic"
+world_annual[world_annual == "Democratic Republic of Congo"] <- "Democratic Republic of the Congo"
+world_annual[world_annual == "England"] 
+world_annual[world_annual == "Eswatini"]
+world_annual[world_annual == "Micronesia (country)"] <- "Micronesia"
+world_annual[world_annual == "Northern Ireland" ]
+world_annual[world_annual == "Scotland"]
+world_annual[world_annual == "Timor"] <- "Timor-Leste"
+world_annual[world_annual == "Tokelau"]
+world_annual[world_annual == "Tokelau"]
+world_annual[world_annual == "Trinidad and Tobago"] <- "Trinidad"
+world_annual[world_annual == "Tuvalu"]
+world_annual[world_annual == "United Kingdom"] <- "UK"
+world_annual[world_annual == "United States"] <- "USA"
+world_annual[world_annual == "United States Virgin Islands"] <- "Virgin Islands"
+world_annual[world_annual == "Wales"]
+
+worlddata <- dplyr::full_join(worldMap, world_annual, by = c(region = "entity"))
+
+
+worlddata <- worlddata %>%
+  dplyr::mutate(dengue.present = dplyr::case_when(incidence == 0 ~ "no", incidence!=0 ~ "yes", incidence == NA ~ "NA"))
+
+worlddata2019 <- dplyr::filter(worlddata, year==2019)
+ggplot(worlddata2019, aes(x = long,
+                          y = lat,
+                          group=group,
+                          fill = dengue.present)) +
+  geom_polygon(color = "black") +
+  labs(x = "Longitude",
+       y = "Latitude") +
+  theme(legend.position = "bottom") + 
+  scale_fill_brewer(palette = "Dark2")
+
+world_data <- worlddata
+usethis::use_data(world_annual, overwrite = TRUE)

data-raw/longitude_latitude.R

@@ -0,0 +1,20 @@
+# data source
+# https://developers.google.com/public-data/docs/canonical/countries_csv
+
+#----
+library(rvest)
+library(magrittr)
+library(here)
+
+#----
+url <- "https://developers.google.com/public-data/docs/canonical/countries_csv"
+css_selector <- "#gc-wrapper > main > devsite-content > article > div.devsite-article-body.clearfix"
+
+world_longitude_latitude <- url %>%
+  read_html() %>%
+  html_element(css=css_selector) %>%
+  html_table()
+
+save(world_longitude_latitude, 
+     file=here("data", 
+               "world_longitude_latitude.rda"))  

data-raw/our_world_data.R

@@ -11,3 +11,5 @@ View(world_annual)
 colnames(world_annual) <- c("entity", "code", "year", "incidence")
 #save(world_annual, file=here("data", "world_annual.rda"))
 usethis::use_data(world_annual, overwrite = TRUE)
+# Teminated on 29 June 2023
+# See the file long_lat_viz.R