Update countrypops dataset

NAMESPACE

@@ -1,5 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method("$",gt_option)
 S3method(add_text_style,character)
 S3method(add_text_style,shiny.tag)
 S3method(as.data.frame,gt_tbl)

R/cols_align.R

@@ -78,9 +78,14 @@
 #' ```r
 #' countrypops |>
 #'   dplyr::select(-contains("code")) |>
-#'   dplyr::filter(country_name == "San Marino") |>
-#'   dplyr::slice_tail(n = 5) |>
-#'   gt(rowname_col = "year", groupname_col = "country_name") |>
+#'   dplyr::filter(
+#'     country_name == "San Marino",
+#'     year %in% 2017:2021
+#'   ) |>
+#'   gt(
+#'     rowname_col = "year",
+#'     groupname_col = "country_name"
+#'   ) |>
 #'   cols_align(
 #'     align = "left",
 #'     columns = population

R/cols_hide.R

@@ -68,8 +68,10 @@
 #'
 #' ```r
 #' countrypops |>
-#'   dplyr::filter(country_name == "Egypt") |>
-#'   dplyr::slice_tail(n = 5) |>
+#'   dplyr::filter(
+#'     country_name == "Egypt",
+#'     year %in% 2017:2021
+#'   ) |>
 #'   gt() |>
 #'   cols_hide(columns = c(country_code_2, country_code_3))
 #' ```
@@ -86,8 +88,10 @@
 #'
 #' ```r
 #' countrypops |>
-#'   dplyr::filter(country_name == "Pakistan") |>
-#'   dplyr::slice_tail(n = 5) |>
+#'   dplyr::filter(
+#'     country_name == "Pakistan",
+#'     year %in% 2017:2021
+#'   ) |>
 #'   gt() |>
 #'   cols_hide(columns = c(country_code_3, population)) |>
 #'   tab_footnote(
@@ -189,8 +193,10 @@ cols_hide <- function(
 #' ```r
 #' tab_1 <-
 #'   countrypops |>
-#'   dplyr::filter(country_name == "Singapore") |>
-#'   dplyr::slice_tail(n = 5) |>
+#'   dplyr::filter(
+#'     country_name == "Singapore",
+#'     year %in% 2017:2021
+#'   ) |>
 #'   gt() |>
 #'   cols_hide(columns = c(country_code_2, country_code_3))
 #'

R/cols_label.R

@@ -142,8 +142,10 @@
 #' ```r
 #' countrypops |>
 #'   dplyr::select(-contains("code")) |>
-#'   dplyr::filter(country_name == "Uganda") |>
-#'   dplyr::slice_tail(n = 5) |>
+#'   dplyr::filter(
+#'     country_name == "Uganda",
+#'     year %in% 2017:2021
+#'   ) |>
 #'   gt() |>
 #'   cols_label(
 #'     country_name = "Name",
@@ -164,8 +166,10 @@
 #' ```r
 #' countrypops |>
 #'   dplyr::select(-contains("code")) |>
-#'   dplyr::filter(country_name == "Uganda") |>
-#'   dplyr::slice_tail(n = 5) |>
+#'   dplyr::filter(
+#'     country_name == "Uganda",
+#'     year %in% 2017:2021
+#'   ) |>
 #'   gt() |>
 #'   cols_label(
 #'     country_name = md("**Name**"),

R/cols_move.R

@@ -79,8 +79,10 @@
 #' ```r
 #' countrypops |>
 #'   dplyr::select(-contains("code")) |>
-#'   dplyr::filter(country_name == "Japan") |>
-#'   dplyr::slice_tail(n = 10) |>
+#'   dplyr::filter(
+#'     country_name == "Japan",
+#'     year %in% 2012:2021
+#'   ) |>
 #'   gt() |>
 #'   cols_move(
 #'     columns = population,
@@ -214,8 +216,10 @@ cols_move <- function(
 #' ```r
 #' countrypops |>
 #'   dplyr::select(-contains("code")) |>
-#'   dplyr::filter(country_name == "Fiji") |>
-#'   dplyr::slice_tail(n = 5) |>
+#'   dplyr::filter(
+#'     country_name == "Fiji",
+#'     year %in% 2017:2021
+#'   ) |>
 #'   gt() |>
 #'   cols_move_to_start(columns = year)
 #' ```
@@ -231,8 +235,10 @@ cols_move <- function(
 #' ```r
 #' countrypops |>
 #'   dplyr::select(-contains("code")) |>
-#'   dplyr::filter(country_name == "Fiji") |>
-#'   dplyr::slice_tail(n = 5) |>
+#'   dplyr::filter(
+#'     country_name == "Fiji",
+#'     year %in% 2017:2021
+#'   ) |>
 #'   gt() |>
 #'   cols_move_to_start(columns = c(year, population))
 #' ```
@@ -336,8 +342,10 @@ cols_move_to_start <- function(
 #' ```r
 #' countrypops |>
 #'   dplyr::select(-contains("code")) |>
-#'   dplyr::filter(country_name == "Benin") |>
-#'   dplyr::slice_tail(n = 5) |>
+#'   dplyr::filter(
+#'     country_name == "Benin",
+#'     year %in% 2017:2021
+#'   ) |>
 #'   gt() |>
 #'   cols_move_to_end(columns = year)
 #' ```
@@ -353,8 +361,10 @@ cols_move_to_start <- function(
 #' ```r
 #' countrypops |>
 #'   dplyr::select(-contains("code")) |>
-#'   dplyr::filter(country_name == "Benin") |>
-#'   dplyr::slice_tail(n = 5) |>
+#'   dplyr::filter(
+#'     country_name == "Benin",
+#'     year %in% 2017:2021
+#'   ) |>
 #'   gt() |>
 #'   cols_move_to_end(columns = c(year, country_name))
 #' ```

R/data_color.R

@@ -439,9 +439,11 @@
 #'
 #' ```r
 #' countrypops |>
-#'   dplyr::filter(country_name == "Bangladesh") |>
 #'   dplyr::select(-contains("code")) |>
-#'   dplyr::slice_tail(n = 10) |>
+#'   dplyr::filter(
+#'     country_name == "Bangladesh",
+#'     year %in% 2012:2021
+#'   ) |>
 #'   gt() |>
 #'   data_color(
 #'     columns = population,
@@ -464,9 +466,11 @@
 #'
 #' ```r
 #' countrypops |>
-#'   dplyr::filter(country_name == "Bangladesh") |>
 #'   dplyr::select(-contains("code")) |>
-#'   dplyr::slice_tail(n = 10) |>
+#'   dplyr::filter(
+#'     country_name == "Bangladesh",
+#'     year %in% 2012:2021
+#'   ) |>
 #'   gt() |>
 #'   data_color(
 #'     columns = population,

R/datasets.R

@@ -22,18 +22,19 @@
 #------------------------------------------------------------------------------#
 
 
-#' Yearly populations of countries from 1960 to 2022
+#' Yearly populations of countries from 1960 to 2023
 #'
 #' @description
 #'
 #' A dataset that presents yearly, total populations of countries. Total
 #' population is based on counts of all residents regardless of legal status or
 #' citizenship. Country identifiers include the English-language country names,
 #' and the 2- and 3-letter ISO 3166-1 country codes. Each row contains a
-#' `population` value for a given `year` (from 1960 to 2022). Any `NA` values
-#' for `populations` indicate the non-existence of the entity during that year.
+#' `population` value for a given `year` (from 1960 to 2023). Any `NA` values
+#' within `population` indicate the non-existence of the entity during that
+#' year.
 #'
-#' @format A tibble with 13,545 rows and 5 variables:
+#' @format A tibble with 13,760 rows and 5 variables:
 #' \describe{
 #' \item{country_name}{The name of the country.}
 #' \item{country_code_2, country_code_3}{The 2- and 3-letter ISO 3166-1 country

R/format_data.R

@@ -8005,7 +8005,7 @@ get_image_hw_ratio <- function(filepath) {
 #' make this work seamlessly, the input cells need to contain some reference to
 #' a country, and this can be in the form of a 2- or 3-letter ISO 3166-1 country
 #' code (e.g., Egypt has the `"EG"` country code). This function will parse the
-#' targeted body cells for those codes (and the [countrypops] dataset contains
+#' targeted body cells for those codes (and the [`countrypops`] dataset contains
 #' all of them) and insert the appropriate flag graphics.
 #'
 #' Multiple flags can be included per cell by separating country codes with

data-raw/01-countrypops.R

@@ -69,9 +69,10 @@ countrypops <-
         `2019` = col_integer(),
         `2020` = col_integer(),
         `2021` = col_integer(),
-        `2022` = col_integer()
+        `2022` = col_integer(),
+        `2023` = col_integer()
     ), na = character()
   ) %>%
-  tidyr::gather("year", "population", `1960`:`2022`) %>%
+  tidyr::gather("year", "population", `1960`:`2023`) %>%
   dplyr::arrange(country_code_3, year) %>%
   dplyr::mutate(year = as.integer(year))