Change "map" variable to "lines" -- maybe less confusing

R/datadoc_cd-info-long.R

@@ -7,7 +7,7 @@
 #'  covering the maps of `r length(unique(cd_info_long$map))` election years
 #'  for each of the 435 congressional districts.
 #'   \describe{
-#'    \item{`map`}{Is the year corresponding to the geography. For example, `map = 2008` and `cd = "AL-01`
+#'    \item{`lines`}{Is the year corresponding to the geography. For example, `map = 2008` and `cd = "AL-01`
 #'     indicates that the row is representing AL-01's geography as used in the 2008 election.}
 #'    \item{`cd`}{Is the CD corresponding to the year of the map.}
 #'    \item{`elec`}{Is the year of the election for the presidential election data that follows}
@@ -20,16 +20,18 @@
 #'    }
 #'
 #' @examples
+#'  library(dplyr)
+#'
 #'  # get only data for proximate years
-#'  cd_info_long |> filter((map == elec) | (elec + 2 == map))
+#'  cd_info_long |> filter((map == lines) | (elec + 2 == lines))
 #'
 #'  # this subset returns exactly 2 * 435 districts per cycle:
-#'  cd_info_long |> filter((map == elec) | (elec + 2 == map)) |> count(map, party)
+#'  cd_info_long |> filter((map == lines) | (elec + 2 == lines)) |> count(lines, party)
 #'
 #' # this will show where the districts lines changed between 2022 and 2024
 #' # (same election, same candidate, different map)
 #' cd_info_long |>
-#'  filter(map %in% c(2022, 2024), elec == 2020, candidate == "biden") |>
-#'  arrange(cd, map)
+#'  filter(lines %in% c(2022, 2024), elec == 2020, candidate == "biden") |>
+#'  arrange(cd, lines)
 #'
 "cd_info_long"

data-raw/create_cd-info-long.R

@@ -48,7 +48,7 @@ cd_info_all <- bind_rows(
 # Republican vote percentages
 R_pct <-
   cd_info_all |>
-  select(map = year, cd, starts_with("pct_")) |>
+  select(lines = year, cd, starts_with("pct_")) |>
   pivot_longer(
     matches("pct_"),
     names_prefix = "pct_",
@@ -58,8 +58,8 @@ R_pct <-
     name == "romney" ~ 2012,
     name == "trump16" ~ 2016,
     name == "trump20" ~ 2020,
-    name == "trump" & map <= 2018 ~ 2016,
-    name == "trump" & map >= 2020 & map <= 2022 ~ 2020
+    name == "trump" & lines <= 2018 ~ 2016,
+    name == "trump" & lines >= 2020 & lines <= 2022 ~ 2020
   ),
   .after = cd
   ) |>
@@ -69,7 +69,7 @@ R_pct <-
 
 D_pct <- R_pct |>
   left_join(pres_names_wide, by = "elec") |>
-  transmute(map,
+  transmute(lines,
             cd,
             elec,
             party = "D",
@@ -79,26 +79,26 @@ D_pct <- R_pct |>
 # same for Ns
 Ns <-
   cd_info_all |>
-  select(map = year, cd, matches("total")) |>
+  select(lines = year, cd, matches("total")) |>
   pivot_longer(
     matches("presvotes_total"),
     names_prefix = "presvotes_",
     values_to = "presvotes_total", values_drop_na = TRUE) |>
   mutate(elec = case_when(
     name == "total20" ~ 2020,
-    map %in% c(2008, 2012, 2016, 2020) ~ map,
-    map == 2010 ~ 2008,
-    map == 2014 ~ 2012,
-    map == 2018 ~ 2016,
-    map == 2022 ~ 2020,
+    lines %in% c(2008, 2012, 2016, 2020) ~ lines,
+    lines == 2010 ~ 2008,
+    lines == 2014 ~ 2012,
+    lines == 2018 ~ 2016,
+    lines == 2022 ~ 2020,
   ),
   .after = cd
   ) |>
   select(-name)
 
 cd_info_long <- bind_rows(D_pct, R_pct) |>
-  tidylog::left_join(Ns, by = c("map", "cd", "elec")) |>
-  arrange(map, elec, cd, party) |>
+  tidylog::left_join(Ns, by = c("lines", "cd", "elec")) |>
+  arrange(lines, elec, cd, party) |>
   rename(candidate = name)
 
 usethis::use_data(cd_info_long, overwrite = TRUE)