fixed community selection logic to match plot function

Author: jcaracappa1

R/plot_community_factors.R

@@ -57,10 +57,19 @@ plot_community_factors <- function(
     )
   }
 
-  fix <- ecodata::engagement |>
+  eng <- ecodata::engagement |>
+    #dplyr::distinct(Time, Var,  EPU, Units, .keep_all = T) |> #hack, remove later
     tidyr::separate(Var, into = c("Town", "StateVar"), sep = ", ") |> #using two steps because some towns have - in the name
     tidyr::separate(StateVar, into = c("State", "Var"), sep = "-") |> # which also seps the variable
     tidyr::unite("Town", c(Town, State), sep = ",") |>
+    dplyr::mutate(Town = dplyr::case_when(
+      stringr::str_detect(Town, "OTHER,VA") ~ "OTHER,VA (includes REEDVILLE)",
+      stringr::str_detect(Town, "Bronx/City Island") ~ "BRONX,NY",
+      stringr::str_detect(Town, "Reedville/District 5") ~ "Reedville,VA",
+      stringr::str_detect(Town, "Harpswell/Bailey Island") ~ "Harpswell,ME",
+      stringr::str_detect(Town, "South Kingstown/Kingston/Wakefield-Peacedale") ~ "South Kingstown,RI",
+      TRUE ~ Town # This keeps everything else the same
+    ))|>
     # tidyr::pivot_wider(names_from = Var, values_from = Value) |>
     dplyr::filter(EPU == filterEPUs) |>
     tidyr::separate(
@@ -72,22 +81,42 @@ plot_community_factors <- function(
     dplyr::mutate(city = stringr::str_to_title(city)) |>
     tidyr::unite("Town", city, state, sep = ", ")
 
-  all.towns = fix |>
-    dplyr::filter(!(Var %in% ('fishing_mean_score'))) |>
-    dplyr::pull(Town) |>
-    unique() |>
-    tolower()
+  eng.ts = eng |>
+    dplyr::filter(Var == 'fishing_mean_score') |>
+    dplyr::mutate(
+      label = dplyr::if_else(
+        Time == max(Time),
+        as.character(Town),
+        NA_character_
+      )
+    )
 
-  top.coms = fix |>
-    dplyr::filter(Var == filterVar & !is.na(Value)) |>
-    dplyr::filter(Time == max(Time) & tolower(Town) %in% all.towns) |>
+  top.coms <- eng |>
+    dplyr::filter(Time == max(Time)) |>
     dplyr::arrange(desc(Value)) |>
-    dplyr::slice_head(n = n) |>
+    head(n = 10) |>
+    dplyr::mutate(
+      Town = dplyr::recode(Town, "Other, VA (includes REEDVILLE)" = "Reedville, VA")
+    ) |>
     dplyr::pull(Town)
 
+  #
+  # all.towns = fix |>
+  #   dplyr::filter(!(Var %in% ('fishing_mean_score'))) |>
+  #   dplyr::pull(Town) |>
+  #   unique() |>
+  #   tolower()
+  #
+  # top.coms = fix |>
+  #   dplyr::filter(Var == filterVar & !is.na(Value)) |>
+  #   dplyr::filter(Time == max(Time) & tolower(Town) %in% all.towns) |>
+  #   dplyr::arrange(desc(Value)) |>
+  #   dplyr::slice_head(n = n) |>
+  #   dplyr::pull(Town)
+
   # optional code to wrangle ecodata object prior to plotting
   # e.g., calculate mean, max or other needed values to join below
-  data = fix |>
+  data = eng |>
     dplyr::filter(Var %in% indgroup) |>
     dplyr::group_by(Town) |>
     dplyr::mutate(total = sum(Value, na.rm = T)) |>