Rationalise colours

scratch.R

@@ -29,7 +29,7 @@ locgraph <- graph(edges = elist)
 sg <- decompose(locgraph, mode="weak")
 
 
-work_travel %>% rename(
+work_travel %>% select(
   res_code = SA2_code_usual_residence_address,
   res_name = SA2_name_usual_residence_address,
   res_east = SA2_usual_residence_easting,
@@ -38,15 +38,15 @@ work_travel %>% rename(
   work_name = SA2_name_workplace_address,
   work_east = SA2_workplace_easting,
   work_north = SA2_workplace_northing,
-  home = Work_at_home,
   private = Drive_a_private_car_truck_or_van,
-  company = Drive_a_company_car_truck_or_van,
   passenger = Passenger_in_a_car_truck_van_or_company_bus,
+  walk = Walk_or_jog,
+  bicycle = Bicycle,
+  company = Drive_a_company_car_truck_or_van,
   bus = Public_bus,
   train = Train,
-  bicycle = Bicycle,
-  walk = Walk_or_jog,
   ferry = Ferry,
+  home = Work_at_home,
   other = Other,
   total = Total
 ) -> work_simp
@@ -57,15 +57,15 @@ work_simp %>%
                          res_east,
                          res_north) %>%
   summarise(
-    home = sum(ifelse(home < 0, 0, home)),
     private = sum(ifelse(private < 0, 0, private)),
-    company = sum(ifelse(company < 0, 0, company)),
     passenger = sum(ifelse(passenger < 0, 0, passenger)),
+    walk = sum(ifelse(walk < 0, 0, walk)),
+    bicycle = sum(ifelse(bicycle < 0, 0, bicycle)),
+    company = sum(ifelse(company < 0, 0, company)),
     bus = sum(ifelse(bus < 0, 0, bus)),
     train = sum(ifelse(train < 0, 0, train)),
-    bicycle = sum(ifelse(bicycle < 0, 0, bicycle)),
-    walk = sum(ifelse(walk < 0, 0, walk)),
     ferry = sum(ifelse(ferry < 0, 0, ferry)),
+    home = sum(ifelse(home < 0, 0, home)),
     other = sum(ifelse(other < 0, 0, other)),
     total = sum(ifelse(total < 0, 0, total)), .groups="drop"
   ) -> work_from
@@ -77,15 +77,15 @@ work_simp %>%
                          work_east,
                          work_north) %>%
   summarise(
-    home = sum(ifelse(home < 0, 0, home)),
     private = sum(ifelse(private < 0, 0, private)),
-    company = sum(ifelse(company < 0, 0, company)),
     passenger = sum(ifelse(passenger < 0, 0, passenger)),
+    walk = sum(ifelse(walk < 0, 0, walk)),
+    bicycle = sum(ifelse(bicycle < 0, 0, bicycle)),
+    company = sum(ifelse(company < 0, 0, company)),
     bus = sum(ifelse(bus < 0, 0, bus)),
     train = sum(ifelse(train < 0, 0, train)),
-    bicycle = sum(ifelse(bicycle < 0, 0, bicycle)),
-    walk = sum(ifelse(walk < 0, 0, walk)),
     ferry = sum(ifelse(ferry < 0, 0, ferry)),
+    home = sum(ifelse(home < 0, 0, home)),
     other = sum(ifelse(other < 0, 0, other)),
     total = sum(ifelse(total < 0, 0, total)), .groups="drop"
   ) -> work_to
@@ -142,20 +142,20 @@ edu_simp %>% group_by(edu_code, edu_name, edu_east, edu_north) %>%
     total = sum(ifelse(total < 0, 0, total)), .groups="drop"
   ) -> edu_to
 
-tencols <-  c("#fb9a99", "#e31a1c", "#1f78b4", "#6a3d9a", "#b2df8a", 
-              "#33a02c", "#fdbf6f", "#ff7f00", "#cab2d6", "#a6cee3")
+tencols <-  c("#f85654", "#e31a1c", "#1f78b4", "#6a3d9a", "#b2df8a", 
+              "#33a02c", "#fdbf6f", "#ff7f00", "#af8ac1", "#cab2d6")
 tencols[which.max(work_from[1, 5:14])]
 
 
-work_from$MAX <- work_from %>% select(home:other) %>% as.matrix() %>% 
+work_from$MAX <- work_from %>% select(private:other) %>% as.matrix() %>% 
   apply(1, function(x) {
     ifelse(max(x) <= 0, NA, which.max(x))
     })
-work_to$MAX <- work_to %>% select(home:other) %>% as.matrix() %>% 
+work_to$MAX <- work_to %>% select(private:other) %>% as.matrix() %>% 
   apply(1, function(x) {
     ifelse(max(x) <= 0, NA, which.max(x))
     })
-work_simp$MAX <- work_simp %>% select(home:other) %>% as.matrix() %>% 
+work_simp$MAX <- work_simp %>% select(private:other) %>% as.matrix() %>% 
   apply(1, function(x) {
     ifelse(max(x) <= 0, NA, which.max(x))
     })

viz/app.R

@@ -14,9 +14,9 @@ load(file="datasets.RData")
 shpf <- readOGR(dsn="sa20025WGSfilcth")
 sa.in.dest <- shpf@data$SA22018_V1 %in% work_to$work_code
 sa.in.home <- shpf@data$SA22018_V1 %in% work_from$res_code
-transport.t <- c("Work at home", "Private car", "Company car", 
-                "Carpool", "Bus", "Train", "Bicycle", "Walk",
-                "Ferry", "Other", "None/Unknown")
+transport.t <- c("Private car", "Passenger in car",
+                 "Bicycle", "Walk", "Company car", "Bus", "Train",
+                 "Ferry", "Work at home", "Other", "None/Unknown")
 edu.t <- c("Drive self", "Passenger in car", "Walk", "Bicycle",
            "School bus", "Public bus", "Train", "Ferry", "Study at home",
            "Other", "None/Unknown")

viz/extras.R

@@ -142,19 +142,23 @@ infotext <- div(class="overflowable",
     a(href=
 "https://www.stats.govt.nz/2018-census/there-and-back-
 again-data-visualisation-competition", "data visualisation competition",
-    .noWS = "after"), ". The source data counts employed persons 15 years or ",
-    "older who gave an employment location in the 2018 census."),
+    .noWS = "after"), ". The employment data counts employed persons 15 years or ",
+    "older who gave an employment location in the 2018 census, while the ",
+    "education data counts people in education 15 years or older who gave an ",
+    "education location in the 2018 census—including older highschoolers and ",
+    "university students, but not e.g. primary school students."),
   h4("Options"),
   p("The top right panel (toggleable via the blue switch) allows ",
     "you to choose between visualising the people who commute from ",
-    "and to each area, and between showing numbers of people or ",
+    "and to each area to both employment and education, and between ",
+    "showing numbers of people or ",
     "their primary mode of transportation."),
   p("Select an area by clicking on it; deselect by clicking it again ",
     "or clicking in the water. When an area is selected the map is ",
     "coloured according to the people who commute to or from that area ",
     "(including the people who commute within the area). When no area is ",
     "selected, colouring is according to commutes to or from all ",
-    "localities."),
+    "localities. Hover over areas for a summary."),
   h4("FAQ"),
   shiny::tags$blockquote("Why are so many areas marked as 'works at ",
                          "home'?"),