added crash_summary_charts.R script

scripts/crash_summary_charts.R

@@ -0,0 +1,130 @@
+library(tidyverse)
+library(RColorBrewer)
+library(tidycensus)
+library(ggrepel)
+
+## Load TOPS data ----
+## To load TOPS data for the whole state for crashes involving bikes and pedestrians):
+## Step 1 - download csv from the TOPS Data Retrieval Tool with the query:  SELECT * FROM DTCRPRD.SUMMARY_COMBINED C WHERE C.CRSHDATE BETWEEN TO_DATE('2023-JAN','YYYY-MM') AND LAST_DAY(TO_DATE('2023-DEC','YYYY-MM')) AND (C.BIKEFLAG = 'Y' OR C.PEDFLAG = 'Y') ORDER BY C.DOCTNMBR 
+## Step 2 - include RACE1 and RACE2 for download in preferences
+## Step 3 - save the csv in the "data" directory as crash-data-download_2023.csv
+TOPS_data <- as.list(NULL)
+for (file in list.files(path = "data/TOPS/", pattern = "crash-data-download")) {
+  message(paste("importing data from file: ", file))
+  year <- substr(file, 21, 24)
+  csv_run <- read_csv(file = paste0("data/TOPS/",file), col_types = cols(.default = "c"))
+  TOPS_data[[file]] <- csv_run
+}
+rm(csv_run, file, year)
+TOPS_data <- bind_rows(TOPS_data)
+
+## clean up data ----
+TOPS_data <- TOPS_data %>% 
+  mutate(date = mdy(CRSHDATE), 
+         age1 = as.double(AGE1),
+         age2 = as.double(AGE2),
+         latitude  = as.double(LATDECDG),
+         longitude = as.double(LONDECDG)) %>% 
+  mutate(month = month(date, label = TRUE),
+         year = as.factor(year(date)))
+
+# Injury Severy Index and Color -----
+injury_severity <- data.frame(InjSevName = c("No apparent injury", "Possible Injury", "Suspected Minor Injury","Suspected Serious Injury","Fatality"), 
+                              code = c("O", "C", "B", "A", "K"),
+                              color = c("#fafa6e", "#edc346", "#d88d2d", "#bd5721", "#9b1c1c"))
+
+TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(INJSVR1 == code)) %>% 
+  mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
+  rename(InjSevName1 = InjSevName)
+TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(INJSVR2 == code)) %>% 
+  mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
+  rename(InjSevName2 = InjSevName)
+
+TOPS_data <- TOPS_data %>% mutate(ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), 
+                                                   INJSVR1, 
+                                                   ifelse(ROLE2 %in% c("BIKE", "PED"), 
+                                                          INJSVR2,
+                                                          NA)))
+
+TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(ped_inj == code)) %>% 
+  mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
+  rename(ped_inj_name = InjSevName)
+
+# Race names
+race <- data.frame(race_name = c("Asian", "Black", "Indian","Hispanic","White"), 
+                   code = c("A", "B", "I", "H", "W"))
+
+TOPS_data <- left_join(TOPS_data, race %>% select(race_name, code), join_by(RACE1 == code)) %>% rename(race_name1 = race_name)
+TOPS_data <- left_join(TOPS_data, race %>% select(race_name, code), join_by(RACE2 == code)) %>% rename(race_name2 = race_name)
+
+## make mutate TOPS_data
+TOPS_data <- TOPS_data %>% 
+  mutate(Year = year,
+         PedestrianInjurySeverity = ped_inj_name,
+         CrashDate = CRSHDATE,
+         CrashTime = CRSHTIME,
+         County = CNTYNAME,
+         Street = ONSTR,
+         CrossStreet = ATSTR) %>%
+  mutate(PedestrianAge = ifelse(ROLE1 %in% c("BIKE", "PED"), age1, age2))
+
+# add population census data ----
+census_api_key(key = substr(read_file(file = "api_keys/census_api_key"), 1, 40))
+county_populations <- get_estimates(geography = "county", year = 2022, product = "population", state = "Wisconsin") %>%
+  filter(variable == "POPESTIMATE") %>%
+  mutate(County = str_to_upper(str_replace(NAME, " County, Wisconsin", "")))
+
+
+## generate county charts ----
+county_focus <- unique(TOPS_data %>%
+                         group_by(CNTYNAME) %>%
+                         summarise(TotalCrashes = n()) %>% 
+                         slice_max(TotalCrashes, n = 8) %>%
+                         pull(CNTYNAME))
+
+
+TOPS_data %>%
+  group_by(CNTYNAME, Year) %>%
+  summarise(TotalCrashes = n()) %>%
+  mutate(County = CNTYNAME) %>%
+  left_join(county_populations, join_by("County")) %>%
+  mutate(CrashesPerPopulation = TotalCrashes/value*100000) %>%
+  filter(County %in% county_focus) %>%
+  ggplot() +
+  geom_line(aes(x = Year,
+                y = CrashesPerPopulation,
+                color = str_to_title(CNTYNAME),
+                group = CNTYNAME),
+            size = 1) +
+  geom_label_repel(data = TOPS_data %>%
+                     group_by(CNTYNAME, Year) %>%
+                     summarise(TotalCrashes = n()) %>%
+                     mutate(County = CNTYNAME) %>%
+                     left_join(county_populations, join_by("County")) %>%
+                     mutate(CrashesPerPopulation = TotalCrashes/value*100000) %>%
+                     filter(County %in% county_focus,
+                            Year == 2023),
+                   aes(x = Year,
+                       y = CrashesPerPopulation,
+                       label = str_to_title(County),
+                       fill = County),
+                   size=3, 
+                   min.segment.length=0, 
+                   segment.size = 0.25,
+                   nudge_x=0.5, 
+                   direction="y") +
+  scale_color_brewer(type = "qual", guide = NULL) +
+  scale_fill_brewer(type = "qual", guide = NULL) +
+  scale_x_discrete(expand = expansion(add = c(0.5,0.75))) +
+  labs(title = "Drivers crashing into pedestrians & bicyclists per 100,000 residents",
+       subtitle = "2017-2023",
+       x = "Year",
+       y = "Total crashes per year per 100,000 residents",
+       color = "County",
+       caption = "data from UW TOPS lab\nretrieved 3/2024 per direction of the WisDOT Bureau of Transportation Safety") +
+  theme(plot.caption = element_text(color = "grey"))
+
+ggsave(file = paste0("figures/crash_summaries/counties_year.pdf"),
+       height = 8.5,
+       width = 11,
+       units = "in")

scripts/dynamic_crash_map.R

@@ -1,6 +1,6 @@
 library(tidyverse)
 library(sf)
-library(tmap)
+#library(tmap)
 library(leaflet)
 library(RColorBrewer)
 library(tidycensus)
@@ -81,17 +81,17 @@ focus_columns <- c("PedestrianInjurySeverity", "CrashDate", "CrashTime", "County
 focus_county <- "DANE"
 
 ## generate map with tmap ----
-tmap_mode("view")
+# tmap_mode("view")
-
+# 
-Pedestrian_Crash_Data <- TOPS_geom %>% 
+# Pedestrian_Crash_Data <- TOPS_geom %>% 
-#  filter(CNTYNAME == focus_county) %>%
+# #  filter(CNTYNAME == focus_county) %>%
-  select(all_of(focus_columns))
+#   select(all_of(focus_columns))
-
+# 
-tm_basemap("Stadia.AlidadeSmooth") +
+# tm_basemap("Stadia.AlidadeSmooth") +
-  tm_shape(Pedestrian_Crash_Data) +
+#   tm_shape(Pedestrian_Crash_Data) +
-  tm_dots("PedestrianInjurySeverity", palette = injury_severity$color, popup.vars	= focus_columns)
+#   tm_dots("PedestrianInjurySeverity", palette = injury_severity$color, popup.vars	= focus_columns)
-  
+#   
-tmap_save(file = "figures/dynamic_crash_maps/dynamic_crash_map.html")
+# tmap_save(file = "figures/dynamic_crash_maps/dynamic_crash_map.html")
 
 
 # generate map with leaflet ----
@@ -138,7 +138,7 @@ tag.map.title <- tags$style(HTML("
 "))
 
 title <- tags$div(
-  tag.map.title, HTML("Pedestrian Crashes</br>2017-2023")
+  tag.map.title, HTML("Pedestrians & Bicyclists hit by cars</br>2017-2023")
 )  
 
 tag.map.subtitle <- tags$style(HTML("
@@ -156,7 +156,7 @@ tag.map.subtitle <- tags$style(HTML("
 "))
 
 subtitle <- tags$div(
-  tag.map.subtitle, HTML("data from UW TOPS lab - retrieved 4/2024</br>per direction of the WisDOT Bureau of Transportation Safety")
+  tag.map.subtitle, HTML("data from UW TOPS lab - retrieved 3/2024</br>per direction of the WisDOT Bureau of Transportation Safety")
 )
 
 leaflet() %>%
@@ -193,3 +193,6 @@ leaflet() %>%
 #  addLegendSize(position = "bottomright", color = "black", shape = "circle", values = County_Crash_Data$value.y, group = "Counties", title = "Population of County") %>%
   groupOptions(group ="Counties", zoomLevels = 1:9)
 
+
+
+