199 lines
8.4 KiB
R
199 lines
8.4 KiB
R
library(tidyverse)
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library(sf)
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#library(tmap)
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library(leaflet)
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library(RColorBrewer)
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library(tidycensus)
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library(htmltools)
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## Load TOPS data ----
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## To load TOPS data for the whole state for crashes involving bikes and pedestrians):
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## 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
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## Step 2 - include RACE1 and RACE2 for download in preferences
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## Step 3 - save the csv in the "data" directory as crash-data-download_2023.csv
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TOPS_data <- as.list(NULL)
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for (file in list.files(path = "data/TOPS/", pattern = "crash-data-download")) {
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message(paste("importing data from file: ", file))
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year <- substr(file, 21, 24)
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csv_run <- read_csv(file = paste0("data/TOPS/",file), col_types = cols(.default = "c"))
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TOPS_data[[file]] <- csv_run
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}
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rm(csv_run, file, year)
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TOPS_data <- bind_rows(TOPS_data)
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## clean up data ----
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TOPS_data <- TOPS_data %>%
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mutate(date = mdy(CRSHDATE),
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age1 = as.double(AGE1),
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age2 = as.double(AGE2),
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latitude = as.double(LATDECDG),
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longitude = as.double(LONDECDG)) %>%
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mutate(month = month(date, label = TRUE),
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year = as.factor(year(date)))
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# Injury Severy Index and Color -----
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injury_severity <- data.frame(InjSevName = c("No apparent injury", "Possible Injury", "Suspected Minor Injury","Suspected Serious Injury","Fatality"),
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code = c("O", "C", "B", "A", "K"),
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color = c("#fafa6e", "#edc346", "#d88d2d", "#bd5721", "#9b1c1c"))
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TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(INJSVR1 == code)) %>%
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mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
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rename(InjSevName1 = InjSevName)
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TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(INJSVR2 == code)) %>%
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mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
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rename(InjSevName2 = InjSevName)
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TOPS_data <- TOPS_data %>% mutate(ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"),
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INJSVR1,
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ifelse(ROLE2 %in% c("BIKE", "PED"),
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INJSVR2,
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NA)))
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TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(ped_inj == code)) %>%
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mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
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rename(ped_inj_name = InjSevName)
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# Race names
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race <- data.frame(race_name = c("Asian", "Black", "Indian","Hispanic","White"),
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code = c("A", "B", "I", "H", "W"))
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TOPS_data <- left_join(TOPS_data, race %>% select(race_name, code), join_by(RACE1 == code)) %>% rename(race_name1 = race_name)
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TOPS_data <- left_join(TOPS_data, race %>% select(race_name, code), join_by(RACE2 == code)) %>% rename(race_name2 = race_name)
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## make mutate TOPS_data
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TOPS_data <- TOPS_data %>%
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mutate(Year = year,
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PedestrianInjurySeverity = ped_inj_name,
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CrashDate = CRSHDATE,
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CrashTime = CRSHTIME,
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County = CNTYNAME,
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Street = ONSTR,
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CrossStreet = ATSTR) %>%
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mutate(PedestrianAge = ifelse(ROLE1 %in% c("BIKE", "PED"), age1, age2))
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TOPS_geom <- st_as_sf(TOPS_data %>% filter(!is.na(latitude)), coords = c("longitude", "latitude"), crs = 4326)
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## add county borders ----
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CountyBoundaries <- read_sf("data/WI_County_Boundaries_24K.geojson")
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focus_columns <- c("PedestrianInjurySeverity", "CrashDate", "CrashTime", "County", "Street", "CrossStreet", "PedestrianAge", "Year")
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focus_county <- "DANE"
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## generate map with tmap ----
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# tmap_mode("view")
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#
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# Pedestrian_Crash_Data <- TOPS_geom %>%
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# # filter(CNTYNAME == focus_county) %>%
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# select(all_of(focus_columns))
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#
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# tm_basemap("Stadia.AlidadeSmooth") +
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# tm_shape(Pedestrian_Crash_Data) +
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# tm_dots("PedestrianInjurySeverity", palette = injury_severity$color, popup.vars = focus_columns)
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#
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# tmap_save(file = "figures/dynamic_crash_maps/dynamic_crash_map.html")
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# generate map with leaflet ----
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Pedestrian_Crash_Data <- TOPS_data %>%
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# filter(CNTYNAME == focus_county) %>%
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select(c(all_of(focus_columns), "longitude", "latitude"))
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injury_severity_pal <- colorFactor(palette = injury_severity$color, domain = injury_severity$InjSevName)
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County_Crash_Data <- Pedestrian_Crash_Data %>%
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group_by(County, Year) %>%
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summarise(TotalCrashes = n(),
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longitude = mean(longitude, na.rm = TRUE),
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latitude = mean(latitude, na.rm = TRUE)) %>%
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group_by(County) %>%
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summarise(MeanCrashes = mean(TotalCrashes, na.rm = TRUE),
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longitude = mean(longitude, na.rm = TRUE),
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latitude = mean(latitude, na.rm = TRUE))
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# add population census data
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census_api_key(key = substr(read_file(file = "api_keys/census_api_key"), 1, 40))
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county_populations <- get_estimates(geography = "county", year = 2022, product = "population", state = "Wisconsin") %>%
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filter(variable == "POPESTIMATE") %>%
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mutate(County = str_to_upper(str_replace(NAME, " County, Wisconsin", "")))
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County_Crash_Data <- left_join(County_Crash_Data, county_populations, join_by("County"))
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County_Crash_Data <- County_Crash_Data %>%
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mutate(CrashesPerPopulation = MeanCrashes/value*100000)
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county_pal <- colorNumeric(palette = "YlOrRd", domain = c(min(County_Crash_Data$CrashesPerPopulation, na.rm = TRUE), max(County_Crash_Data$CrashesPerPopulation, na.rm = TRUE)))
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#title style
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tag.map.title <- tags$style(HTML("
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.leaflet-control.map-title {
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transform: translate(-50%,20%);
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position: fixed !important;
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left: 50%;
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text-align: center;
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padding-left: 10px;
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padding-right: 10px;
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background: rgba(255,255,255,0.75);
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font-weight: bold;
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font-size: 28px;
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}
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"))
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title <- tags$div(
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tag.map.title, HTML("Pedestrians & Bicyclists hit by cars</br>2017-2023")
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)
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tag.map.subtitle <- tags$style(HTML("
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.leaflet-control.map-subtitle {
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transform: translate(0%,0%);
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position: fixed !important;
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left: 90%;
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text-align: center;
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padding-left: 10px;
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padding-right: 10px;
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background: rgba(255,255,255,0.75);
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font-weight: regular;
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font-size: 12px;
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}
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"))
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subtitle <- tags$div(
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tag.map.subtitle, HTML("data from UW TOPS lab - retrieved 3/2024</br>per direction of the WisDOT Bureau of Transportation Safety")
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)
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leaflet() %>%
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addControl(title, position = "topleft", className="map-title") %>%
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addControl(subtitle, position = "bottomleft", className="map-subtitle") %>%
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addProviderTiles(providers$Stadia.AlidadeSmooth) %>%
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addCircleMarkers(data = Pedestrian_Crash_Data,
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lng=Pedestrian_Crash_Data$longitude,
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lat=Pedestrian_Crash_Data$latitude,
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fillColor=injury_severity_pal(Pedestrian_Crash_Data$PedestrianInjurySeverity),
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radius=3,
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stroke=TRUE,
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color = "black",
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weight = 1,
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fillOpacity = 0.8,
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group = "Crash Points") %>%
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addLegend(position = "bottomleft", labels = injury_severity$InjSevName, colors = injury_severity$color, group = "Crash Points", title = "Injury Severity") %>%
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groupOptions(group = "Crash Points", zoomLevels = 10:20) %>%
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addPolygons(data = CountyBoundaries,
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fill = FALSE,
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color = "black",
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weight = 1) %>%
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addCircleMarkers(data = County_Crash_Data,
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lng=County_Crash_Data$longitude,
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lat=County_Crash_Data$latitude,
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fillColor=county_pal(County_Crash_Data$CrashesPerPopulation),
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radius=County_Crash_Data$value/20000,
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stroke = TRUE,
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color = "black",
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weight = 1,
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fillOpacity = 0.7,
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group = "Counties") %>%
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addLegend(position = "bottomleft", pal = county_pal, values = County_Crash_Data$CrashesPerPopulation, group = "Counties", title = "Mean Crashes/Year in County</br>(per 100,000 residents)") %>%
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# addLegendSize(position = "bottomright", color = "black", shape = "circle", values = County_Crash_Data$value.y, group = "Counties", title = "Population of County") %>%
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groupOptions(group ="Counties", zoomLevels = 1:9)
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