changed directory structure, first move to Makefile
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21 changed files with 47 additions and 7 deletions
171
R/crash_summary_charts.R
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171
R/crash_summary_charts.R
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library(tidyverse)
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library(RColorBrewer)
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library(tidycensus)
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library(ggrepel)
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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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csv_run["retreive_date"] <- file.info(file = paste0("data/TOPS/",file))$mtime
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TOPS_data[[file]] <- csv_run
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}
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rm(csv_run, file, year)
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TOPS_data <- bind_rows(TOPS_data)
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## clean up data ----
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TOPS_data <- TOPS_data %>%
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mutate(date = mdy(CRSHDATE),
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age1 = as.double(AGE1),
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age2 = as.double(AGE2),
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latitude = as.double(LATDECDG),
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longitude = as.double(LONDECDG)) %>%
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mutate(month = month(date, label = TRUE),
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year = as.factor(year(date)))
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retrieve_date <- max(TOPS_data %>% filter(year %in% max(year(TOPS_data$date), na.rm = TRUE)) %>% pull(retreive_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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# add bike or pedestrian roles ----
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bike_roles <- c("BIKE", "O BIKE")
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ped_roles <- c("PED", "O PED", "PED NO")
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vuln_roles <- c(bike_roles, ped_roles)
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TOPS_data <- TOPS_data %>% mutate(ped_inj = ifelse(ROLE1 %in% vuln_roles,
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INJSVR1,
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ifelse(ROLE2 %in% vuln_roles,
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INJSVR2,
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NA)))
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# bike or ped
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TOPS_data <- TOPS_data %>% mutate(vulnerable_role = ifelse(ROLE1 %in% bike_roles | ROLE2 %in% bike_roles,
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"Bicyclist",
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ifelse(ROLE1 %in% ped_roles | ROLE2 %in% ped_roles,
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"Pedestrian",
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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% vuln_roles, age1, age2))
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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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## generate county charts ----
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county_focus <- unique(TOPS_data %>%
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group_by(CNTYNAME) %>%
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summarise(TotalCrashes = n()) %>%
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slice_max(TotalCrashes, n = 8) %>%
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pull(CNTYNAME))
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TOPS_data %>%
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filter(ped_inj %in% c("A", "K")) %>%
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group_by(CNTYNAME, Year) %>%
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summarise(TotalCrashes = n()) %>%
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mutate(County = CNTYNAME) %>%
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left_join(county_populations, join_by("County")) %>%
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mutate(CrashesPerPopulation = TotalCrashes/value*100000) %>%
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filter(County %in% county_focus) %>%
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ggplot() +
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geom_line(aes(x = Year,
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y = CrashesPerPopulation,
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color = str_to_title(CNTYNAME),
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group = CNTYNAME),
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size = 1) +
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geom_label_repel(data = TOPS_data %>%
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filter(ped_inj %in% c("A", "K")) %>%
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group_by(CNTYNAME, Year) %>%
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summarise(TotalCrashes = n()) %>%
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mutate(County = CNTYNAME) %>%
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left_join(county_populations, join_by("County")) %>%
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mutate(CrashesPerPopulation = TotalCrashes/value*100000) %>%
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filter(County %in% county_focus,
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Year == 2023),
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aes(x = Year,
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y = CrashesPerPopulation,
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label = str_to_title(County),
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fill = County),
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size=3,
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min.segment.length=0,
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segment.size = 0.25,
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nudge_x=0.5,
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direction="y") +
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scale_color_brewer(type = "qual", guide = NULL) +
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scale_fill_brewer(type = "qual", guide = NULL) +
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scale_x_discrete(expand = expansion(add = c(0.5,0.75))) +
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labs(title = "Drivers crashing into pedestrians & bicyclists per 100,000 residents",
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subtitle = "Fatalities and Severe Injuries | 2017-2023",
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x = "Year",
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y = "Total crashes per year per 100,000 residents",
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color = "County",
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caption = paste0("crash data from UW TOPS lab - retrieved ",
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strftime(retrieve_date, format = "%m/%Y"),
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" per direction of the WisDOT Bureau of Transportation Safety",
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"\nbasemap from StadiaMaps and OpenStreetMap Contributers")) +
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theme(plot.caption = element_text(color = "grey"))
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ggsave(file = paste0("figures/crash_summaries/counties_year.pdf"),
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height = 8.5,
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width = 11,
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units = "in")
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TOPS_data %>%
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filter(County %in% county_focus) %>%
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group_by(County, vulnerable_role) %>%
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summarise(count = n()) %>%
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ggplot() +
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geom_col(aes(x = County,
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y = count,
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fill = vulnerable_role))
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TOPS_data %>%
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filter(County %in% "DANE") %>%
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group_by(County, vulnerable_role, year) %>%
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summarise(count = n()) %>%
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ggplot() +
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geom_col(aes(x = year,
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y = count,
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fill = vulnerable_role),
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position = position_dodge())
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