263 lines
14 KiB
R
263 lines
14 KiB
R
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library(tidyverse)
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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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# 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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## set parameters ----
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county_focus <- c("MILWAUKEE")
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municipality_focus <- c("MILWAUKEE")
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## build data summaries for city ----
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data_summary <- list(NULL)
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# crashes by year that resulted in a pedestrian fatality or severe injury
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data_summary[["crash_by_year"]] <- TOPS_data %>%
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filter(MUNINAME %in% municipality_focus) %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& INJSVR1 %in% c("A", "K")
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| ROLE2 %in% c("BIKE", "PED")
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& INJSVR2 %in% c("A", "K")
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) %>%
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mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
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ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA))) %>%
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group_by(MUNINAME, year, ped_type, ped_inj) %>%
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summarise(count = n_distinct(DOCTNMBR))
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# crashes by race of pedestrian/bicyclist for focus year
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data_summary[["crash_by_race"]] <- TOPS_data %>%
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filter(MUNINAME %in% municipality_focus) %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& INJSVR1 %in% c("A", "K")
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| ROLE2 %in% c("BIKE", "PED")
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& INJSVR2 %in% c("A", "K")
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) %>%
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mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
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ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA)),
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ped_race = ifelse(ROLE1 %in% c("BIKE", "PED"), race_name1, ifelse(ROLE2 %in% c("BIKE", "PED"), race_name2, NA))) %>%
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group_by(MUNINAME, ped_type, ped_inj, ped_race) %>%
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summarise(count = n_distinct(DOCTNMBR))
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# crashes by race of driver that resulted in a pedestrian fatality or severe injury
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data_summary[["crash_by_driver_race"]] <- TOPS_data %>%
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filter(MUNINAME %in% municipality_focus) %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& INJSVR1 %in% c("A", "K")
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| ROLE2 %in% c("BIKE", "PED")
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& INJSVR2 %in% c("A", "K")
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) %>%
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mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
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ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA)),
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driver_race = ifelse(ROLE1 %in% c("DR"), race_name1, ifelse(ROLE2 %in% c("DR"), race_name2, NA))) %>%
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group_by(MUNINAME, year, ped_type, ped_inj, driver_race) %>%
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summarise(count = n_distinct(DOCTNMBR))
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# crashes by age of pedestrian/bicyclist
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data_summary[["crash_by_age"]] <- TOPS_data %>%
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filter(MUNINAME %in% municipality_focus) %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& INJSVR1 %in% c("A", "K")
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| ROLE2 %in% c("BIKE", "PED")
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& INJSVR2 %in% c("A", "K")
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) %>%
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mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
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ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA)),
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ped_age = ifelse(ROLE1 %in% c("BIKE", "PED"), age1, ifelse(ROLE2 %in% c("BIKE", "PED"), age2, NA))) %>%
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group_by(MUNINAME, year, ped_type, ped_inj, ped_age) %>%
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summarise(count = n_distinct(DOCTNMBR))
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# crashes by age of driver that resulted in a severe injury or fatality of a pedestrian/bicyclist
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data_summary[["crash_by_driver_age"]] <- TOPS_data %>%
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filter(MUNINAME %in% municipality_focus) %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& INJSVR1 %in% c("A", "K")
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| ROLE2 %in% c("BIKE", "PED")
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& INJSVR2 %in% c("A", "K")
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) %>%
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mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
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ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA)),
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driver_age = ifelse(ROLE1 %in% c("DR"), age1, ifelse(ROLE2 %in% c("BIKE", "PED"), age2, NA))) %>%
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group_by(MUNINAME, year, ped_type, ped_inj, driver_age) %>%
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summarise(count = n_distinct(DOCTNMBR))
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# crashes by sex of pedestrian/bicyclist
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data_summary[["crash_by_sex"]] <- TOPS_data %>%
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filter(MUNINAME %in% municipality_focus) %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& INJSVR1 %in% c("A", "K")
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| ROLE2 %in% c("BIKE", "PED")
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& INJSVR2 %in% c("A", "K")
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) %>%
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mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
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ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA)),
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ped_sex = ifelse(ROLE1 %in% c("BIKE", "PED"), SEX1, ifelse(ROLE2 %in% c("BIKE", "PED"), SEX1, NA))) %>%
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group_by(MUNINAME, year, ped_type, ped_inj, ped_sex) %>%
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summarise(count = n_distinct(DOCTNMBR))
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# crashes by sex of driver that resulted in a severe injury or fatality of a pedestrian/bicyclist
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data_summary[["crash_by_driver_sex"]] <- TOPS_data %>%
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filter(MUNINAME %in% municipality_focus) %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& INJSVR1 %in% c("A", "K")
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| ROLE2 %in% c("BIKE", "PED")
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& INJSVR2 %in% c("A", "K")
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) %>%
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mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
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ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA)),
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driver_sex = ifelse(ROLE1 %in% c("DR"), SEX1, ifelse(ROLE2 %in% c("BIKE", "PED"), SEX1, NA))) %>%
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group_by(MUNINAME, year, ped_type, ped_inj, driver_sex) %>%
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summarise(count = n_distinct(DOCTNMBR))
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## export csv files for city ----
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for(table_name in as.vector(names(data_summary[-1]))) {
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write_csv(data_summary[[table_name]], file = paste0("data_summaries/city/",table_name, ".csv"))
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}
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## build data summaries for county ----
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data_summary <- list(NULL)
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# crashes by year that resulted in a pedestrian fatality or severe injury
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data_summary[["crash_by_year"]] <- TOPS_data %>%
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filter(CNTYNAME %in% county_focus) %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& INJSVR1 %in% c("A", "K")
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| ROLE2 %in% c("BIKE", "PED")
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& INJSVR2 %in% c("A", "K")
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) %>%
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mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
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ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA))) %>%
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group_by(CNTYNAME, year, ped_type, ped_inj) %>%
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summarise(count = n_distinct(DOCTNMBR))
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# crashes by race of pedestrian/bicyclist for focus year
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data_summary[["crash_by_race"]] <- TOPS_data %>%
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filter(CNTYNAME %in% county_focus) %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& INJSVR1 %in% c("A", "K")
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| ROLE2 %in% c("BIKE", "PED")
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& INJSVR2 %in% c("A", "K")
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) %>%
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mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
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ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA)),
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ped_race = ifelse(ROLE1 %in% c("BIKE", "PED"), race_name1, ifelse(ROLE2 %in% c("BIKE", "PED"), race_name2, NA))) %>%
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group_by(CNTYNAME, ped_type, ped_inj, ped_race) %>%
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summarise(count = n_distinct(DOCTNMBR))
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# crashes by race of driver that resulted in a pedestrian fatality or severe injury
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data_summary[["crash_by_driver_race"]] <- TOPS_data %>%
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filter(CNTYNAME %in% county_focus) %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& INJSVR1 %in% c("A", "K")
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| ROLE2 %in% c("BIKE", "PED")
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& INJSVR2 %in% c("A", "K")
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) %>%
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mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
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ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA)),
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driver_race = ifelse(ROLE1 %in% c("DR"), race_name1, ifelse(ROLE2 %in% c("DR"), race_name2, NA))) %>%
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group_by(CNTYNAME, year, ped_type, ped_inj, driver_race) %>%
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summarise(count = n_distinct(DOCTNMBR))
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# crashes by age of pedestrian/bicyclist
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data_summary[["crash_by_age"]] <- TOPS_data %>%
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filter(CNTYNAME %in% county_focus) %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& INJSVR1 %in% c("A", "K")
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| ROLE2 %in% c("BIKE", "PED")
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& INJSVR2 %in% c("A", "K")
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) %>%
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mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
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ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA)),
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ped_age = ifelse(ROLE1 %in% c("BIKE", "PED"), age1, ifelse(ROLE2 %in% c("BIKE", "PED"), age2, NA))) %>%
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group_by(CNTYNAME, year, ped_type, ped_inj, ped_age) %>%
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summarise(count = n_distinct(DOCTNMBR))
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# crashes by age of driver that resulted in a severe injury or fatality of a pedestrian/bicyclist
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data_summary[["crash_by_driver_age"]] <- TOPS_data %>%
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filter(CNTYNAME %in% county_focus) %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& INJSVR1 %in% c("A", "K")
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| ROLE2 %in% c("BIKE", "PED")
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& INJSVR2 %in% c("A", "K")
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) %>%
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mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
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ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA)),
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driver_age = ifelse(ROLE1 %in% c("DR"), age1, ifelse(ROLE2 %in% c("BIKE", "PED"), age2, NA))) %>%
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group_by(CNTYNAME, year, ped_type, ped_inj, driver_age) %>%
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summarise(count = n_distinct(DOCTNMBR))
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# crashes by sex of pedestrian/bicyclist
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data_summary[["crash_by_sex"]] <- TOPS_data %>%
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filter(CNTYNAME %in% county_focus) %>%
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filter(ROLE1 %in% c("BIKE", "PED")
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& INJSVR1 %in% c("A", "K")
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| ROLE2 %in% c("BIKE", "PED")
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& INJSVR2 %in% c("A", "K")
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) %>%
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mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
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ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA)),
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ped_sex = ifelse(ROLE1 %in% c("BIKE", "PED"), SEX1, ifelse(ROLE2 %in% c("BIKE", "PED"), SEX1, NA))) %>%
|
||
|
|
group_by(CNTYNAME, year, ped_type, ped_inj, ped_sex) %>%
|
||
|
|
summarise(count = n_distinct(DOCTNMBR))
|
||
|
|
|
||
|
|
# crashes by sex of driver that resulted in a severe injury or fatality of a pedestrian/bicyclist
|
||
|
|
data_summary[["crash_by_driver_sex"]] <- TOPS_data %>%
|
||
|
|
filter(CNTYNAME %in% county_focus) %>%
|
||
|
|
filter(ROLE1 %in% c("BIKE", "PED")
|
||
|
|
& INJSVR1 %in% c("A", "K")
|
||
|
|
| ROLE2 %in% c("BIKE", "PED")
|
||
|
|
& INJSVR2 %in% c("A", "K")
|
||
|
|
) %>%
|
||
|
|
mutate(ped_type = ifelse(ROLE1 %in% c("BIKE", "PED"), ROLE1, ifelse(ROLE2 %in% c("BIKE", "PED"), ROLE2, NA)),
|
||
|
|
ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), as.character(InjSevName1), ifelse(ROLE2 %in% c("BIKE", "PED"), as.character(InjSevName2), NA)),
|
||
|
|
driver_sex = ifelse(ROLE1 %in% c("DR"), SEX1, ifelse(ROLE2 %in% c("BIKE", "PED"), SEX1, NA))) %>%
|
||
|
|
group_by(CNTYNAME, year, ped_type, ped_inj, driver_sex) %>%
|
||
|
|
summarise(count = n_distinct(DOCTNMBR))
|
||
|
|
|
||
|
|
## export csv files for county ----
|
||
|
|
for(table_name in as.vector(names(data_summary[-1]))) {
|
||
|
|
write_csv(data_summary[[table_name]], file = paste0("data_summaries/county/",table_name, ".csv"))
|
||
|
|
}
|