edited crash_data_summaries.Rmd again
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Ben Varick
parent
ebf40f7e63
commit
8810ca19a9
1 changed files with 69 additions and 17 deletions
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@ -24,7 +24,7 @@ library(tidycensus)
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library(ggrepel)
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library(leaflet)
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county_focus <- c("DANE")
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municipality_focus <- c("DANE")
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municipality_focus <- c("MADISON")
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```
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## Load TOPS data
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@ -37,6 +37,13 @@ injury_severity_pal <- colorFactor(palette = injury_severity$color, levels = inj
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```
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## Set parameters
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```{r parameters, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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county_focus <- c("DANE")
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municipality_focus <- c("MADISON")
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injury_severy_focus <- c("A", "K", "B")
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```
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## build data summaries for city
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```{r citysummaries, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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@ -44,21 +51,21 @@ 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(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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group_by(MUNINAME, year, vulnerable_role, ped_inj_name) %>%
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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(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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group_by(MUNINAME, vulnerable_role, ped_inj_name, vulnerable_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(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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mutate(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, vulnerable_role, ped_inj_name, driver_race) %>%
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summarise(count = n_distinct(DOCTNMBR))
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@ -66,7 +73,7 @@ data_summary[["crash_by_driver_race"]] <- TOPS_data %>%
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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(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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mutate(vulnerable_age = ifelse(ROLE1 %in% vuln_roles, age1, ifelse(ROLE2 %in% vuln_roles, age2, NA))) %>%
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group_by(MUNINAME, year, vulnerable_role, ped_inj_name, vulnerable_age) %>%
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summarise(count = n_distinct(DOCTNMBR))
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@ -74,7 +81,7 @@ data_summary[["crash_by_age"]] <- TOPS_data %>%
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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(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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mutate(driver_age = ifelse(ROLE1 %in% c("DR"), age1, ifelse(ROLE2 %in% c("DR"), age2, NA))) %>%
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group_by(MUNINAME, year, vulnerable_role, ped_inj_name, driver_age) %>%
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summarise(count = n_distinct(DOCTNMBR))
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@ -82,7 +89,7 @@ data_summary[["crash_by_driver_age"]] <- TOPS_data %>%
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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(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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mutate(vulnerable_sex = ifelse(ROLE1 %in% vuln_roles, SEX1, ifelse(ROLE2 %in% vuln_roles, SEX1, NA))) %>%
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group_by(MUNINAME, year, vulnerable_role, ped_inj_name, vulnerable_sex) %>%
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summarise(count = n_distinct(DOCTNMBR))
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@ -90,14 +97,19 @@ data_summary[["crash_by_sex"]] <- TOPS_data %>%
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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(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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mutate(driver_sex = ifelse(ROLE1 %in% c("DR"), SEX1, ifelse(ROLE2 %in% c("DR"), SEX2, NA))) %>%
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group_by(MUNINAME, year, vulnerable_role, ped_inj_name, driver_sex) %>%
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summarise(count = n_distinct(DOCTNMBR))
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## export csv files for city ----
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# make directories for city summaries
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city_directory <- paste0("data_summaries/city/",municipality_focus,"/")
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ifelse(!dir.exists(city_directory), dir.create(city_directory), "Folder exists already")
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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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write_csv(data_summary[[table_name]], file = paste0(city_directory, municipality_focus, "-", table_name, ".csv"))
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}
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```
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@ -110,21 +122,21 @@ 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(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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group_by(CNTYNAME, year, vulnerable_role, ped_inj_name) %>%
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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(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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group_by(CNTYNAME, vulnerable_role, ped_inj_name, vulnerable_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(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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mutate(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, vulnerable_role, ped_inj_name, driver_race) %>%
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summarise(count = n_distinct(DOCTNMBR))
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@ -132,7 +144,7 @@ data_summary[["crash_by_driver_race"]] <- TOPS_data %>%
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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(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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mutate(vulnerable_age = ifelse(ROLE1 %in% vuln_roles, age1, ifelse(ROLE2 %in% vuln_roles, age2, NA))) %>%
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group_by(CNTYNAME, year, vulnerable_role, ped_inj_name, vulnerable_age) %>%
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summarise(count = n_distinct(DOCTNMBR))
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@ -140,7 +152,7 @@ data_summary[["crash_by_age"]] <- TOPS_data %>%
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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(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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mutate(driver_age = ifelse(ROLE1 %in% c("DR"), age1, ifelse(ROLE2 %in% c("DR"), age2, NA))) %>%
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group_by(CNTYNAME, year, vulnerable_role, ped_inj_name, driver_age) %>%
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summarise(count = n_distinct(DOCTNMBR))
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@ -148,7 +160,7 @@ data_summary[["crash_by_driver_age"]] <- TOPS_data %>%
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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(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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mutate(vulnerable_sex = ifelse(ROLE1 %in% vuln_roles, SEX1, ifelse(ROLE2 %in% vuln_roles, SEX1, NA))) %>%
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group_by(CNTYNAME, year, vulnerable_role, ped_inj_name, vulnerable_sex) %>%
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summarise(count = n_distinct(DOCTNMBR))
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@ -156,15 +168,55 @@ data_summary[["crash_by_sex"]] <- TOPS_data %>%
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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(CNTYNAME %in% county_focus) %>%
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filter(ped_inj %in% c("A", "K")) %>%
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filter(ped_inj %in% injury_severy_focus) %>%
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mutate(driver_sex = ifelse(ROLE1 %in% c("DR"), SEX1, ifelse(ROLE2 %in% c("DR"), SEX2, NA))) %>%
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group_by(CNTYNAME, year, vulnerable_role, ped_inj_name, driver_sex) %>%
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summarise(count = n_distinct(DOCTNMBR))
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## export csv files for county ----
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# make directories for county summaries
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county_directory <- paste0("data_summaries/county/",county_focus,"/")
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ifelse(!dir.exists(county_directory), dir.create(county_directory), "Folder exists already")
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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/county/",table_name, ".csv"))
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write_csv(data_summary[[table_name]], file = paste0("data_summaries/county/", county_focus, "/", county_focus, "_", table_name, ".csv"))
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}
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```
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## Make figures for county summaries
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```{r countysummaryfigures, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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data_summary[["crash_by_age"]] %>%
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filter(vulnerable_age < 18,
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year != 2025) %>%
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group_by(year, ped_inj_name) %>%
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summarise(count = sum(count)) %>%
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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 = ped_inj_name)) +
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scale_fill_manual(values = injury_severity_pal(injury_severity %>% filter(code %in% injury_severy_focus) %>% pull(InjSevName))) +
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labs(title = paste0("Pedestrian & bicyclists under 18yo killed or injured in ", str_to_title(county_focus), " county"),
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x = "Year",
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y = "Total crashes",
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fill = "Injury Severity",
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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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theme(plot.caption = element_text(color = "grey"))
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ggsave(file = paste0(county_directory, county_focus, "_youth_crashes.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(CNTYNAME %in% county_focus) %>%
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filter(year == 2024) %>%
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filter(ped_inj %in% "K") %>%
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mutate(vulnerable_age = ifelse(ROLE1 %in% vuln_roles, age1, ifelse(ROLE2 %in% vuln_roles, age2, NA))) %>%
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filter(vulnerable_age < 18)
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```
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