moved over crash_data_summaries to RMarkdown
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20 changed files with 2893 additions and 2374 deletions
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@ -105,6 +105,20 @@ TOPS_data <- TOPS_data %>% mutate(vulnerable_role = ifelse(ROLE1 %in% bike_roles
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NA)))
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```
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## Add race names to TOPS data
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```{r TOPSrace, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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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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TOPS_data <- TOPS_data %>% mutate(vulnerable_race = ifelse(ROLE1 %in% vuln_roles,
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race_name1,
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ifelse(ROLE2 %in% vuln_roles,
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race_name2,
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NA)))
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```
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## Save resulting data table as an Rda file for use in other documents
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```{r savecleaneddata, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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save(TOPS_data, file = "data/TOPS/TOPS_data.Rda")
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169
R/crash_data_summaries.Rmd
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169
R/crash_data_summaries.Rmd
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@ -0,0 +1,169 @@
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---
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title: "Crash Data Summaries"
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output:
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html_document:
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toc: true
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toc_depth: 5
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toc_float:
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collapsed: false
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smooth_scroll: true
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editor_options:
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chunk_output_type: console
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---
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# Input Data & Configuration
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## Libraries
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```{r libs, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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date()
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rm(list=ls())
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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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county_focus <- c("MILWAUKEE")
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municipality_focus <- c("MILWAUKEE")
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```
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## Load TOPS data
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```{r loadTOPS, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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load(file = "data/TOPS/TOPS_data.Rda")
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load(file = "data/TOPS/vuln_roles.Rda")
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load(file = "data/TOPS/retrieve_date.Rda")
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load(file = "data/TOPS/injury_severity.Rda")
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injury_severity_pal <- colorFactor(palette = injury_severity$color, levels = injury_severity$InjSevName)
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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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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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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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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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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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# 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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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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# 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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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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# 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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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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# 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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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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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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```
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## build data summaries for county ----
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```{r countysummaries, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
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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(ped_inj %in% c("A", "K")) %>%
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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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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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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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# 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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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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# 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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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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# 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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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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# 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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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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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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}
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```
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