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added to data_summaries

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Ben Varick 2025-04-09 13:35:00 -05:00
parent 8810ca19a9
commit 8feb47053e
Signed by: ben
SSH key fingerprint: SHA256:jWnpFDAcacYM5aPFpYRqlsamlDyKNpSj3jj+k4ojtUo
1 changed files with 85 additions and 2 deletions
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87
R/crash_data_summaries.Rmd
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@ -23,8 +23,6 @@ library(RColorBrewer)
library(tidycensus)
library(ggrepel)
library(leaflet)
county_focus <- c("DANE")
municipality_focus <- c("MADISON")
```
## Load TOPS data
@ -220,3 +218,88 @@ TOPS_data %>%
filter(vulnerable_age < 18)
```
## Make figures for statewide summaries
```{r statesummary, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
TOPS_data %>%
filter(!is.na(vulnerable_role)) %>%
filter(vulnerable_role == "Pedestrian") %>%
mutate(driver_vehtype = ifelse(ROLE1 %in% c("DR"), VEHTYPE1, ifelse(ROLE2 %in% c("DR"), VEHTYPE2, NA))) %>%
filter(driver_vehtype %in% c("CAR", "SUV", "UT TRK")) %>%
group_by(vulnerable_role, ped_inj_name, driver_vehtype) %>%
summarise(count = n_distinct(DOCTNMBR)) %>%
pivot_wider(names_from = driver_vehtype, values_from = count) %>%
ungroup() %>%
mutate(total = (CAR + SUV + `UT TRK`)) %>%
mutate(car_percent = CAR/total * 100,
suv_percent = SUV/total * 100,
pickup_percent = `UT TRK`/total * 100) %>%
select(vulnerable_role, ped_inj_name, car_percent, suv_percent, pickup_percent) %>%
mutate(Car = car_percent, SUV = suv_percent, "Pickup Truck" = pickup_percent) %>%
pivot_longer(cols = c(Car, SUV, "Pickup Truck")) %>%
ggplot(aes(x = ped_inj_name,
y = value,
fill = name)) +
geom_col(position = position_stack()) +
geom_text(aes(label = paste0(round(value, 1), "%")),
position = position_stack(vjust = 0.5)) +
theme(axis.text.x = element_text(angle = 30, hjust = 1),
plot.caption = element_text(color = "grey")) +
scale_y_continuous(expand = expansion(mult = c(0, 0))) +
labs(title = "Impact of type of vehicle on severity of injury",
subtitle = "Crashes statewide - Wisconsin",
y = "Percent of crashes",
x = "Severity of injury",
fill = "Type of vehicle",
caption = paste0("crash data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"),
" per direction of the WisDOT Bureau of Transportation Safety"))
ggsave(file = paste0("figures/crash_summaries/state/", "VehType_InjSev.pdf"),
height = 8.5,
width = 11,
units = "in")
TOPS_data %>%
filter(!is.na(vulnerable_role)) %>%
filter(vulnerable_role == "Pedestrian") %>%
filter(ped_inj %in% injury_severy_focus) %>%
mutate(driver_vehtype = ifelse(ROLE1 %in% c("DR"), VEHTYPE1, ifelse(ROLE2 %in% c("DR"), VEHTYPE2, NA))) %>%
filter(driver_vehtype %in% c("CAR", "SUV", "UT TRK")) %>%
group_by(vulnerable_role, driver_vehtype, year) %>%
summarise(count = n_distinct(DOCTNMBR)) %>%
pivot_wider(names_from = driver_vehtype, values_from = count) %>%
ungroup() %>%
mutate(total = (CAR + SUV + `UT TRK`)) %>%
mutate(car_percent = CAR/total * 100,
suv_percent = SUV/total * 100,
pickup_percent = `UT TRK`/total * 100) %>%
select(year, vulnerable_role, car_percent, suv_percent, pickup_percent) %>%
pivot_longer(cols = c(car_percent, suv_percent, pickup_percent)) %>%
ggplot() +
geom_line(aes(x = as.double(as.character(year)),
y = value,
color = name))
TOPS_data %>%
filter(!is.na(vulnerable_role)) %>%
filter(vulnerable_role == "Pedestrian") %>%
filter(ped_inj %in% "K") %>%
mutate(driver_vehtype = ifelse(ROLE1 %in% c("DR"), VEHTYPE1, ifelse(ROLE2 %in% c("DR"), VEHTYPE2, NA))) %>%
filter(driver_vehtype %in% c("CAR", "SUV", "UT TRK")) %>%
group_by(vulnerable_role, driver_vehtype, year) %>%
summarise(count = n_distinct(DOCTNMBR)) %>%
pivot_wider(names_from = driver_vehtype, values_from = count) %>%
ungroup() %>%
mutate(total = (CAR + SUV + `UT TRK`)) %>%
mutate(car_percent = CAR/total * 100,
suv_percent = SUV/total * 100,
pickup_percent = `UT TRK`/total * 100) %>%
select(year, vulnerable_role, car_percent, suv_percent, pickup_percent) %>%
pivot_longer(cols = c(car_percent, suv_percent, pickup_percent)) %>%
ggplot() +
geom_line(aes(x = as.double(as.character(year)),
y = value,
color = name))
```