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added crash summary charts

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This commit is contained in:
Ben Varick 2024-04-08 13:16:11 -05:00
parent 71db97a5a0
commit ff64e72a65
Signed by: ben
SSH Key Fingerprint: SHA256:758jG979jvr5HnQJl1AQ/NYTyzXRgnuoVM/yCR024sE
2 changed files with 39 additions and 5 deletions
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figures/crash_summaries/counties_year.pdf
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scripts/crash_summary_charts.R
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@ -43,12 +43,25 @@ TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code),
mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>% mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
rename(InjSevName2 = InjSevName) rename(InjSevName2 = InjSevName)
TOPS_data <- TOPS_data %>% mutate(ped_inj = ifelse(ROLE1 %in% c("BIKE", "PED"), # add bike or pedestrian roles ----
bike_roles <- c("BIKE", "O BIKE")
ped_roles <- c("PED", "O PED", "PED NO")
vuln_roles <- c(bike_roles, ped_roles)
TOPS_data <- TOPS_data %>% mutate(ped_inj = ifelse(ROLE1 %in% vuln_roles,
INJSVR1, INJSVR1,
ifelse(ROLE2 %in% c("BIKE", "PED"), ifelse(ROLE2 %in% vuln_roles,
INJSVR2, INJSVR2,
NA))) NA)))
# bike or ped
TOPS_data <- TOPS_data %>% mutate(vulnerable_role = ifelse(ROLE1 %in% bike_roles | ROLE2 %in% bike_roles,
"Bicyclist",
ifelse(ROLE1 %in% ped_roles | ROLE2 %in% ped_roles,
"Pedestrian",
NA)))
TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(ped_inj == code)) %>% TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(ped_inj == code)) %>%
mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>% mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
rename(ped_inj_name = InjSevName) rename(ped_inj_name = InjSevName)
@ -69,7 +82,7 @@ TOPS_data <- TOPS_data %>%
County = CNTYNAME, County = CNTYNAME,
Street = ONSTR, Street = ONSTR,
CrossStreet = ATSTR) %>% CrossStreet = ATSTR) %>%
mutate(PedestrianAge = ifelse(ROLE1 %in% c("BIKE", "PED"), age1, age2)) mutate(PedestrianAge = ifelse(ROLE1 %in% vuln_roles, age1, age2))
# add population census data ---- # add population census data ----
census_api_key(key = substr(read_file(file = "api_keys/census_api_key"), 1, 40)) census_api_key(key = substr(read_file(file = "api_keys/census_api_key"), 1, 40))
@ -87,6 +100,7 @@ county_focus <- unique(TOPS_data %>%
TOPS_data %>% TOPS_data %>%
filter(ped_inj %in% c("A", "K")) %>%
group_by(CNTYNAME, Year) %>% group_by(CNTYNAME, Year) %>%
summarise(TotalCrashes = n()) %>% summarise(TotalCrashes = n()) %>%
mutate(County = CNTYNAME) %>% mutate(County = CNTYNAME) %>%
@ -100,6 +114,7 @@ TOPS_data %>%
group = CNTYNAME), group = CNTYNAME),
size = 1) + size = 1) +
geom_label_repel(data = TOPS_data %>% geom_label_repel(data = TOPS_data %>%
filter(ped_inj %in% c("A", "K")) %>%
group_by(CNTYNAME, Year) %>% group_by(CNTYNAME, Year) %>%
summarise(TotalCrashes = n()) %>% summarise(TotalCrashes = n()) %>%
mutate(County = CNTYNAME) %>% mutate(County = CNTYNAME) %>%
@ -120,7 +135,7 @@ TOPS_data %>%
scale_fill_brewer(type = "qual", guide = NULL) + scale_fill_brewer(type = "qual", guide = NULL) +
scale_x_discrete(expand = expansion(add = c(0.5,0.75))) + scale_x_discrete(expand = expansion(add = c(0.5,0.75))) +
labs(title = "Drivers crashing into pedestrians & bicyclists per 100,000 residents", labs(title = "Drivers crashing into pedestrians & bicyclists per 100,000 residents",
subtitle = "2017-2023", subtitle = "Fatalities and Severe Injuries | 2017-2023",
x = "Year", x = "Year",
y = "Total crashes per year per 100,000 residents", y = "Total crashes per year per 100,000 residents",
color = "County", color = "County",
@ -134,4 +149,23 @@ ggsave(file = paste0("figures/crash_summaries/counties_year.pdf"),
height = 8.5, height = 8.5,
width = 11, width = 11,
units = "in") units = "in")
e
TOPS_data %>%
filter(County %in% county_focus) %>%
group_by(County, vulnerable_role) %>%
summarise(count = n()) %>%
ggplot() +
geom_col(aes(x = County,
y = count,
fill = vulnerable_role))
TOPS_data %>%
filter(County %in% "DANE") %>%
group_by(County, vulnerable_role, year) %>%
summarise(count = n()) %>%
ggplot() +
geom_col(aes(x = year,
y = count,
fill = vulnerable_role),
position = position_dodge())