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edited school_map.R script to match changes in the parallel script

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Ben Varick 2024-06-04 15:21:14 -05:00
parent 428af27b98
commit 966c2d33fd
Signed by: ben
SSH Key Fingerprint: SHA256:758jG979jvr5HnQJl1AQ/NYTyzXRgnuoVM/yCR024sE
1 changed files with 41 additions and 13 deletions
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scripts/school_maps.R
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@ -46,14 +46,41 @@ retrieve_date <- max(TOPS_data %>% filter(year %in% max(year(TOPS_data$date), na
counties <- data.frame(name = c("Dane", "Milwaukee"), counties <- data.frame(name = c("Dane", "Milwaukee"),
CNTYCODE = c(13, 40), CNTYCODE = c(13, 40),
COUNTY = c("DANE", "MILWAUKEE")) COUNTY = c("DANE", "MILWAUKEE"))
# Injury Severy Index and Color ------------------------------------------- # Injury Severy Index and Color -------------------------------------------
# injury severity index # injury severity index
injury_severity <- data.frame(InjSevName = c("No apparent injury", "Possible Injury", "Suspected Minor Injury","Suspected Serious Injury","Fatality"), injury_severity <- data.frame(InjSevName = c("Injury severity unknown", "No apparent injury", "Possible Injury", "Suspected Minor Injury","Suspected Serious Injury","Fatality"),
code = c("O", "C", "B", "A", "K"), code = c(NA, "O", "C", "B", "A", "K"),
color = c("#fafa6e", "#edc346", "#d88d2d", "#bd5721", "#9b1c1c")) color = c("grey", "#fafa6e", "#edc346", "#d88d2d", "#bd5721", "#9b1c1c"))
TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(INJSVR == code)) %>% mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(INJSVR1 == code)) %>%
mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
rename(InjSevName1 = InjSevName)
TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(INJSVR2 == code)) %>%
mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
rename(InjSevName2 = InjSevName)
# 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,
ifelse(ROLE2 %in% vuln_roles,
INJSVR2,
NA)))
TOPS_data <- left_join(TOPS_data, injury_severity %>% select(InjSevName, code), join_by(ped_inj == code)) %>%
mutate(InjSevName = factor(InjSevName, levels = injury_severity$InjSevName)) %>%
rename(ped_inj_name = InjSevName)
# 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)))
# ---- add additional data # ---- add additional data
## add school enrollment data ## add school enrollment data
@ -116,14 +143,13 @@ school_symbol <- image_read_svg(path = "other/school_FILL0_wght400_GRAD0_opsz24.
#county_focus <- str_to_upper(unique(WI_schools %>% pull(CTY_DIST))) #county_focus <- str_to_upper(unique(WI_schools %>% pull(CTY_DIST)))
#county_focus <- c("DANE") #county_focus <- c("DANE")
county_focus <- c("MILWAUKEE") county_focus <- c("DANE")
#school_type_focus <- unique(WI_schools %>% filter(CTY_DIST %in% str_to_title(county_focus)) %>% pull(SCHOOLTYPE)) #school_type_focus <- unique(WI_schools %>% filter(CTY_DIST %in% str_to_title(county_focus)) %>% pull(SCHOOLTYPE))
school_type_focus <- c("High School") school_type_focus <- c("High School")
#district_focus <- unique(WI_schools %>% filter(CTY_DIST %in% str_to_title(county_focus), SCHOOLTYPE %in% school_type_focus, !is.na(DISTRICT_NAME)) %>% pull(DISTRICT_NAME)) #district_focus <- unique(WI_schools %>% filter(CTY_DIST %in% str_to_title(county_focus), SCHOOLTYPE %in% school_type_focus, !is.na(DISTRICT_NAME)) %>% pull(DISTRICT_NAME))
#district_focus <- c("Madison Metropolitan") district_focus <- c("Madison Metropolitan")
district_focus <- c("Milwaukee")
school_number <- length(unique(WI_schools %>% filter(CTY_DIST %in% str_to_title(county_focus), school_number <- length(unique(WI_schools %>% filter(CTY_DIST %in% str_to_title(county_focus),
SCHOOLTYPE %in% school_type_focus, SCHOOLTYPE %in% school_type_focus,
@ -290,7 +316,9 @@ for(district in district_focus) {
# generate map # generate map
ggmap(basemap) + ggmap(basemap) +
labs(title = paste0("Crashes between cars and youth (<18) pedestrians/bicyclists near ", labs(title = paste0(
"Crashes between cars and youth (<18) pedestrians/bicyclists near ",
# "Crashes between cars and all pedestrians/bicyclists near ",
school_data %>% pull(SCHOOL_NAME), school_data %>% pull(SCHOOL_NAME),
" School"), " School"),
subtitle = paste0(school_data %>% pull(DISTRICT_NAME), subtitle = paste0(school_data %>% pull(DISTRICT_NAME),
@ -328,7 +356,7 @@ for(district in district_focus) {
latitude <= as.double(bbox[4])), latitude <= as.double(bbox[4])),
aes(x = longitude, aes(x = longitude,
y = latitude, y = latitude,
fill = InjSevName), fill = ped_inj_name),
shape = 23, shape = 23,
size = 3) + size = 3) +
scale_fill_manual(values = injury_severity$color, name = "Crash Severity") + scale_fill_manual(values = injury_severity$color, name = "Crash Severity") +
@ -380,8 +408,8 @@ for(district in district_focus) {
units = "in", units = "in",
create.dir = TRUE) create.dir = TRUE)
} }
districts_done <- bind_rows(districts_done, data.frame(district = c(district))) # districts_done <- bind_rows(districts_done, data.frame(district = c(district)))
write_csv(districts_done, file = "other/districts_done.csv") # write_csv(districts_done, file = "other/districts_done.csv")
} }
# double check that all schools have a map ---- # double check that all schools have a map ----