ben/wisconsin_crashes
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

edited MilWALKeeWalks.Rmd

Browse source
This commit is contained in:
Ben Varick 2025-09-22 15:21:39 -07:00
parent e71f197046
commit 8ffee34eeb
Signed by: ben
SSH key fingerprint: SHA256:jWnpFDAcacYM5aPFpYRqlsamlDyKNpSj3jj+k4ojtUo
1 changed files with 310 additions and 66 deletions
Download patch file Download diff file Expand all files Collapse all files

376
R/MilWALKeeWalks.Rmd
View file

@ -29,6 +29,8 @@ library(magick)
library(rsvg) library(rsvg)
library(parallel) library(parallel)
library(tidycensus) library(tidycensus)
library(MASS)
library(raster)
``` ```
@ -56,8 +58,9 @@ year_max <- max(year(TOPS_data_filtered$date))
```{r introCharts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r introCharts, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
ggplot() + ggplot() +
geom_col(data = TOPS_data_filtered %>% geom_col(data = TOPS_data_filtered %>%
filter(year != year_max) %>% # filter(MUNINAME %in% "MILWAUKEE") %>%
filter(!is.na(vulnerable_role)) %>% filter(! year %in% c(year_max)) %>%
filter(! is.na(vulnerable_role)) %>%
group_by(month, vulnerable_role) %>% group_by(month, vulnerable_role) %>%
summarize(total = n()), summarize(total = n()),
aes(x = month, aes(x = month,
@ -65,6 +68,7 @@ ggplot() +
fill = vulnerable_role), fill = vulnerable_role),
position = position_dodge()) + position = position_dodge()) +
geom_line(data = TOPS_data_filtered %>% geom_line(data = TOPS_data_filtered %>%
# filter(MUNINAME %in% "MILWAUKEE") %>%
filter(year == year_max) %>% filter(year == year_max) %>%
filter(!is.na(vulnerable_role)) %>% filter(!is.na(vulnerable_role)) %>%
group_by(month, vulnerable_role) %>% group_by(month, vulnerable_role) %>%
@ -80,8 +84,8 @@ ggplot() +
labs(title = paste0("Crashes involved pedestrians and bicyclists"), labs(title = paste0("Crashes involved pedestrians and bicyclists"),
subtitle = paste0(str_to_title(focus_county), " County"), subtitle = paste0(str_to_title(focus_county), " County"),
x = "Month", x = "Month",
y = "Average crashes per year", y = "Crashes per month",
fill = paste0("Yearly average\n", year_min, " - ", year_max - 1), fill = paste0("Average\n", year_min, " - ", year_max - 1),
color = year_max, color = year_max,
caption = paste0("crash data from UW TOPS lab - retrieved ", caption = paste0("crash data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"), strftime(retrieve_date, format = "%m/%Y"),
@ -96,9 +100,10 @@ ggsave(filename = paste0("figures/MilWALKee_Walks/", "month_role.png"),
ggplot() + ggplot() +
geom_col(data = TOPS_data_filtered %>% geom_col(data = TOPS_data_filtered %>%
# filter(MUNINAME %in% "MILWAUKEE") %>%
filter(vulnerable_role == "Pedestrian", filter(vulnerable_role == "Pedestrian",
!is.na(ped_age)) %>% !is.na(ped_age)) %>%
filter(year != year_max) %>% filter(! year %in% c(year_max)) %>%
mutate(age = ifelse(ped_age <= 18, "child", "adult")) %>% mutate(age = ifelse(ped_age <= 18, "child", "adult")) %>%
group_by(month, age) %>% group_by(month, age) %>%
summarize(total = n()/((year_max - 1) - year_min + 1)), summarize(total = n()/((year_max - 1) - year_min + 1)),
@ -107,25 +112,26 @@ ggplot() +
fill = age), fill = age),
position = position_dodge()) + position = position_dodge()) +
geom_line(data = TOPS_data_filtered %>% geom_line(data = TOPS_data_filtered %>%
filter(year == year_max) %>% # filter(MUNINAME %in% "MILWAUKEE") %>%
filter(vulnerable_role == "Pedestrian", filter(year == year_max) %>%
!is.na(ped_age)) %>% filter(vulnerable_role == "Pedestrian",
mutate(age = ifelse(ped_age <= 18, "child", "adult")) %>% !is.na(ped_age)) %>%
group_by(month, age, year) %>% mutate(age = ifelse(ped_age <= 18, "child", "adult")) %>%
summarize(total = n()), group_by(month, age, year) %>%
aes(x = month, summarize(total = n()),
y = total, aes(x = month,
color = age, y = total,
group = age), color = age,
linewidth = 1) + group = age),
linewidth = 1) +
scale_y_continuous(expand = expansion(mult = c(0,0.1))) + scale_y_continuous(expand = expansion(mult = c(0,0.1))) +
scale_fill_manual(values = c("deeppink1", "darkgoldenrod1")) + scale_fill_manual(values = c("deeppink1", "darkgoldenrod1")) +
scale_color_manual(values = c("deeppink3", "darkgoldenrod3")) + scale_color_manual(values = c("deeppink3", "darkgoldenrod3")) +
labs(title = paste0("Crashes involved pedestrians"), labs(title = paste0("Crashes involved pedestrians"),
subtitle = paste0(str_to_title(focus_county), " County"), subtitle = paste0(str_to_title(focus_county), " County"),
x = "Month", x = "Month",
y = "Crashes", y = "Crashes per month",
fill = paste0("Yearly average\n", year_min, " - ", year_max - 1), fill = paste0("Average\n", year_min, " - ", year_max - 1),
color = year_max, color = year_max,
caption = paste0("crash data from UW TOPS lab - retrieved ", caption = paste0("crash data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"), strftime(retrieve_date, format = "%m/%Y"),
@ -140,7 +146,10 @@ ggsave(filename = paste0("figures/MilWALKee_Walks/", "month_age.png"),
ggplot(data = TOPS_data_filtered %>% ggplot(data = TOPS_data_filtered %>%
filter(vulnerable_role == "Pedestrian", filter(vulnerable_role == "Pedestrian",
month(date) <= 8) %>% month(date) <= 8,
) %>%
# filter(ped_age < 18,
# MUNINAME %in% "MILWAUKEE") %>%
group_by(year) %>% group_by(year) %>%
summarize(total = n())) + summarize(total = n())) +
geom_col(aes(x = year, geom_col(aes(x = year,
@ -150,7 +159,7 @@ ggplot(data = TOPS_data_filtered %>%
labs(title = paste0("Crashes involved pedestrians"), labs(title = paste0("Crashes involved pedestrians"),
subtitle = paste0(str_to_title(focus_county), " County | ", "January - August"), subtitle = paste0(str_to_title(focus_county), " County | ", "January - August"),
x = NULL, x = NULL,
y = "Crashes", y = "Crashes per year",
caption = paste0("crash data from UW TOPS lab - retrieved ", caption = paste0("crash data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"), strftime(retrieve_date, format = "%m/%Y"),
"\nper direction of the WisDOT Bureau of Transportation Safety")) + "\nper direction of the WisDOT Bureau of Transportation Safety")) +
@ -161,6 +170,35 @@ ggsave(filename = paste0("figures/MilWALKee_Walks/", "ped_years.png"),
width = 11, width = 11,
units = "in", units = "in",
create.dir = TRUE) create.dir = TRUE)
ggplot(data = TOPS_data_filtered %>%
filter(vulnerable_role == "Pedestrian",
month(date) <= 8) %>%
mutate(mke_city = factor(ifelse(MUNINAME %in% "MILWAUKEE", "City of Milwaukee", "Other municipalities"),
levels = c("Other municipalities", "City of Milwaukee"))) %>%
group_by(year, mke_city) %>%
summarize(total = n())) +
geom_col(aes(x = year,
y = total,
fill = mke_city),
position = position_dodge()) +
scale_y_continuous(expand = expansion(mult = c(0,0.1))) +
labs(title = paste0("Crashes involved pedestrians"),
subtitle = paste0(str_to_title(focus_county), " County | ", "January - August"),
x = NULL,
y = "Crashes",
fill = NULL,
caption = paste0("crash data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"),
"\nper direction of the WisDOT Bureau of Transportation Safety")) +
theme(plot.caption = element_text(color = "grey"))
ggsave(filename = paste0("figures/MilWALKee_Walks/", "ped_years_MKEcity.png"),
device = png,
height = 8.5,
width = 11,
units = "in",
create.dir = TRUE)
``` ```
## Milwaukee maps ## Milwaukee maps
@ -182,30 +220,24 @@ county_populations <- get_estimates(geography = "county",
filter(variable == "POPESTIMATE") %>% filter(variable == "POPESTIMATE") %>%
mutate(County = str_to_upper(str_replace(NAME, " County, Wisconsin", ""))) mutate(County = str_to_upper(str_replace(NAME, " County, Wisconsin", "")))
county_populations <- st_transform(county_populations, crs = 4326) %>% filter(County %in% focus_county) county_populations <- st_transform(county_populations, crs = 4326) %>% filter(County %in% focus_county)
census_tract_populations <- st_transform(get_decennial(
year = 2020,
geography = "block",
variables = "P1_001N",
state = "WI",
county = focus_county,
geometry = TRUE
), crs = 4326)
census_tract_crashes <- st_join(census_tract_populations, st_as_sf(TOPS_data_filtered %>% filter(!is.na(latitude)), coords = c("longitude", "latitude"), crs = 4326), join = st_contains) %>%
group_by(GEOID) %>%
summarise(count = n(), .groups = 'drop')
hexgrid <- rowid_to_column(st_transform(st_as_sf(st_make_grid(st_transform(county_populations, crs = 32616), hexgrid <- rowid_to_column(st_transform(st_as_sf(st_make_grid(st_transform(county_populations, crs = 32616),
cellsize = 3000, cellsize = 3000,
what = 'polygons', what = 'polygons',
square = FALSE square = FALSE
)), crs = 4326), "ID") )), crs = 4326), "ID")
yearsforprior <- 8
hex_crashes <- st_join(hexgrid, st_as_sf(TOPS_data_filtered %>% filter(!is.na(latitude)), coords = c("longitude", "latitude"), crs = 4326), join = st_contains) %>% hex_crashes <- st_join(hexgrid,
st_as_sf(TOPS_data_filtered %>%
filter(vulnerable_role %in% "Pedestrian") %>%
filter(!is.na(latitude)),
coords = c("longitude", "latitude"),
crs = 4326),
join = st_contains) %>%
filter(!is.na(year)) %>% filter(!is.na(year)) %>%
filter(date >= (max(date) - (365 * 5))) %>% filter(date >= (max(date) - (365 * yearsforprior))) %>%
mutate(lastyear = ifelse((date <= max(date) - 365), mutate(lastyear = ifelse((date <= max(date) - 365),
"priorfive", "prior",
"lastyear")) %>% "lastyear")) %>%
group_by(ID, lastyear) %>% group_by(ID, lastyear) %>%
summarise(count = n(), .groups = 'drop') %>% summarise(count = n(), .groups = 'drop') %>%
@ -217,10 +249,45 @@ hex_crashes <- st_join(hexgrid, st_as_sf(TOPS_data_filtered %>% filter(!is.na(la
hex_crashes <- st_as_sf(left_join(hexgrid, hex_crashes), crs = 4326) hex_crashes <- st_as_sf(left_join(hexgrid, hex_crashes), crs = 4326)
hex_crashes <- hex_crashes %>% hex_crashes <- hex_crashes %>%
mutate(lastyearchange = (lastyear - priorfive/5)/(priorfive/5)) mutate(lastyearchange = (lastyear - prior/yearsforprior)/(prior/yearsforprior))
hex_crashes_points <- st_centroid(hex_crashes) hex_crashes_points <- st_centroid(hex_crashes)
#hex_small_crashes
hexgrid_small <- rowid_to_column(st_transform(st_as_sf(st_make_grid(st_transform(county_populations, crs = 32616),
cellsize = 500,
what = 'polygons',
square = FALSE
)), crs = 4326), "ID")
hex_crashes_small <- st_join(hexgrid_small,
st_as_sf(TOPS_data_filtered %>%
filter(vulnerable_role %in% "Pedestrian") %>%
filter(!is.na(latitude)),
coords = c("longitude", "latitude"),
crs = 4326),
join = st_contains) %>%
filter(!is.na(year)) %>%
filter(date >= (max(date) - (365 * yearsforprior))) %>%
mutate(lastyear = ifelse((date <= max(date) - 365),
"prior",
"lastyear")) %>%
group_by(ID, lastyear) %>%
summarise(count = n(), .groups = 'drop') %>%
st_drop_geometry() %>%
pivot_wider(id_cols = ID, names_from = lastyear, values_from = count) %>%
mutate(across(-ID, ~ replace_na(., 0))) %>%
mutate(total = rowSums(dplyr::select(., -ID), na.rm = TRUE))
hex_crashes_small <- st_as_sf(left_join(hexgrid_small, hex_crashes_small), crs = 4326)
hex_crashes_small <- hex_crashes_small %>%
mutate(lastyearchange = (lastyear - prior/yearsforprior)/(prior/yearsforprior))
hex_crashes_small_points <- st_centroid(hex_crashes_small)
``` ```
```{r MilwaukeeMaps, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r MilwaukeeMaps, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
@ -230,7 +297,7 @@ bbox <- c(left = as.double(bbox[1]),
bottom = as.double(bbox[2]), bottom = as.double(bbox[2]),
right = as.double(bbox[3]), right = as.double(bbox[3]),
top = as.double(bbox[4])) top = as.double(bbox[4]))
basemap <- get_stadiamap(bbox = bbox, zoom = 13, maptype = "stamen_toner_lite") basemap <- get_stadiamap(bbox = bbox, zoom = 12, maptype = "stamen_toner_lite")
# generate map with bubbles # generate map with bubbles
ggmap(basemap) + ggmap(basemap) +
@ -247,7 +314,7 @@ ggmap(basemap) +
x = NULL, x = NULL,
y = NULL, y = NULL,
size = paste0("Total crashes"), size = paste0("Total crashes"),
fill = "last 12 months\ncompared to previous") + fill = paste0("last 12 months\ncompared to previous\n", yearsforprior, " years")) +
theme(axis.text=element_blank(), theme(axis.text=element_blank(),
axis.ticks=element_blank(), axis.ticks=element_blank(),
plot.caption = element_text(color = "grey", size = 8)) + plot.caption = element_text(color = "grey", size = 8)) +
@ -277,6 +344,168 @@ ggsave(file = paste0("figures/MilWALKee_Walks/",
width = 11, width = 11,
units = "in", units = "in",
create.dir = TRUE) create.dir = TRUE)
ggmap(basemap) +
labs(title = paste0("Crashes between cars and pedestrians"),
subtitle = paste0(str_to_title(focus_county),
" County | ",
min(year(TOPS_data$date), na.rm = TRUE),
" - ",
max(year(TOPS_data$date), na.rm = TRUE)),
caption = paste0("crash data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"),
"\nper direction of the WisDOT Bureau of Transportation Safety",
"\nbasemap from StadiaMaps and OpenStreetMap Contributers"),
x = NULL,
y = NULL,
size = paste0("Total crashes"),
fill = "Crashes in the last 12 months\ncompared to previous average") +
theme(axis.text=element_blank(),
axis.ticks=element_blank(),
plot.caption = element_text(color = "grey", size = 8)) +
# add crash locations
geom_sf(data = hex_crashes %>% mutate(lastyearchange = (lastyear - prior/yearsforprior)),
inherit.aes = FALSE,
aes(fill = lastyearchange),
alpha = 0.5) +
scale_size_area() +
scale_fill_gradient2(
low = "darkgreen",
mid = "white",
high = "red",
midpoint = 0,
# limits = c(-2, 2),
# oob = scales::squish
)
ggsave(file = paste0("figures/MilWALKee_Walks/",
"milwaukee_map_crashchange.png"),
device = png,
height = 8.5,
width = 11,
units = "in",
create.dir = TRUE)
highlighted_areas <- hex_crashes %>%
mutate(lastyearchange = (lastyear - prior/yearsforprior)) %>%
filter(prior > 100,
(lastyearchange > 5 |
lastyearchange < -1)) %>% pull(ID)
ggmap(basemap) +
labs(title = paste0("Crashes between cars and pedestrians\nselect areas of the county"),
subtitle = paste0(str_to_title(focus_county),
" County | ",
min(year(TOPS_data$date), na.rm = TRUE),
" - ",
max(year(TOPS_data$date), na.rm = TRUE)),
caption = paste0("crash data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"),
"\nper direction of the WisDOT Bureau of Transportation Safety",
"\nbasemap from StadiaMaps and OpenStreetMap Contributers"),
x = NULL,
y = NULL,
size = paste0("Total crashes"),
fill = "last 12 months\ncompared to previous") +
theme(axis.text=element_blank(),
axis.ticks=element_blank(),
plot.caption = element_text(color = "grey", size = 8)) +
# add crash locations
geom_sf(data = hex_crashes %>% filter(ID %in% highlighted_areas),
inherit.aes = FALSE,
aes(fill = lastyearchange),
alpha = 0.5) +
geom_sf_text(data = hex_crashes_points %>% filter(ID %in% highlighted_areas),
inherit.aes = FALSE,
aes(label = paste0(ifelse(lastyearchange > 0, "+",""),round(lastyearchange * 100, 0), "%")),
size = 3) +
scale_size_area() +
scale_fill_gradient2(
low = "darkgreen",
mid = "white",
high = "red",
midpoint = 0,
limits = c(-2, 2),
oob = scales::squish,
labels = scales::percent
)
ggsave(file = paste0("figures/MilWALKee_Walks/",
"milwaukee_map_highlighted.png"),
device = png,
height = 8.5,
width = 11,
units = "in",
create.dir = TRUE)
bbox <- st_bbox(st_union(hexgrid %>% filter(ID %in% c(78, 85, 94))))
bbox <- c(left = as.double(bbox[1]),
bottom = as.double(bbox[2]),
right = as.double(bbox[3]),
top = as.double(bbox[4]))
basemap <- get_stadiamap(bbox = bbox, zoom = 14, maptype = "stamen_toner_lite")
# Map of high increase areas
ggmap(basemap) +
labs(title = paste0("Crashes between cars and pedestrians"),
subtitle = paste0(str_to_title(focus_county),
" County | ",
min(year(TOPS_data$date), na.rm = TRUE),
" - ",
max(year(TOPS_data$date), na.rm = TRUE)),
caption = paste0("crash data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"),
"\nper direction of the WisDOT Bureau of Transportation Safety",
"\nbasemap from StadiaMaps and OpenStreetMap Contributers"),
x = NULL,
y = NULL,
size = paste0("Total crashes"),
fill = paste0("last 12 months\ncompared to previous\n", yearsforprior, " years")) +
theme(axis.text=element_blank(),
axis.ticks=element_blank(),
plot.caption = element_text(color = "grey", size = 8)) +
#add hexagons
new_scale_fill() +
geom_sf(data = hex_crashes_small %>% mutate(lastyearchange = (lastyear - prior/yearsforprior)),
inherit.aes = FALSE,
aes(fill = lastyearchange),
alpha = 0.5) +
scale_size_area() +
scale_fill_gradient2(
low = "darkgreen",
mid = "white",
high = "red",
midpoint = 0,
# limits = c(-2, 2),
# oob = scales::squish
name = "Crashes last year compared to previous average") +
# add crash locations
new_scale_fill() +
geom_point(data = TOPS_data_filtered %>%
filter(date >= max(TOPS_data_filtered$date) - 365) %>%
#filter(ped_inj %in% c("K", "A")) %>%
filter(vulnerable_role %in% "Pedestrian") %>%
filter(longitude >= as.double(bbox[1]),
latitude >= as.double(bbox[2]),
longitude <= as.double(bbox[3]),
latitude <= as.double(bbox[4])) %>%
arrange(ped_inj_name),
aes(x = longitude,
y = latitude,
fill = ped_inj_name),
shape = 23,
size = 3) +
scale_fill_manual(values = setNames(injury_severity$color, injury_severity$InjSevName), name = "Crash Severity")
ggsave(file = paste0("figures/MilWALKee_Walks/",
"milwaukee_map_zoomchange.png"),
device = png,
height = 8.5,
width = 11,
units = "in",
create.dir = TRUE)
``` ```
@ -300,7 +529,7 @@ TOPS_data <- TOPS_data %>% mutate(trickortreat = ifelse(date %in% trickortreatda
ggplot(data = TOPS_data_filtered %>% ggplot(data = TOPS_data_filtered %>%
# filter(ped_inj %in% c("K", "A", "B")) %>% # filter(ped_inj %in% c("K", "A", "B")) %>%
# filter(ped_age <=18) %>% # filter(ped_age <=18) %>%
# filter(vulnerable_role == "Pedestrian") %>% filter(vulnerable_role == "Pedestrian") %>%
mutate(mday = mday(date)) %>% mutate(mday = mday(date)) %>%
mutate(date_yearagnostic = ymd(paste("2025", month, mday))) %>% mutate(date_yearagnostic = ymd(paste("2025", month, mday))) %>%
group_by(date_yearagnostic, year, trickortreat) %>% group_by(date_yearagnostic, year, trickortreat) %>%
@ -309,40 +538,55 @@ ggplot(data = TOPS_data_filtered %>%
y = total, y = total,
fill = trickortreat)) + fill = trickortreat)) +
scale_x_date(minor_breaks = "month", date_labels = "%b", expand = expansion(mult = c(0,0))) + scale_x_date(minor_breaks = "month", date_labels = "%b", expand = expansion(mult = c(0,0))) +
scale_y_continuous(breaks = seq(0, 9, 3), labels = c(0, 3, 6, "")) +
scale_fill_manual(values = c("black", "orange")) + scale_fill_manual(values = c("black", "orange")) +
facet_grid(year ~ .) + facet_grid(year ~ .) +
labs(title = paste0("Crashes involved pedestrians - Halloween"), labs(title = paste0("Car crashes involving pedestrians - Halloween"),
subtitle = paste0(str_to_title(focus_county), " County | ", year_min, " - ", year_max), subtitle = paste0(str_to_title(focus_county), " County | ", year_min, " - ", year_max),
x = NULL, x = NULL,
y = "Crashes", y = "Crashes per day",
fill = "Days with\nTrick-or-Treating",
caption = paste0("crash data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"),
"\nper direction of the WisDOT Bureau of Transportation Safety")) +
theme(plot.caption = element_text(color = "grey"))
ggsave(filename = paste0("figures/MilWALKee_Walks/", "halloween.png"),
device = png,
height = 8.5,
width = 11,
units = "in",
create.dir = TRUE)
ggplot(data = TOPS_data_filtered %>%
# filter(ped_inj %in% c("K", "A", "B")) %>%
# filter(ped_age <=18) %>%
mutate(wday = wday(date, label = TRUE)) %>%
filter(wday(date) %in% c(1, 7)) %>%
filter(vulnerable_role == "Pedestrian") %>%
group_by(date, wday, trickortreat) %>%
summarize(total = n()) %>%
group_by(wday, trickortreat) %>%
summarize(average = mean(total, na.rm = TRUE))) +
geom_col(aes(x = wday,
y = average,
fill = trickortreat),
position = position_dodge()) +
scale_fill_manual(values = c("black", "orange")) +
labs(title = paste0("Car crashes involving pedestrians - Halloween"),
subtitle = paste0(str_to_title(focus_county), " County | ", year_min, " - ", year_max),
x = NULL,
y = "Crashes per day",
fill = "Days with\nTrick-or-Treating",
caption = paste0("crash data from UW TOPS lab - retrieved ", caption = paste0("crash data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"), strftime(retrieve_date, format = "%m/%Y"),
"\nper direction of the WisDOT Bureau of Transportation Safety")) + "\nper direction of the WisDOT Bureau of Transportation Safety")) +
theme(plot.caption = element_text(color = "grey")) theme(plot.caption = element_text(color = "grey"))
ggsave(filename = paste0("figures/MilWALKee_Walks/", "halloween_wday.png"),
ggplot(data = TOPS_data_filtered %>% device = png,
# filter(ped_inj %in% c("K", "A", "B")) %>% height = 8.5,
mutate(age = ifelse(ped_age <= 18, "child", "adult"))) + width = 11,
geom_bar(aes(x = month, units = "in",
fill = age), create.dir = TRUE)
position = "fill")
ggplot(data = TOPS_data_filtered %>%
# filter(ped_age <=18) %>%
# filter(vulnerable_role == "Pedestrian") %>%
mutate(age = ifelse(ped_age <= 18, "child", "adult")) %>%
mutate(date_yearagnostic = ymd(paste("2025", month, mday(date)))) %>%
group_by(date_yearagnostic, year, age, trickortreat) %>%
summarize(total = n())) +
# geom_vline(aes(xintercept = ymd("2025-10-31")),
# linetype = "dashed",
# alpha = 0.5) +
geom_col(aes(x = date_yearagnostic,
y = total,
fill = trickortreat)) +
scale_x_date(minor_breaks = "month", date_labels = "%b", expand = expansion(mult = c(0,0))) +
scale_fill_manual(values = c("black", "orange")) +
facet_grid(year ~ .)
``` ```