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edited MilWALKeeWalks.Rmd to make months more dynamic

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Ben Varick 2025-10-09 10:25:54 -07:00
parent 7d88bd3d48
commit 8e86ece8c3
Signed by: ben
SSH key fingerprint: SHA256:jWnpFDAcacYM5aPFpYRqlsamlDyKNpSj3jj+k4ojtUo
1 changed files with 30 additions and 28 deletions
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R/MilWALKeeWalks.Rmd
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@ -49,6 +49,8 @@ TOPS_data_filtered <- TOPS_data %>% filter(CNTYNAME == focus_county)
```{r startenddates, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r startenddates, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
year_min <- min(year(TOPS_data_filtered$date)) year_min <- min(year(TOPS_data_filtered$date))
year_max <- max(year(TOPS_data_filtered$date)) year_max <- max(year(TOPS_data_filtered$date))
month_end <- 9
date_max <- ymd(paste(year_max, month_end + 1,1)) - 1
``` ```
## intro charts ## intro charts
@ -144,7 +146,7 @@ ggsave(filename = paste0("figures/MilWALKee_Walks/", "month_age.png"),
ggplot(data = TOPS_data_filtered %>% ggplot(data = TOPS_data_filtered %>%
filter( filter(
#vulnerable_role == "Pedestrian", #vulnerable_role == "Pedestrian",
month(date) <= 8, month(date) <= month_end,
) %>% ) %>%
# filter(ped_age < 18, # filter(ped_age < 18,
# MUNINAME %in% "MILWAUKEE") %>% # MUNINAME %in% "MILWAUKEE") %>%
@ -155,7 +157,7 @@ ggplot(data = TOPS_data_filtered %>%
fill = "lightblue4") + fill = "lightblue4") +
scale_y_continuous(expand = expansion(mult = c(0,0.1))) + scale_y_continuous(expand = expansion(mult = c(0,0.1))) +
labs(title = paste0("Car crashes involving pedestrians & bicyclists"), labs(title = paste0("Car crashes involving pedestrians & bicyclists"),
subtitle = paste0(str_to_title(focus_county), " County | ", "January - August"), subtitle = paste0(str_to_title(focus_county), " County | ", "January - ", format(ymd(paste(2025, month_end, 01)), "%B")),
x = NULL, x = NULL,
y = "Crashes per year", y = "Crashes per year",
caption = paste0("crash data from UW TOPS lab - retrieved ", caption = paste0("crash data from UW TOPS lab - retrieved ",
@ -172,7 +174,7 @@ ggsave(filename = paste0("figures/MilWALKee_Walks/", "vuln_years.png"),
ggplot(data = TOPS_data_filtered %>% ggplot(data = TOPS_data_filtered %>%
filter( filter(
#vulnerable_role == "Pedestrian", #vulnerable_role == "Pedestrian",
month(date) <= 8, month(date) <= month_end,
ped_inj %in% c("K", "A"), ped_inj %in% c("K", "A"),
) %>% ) %>%
group_by(year, ped_inj_name) %>% group_by(year, ped_inj_name) %>%
@ -184,7 +186,7 @@ ggplot(data = TOPS_data_filtered %>%
scale_y_continuous(expand = expansion(mult = c(0,0.1))) + scale_y_continuous(expand = expansion(mult = c(0,0.1))) +
scale_fill_manual(values = setNames(injury_severity$color, injury_severity$InjSevName), name = "Injury severity") + scale_fill_manual(values = setNames(injury_severity$color, injury_severity$InjSevName), name = "Injury severity") +
labs(title = paste0("Car crashes involving pedestrians & bicyclists - fatal and serious injuries"), labs(title = paste0("Car crashes involving pedestrians & bicyclists - fatal and serious injuries"),
subtitle = paste0(str_to_title(focus_county), " County | ", "January - August"), subtitle = paste0(str_to_title(focus_county), " County | ", "January - ", format(ymd(paste(2025, month_end, 01)), "%B")),
x = NULL, x = NULL,
y = "Crashes per year", y = "Crashes per year",
caption = paste0("crash data from UW TOPS lab - retrieved ", caption = paste0("crash data from UW TOPS lab - retrieved ",
@ -202,7 +204,7 @@ ggplot(data = TOPS_data_filtered %>%
filter( filter(
#vulnerable_role == "Pedestrian", #vulnerable_role == "Pedestrian",
ped_inj %in% c("K", "A"), ped_inj %in% c("K", "A"),
month(date) <= 8) %>% month(date) <= month_end) %>%
mutate(mke_city = factor(ifelse(MUNINAME %in% "MILWAUKEE", "City of Milwaukee", "Other municipalities"), mutate(mke_city = factor(ifelse(MUNINAME %in% "MILWAUKEE", "City of Milwaukee", "Other municipalities"),
levels = c("Other municipalities", "City of Milwaukee"))) %>% levels = c("Other municipalities", "City of Milwaukee"))) %>%
group_by(year, mke_city) %>% group_by(year, mke_city) %>%
@ -213,7 +215,7 @@ ggplot(data = TOPS_data_filtered %>%
position = position_dodge()) + position = position_dodge()) +
scale_y_continuous(expand = expansion(mult = c(0,0.1))) + scale_y_continuous(expand = expansion(mult = c(0,0.1))) +
labs(title = paste0("Car crashes involving pedestrians - fatal and severe injuries"), labs(title = paste0("Car crashes involving pedestrians - fatal and severe injuries"),
subtitle = paste0(str_to_title(focus_county), " County | ", "January - August"), subtitle = paste0(str_to_title(focus_county), " County | ", "January - ", format(ymd(paste(2025, month_end, 01)), "%B")),
x = NULL, x = NULL,
y = "Crashes", y = "Crashes",
fill = NULL, fill = NULL,
@ -293,7 +295,7 @@ hexgrid <- rowid_to_column(st_transform(st_as_sf(st_make_grid(st_transform(count
what = 'polygons', what = 'polygons',
square = FALSE square = FALSE
)), crs = 4326), "ID") )), crs = 4326), "ID")
yearsforprior <- 7 yearsforprior <- year_max - year_min - 1
hex_crashes <- st_join(hexgrid, hex_crashes <- st_join(hexgrid,
st_as_sf(TOPS_data_filtered %>% st_as_sf(TOPS_data_filtered %>%
filter(vulnerable_role %in% "Pedestrian") %>% filter(vulnerable_role %in% "Pedestrian") %>%
@ -303,8 +305,8 @@ hex_crashes <- st_join(hexgrid,
crs = 4326), crs = 4326),
join = st_contains) %>% join = st_contains) %>%
filter(!is.na(year)) %>% filter(!is.na(year)) %>%
filter(date > (max(date) - (365 * (yearsforprior + 1)))) %>% filter(date > (date_max - (365 * (yearsforprior + 1)))) %>%
mutate(lastyear = ifelse((date <= max(date) - 365), mutate(lastyear = ifelse((date <= date_max - 365),
"prior", "prior",
"lastyear")) %>% "lastyear")) %>%
group_by(ID, lastyear) %>% group_by(ID, lastyear) %>%
@ -340,8 +342,8 @@ hex_crashes_small <- st_join(hexgrid_small,
crs = 4326), crs = 4326),
join = st_contains) %>% join = st_contains) %>%
filter(!is.na(year)) %>% filter(!is.na(year)) %>%
filter(date > (max(date) - (365 * (yearsforprior + 1)))) %>% filter(date > (date_max - (365 * (yearsforprior + 1)))) %>%
mutate(lastyear = ifelse((date <= max(date) - 365), mutate(lastyear = ifelse((date <= date_max - 365),
"prior", "prior",
"lastyear")) %>% "lastyear")) %>%
group_by(ID, lastyear) %>% group_by(ID, lastyear) %>%
@ -365,7 +367,7 @@ hex_crashes_small_points <- st_centroid(hex_crashes_small)
```{r previousyearstring, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r previousyearstring, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
previousyearstring <- paste0(format(max(TOPS_data_filtered$date, na.rm = TRUE) - 365 + 1, "%m/%Y"), " - ", format(max(TOPS_data_filtered$date, na.rm = TRUE), "%m/%Y")) previousyearstring <- paste0(format(date_max - 365 + 1, "%m/%Y"), " - ", format(date_max, "%m/%Y"))
``` ```
```{r importprojects, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r importprojects, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
@ -388,9 +390,9 @@ ggmap(basemap) +
labs(title = paste0("Car crashes involving pedestrians"), labs(title = paste0("Car crashes involving pedestrians"),
subtitle = paste0(str_to_title(focus_county), subtitle = paste0(str_to_title(focus_county),
" County | ", " County | ",
year(min(TOPS_data_filtered$date, na.rm = TRUE)), year_min,
" - ", " - ",
year(max(TOPS_data_filtered$date, na.rm = TRUE))), 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"),
"\nper direction of the WisDOT Bureau of Transportation Safety", "\nper direction of the WisDOT Bureau of Transportation Safety",
@ -479,9 +481,9 @@ ggmap(basemap) +
labs(title = paste0("Car crashes involving pedestrians"), labs(title = paste0("Car crashes involving pedestrians"),
subtitle = paste0(str_to_title(focus_county), subtitle = paste0(str_to_title(focus_county),
" County | ", " County | ",
min(year(TOPS_data$date), na.rm = TRUE), year_min,
" - ", " - ",
max(year(TOPS_data$date), na.rm = TRUE)), 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"),
"\nper direction of the WisDOT Bureau of Transportation Safety", "\nper direction of the WisDOT Bureau of Transportation Safety",
@ -533,9 +535,9 @@ ggmap(basemap) +
labs(title = paste0("Car crashes involving pedestrians"), labs(title = paste0("Car crashes involving pedestrians"),
subtitle = paste0(str_to_title(focus_county), subtitle = paste0(str_to_title(focus_county),
" County | ", " County | ",
min(year(TOPS_data$date), na.rm = TRUE), year_min,
" - ", " - ",
max(year(TOPS_data$date), na.rm = TRUE)), 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"),
"\nper direction of the WisDOT Bureau of Transportation Safety", "\nper direction of the WisDOT Bureau of Transportation Safety",
@ -566,7 +568,7 @@ ggmap(basemap) +
# add crash locations # add crash locations
new_scale_fill() + new_scale_fill() +
geom_point(data = TOPS_data_filtered %>% geom_point(data = TOPS_data_filtered %>%
filter(date > max(TOPS_data_filtered$date) - 365) %>% filter(date > date_max - 365) %>%
#filter(ped_inj %in% c("K", "A", "B")) %>% #filter(ped_inj %in% c("K", "A", "B")) %>%
filter(vulnerable_role %in% "Pedestrian") %>% filter(vulnerable_role %in% "Pedestrian") %>%
filter(longitude >= as.double(bbox[1]), filter(longitude >= as.double(bbox[1]),
@ -591,7 +593,7 @@ ggsave(file = paste0("figures/MilWALKee_Walks/",
## compare crashes in area ## compare crashes in area
nrow(TOPS_data_filtered %>% nrow(TOPS_data_filtered %>%
filter(date > max(TOPS_data_filtered$date) - 365) %>% filter(date > date_max - 365) %>%
filter(ped_inj %in% c("K", "A")) %>% filter(ped_inj %in% c("K", "A")) %>%
filter(vulnerable_role %in% "Pedestrian") %>% filter(vulnerable_role %in% "Pedestrian") %>%
filter(longitude >= as.double(bbox[1]), filter(longitude >= as.double(bbox[1]),
@ -599,7 +601,7 @@ nrow(TOPS_data_filtered %>%
longitude <= as.double(bbox[3]), longitude <= as.double(bbox[3]),
latitude <= as.double(bbox[4]))) latitude <= as.double(bbox[4])))
nrow(TOPS_data_filtered %>% nrow(TOPS_data_filtered %>%
filter(date > (max(TOPS_data_filtered$date) - 365 * (yearsforprior + 1))) %>% filter(date > (date_max - 365 * (yearsforprior + 1))) %>%
filter(ped_inj %in% c("K", "A")) %>% filter(ped_inj %in% c("K", "A")) %>%
filter(vulnerable_role %in% "Pedestrian") %>% filter(vulnerable_role %in% "Pedestrian") %>%
filter(longitude >= as.double(bbox[1]), filter(longitude >= as.double(bbox[1]),
@ -617,9 +619,9 @@ ggmap(basemap) +
labs(title = paste0("Crashes between cars and pedestrians"), labs(title = paste0("Crashes between cars and pedestrians"),
subtitle = paste0(str_to_title(focus_county), subtitle = paste0(str_to_title(focus_county),
" County | ", " County | ",
min(year(TOPS_data$date), na.rm = TRUE), year_min,
" - ", " - ",
max(year(TOPS_data$date), na.rm = TRUE)), year_max),
caption = paste0("crash data from UW TOPS lab - retrieved ", caption = paste0("crash data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"), strftime(retrieve_date, format = "%m/%Y"),
"\nper direction of the WisDOT Bureau of Transportation Safety", "\nper direction of the WisDOT Bureau of Transportation Safety",
@ -658,9 +660,9 @@ ggmap(basemap) +
labs(title = paste0("Crashes between cars and pedestrians"), labs(title = paste0("Crashes between cars and pedestrians"),
subtitle = paste0(str_to_title(focus_county), subtitle = paste0(str_to_title(focus_county),
" County | ", " County | ",
min(year(TOPS_data$date), na.rm = TRUE), year_min,
" - ", " - ",
max(year(TOPS_data$date), na.rm = TRUE)), year_max),
caption = paste0("crash data from UW TOPS lab - retrieved ", caption = paste0("crash data from UW TOPS lab - retrieved ",
strftime(retrieve_date, format = "%m/%Y"), strftime(retrieve_date, format = "%m/%Y"),
"\nper direction of the WisDOT Bureau of Transportation Safety", "\nper direction of the WisDOT Bureau of Transportation Safety",
@ -694,7 +696,7 @@ ggmap(basemap) +
## identify Halloween trick-or-treating days ## identify Halloween trick-or-treating days
```{r trickortreatdays, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE} ```{r trickortreatdays, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
trickortreatdays <- data_frame(year = seq(year(min(TOPS_data$date)), year(max(TOPS_data$date)), 1)) trickortreatdays <- data_frame(year = seq(year_min, year_max, 1))
trickortreatdays <- trickortreatdays %>% trickortreatdays <- trickortreatdays %>%
mutate(halloween = ymd(paste(year, "10, 31"))) %>% mutate(halloween = ymd(paste(year, "10, 31"))) %>%
mutate(wday = wday(halloween, label = TRUE)) %>% mutate(wday = wday(halloween, label = TRUE)) %>%
@ -879,9 +881,9 @@ ggmap(basemap) +
labs(title = paste0("Car crashes involving pedestrians"), labs(title = paste0("Car crashes involving pedestrians"),
subtitle = paste0(str_to_title(focus_county), subtitle = paste0(str_to_title(focus_county),
" County | ", " County | ",
min(year(TOPS_data$date), na.rm = TRUE), year_min,
" - ", " - ",
max(year(TOPS_data$date), na.rm = TRUE)), 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"),
"\nper direction of the WisDOT Bureau of Transportation Safety", "\nper direction of the WisDOT Bureau of Transportation Safety",