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edited titles of figures

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Ben Varick 2025-09-24 13:31:56 -07:00
parent cf85f8b2a8
commit e07a2efee1
Signed by: ben
SSH key fingerprint: SHA256:jWnpFDAcacYM5aPFpYRqlsamlDyKNpSj3jj+k4ojtUo
1 changed files with 29 additions and 12 deletions
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R/MilWALKeeWalks.Rmd
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@ -78,7 +78,7 @@ ggplot() +
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("sienna3", "deepskyblue3")) + scale_fill_manual(values = c("sienna3", "deepskyblue3")) +
scale_color_manual(values = c("sienna4", "deepskyblue4")) + scale_color_manual(values = c("sienna4", "deepskyblue4")) +
labs(title = paste0("Crashes involved pedestrians and bicyclists"), labs(title = paste0("Car crashes involving pedestrians & bicyclists"),
subtitle = paste0(str_to_title(focus_county), " County"), subtitle = paste0(str_to_title(focus_county), " County"),
x = "Month", x = "Month",
y = "Crashes per month", y = "Crashes per month",
@ -124,7 +124,7 @@ ggplot() +
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("Car crashes involving pedestrians"),
subtitle = paste0(str_to_title(focus_county), " County"), subtitle = paste0(str_to_title(focus_county), " County"),
x = "Month", x = "Month",
y = "Crashes per month", y = "Crashes per month",
@ -154,7 +154,7 @@ ggplot(data = TOPS_data_filtered %>%
y = total), y = total),
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("Crashes involved 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 - August"),
x = NULL, x = NULL,
y = "Crashes per year", y = "Crashes per year",
@ -183,7 +183,7 @@ 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))) +
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("Crashes involved 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 - August"),
x = NULL, x = NULL,
y = "Crashes per year", y = "Crashes per year",
@ -212,7 +212,7 @@ ggplot(data = TOPS_data_filtered %>%
fill = mke_city), fill = mke_city),
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("Crashes involved 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 - August"),
x = NULL, x = NULL,
y = "Crashes", y = "Crashes",
@ -246,7 +246,7 @@ 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_color_brewer(palette = "Set1") + scale_color_brewer(palette = "Set1") +
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("Crashes involved 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"), subtitle = paste0(str_to_title(focus_county), " County"),
x = NULL, x = NULL,
y = "Cumulative crashes", y = "Cumulative crashes",
@ -385,7 +385,7 @@ basemap <- get_stadiamap(bbox = bbox, zoom = 12, maptype = "stamen_toner_lite")
# generate map with bubbles # generate map with bubbles
ggmap(basemap) + ggmap(basemap) +
labs(title = paste0("Crashes between cars and 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$date, na.rm = TRUE)), year(min(TOPS_data_filtered$date, na.rm = TRUE)),
@ -430,7 +430,7 @@ ggsave(file = paste0("figures/MilWALKee_Walks/",
create.dir = TRUE) create.dir = TRUE)
ggmap(basemap) + ggmap(basemap) +
labs(title = paste0("Crashes between cars and pedestrians"), labs(title = paste0("Car crashes involving pedestrians"),
subtitle = paste0(str_to_title(focus_county), subtitle = paste0(str_to_title(focus_county),
" County | ", " County | ",
previousyearstring), previousyearstring),
@ -476,7 +476,7 @@ highlighted_areas <- hex_crashes %>%
highlighted_areas <- c(62, 69, 78, 85) highlighted_areas <- c(62, 69, 78, 85)
ggmap(basemap) + ggmap(basemap) +
labs(title = paste0("Crashes between cars and pedestrians\nselect areas of the county"), 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$date), na.rm = TRUE), min(year(TOPS_data$date), na.rm = TRUE),
@ -489,7 +489,7 @@ 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 = "last year\ncompared to previous") +
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)) +
@ -530,7 +530,7 @@ basemap <- get_stadiamap(bbox = bbox, zoom = 14, maptype = "stamen_toner_lite")
# Map of high increase areas # Map of high increase areas
ggmap(basemap) + ggmap(basemap) +
labs(title = paste0("Crashes between cars and 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$date), na.rm = TRUE), min(year(TOPS_data$date), na.rm = TRUE),
@ -579,7 +579,7 @@ ggmap(basemap) +
fill = ped_inj_name), fill = ped_inj_name),
shape = 23, shape = 23,
size = 3) + size = 3) +
scale_fill_manual(values = setNames(injury_severity$color, injury_severity$InjSevName), name = paste0("Crashes ", previousyearstring)) + geom_sf(data = projects_2023, inherit.aes = FALSE) scale_fill_manual(values = setNames(injury_severity$color, injury_severity$InjSevName), name = paste0("Crashes ", previousyearstring))# + geom_sf(data = projects_2023, inherit.aes = FALSE)
ggsave(file = paste0("figures/MilWALKee_Walks/", ggsave(file = paste0("figures/MilWALKee_Walks/",
"milwaukee_map_zoomchange.png"), "milwaukee_map_zoomchange.png"),
@ -589,6 +589,23 @@ ggsave(file = paste0("figures/MilWALKee_Walks/",
units = "in", units = "in",
create.dir = TRUE) create.dir = TRUE)
## compare crashes in area
nrow(TOPS_data_filtered %>%
filter(date > max(TOPS_data_filtered$date) - 365) %>%
filter(ped_inj %in% c("K", "A")) %>%
filter(vulnerable_role %in% "Pedestrian") %>%
filter(longitude >= as.double(bbox[1]),
latitude >= as.double(bbox[2]),
longitude <= as.double(bbox[3]),
latitude <= as.double(bbox[4])))
nrow(TOPS_data_filtered %>%
filter(date > (max(TOPS_data_filtered$date) - 365 * (yearsforprior + 1))) %>%
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])))/(yearsforprior + 1)
##highland ave ##highland ave
bbox <- c(left = -87.967, bbox <- c(left = -87.967,