edited titles of figures
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1 changed files with 29 additions and 12 deletions
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@ -78,7 +78,7 @@ ggplot() +
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scale_y_continuous(expand = expansion(mult = c(0,0.1))) +
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scale_fill_manual(values = c("sienna3", "deepskyblue3")) +
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scale_color_manual(values = c("sienna4", "deepskyblue4")) +
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labs(title = paste0("Crashes involved pedestrians and bicyclists"),
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labs(title = paste0("Car crashes involving pedestrians & bicyclists"),
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subtitle = paste0(str_to_title(focus_county), " County"),
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x = "Month",
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y = "Crashes per month",
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@ -124,7 +124,7 @@ ggplot() +
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scale_y_continuous(expand = expansion(mult = c(0,0.1))) +
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scale_fill_manual(values = c("deeppink1", "darkgoldenrod1")) +
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scale_color_manual(values = c("deeppink3", "darkgoldenrod3")) +
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labs(title = paste0("Crashes involved pedestrians"),
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labs(title = paste0("Car crashes involving pedestrians"),
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subtitle = paste0(str_to_title(focus_county), " County"),
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x = "Month",
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y = "Crashes per month",
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@ -154,7 +154,7 @@ ggplot(data = TOPS_data_filtered %>%
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y = total),
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fill = "lightblue4") +
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scale_y_continuous(expand = expansion(mult = c(0,0.1))) +
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labs(title = paste0("Crashes involved pedestrians & bicyclists"),
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labs(title = paste0("Car crashes involving pedestrians & bicyclists"),
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subtitle = paste0(str_to_title(focus_county), " County | ", "January - August"),
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x = NULL,
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y = "Crashes per year",
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@ -183,7 +183,7 @@ ggplot(data = TOPS_data_filtered %>%
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position = position_dodge()) +
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scale_y_continuous(expand = expansion(mult = c(0,0.1))) +
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scale_fill_manual(values = setNames(injury_severity$color, injury_severity$InjSevName), name = "Injury severity") +
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labs(title = paste0("Crashes involved pedestrians & bicyclists - fatal and serious injuries"),
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labs(title = paste0("Car crashes involving pedestrians & bicyclists - fatal and serious injuries"),
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subtitle = paste0(str_to_title(focus_county), " County | ", "January - August"),
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x = NULL,
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y = "Crashes per year",
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@ -212,7 +212,7 @@ ggplot(data = TOPS_data_filtered %>%
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fill = mke_city),
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position = position_dodge()) +
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scale_y_continuous(expand = expansion(mult = c(0,0.1))) +
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labs(title = paste0("Crashes involved pedestrians - fatal and severe injuries"),
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labs(title = paste0("Car crashes involving pedestrians - fatal and severe injuries"),
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subtitle = paste0(str_to_title(focus_county), " County | ", "January - August"),
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x = NULL,
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y = "Crashes",
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@ -246,7 +246,7 @@ ggplot(data = TOPS_data_filtered %>%
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scale_y_continuous(expand = expansion(mult = c(0,0.1))) +
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scale_color_brewer(palette = "Set1") +
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scale_fill_manual(values = setNames(injury_severity$color, injury_severity$InjSevName), name = "Injury severity") +
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labs(title = paste0("Crashes involved pedestrians & bicyclists - fatal and serious injuries"),
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labs(title = paste0("Car crashes involving pedestrians & bicyclists - fatal and serious injuries"),
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subtitle = paste0(str_to_title(focus_county), " County"),
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x = NULL,
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y = "Cumulative crashes",
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@ -385,7 +385,7 @@ basemap <- get_stadiamap(bbox = bbox, zoom = 12, maptype = "stamen_toner_lite")
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# generate map with bubbles
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ggmap(basemap) +
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labs(title = paste0("Crashes between cars and pedestrians"),
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labs(title = paste0("Car crashes involving pedestrians"),
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subtitle = paste0(str_to_title(focus_county),
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" County | ",
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year(min(TOPS_data_filtered$date, na.rm = TRUE)),
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@ -430,7 +430,7 @@ ggsave(file = paste0("figures/MilWALKee_Walks/",
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create.dir = TRUE)
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ggmap(basemap) +
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labs(title = paste0("Crashes between cars and pedestrians"),
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labs(title = paste0("Car crashes involving pedestrians"),
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subtitle = paste0(str_to_title(focus_county),
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" County | ",
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previousyearstring),
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@ -476,7 +476,7 @@ highlighted_areas <- hex_crashes %>%
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highlighted_areas <- c(62, 69, 78, 85)
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ggmap(basemap) +
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labs(title = paste0("Crashes between cars and pedestrians\nselect areas of the county"),
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labs(title = paste0("Car crashes involving pedestrians"),
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subtitle = paste0(str_to_title(focus_county),
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" County | ",
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min(year(TOPS_data$date), na.rm = TRUE),
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@ -489,7 +489,7 @@ ggmap(basemap) +
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x = NULL,
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y = NULL,
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size = paste0("Total crashes"),
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fill = "last 12 months\ncompared to previous") +
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fill = "last year\ncompared to previous") +
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theme(axis.text=element_blank(),
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axis.ticks=element_blank(),
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plot.caption = element_text(color = "grey", size = 8)) +
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@ -530,7 +530,7 @@ basemap <- get_stadiamap(bbox = bbox, zoom = 14, maptype = "stamen_toner_lite")
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# Map of high increase areas
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ggmap(basemap) +
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labs(title = paste0("Crashes between cars and pedestrians"),
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labs(title = paste0("Car crashes involving pedestrians"),
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subtitle = paste0(str_to_title(focus_county),
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" County | ",
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min(year(TOPS_data$date), na.rm = TRUE),
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@ -579,7 +579,7 @@ ggmap(basemap) +
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fill = ped_inj_name),
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shape = 23,
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size = 3) +
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scale_fill_manual(values = setNames(injury_severity$color, injury_severity$InjSevName), name = paste0("Crashes ", previousyearstring)) + geom_sf(data = projects_2023, inherit.aes = FALSE)
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scale_fill_manual(values = setNames(injury_severity$color, injury_severity$InjSevName), name = paste0("Crashes ", previousyearstring))# + geom_sf(data = projects_2023, inherit.aes = FALSE)
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ggsave(file = paste0("figures/MilWALKee_Walks/",
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"milwaukee_map_zoomchange.png"),
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@ -589,6 +589,23 @@ ggsave(file = paste0("figures/MilWALKee_Walks/",
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units = "in",
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create.dir = TRUE)
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## compare crashes in area
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nrow(TOPS_data_filtered %>%
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filter(date > max(TOPS_data_filtered$date) - 365) %>%
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filter(ped_inj %in% c("K", "A")) %>%
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filter(vulnerable_role %in% "Pedestrian") %>%
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filter(longitude >= as.double(bbox[1]),
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latitude >= as.double(bbox[2]),
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longitude <= as.double(bbox[3]),
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latitude <= as.double(bbox[4])))
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nrow(TOPS_data_filtered %>%
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filter(date > (max(TOPS_data_filtered$date) - 365 * (yearsforprior + 1))) %>%
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filter(ped_inj %in% c("K", "A")) %>%
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filter(vulnerable_role %in% "Pedestrian") %>%
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filter(longitude >= as.double(bbox[1]),
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latitude >= as.double(bbox[2]),
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longitude <= as.double(bbox[3]),
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latitude <= as.double(bbox[4])))/(yearsforprior + 1)
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##highland ave
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bbox <- c(left = -87.967,
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