edited MilWALKeeWalks.Rmd again
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1 changed files with 100 additions and 15 deletions
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@ -254,7 +254,9 @@ hex_crashes <- st_join(hexgrid,
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hex_crashes <- st_as_sf(left_join(hexgrid, hex_crashes), crs = 4326)
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hex_crashes <- hex_crashes %>%
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mutate(lastyearchange = (lastyear - prior/yearsforprior)/(prior/yearsforprior))
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mutate(prioryearlyaverage = prior/yearsforprior) %>%
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mutate(lastyearchangepercent = (lastyear - prioryearlyaverage)/prioryearlyaverage,
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lastyearchangecrashes = lastyear - prioryearlyaverage)
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hex_crashes_points <- st_centroid(hex_crashes)
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@ -289,7 +291,9 @@ hex_crashes_small <- st_join(hexgrid_small,
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hex_crashes_small <- st_as_sf(left_join(hexgrid_small, hex_crashes_small), crs = 4326)
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hex_crashes_small <- hex_crashes_small %>%
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mutate(lastyearchange = (lastyear - prior/yearsforprior)/(prior/yearsforprior))
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mutate(prioryearlyaverage = prior/yearsforprior) %>%
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mutate(lastyearchangepercent = (lastyear - prioryearlyaverage)/prioryearlyaverage,
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lastyearchangecrashes = lastyear - prioryearlyaverage)
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hex_crashes_small_points <- st_centroid(hex_crashes_small)
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@ -339,7 +343,7 @@ ggmap(basemap) +
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geom_sf(data = hex_crashes_points %>% filter(is.double(total), !is.na(total)),
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inherit.aes = FALSE,
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aes(size = total,
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fill = lastyearchange),
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fill = lastyearchangepercent),
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linewidth = 0,
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shape = 21,
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color = "black") +
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@ -379,9 +383,9 @@ ggmap(basemap) +
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axis.ticks=element_blank(),
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plot.caption = element_text(color = "grey", size = 8)) +
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# add crash locations
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geom_sf(data = hex_crashes %>% mutate(lastyearchange = (lastyear - prior/yearsforprior)),
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geom_sf(data = hex_crashes,
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inherit.aes = FALSE,
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aes(fill = lastyearchange),
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aes(fill = lastyearchangecrashes),
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alpha = 0.5) +
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scale_size_area() +
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scale_fill_gradient2(
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@ -389,7 +393,7 @@ ggmap(basemap) +
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mid = "white",
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high = "red",
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midpoint = 0,
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# limits = c(-2, 2),
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# limits = c(-20, 20),
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# oob = scales::squish
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) # + geom_sf(data = projects_2023, inherit.aes = FALSE)
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@ -429,11 +433,11 @@ ggmap(basemap) +
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# add crash locations
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geom_sf(data = hex_crashes %>% filter(ID %in% highlighted_areas),
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inherit.aes = FALSE,
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aes(fill = lastyearchange),
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aes(fill = lastyearchangepercent),
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alpha = 0.5) +
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geom_sf_text(data = hex_crashes_points %>% filter(ID %in% highlighted_areas),
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inherit.aes = FALSE,
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aes(label = paste0(ifelse(lastyearchange > 0, "+",""),round(lastyearchange * 100, 0), "%")),
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aes(label = paste0(ifelse(lastyearchangepercent > 0, "+",""),round(lastyearchangepercent * 100, 0), "%")),
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size = 3) +
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scale_size_area() +
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scale_fill_gradient2(
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@ -483,10 +487,9 @@ ggmap(basemap) +
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#add hexagons
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new_scale_fill() +
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geom_sf(data = hex_crashes_small %>%
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mutate(lastyearchange = (lastyear - prior/yearsforprior)) %>%
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filter(!is.na(lastyearchange)),
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filter(!is.na(lastyearchangecrashes)),
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inherit.aes = FALSE,
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aes(fill = lastyearchange),
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aes(fill = lastyearchangecrashes),
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alpha = 0.5) +
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scale_size_area() +
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scale_fill_gradient2(
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@ -494,8 +497,8 @@ ggmap(basemap) +
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mid = "white",
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high = "red",
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midpoint = 0,
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# limits = c(-2, 2),
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# oob = scales::squish
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# limits = c(-10, 10),
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# oob = scales::squish,
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name = "Crashes last year\ncompared to previous average") +
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# add crash locations
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new_scale_fill() +
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@ -513,7 +516,7 @@ 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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@ -523,7 +526,89 @@ ggsave(file = paste0("figures/MilWALKee_Walks/",
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units = "in",
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create.dir = TRUE)
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##highland ave
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bbox <- c(left = -87.967,
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bottom = 43.043,
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right = -87.944,
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top = 43.051)
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basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
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ggmap(basemap) +
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labs(title = paste0("Crashes between cars and 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$date), na.rm = TRUE),
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" - ",
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max(year(TOPS_data$date), na.rm = TRUE)),
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caption = paste0("crash data from UW TOPS lab - retrieved ",
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strftime(retrieve_date, format = "%m/%Y"),
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"\nper direction of the WisDOT Bureau of Transportation Safety",
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"\nbasemap from StadiaMaps and OpenStreetMap Contributers"),
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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 = paste0("last 12 months\ncompared to previous\n", yearsforprior, " years")) +
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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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# add crash locations
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new_scale_fill() +
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geom_point(data = TOPS_data_filtered %>%
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filter(year == 2025) %>%
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#filter(ped_inj %in% c("K", "A", "B")) %>%
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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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arrange(ped_inj_name),
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aes(x = longitude,
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y = latitude,
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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))
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bbox <- c(left = -87.910,
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bottom = 43.032,
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right = -87.896,
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top = 43.044)
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basemap <- get_stadiamap(bbox = bbox, zoom = 15, maptype = "stamen_toner_lite")
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ggmap(basemap) +
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labs(title = paste0("Crashes between cars and 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$date), na.rm = TRUE),
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" - ",
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max(year(TOPS_data$date), na.rm = TRUE)),
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caption = paste0("crash data from UW TOPS lab - retrieved ",
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strftime(retrieve_date, format = "%m/%Y"),
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"\nper direction of the WisDOT Bureau of Transportation Safety",
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"\nbasemap from StadiaMaps and OpenStreetMap Contributers"),
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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 = paste0("last 12 months\ncompared to previous\n", yearsforprior, " years")) +
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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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# add crash locations
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new_scale_fill() +
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geom_point(data = TOPS_data_filtered %>%
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filter(year != 2025) %>%
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#filter(ped_inj %in% c("K", "A", "B")) %>%
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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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arrange(ped_inj_name),
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aes(x = longitude,
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y = latitude,
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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))
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```
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