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