512 lines
19 KiB
Text
512 lines
19 KiB
Text
~st_linestring(matrix(c(..2, ..1, ..4, ..3), ncol = 2, byrow = TRUE)))
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) %>%
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st_as_sf(sf_column_name = "geometry")
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bucket_feet <- 500
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lat_round <- bucket_feet/364481.35
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lon_round <- bucket_feet/267203.05
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metro_summary <- metro_data %>%
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left_join(routes_categorized, by = "pid") %>%
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mutate(lat_bucket = round(lat / lat_round) * lat_round,
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lon_bucket = round(lon / lon_round) * lon_round) %>%
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group_by(rt, des, pid, lat_bucket, lon_bucket) %>%
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summarise(spd = median(spd, na.rm = TRUE),
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spd_calc = median(spd_calc, na.rm = TRUE),
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pdist = median(pdist),
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trip_count = length(unique(origtatripno)))
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metro_data_sf <- st_as_sf(metro_data %>% filter(!is.na(lon)), coords = c("lon", "lat"), remove = FALSE)
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metro_summary_sf <- st_as_sf(metro_summary %>% filter(!is.na(lon_bucket)), coords = c("lon_bucket", "lat_bucket"), remove = FALSE)
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metro_segments <- metro_summary %>%
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group_by(rt, pid) %>%
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arrange(pdist) %>%
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mutate(lat_bucket_lag = lag(lat_bucket),
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lon_bucket_lag = lag(lon_bucket)) %>%
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filter(!is.na(lat_bucket) & !is.na(lon_bucket) & !is.na(lat_bucket_lag) & !is.na(lon_bucket_lag)) %>%
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mutate(
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geometry = pmap(list(lat_bucket, lon_bucket, lat_bucket_lag, lon_bucket_lag),
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~st_linestring(matrix(c(..2, ..1, ..4, ..3), ncol = 2, byrow = TRUE)))
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) %>%
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st_as_sf(sf_column_name = "geometry")
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# get counts of routes
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route_counts <- metro_data %>% group_by(pid, rt, des) %>% summarise(route_count = length(unique(origtatripno)))
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# make charts
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ggplot(data = metro_summary %>% filter(pid %in% (routes_categorized %>% filter(name %in% c("B_North", "B_South")) %>% pull (pid))),
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aes(x = pdist,
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y = spd_calc)) +
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geom_point() +
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geom_smooth() +
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facet_grid(paste0(rt, "-", des) ~ .)
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register_stadiamaps(key = substr(read_file(file = "api_keys/stadia_api_key"), 1, 36))
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bbox <- c(left = min(metro_data$lon),
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bottom = min(metro_data$lat),
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right = max(metro_data$lon),
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top = max(metro_data$lat))
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#get basemap
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basemap <- get_stadiamap(bbox = bbox, zoom = 13, maptype = "stamen_toner_lite")
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# A West
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quantile(metro_segments %>% filter(pid %in% c("469")) %>% pull(spd_calc), c(0,0.25, 0.5, 0.75, 1), na.rm = TRUE)
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for (route in unique(routes_categorized$name)){
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route_focus <- routes_categorized %>% filter(name == route) %>% pull(pid)
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ggmap(basemap) +
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labs(title = paste0("Metro Route Speed - ", route),
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subtitle = paste0("averaged between ",
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sum(route_counts %>% filter(pid %in% route_focus) %>% pull(route_count)),
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" bus trips - ",
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min(date(metro_data$time)),
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" to ",
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max(date(metro_data$time))),
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x = NULL,
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y = NULL) +
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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")) +
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geom_sf(data = metro_segments %>% filter(pid %in% route_focus),
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inherit.aes = FALSE,
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aes(color = spd_calc),
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linewidth = 1) +
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scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed or segment\n(calculated with locations, not reported speed)")
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ggsave(file = paste0("figures/",
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route,
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".pdf"),
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title = paste0("Metro Route Speed - ", route),
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device = pdf,
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height = 8.5,
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width = 11,
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units = "in",
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create.dir = TRUE)
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}
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View(metro_data)
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View(metro_summary)
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metro_summary <- metro_data %>%
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left_join(routes_categorized, by = "pid") %>%
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mutate(lat_bucket = round(lat / lat_round) * lat_round,
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lon_bucket = round(lon / lon_round) * lon_round)
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View(metro_summary)
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metro_summary <- metro_data %>%
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left_join(routes_categorized, by = "pid") %>%
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mutate(lat_bucket = round(lat / lat_round) * lat_round,
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lon_bucket = round(lon / lon_round) * lon_round) %>%
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group_by(rt, name, pid, lat_bucket, lon_bucket) %>%
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summarise(spd = median(spd, na.rm = TRUE),
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spd_calc = median(spd_calc, na.rm = TRUE),
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pdist = median(pdist),
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trip_count = length(unique(origtatripno)))
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metro_data_sf <- st_as_sf(metro_data %>% filter(!is.na(lon)), coords = c("lon", "lat"), remove = FALSE)
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metro_summary_sf <- st_as_sf(metro_summary %>% filter(!is.na(lon_bucket)), coords = c("lon_bucket", "lat_bucket"), remove = FALSE)
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metro_segments <- metro_summary %>%
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group_by(rt, pid) %>%
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arrange(pdist) %>%
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mutate(lat_bucket_lag = lag(lat_bucket),
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lon_bucket_lag = lag(lon_bucket)) %>%
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filter(!is.na(lat_bucket) & !is.na(lon_bucket) & !is.na(lat_bucket_lag) & !is.na(lon_bucket_lag)) %>%
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mutate(
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geometry = pmap(list(lat_bucket, lon_bucket, lat_bucket_lag, lon_bucket_lag),
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~st_linestring(matrix(c(..2, ..1, ..4, ..3), ncol = 2, byrow = TRUE)))
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) %>%
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st_as_sf(sf_column_name = "geometry") %>%
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group_by(rt, name, lat_bucket, lon_bucket) %>%
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summarise(weighted.mean(spd_calc, trip_count))
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View(metro_segments)
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metro_segments <- metro_summary %>%
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group_by(rt, pid) %>%
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arrange(pdist) %>%
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mutate(lat_bucket_lag = lag(lat_bucket),
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lon_bucket_lag = lag(lon_bucket)) %>%
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filter(!is.na(lat_bucket) & !is.na(lon_bucket) & !is.na(lat_bucket_lag) & !is.na(lon_bucket_lag)) %>%
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mutate(
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geometry = pmap(list(lat_bucket, lon_bucket, lat_bucket_lag, lon_bucket_lag),
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~st_linestring(matrix(c(..2, ..1, ..4, ..3), ncol = 2, byrow = TRUE)))
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) %>%
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st_as_sf(sf_column_name = "geometry") %>%
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group_by(rt, name, lat_bucket, lon_bucket) %>%
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summarise(spd_calc = weighted.mean(spd_calc, trip_count))
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View(metro_segments)
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for (route in unique(routes_categorized$name)){
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route_focus <- routes_categorized %>% filter(name == route) %>% pull(pid)
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ggmap(basemap) +
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labs(title = paste0("Metro Route Speed - ", route),
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subtitle = paste0("averaged between ",
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sum(route_counts %>% filter(pid %in% route_focus) %>% pull(route_count)),
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" bus trips - ",
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min(date(metro_data$time)),
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" to ",
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max(date(metro_data$time))),
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x = NULL,
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y = NULL) +
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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")) +
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geom_sf(data = metro_segments %>% filter(pid %in% route_focus),
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inherit.aes = FALSE,
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aes(color = spd_calc),
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linewidth = 1) +
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scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed or segment\n(calculated with locations, not reported speed)")
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ggsave(file = paste0("figures/",
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route,
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".pdf"),
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title = paste0("Metro Route Speed - ", route),
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device = pdf,
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height = 8.5,
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width = 11,
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units = "in",
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create.dir = TRUE)
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}
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for (route in unique(routes_categorized$name)){
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route_focus <- routes_categorized %>% filter(name == route) %>% pull(pid)
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ggmap(basemap) +
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labs(title = paste0("Metro Route Speed - ", route),
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subtitle = paste0("averaged between ",
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sum(route_counts %>% filter(pid %in% route_focus) %>% pull(route_count)),
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" bus trips - ",
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min(date(metro_data$time)),
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" to ",
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max(date(metro_data$time))),
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x = NULL,
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y = NULL) +
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theme(axis.text=element_blank(),
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axis.ticks=element_blank(),
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plot.caption = element_text(color = "grey")) +
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geom_sf(data = metro_segments %>% filter(name %in route),
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ggmap(basemap) +
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labs(title = paste0("Metro Route Speed - ", route),
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subtitle = paste0("averaged between ",
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sum(route_counts %>% filter(pid %in% route_focus) %>% pull(route_count)),
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" bus trips - ",
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min(date(metro_data$time)),
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" to ",
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max(date(metro_data$time))),
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x = NULL,
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y = NULL) +
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theme(axis.text=element_blank(),
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axis.ticks=element_blank(),
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plot.caption = element_text(color = "grey")) +
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geom_sf(data = metro_segments %>% filter(name %in% route),
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inherit.aes = FALSE,
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aes(color = spd_calc),
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linewidth = 1) +
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scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed or segment\n(calculated with locations, not reported speed)")
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for (route in unique(routes_categorized$name)){
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route_focus <- routes_categorized %>% filter(name == route) %>% pull(pid)
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ggmap(basemap) +
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labs(title = paste0("Metro Route Speed - ", route),
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subtitle = paste0("averaged between ",
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sum(route_counts %>% filter(pid %in% route_focus) %>% pull(route_count)),
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" bus trips - ",
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min(date(metro_data$time)),
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" to ",
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max(date(metro_data$time))),
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x = NULL,
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y = NULL) +
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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")) +
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geom_sf(data = metro_segments %>% filter(name %in% route),
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inherit.aes = FALSE,
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aes(color = spd_calc),
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linewidth = 1) +
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scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed or segment\n(calculated with locations, not reported speed)")
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ggsave(file = paste0("figures/",
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route,
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".pdf"),
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title = paste0("Metro Route Speed - ", route),
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device = pdf,
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height = 8.5,
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width = 11,
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units = "in",
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create.dir = TRUE)
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}
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# A West
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quantile(metro_segments %>% filter(pid %in% c("469")) %>% pull(spd_calc), c(0,0.25, 0.5, 0.75, 1), na.rm = TRUE)
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quantile(metro_segments %>% filter(name %in% c("A_West")) %>% pull(spd_calc), c(0,0.25, 0.5, 0.75, 1), na.rm = TRUE)
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metro_data <- metro_raw %>%
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mutate(time = with_tz(time, "America/Chicago"),
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spd = as.double(spd),
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pdist = as.double(pdist),
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lon = as.double(lon),
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lat = as.double(lat)) %>%
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group_by(pid, vid) %>%
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arrange(time) %>%
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mutate(pdist_lag = lag(pdist),
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time_lag = lag(time)) %>%
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mutate(spd_calc = case_when(pdist_lag > pdist ~ NA,
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pdist_lag <= pdist ~ (pdist - pdist_lag)/as.double(difftime(time, time_lag, units = "hours"))/5280)) %>%
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left_join(routes_categorized, by = "pid")
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ggplot(data = metro_data %>% filter(name %in% route)) +
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geom_violin(aes(x = time,
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y = spd_calc))
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metro_data <- metro_raw %>%
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mutate(time = with_tz(time, "America/Chicago"),
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spd = as.double(spd),
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pdist = as.double(pdist),
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lon = as.double(lon),
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lat = as.double(lat)) %>%
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mutate(date = date(time)) %>%
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group_by(pid, vid) %>%
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arrange(time) %>%
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mutate(pdist_lag = lag(pdist),
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time_lag = lag(time)) %>%
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mutate(spd_calc = case_when(pdist_lag > pdist ~ NA,
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pdist_lag <= pdist ~ (pdist - pdist_lag)/as.double(difftime(time, time_lag, units = "hours"))/5280)) %>%
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left_join(routes_categorized, by = "pid")
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ggplot(data = metro_data %>% filter(name %in% route)) +
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geom_violin(aes(x = time,
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y = spd_calc,
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group = date))
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ggplot(data = metro_data %>% filter(name %in% route)) +
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geom_violin(aes(x = date,
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y = spd_calc))
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ggplot(data = metro_data %>% filter(name %in% route)) +
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geom_boxplot(aes(x = date,
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y = spd_calc))
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library(tidyverse)
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library(influxdbclient)
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library(glue)
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library(ggmap)
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library(sf)
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# parameters needed to make connection to Database
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token <- substr(read_file(file = 'api_keys/influxdb_madison-metro'), 1, 88)
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org <- "e2581d54779b077f"
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bucket <- "madison-metro"
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days <- 1
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influx_connection <- InfluxDBClient$new(url = "https://influxdb.dendroalsia.net",
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token = token,
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org = org)
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#---
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# Fields you want to query
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fields <- c("des", "spd", "pdist", "lon", "lat", "dly", "origtatripno")
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# Creating an empty list to store results for each field
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results <- vector("list", length(fields))
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# Loop through each field, get data, and coerce types if needed
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for (i in seq_along(fields)) {
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field <- fields[i]
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query_string <- glue('from(bucket: "{bucket}") ',
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'|> range(start: -{days}d) ',
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'|> filter(fn: (r) => r["_measurement"] == "vehicle_data")',
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'|> filter(fn: (r) => r["_field"] == "{field}")')
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data <- influx_connection$query(query_string)
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# Ensure the columns are coerced to consistent types
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# (Optionally add coercion based on your expected types)
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data <- bind_rows(data) %>%
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mutate(value = as.character(`_value`),
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field = `_field`) %>%
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select(time, rt, pid, vid, value, field)
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results[[i]] <- data
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}
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# Bind all results together
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metro_raw <- bind_rows(results)
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metro_raw <- pivot_wider(metro_raw, values_from = value, names_from = field) %>%
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distinct(pid, vid, lat, lon, spd, .keep_all = TRUE)
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routes_categorized <- read_csv(file = "routes_categorized.csv", col_types = "cc")
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metro_data <- metro_raw %>%
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mutate(time = with_tz(time, "America/Chicago"),
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spd = as.double(spd),
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pdist = as.double(pdist),
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lon = as.double(lon),
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lat = as.double(lat)) %>%
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mutate(date = date(time)) %>%
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group_by(pid, vid) %>%
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arrange(time) %>%
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mutate(pdist_lag = lag(pdist),
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time_lag = lag(time)) %>%
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mutate(spd_calc = case_when(pdist_lag > pdist ~ NA,
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pdist_lag <= pdist ~ (pdist - pdist_lag)/as.double(difftime(time, time_lag, units = "hours"))/5280)) %>%
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left_join(routes_categorized, by = "pid")
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bucket_feet <- 500
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lat_round <- bucket_feet/364481.35
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lon_round <- bucket_feet/267203.05
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metro_summary <- metro_data %>%
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mutate(lat_bucket = round(lat / lat_round) * lat_round,
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lon_bucket = round(lon / lon_round) * lon_round) %>%
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group_by(rt, name, pid, lat_bucket, lon_bucket) %>%
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summarise(spd = median(spd, na.rm = TRUE),
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spd_calc = median(spd_calc, na.rm = TRUE),
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pdist = median(pdist),
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trip_count = length(unique(origtatripno)))
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metro_data_sf <- st_as_sf(metro_data %>% filter(!is.na(lon)), coords = c("lon", "lat"), remove = FALSE)
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metro_summary_sf <- st_as_sf(metro_summary %>% filter(!is.na(lon_bucket)), coords = c("lon_bucket", "lat_bucket"), remove = FALSE)
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metro_segments <- metro_summary %>%
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group_by(rt, pid) %>%
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arrange(pdist) %>%
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mutate(lat_bucket_lag = lag(lat_bucket),
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lon_bucket_lag = lag(lon_bucket)) %>%
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filter(!is.na(lat_bucket) & !is.na(lon_bucket) & !is.na(lat_bucket_lag) & !is.na(lon_bucket_lag)) %>%
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mutate(
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geometry = pmap(list(lat_bucket, lon_bucket, lat_bucket_lag, lon_bucket_lag),
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~st_linestring(matrix(c(..2, ..1, ..4, ..3), ncol = 2, byrow = TRUE)))
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) %>%
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st_as_sf(sf_column_name = "geometry") %>%
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group_by(rt, name, lat_bucket, lon_bucket) %>%
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summarise(spd_calc = weighted.mean(spd_calc, trip_count))
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# get counts of routes
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route_counts <- metro_data %>% group_by(pid, rt, des) %>% summarise(route_count = length(unique(origtatripno)))
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# make charts
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ggplot(data = metro_summary %>% filter(pid %in% (routes_categorized %>% filter(name %in% c("B_North", "B_South")) %>% pull (pid))),
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aes(x = pdist,
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y = spd_calc)) +
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geom_point() +
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geom_smooth() +
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facet_grid(paste0(rt, "-", des) ~ .)
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# make charts
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ggplot(data = metro_summary %>% filter(pid %in% (routes_categorized %>% filter(name %in% c("B_North", "B_South")) %>% pull (pid))),
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aes(x = pdist,
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y = spd_calc)) +
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geom_point() +
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geom_smooth() +
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facet_grid(name ~ .)
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library(tidyverse)
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library(influxdbclient)
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library(glue)
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library(ggmap)
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library(sf)
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# parameters needed to make connection to Database
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token <- substr(read_file(file = 'api_keys/influxdb_madison-metro'), 1, 88)
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org <- "e2581d54779b077f"
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bucket <- "madison-metro"
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days <- 1
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influx_connection <- InfluxDBClient$new(url = "https://influxdb.dendroalsia.net",
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token = token,
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org = org)
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#---
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# Fields you want to query
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fields <- c("des", "spd", "pdist", "lon", "lat", "dly", "origtatripno")
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# Creating an empty list to store results for each field
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results <- vector("list", length(fields))
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# Loop through each field, get data, and coerce types if needed
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for (i in seq_along(fields)) {
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field <- fields[i]
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query_string <- glue('from(bucket: "{bucket}") ',
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'|> range(start: -{days}d) ',
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'|> filter(fn: (r) => r["_measurement"] == "vehicle_data")',
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'|> filter(fn: (r) => r["_field"] == "{field}")')
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data <- influx_connection$query(query_string)
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# Ensure the columns are coerced to consistent types
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# (Optionally add coercion based on your expected types)
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data <- bind_rows(data) %>%
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mutate(value = as.character(`_value`),
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field = `_field`) %>%
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select(time, rt, pid, vid, value, field)
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results[[i]] <- data
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|
}
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|
# Bind all results together
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metro_raw <- bind_rows(results)
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metro_raw <- pivot_wider(metro_raw, values_from = value, names_from = field) %>%
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distinct(pid, vid, lat, lon, spd, .keep_all = TRUE)
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routes_categorized <- read_csv(file = "routes_categorized.csv", col_types = "cc")
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|
metro_data <- metro_raw %>%
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mutate(time = with_tz(time, "America/Chicago"),
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|
spd = as.double(spd),
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|
pdist = as.double(pdist),
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|
lon = as.double(lon),
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|
lat = as.double(lat)) %>%
|
|
mutate(date = date(time)) %>%
|
|
group_by(pid, vid) %>%
|
|
arrange(time) %>%
|
|
mutate(pdist_lag = lag(pdist),
|
|
time_lag = lag(time)) %>%
|
|
mutate(spd_calc = case_when(pdist_lag > pdist ~ NA,
|
|
pdist_lag <= pdist ~ (pdist - pdist_lag)/as.double(difftime(time, time_lag, units = "hours"))/5280)) %>%
|
|
left_join(routes_categorized, by = "pid")
|
|
bucket_feet <- 500
|
|
lat_round <- bucket_feet/364481.35
|
|
lon_round <- bucket_feet/267203.05
|
|
metro_summary <- metro_data %>%
|
|
mutate(lat_bucket = round(lat / lat_round) * lat_round,
|
|
lon_bucket = round(lon / lon_round) * lon_round) %>%
|
|
group_by(rt, name, pid, lat_bucket, lon_bucket) %>%
|
|
summarise(spd = median(spd, na.rm = TRUE),
|
|
spd_calc = median(spd_calc, na.rm = TRUE),
|
|
pdist = median(pdist),
|
|
trip_count = length(unique(origtatripno)))
|
|
metro_data_sf <- st_as_sf(metro_data %>% filter(!is.na(lon)), coords = c("lon", "lat"), remove = FALSE)
|
|
metro_summary_sf <- st_as_sf(metro_summary %>% filter(!is.na(lon_bucket)), coords = c("lon_bucket", "lat_bucket"), remove = FALSE)
|
|
metro_segments <- metro_summary %>%
|
|
group_by(rt, pid) %>%
|
|
arrange(pdist) %>%
|
|
mutate(lat_bucket_lag = lag(lat_bucket),
|
|
lon_bucket_lag = lag(lon_bucket)) %>%
|
|
filter(!is.na(lat_bucket) & !is.na(lon_bucket) & !is.na(lat_bucket_lag) & !is.na(lon_bucket_lag)) %>%
|
|
mutate(
|
|
geometry = pmap(list(lat_bucket, lon_bucket, lat_bucket_lag, lon_bucket_lag),
|
|
~st_linestring(matrix(c(..2, ..1, ..4, ..3), ncol = 2, byrow = TRUE)))
|
|
) %>%
|
|
st_as_sf(sf_column_name = "geometry") %>%
|
|
group_by(rt, name, lat_bucket, lon_bucket) %>%
|
|
summarise(spd_calc = weighted.mean(spd_calc, trip_count))
|
|
# get counts of routes
|
|
route_counts <- metro_data %>% group_by(pid, rt, des) %>% summarise(route_count = length(unique(origtatripno)))
|
|
# make charts
|
|
ggplot(data = metro_summary %>% filter(pid %in% (routes_categorized %>% filter(name %in% c("B_North", "B_South")) %>% pull (pid))),
|
|
aes(x = pdist,
|
|
y = spd_calc)) +
|
|
geom_point() +
|
|
geom_smooth() +
|
|
facet_grid(name ~ .)
|
|
register_stadiamaps(key = substr(read_file(file = "api_keys/stadia_api_key"), 1, 36))
|
|
bbox <- c(left = min(metro_data$lon),
|
|
bottom = min(metro_data$lat),
|
|
right = max(metro_data$lon),
|
|
top = max(metro_data$lat))
|
|
#get basemap
|
|
basemap <- get_stadiamap(bbox = bbox, zoom = 13, maptype = "stamen_toner_lite")
|
|
quantile(metro_segments %>% filter(name %in% c("A_West")) %>% pull(spd_calc), c(0,0.25, 0.5, 0.75, 1), na.rm = TRUE)
|
|
for (route in unique(routes_categorized$name)){
|
|
route_focus <- routes_categorized %>% filter(name == route) %>% pull(pid)
|
|
ggmap(basemap) +
|
|
labs(title = paste0("Metro Route Speed - ", route),
|
|
subtitle = paste0("averaged between ",
|
|
sum(route_counts %>% filter(pid %in% route_focus) %>% pull(route_count)),
|
|
" bus trips - ",
|
|
min(date(metro_data$time)),
|
|
" to ",
|
|
max(date(metro_data$time))),
|
|
x = NULL,
|
|
y = NULL) +
|
|
theme(axis.text=element_blank(),
|
|
axis.ticks=element_blank(),
|
|
plot.caption = element_text(color = "grey")) +
|
|
geom_sf(data = metro_segments %>% filter(name %in% route),
|
|
inherit.aes = FALSE,
|
|
aes(color = spd_calc),
|
|
linewidth = 1) +
|
|
scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed or segment\n(calculated with locations, not reported speed)")
|
|
ggsave(file = paste0("figures/",
|
|
route,
|
|
"_map.pdf"),
|
|
title = paste0("Metro Route Speed - ", route),
|
|
device = pdf,
|
|
height = 8.5,
|
|
width = 11,
|
|
units = "in",
|
|
create.dir = TRUE)
|
|
ggplot(data = metro_data %>% filter(name %in% route)) +
|
|
geom_boxplot(aes(x = date,
|
|
y = spd_calc))
|
|
ggsave(file = paste0("figures/",
|
|
route,
|
|
"_date.pdf"),
|
|
title = paste0("Metro Route Speed - ", route),
|
|
device = pdf,
|
|
height = 8.5,
|
|
width = 11,
|
|
units = "in",
|
|
create.dir = TRUE)
|
|
}
|
|
ggplot(data = metro_summary %>% filter(!is.blank(name)),
|
|
aes(x = pdist,
|
|
y = spd_calc)) +
|
|
geom_boxplot()
|
|
ggplot(data = metro_summary %>% filter(!is.na(name)),
|
|
aes(x = pdist,
|
|
y = spd_calc)) +
|
|
geom_boxplot()
|
|
ggplot(data = metro_summary %>% filter(!is.na(name)),
|
|
aes(x = name,
|
|
y = spd_calc)) +
|
|
geom_boxplot()
|
|
ggplot(data = metro_summary %>% filter(!is.na(name)),
|
|
aes(x = name,
|
|
y = spd_calc)) +
|
|
geom_violin()
|
|
ggplot(data = metro_summary %>% filter(!is.na(name)),
|
|
aes(x = name,
|
|
y = spd_calc)) +
|
|
geom_boxplot()
|