diff --git a/.Rhistory b/.Rhistory
deleted file mode 100644
index a9ea7e0..0000000
--- a/.Rhistory
+++ /dev/null
@@ -1,512 +0,0 @@
-~st_linestring(matrix(c(..2, ..1, ..4, ..3), ncol = 2, byrow = TRUE)))
-) %>%
-st_as_sf(sf_column_name = "geometry")
-bucket_feet <- 500
-lat_round <- bucket_feet/364481.35
-lon_round <- bucket_feet/267203.05
-metro_summary <- metro_data %>%
-left_join(routes_categorized, by = "pid") %>%
-mutate(lat_bucket = round(lat / lat_round) * lat_round,
-lon_bucket = round(lon / lon_round) * lon_round) %>%
-group_by(rt, des, 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")
-# 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(paste0(rt, "-", des) ~ .)
-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")
-# A West
-quantile(metro_segments %>% filter(pid %in% c("469")) %>% 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(pid %in% route_focus),
-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,
-".pdf"),
-title = paste0("Metro Route Speed - ", route),
-device = pdf,
-height = 8.5,
-width = 11,
-units = "in",
-create.dir = TRUE)
-}
-View(metro_data)
-View(metro_summary)
-metro_summary <- metro_data %>%
-left_join(routes_categorized, by = "pid") %>%
-mutate(lat_bucket = round(lat / lat_round) * lat_round,
-lon_bucket = round(lon / lon_round) * lon_round)
-View(metro_summary)
-metro_summary <- metro_data %>%
-left_join(routes_categorized, by = "pid") %>%
-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(weighted.mean(spd_calc, trip_count))
-View(metro_segments)
-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))
-View(metro_segments)
-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(pid %in% route_focus),
-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,
-".pdf"),
-title = paste0("Metro Route Speed - ", route),
-device = pdf,
-height = 8.5,
-width = 11,
-units = "in",
-create.dir = 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$time))),
-x = NULL,
-y = NULL) +
-theme(axis.text=element_blank(),
-axis.ticks=element_blank(),
-plot.caption = element_text(color = "grey")) +
-geom_sf(data = metro_segments %>% filter(name %in route),
-ggmap(basemap) +
-labs(title = paste0("Metro Route Speed - ", route),
-subtitle = paste0("averaged between ",
-sum(route_counts %>% filter(pid %in% route_focus) %>% pull(route_count)),
-" bus trips - ",
-min(date(metro_data$time)),
-" to ",
-max(date(metro_data$time))),
-x = NULL,
-y = NULL) +
-theme(axis.text=element_blank(),
-axis.ticks=element_blank(),
-plot.caption = element_text(color = "grey")) +
-geom_sf(data = metro_segments %>% filter(name %in% route),
-inherit.aes = FALSE,
-aes(color = spd_calc),
-linewidth = 1) +
-scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed or segment\n(calculated with locations, not reported speed)")
-for (route in unique(routes_categorized$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,
-".pdf"),
-title = paste0("Metro Route Speed - ", route),
-device = pdf,
-height = 8.5,
-width = 11,
-units = "in",
-create.dir = TRUE)
-}
-# A West
-quantile(metro_segments %>% filter(pid %in% c("469")) %>% pull(spd_calc), c(0,0.25, 0.5, 0.75, 1), na.rm = TRUE)
-quantile(metro_segments %>% filter(name %in% c("A_West")) %>% pull(spd_calc), c(0,0.25, 0.5, 0.75, 1), na.rm = TRUE)
-metro_data <- metro_raw %>%
-mutate(time = with_tz(time, "America/Chicago"),
-spd = as.double(spd),
-pdist = as.double(pdist),
-lon = as.double(lon),
-lat = as.double(lat)) %>%
-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")
-ggplot(data = metro_data %>% filter(name %in% route)) +
-geom_violin(aes(x = time,
-y = spd_calc))
-metro_data <- metro_raw %>%
-mutate(time = with_tz(time, "America/Chicago"),
-spd = as.double(spd),
-pdist = as.double(pdist),
-lon = as.double(lon),
-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")
-ggplot(data = metro_data %>% filter(name %in% route)) +
-geom_violin(aes(x = time,
-y = spd_calc,
-group = date))
-ggplot(data = metro_data %>% filter(name %in% route)) +
-geom_violin(aes(x = date,
-y = spd_calc))
-ggplot(data = metro_data %>% filter(name %in% route)) +
-geom_boxplot(aes(x = date,
-y = spd_calc))
-library(tidyverse)
-library(influxdbclient)
-library(glue)
-library(ggmap)
-library(sf)
-# parameters needed to make connection to Database
-token <- substr(read_file(file = 'api_keys/influxdb_madison-metro'), 1, 88)
-org <- "e2581d54779b077f"
-bucket <- "madison-metro"
-days <- 1
-influx_connection <- InfluxDBClient$new(url = "https://influxdb.dendroalsia.net",
-token = token,
-org = org)
-#---
-# Fields you want to query
-fields <- c("des", "spd", "pdist", "lon", "lat", "dly", "origtatripno")
-# Creating an empty list to store results for each field
-results <- vector("list", length(fields))
-# Loop through each field, get data, and coerce types if needed
-for (i in seq_along(fields)) {
-field <- fields[i]
-query_string <- glue('from(bucket: "{bucket}") ',
-'|> range(start: -{days}d) ',
-'|> filter(fn: (r) => r["_measurement"] == "vehicle_data")',
-'|> filter(fn: (r) => r["_field"] == "{field}")')
-data <- influx_connection$query(query_string)
-# Ensure the columns are coerced to consistent types
-# (Optionally add coercion based on your expected types)
-data <- bind_rows(data) %>%
-mutate(value = as.character(`_value`),
-field = `_field`) %>%
-select(time, rt, pid, vid, value, field)
-results[[i]] <- data
-}
-# Bind all results together
-metro_raw <- bind_rows(results)
-metro_raw <- pivot_wider(metro_raw, values_from = value, names_from = field) %>%
-distinct(pid, vid, lat, lon, spd, .keep_all = TRUE)
-routes_categorized <- read_csv(file = "routes_categorized.csv", col_types = "cc")
-metro_data <- metro_raw %>%
-mutate(time = with_tz(time, "America/Chicago"),
-spd = as.double(spd),
-pdist = as.double(pdist),
-lon = as.double(lon),
-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(paste0(rt, "-", des) ~ .)
-# 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 ~ .)
-library(tidyverse)
-library(influxdbclient)
-library(glue)
-library(ggmap)
-library(sf)
-# parameters needed to make connection to Database
-token <- substr(read_file(file = 'api_keys/influxdb_madison-metro'), 1, 88)
-org <- "e2581d54779b077f"
-bucket <- "madison-metro"
-days <- 1
-influx_connection <- InfluxDBClient$new(url = "https://influxdb.dendroalsia.net",
-token = token,
-org = org)
-#---
-# Fields you want to query
-fields <- c("des", "spd", "pdist", "lon", "lat", "dly", "origtatripno")
-# Creating an empty list to store results for each field
-results <- vector("list", length(fields))
-# Loop through each field, get data, and coerce types if needed
-for (i in seq_along(fields)) {
-field <- fields[i]
-query_string <- glue('from(bucket: "{bucket}") ',
-'|> range(start: -{days}d) ',
-'|> filter(fn: (r) => r["_measurement"] == "vehicle_data")',
-'|> filter(fn: (r) => r["_field"] == "{field}")')
-data <- influx_connection$query(query_string)
-# Ensure the columns are coerced to consistent types
-# (Optionally add coercion based on your expected types)
-data <- bind_rows(data) %>%
-mutate(value = as.character(`_value`),
-field = `_field`) %>%
-select(time, rt, pid, vid, value, field)
-results[[i]] <- data
-}
-# Bind all results together
-metro_raw <- bind_rows(results)
-metro_raw <- pivot_wider(metro_raw, values_from = value, names_from = field) %>%
-distinct(pid, vid, lat, lon, spd, .keep_all = TRUE)
-routes_categorized <- read_csv(file = "routes_categorized.csv", col_types = "cc")
-metro_data <- metro_raw %>%
-mutate(time = with_tz(time, "America/Chicago"),
-spd = as.double(spd),
-pdist = as.double(pdist),
-lon = as.double(lon),
-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()
diff --git a/madison-metro.R b/madison-metro.R
index aad0eae..6c858c8 100644
--- a/madison-metro.R
+++ b/madison-metro.R
@@ -5,9 +5,14 @@ library(ggmap)
 library(sf)
 
 # parameters needed to make connection to Database
-token <- substr(read_file(file = 'api_keys/influxdb_madison-metro'), 1, 88)
-org <- "e2581d54779b077f"
-bucket <- "madison-metro"
+#token <- substr(read_file(file = 'api_keys/influxdb_madison-metro'), 1, 88)
+#org <- "e2581d54779b077f"
+#bucket <- "madison-metro"
+
+token <- substr(read_file(file = 'api_keys/influxdb_madison-metro_new'), 1, 88)
+org <- "32b7fde0efd8a3b3"
+bucket <- "metro_vehicles"
+  
 
 days <- 1