From e59afa8c599ed5c63ea61ea99778b3fe7a06a47e Mon Sep 17 00:00:00 2001
From: Ben Varick <ben@dendroalsia.net>
Date: Wed, 20 Nov 2024 09:47:26 -0600
Subject: [PATCH] removed .Rhistory

---
 .Rhistory  | 1004 ++++++++++++++++++++++++++--------------------------
 .gitignore |    1 +
 2 files changed, 503 insertions(+), 502 deletions(-)

diff --git a/.Rhistory b/.Rhistory
index 8cd9e28..16d0144 100644
--- a/.Rhistory
+++ b/.Rhistory
@@ -1,512 +1,512 @@
-geom_sf(data = segments_sf %>% filter(pid %in% c("422")),
-inherit.aes = FALSE,
-aes(color = lag_spd),
-linewidth = 1) +
-scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed\n(calculated with consecutive points)") +
-facet_wrap(paste0(rt, "-", des) ~ .)
-segments_sf %>% filter(pid %in% c("422")) %>% pull(trip_count)
-metro_data_sf <- st_as_sf(metro_data, coords = c("lon", "lat"), remove = FALSE)
-metro_summary_sf <- st_as_sf(metro_summary, coords = c("lon", "lat"), remove = FALSE)
-segments_sf <- metro_summary_sf %>%
-group_by(rt, pid, des) %>%
-arrange(pid, pdist_bucket) %>%  # Ensure points within each route are sorted if needed
-mutate(
-lead_geom = lead(geometry),
-lead_spd = lead(spd)
-) %>%
-filter(!is.na(lead_geom)) %>%
-# Create a segment for each pair of points
-rowwise() %>%
-mutate(
-segment = st_cast(st_union(geometry, lead_geom), "LINESTRING")
-) %>%
-ungroup() %>%
-as.data.frame() %>%
-select(rt, pid, des, pdist_bucket, spd, segment, lag_spd) %>%
-st_as_sf()
-ggmap(basemap) +
-labs(title = "Metro Route Speed",
-subtitle = paste0("averaged between ",
-segments_sf %>% filter(pid %in% c("422")) %>% pull(trip_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 = segments_sf %>% filter(pid %in% c("422")),
-inherit.aes = FALSE,
-aes(color = lag_spd),
-linewidth = 1) +
-scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed\n(calculated with consecutive points)") +
-facet_wrap(paste0(rt, "-", des) ~ .)
-ggmap(basemap) +
-labs(title = "Metro Route Speed",
-subtitle = paste0("averaged between ",
-metro_summary %>% filter(pid %in% c("422")) %>% pull(trip_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 = segments_sf %>% filter(pid %in% c("422")),
-inherit.aes = FALSE,
-aes(color = lag_spd),
-linewidth = 1) +
-scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed\n(calculated with consecutive points)") +
-facet_wrap(paste0(rt, "-", des) ~ .)
-metro_summary %>% filter(pid %in% c("422"))
-max(metro_summary %>% filter(pid %in% c("422")) %>% pull(trip_count))
-ggmap(basemap) +
-labs(title = "Metro Route Speed",
-subtitle = paste0("averaged between ",
-metro_summary %>% filter(pid %in% c("469")) %>% pull(trip_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 = segments_sf %>% filter(pid %in% c("469")),
-inherit.aes = FALSE,
-aes(color = lag_spd),
-linewidth = 1) +
-scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed\n(calculated with consecutive points)") +
-facet_wrap(paste0(rt, "-", des) ~ .)
-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$new(url = "https://influxdb.dendroalsia.net",
-token = token,
-org = org)
-#---
-# Fields you want to query
-fields <- c("spd", "pdist", "pid", "lon", "lat", "vid", "dly", "origtatripno")
-# Creating an empty list to store results for each field
-results <- vector("list", length(fields))
-# Loop through each field, get data, and coerce types if needed
-for (i in seq_along(fields)) {
-field <- fields[i]
-query_string <- glue('from(bucket: "{bucket}") ',
-'|> range(start: -{days}d) ',
-'|> filter(fn: (r) => r["_measurement"] == "vehicle_data")',
-'|> filter(fn: (r) => r["_field"] == "{field}")')
-data <- influx_connection$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, des, 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)
-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(origtatripno) %>%
-arrange(time) %>%
-mutate(lag_pdist = lag(pdist),
-lag_time = lag(time)) %>%
-mutate(lag_spd = (pdist - lag_pdist)/as.double(difftime(time, lag_time, units = "hours"))/5280)
-metro_summary <- metro_data %>%
-mutate(pdist_bucket = round(pdist / 500) * 500) %>%
-group_by(pdist_bucket, rt, des, pid) %>%
-summarise(lat = median(lat),
-lon = median(lon),
-spd = median(spd),
-lag_spd = median(lag_spd),
-trip_count = length(unique(origtatripno)))
-metro_data_sf <- st_as_sf(metro_data, coords = c("lon", "lat"), remove = FALSE)
-metro_summary_sf <- st_as_sf(metro_summary, coords = c("lon", "lat"), remove = FALSE)
-segments_sf <- metro_summary_sf %>%
-group_by(rt, pid, des) %>%
-arrange(pid, pdist_bucket) %>%  # Ensure points within each route are sorted if needed
-mutate(
-lead_geom = lead(geometry),
-lead_spd = lead(spd)
-) %>%
-filter(!is.na(lead_geom)) %>%
-# Create a segment for each pair of points
-rowwise() %>%
-mutate(
-segment = st_cast(st_union(geometry, lead_geom), "LINESTRING")
-) %>%
-ungroup() %>%
-as.data.frame() %>%
-select(rt, pid, des, pdist_bucket, spd, segment, lag_spd) %>%
-st_as_sf()
-ggplot(data = metro_summary %>% filter(pid %in% c("421", "422")),
-aes(x = pdist_bucket,
-y = lag_spd)) +
-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")
-ggmap(basemap) +
-labs(title = "Metro Route Speed",
-subtitle = paste0("averaged between ",
-metro_summary %>% filter(pid %in% c("469")) %>% pull(trip_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 = segments_sf %>% filter(pid %in% c("469")),
-inherit.aes = FALSE,
-aes(color = lag_spd),
-linewidth = 1) +
-scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed\n(calculated with consecutive points)") +
-facet_wrap(paste0(rt, "-", des) ~ .)
-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$new(url = "https://influxdb.dendroalsia.net",
-token = token,
-org = org)
-#---
-# Fields you want to query
-fields <- c("spd", "pdist", "pid", "lon", "lat", "vid", "dly", "origtatripno")
-# Creating an empty list to store results for each field
-results <- vector("list", length(fields))
-# Loop through each field, get data, and coerce types if needed
-for (i in seq_along(fields)) {
-field <- fields[i]
-query_string <- glue('from(bucket: "{bucket}") ',
-'|> range(start: -{days}d) ',
-'|> filter(fn: (r) => r["_measurement"] == "vehicle_data")',
-'|> filter(fn: (r) => r["_field"] == "{field}")')
-data <- influx_connection$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, des, 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)
-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(origtatripno) %>%
-arrange(time) %>%
-mutate(lag_pdist = lag(pdist),
-lag_time = lag(time)) %>%
-mutate(lag_spd = (pdist - lag_pdist)/as.double(difftime(time, lag_time, units = "hours"))/5280)
-metro_summary <- metro_data %>%
-mutate(pdist_bucket = round(pdist / 500) * 500) %>%
-group_by(pdist_bucket, rt, des, pid) %>%
-summarise(lat = median(lat),
-lon = median(lon),
-spd = median(spd),
-lag_spd = median(lag_spd),
-trip_count = length(unique(origtatripno)))
-metro_data_sf <- st_as_sf(metro_data, coords = c("lon", "lat"), remove = FALSE)
-metro_data_sf <- st_as_sf(metro_data, coords = c("lon", "lat"), remove = FALSE)
-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$new(url = "https://influxdb.dendroalsia.net",
-token = token,
-org = org)
-#---
-# Fields you want to query
-fields <- c("spd", "pdist", "pid", "lon", "lat", "vid", "dly", "origtatripno")
-# Creating an empty list to store results for each field
-results <- vector("list", length(fields))
-# Loop through each field, get data, and coerce types if needed
-for (i in seq_along(fields)) {
-field <- fields[i]
-query_string <- glue('from(bucket: "{bucket}") ',
-'|> range(start: -{days}d) ',
-'|> filter(fn: (r) => r["_measurement"] == "vehicle_data")',
-'|> filter(fn: (r) => r["_field"] == "{field}")')
-data <- influx_connection$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, des, 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)
-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(origtatripno) %>%
-arrange(time) %>%
-mutate(lag_pdist = lag(pdist),
-lag_time = lag(time)) %>%
-mutate(lag_spd = (pdist - lag_pdist)/as.double(difftime(time, lag_time, units = "hours"))/5280)
-metro_summary <- metro_data %>%
-mutate(pdist_bucket = round(pdist / 500) * 500) %>%
-group_by(pdist_bucket, rt, des, pid) %>%
-summarise(lat = median(lat),
-lon = median(lon),
-spd = median(spd),
-lag_spd = median(lag_spd),
-trip_count = length(unique(origtatripno)))
-metro_data_sf <- st_as_sf(metro_data, coords = c("lon", "lat"), remove = FALSE)
-metro_summary_sf <- st_as_sf(metro_summary, coords = c("lon", "lat"), remove = FALSE)
-metro_summary <- metro_data %>%
-mutate(pdist_bucket = round(pdist / 500) * 500) %>%
-group_by(pdist_bucket, rt, des, pid) %>%
-summarise(lat = median(lat, na.rm = TRUE),
-lon = median(lon, na.rm = TRUE),
-spd = median(spd, na.rm = TRUE),
-lag_spd = median(lag_spd, na.rm = TRUE),
-trip_count = length(unique(origtatripno)))
-metro_data_sf <- st_as_sf(metro_data %>% filter(is.double(lat)), coords = c("lon", "lat"), remove = FALSE)
-View(metro_data)
-metro_data_sf <- st_as_sf(metro_data %>% filter(!is.na(lat)), coords = c("lon", "lat"), remove = FALSE)
-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, coords = c("lon", "lat"), remove = FALSE)
-View(metro_summary)
-nrow(metro_data %>% filter(is.na(lon)))
-metro_summary <- metro_data %>%
-mutate(pdist_bucket = round(pdist / 500) * 500) %>%
-group_by(pdist_bucket, rt, des, pid) %>%
-summarise(lat = median(lat, na.rm = TRUE),
-lon = median(lon, na.rm = TRUE),
-spd = median(spd, na.rm = TRUE),
-lag_spd = median(lag_spd, na.rm = TRUE),
-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)), coords = c("lon", "lat"), remove = FALSE)
-segments_sf <- metro_summary_sf %>%
-group_by(rt, pid, des) %>%
-arrange(pid, pdist_bucket) %>%  # Ensure points within each route are sorted if needed
-mutate(
-lead_geom = lead(geometry),
-lead_spd = lead(spd)
-) %>%
-filter(!is.na(lead_geom)) %>%
-# Create a segment for each pair of points
-rowwise() %>%
-mutate(
-segment = st_cast(st_union(geometry, lead_geom), "LINESTRING")
-) %>%
-ungroup() %>%
-as.data.frame() %>%
-select(rt, pid, des, pdist_bucket, spd, segment, lag_spd) %>%
-st_as_sf()
-ggplot(data = metro_summary %>% filter(pid %in% c("421", "422")),
-aes(x = pdist_bucket,
-y = lag_spd)) +
-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")
-ggmap(basemap) +
-labs(title = "Metro Route Speed",
-subtitle = paste0("averaged between ",
-metro_summary %>% filter(pid %in% c("469")) %>% pull(trip_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 = segments_sf %>% filter(pid %in% c("469")),
-inherit.aes = FALSE,
-aes(color = lag_spd),
-linewidth = 1) +
-scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed\n(calculated with consecutive points)") +
-facet_wrap(paste0(rt, "-", des) ~ .)
-quantile(segments_sf %>% filter(pid %in% c("469") %>% pull(lag_spd), c(0,0.25, 0.5, 0.75, 1))
-)
-quantile(segments_sf %>% filter(pid %in% c("469") %>% pull(lag_spd)), c(0,0.25, 0.5, 0.75, 1))
-quantile(segments_sf %>% filter(pid %in% c("469")) %>% pull(lag_spd), c(0,0.25, 0.5, 0.75, 1))
-quantile(segments_sf %>% filter(pid %in% c("469")) %>% pull(lag_spd), c(0,0.25, 0.5, 0.75, 1))
-ggmap(basemap) +
-labs(title = "Metro Route Speed",
-subtitle = paste0("averaged between ",
-metro_summary %>% filter(pid %in% c("469")) %>% pull(trip_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 = segments_sf %>% filter(pid %in% c("469")),
-inherit.aes = FALSE,
-aes(color = lag_spd),
-linewidth = 1) +
-scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed\n(calculated with consecutive points)") +
-facet_wrap(paste0(rt, "-", des) ~ .)
-metro_data %>% group_by(pid, rt, des) %>% summarise(route_count = length(unique(origtatripno)))
-# get counts of routes
-route_counts <- metro_data %>% group_by(pid, rt, des) %>% summarise(route_count = length(unique(origtatripno)))
-View(route_counts)
-ggmap(basemap) +
-labs(title = "Metro Route Speed",
-subtitle = paste0("averaged between ",
-metro_summary %>% filter(pid %in% c("1280")) %>% pull(trip_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 = segments_sf %>% filter(pid %in% c("469")),
-inherit.aes = FALSE,
-aes(color = lag_spd),
-linewidth = 1) +
-scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed\n(calculated with consecutive points)") +
-facet_wrap(paste0(rt, "-", des) ~ .)
-ggmap(basemap) +
-labs(title = "Metro Route Speed",
-subtitle = paste0("averaged between ",
-metro_summary %>% filter(pid %in% c("1280")) %>% pull(trip_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 = segments_sf %>% filter(pid %in% c("1280")),
-inherit.aes = FALSE,
-aes(color = lag_spd),
-linewidth = 1) +
-scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed\n(calculated with consecutive points)") +
-facet_wrap(paste0(rt, "-", des) ~ .)
-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$new(url = "https://influxdb.dendroalsia.net",
-token = token,
-org = org)
-#---
-# Fields you want to query
-fields <- c("spd", "pdist", "pid", "lon", "lat", "vid", "dly", "origtatripno")
-# Creating an empty list to store results for each field
-results <- vector("list", length(fields))
-# Loop through each field, get data, and coerce types if needed
-for (i in seq_along(fields)) {
-field <- fields[i]
-query_string <- glue('from(bucket: "{bucket}") ',
-'|> range(start: -{days}d) ',
-'|> filter(fn: (r) => r["_measurement"] == "vehicle_data")',
-'|> filter(fn: (r) => r["_field"] == "{field}")')
-data <- influx_connection$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, des, 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)
-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(origtatripno) %>%
-arrange(time) %>%
-mutate(lag_pdist = lag(pdist),
-lag_time = lag(time)) %>%
 mutate(lag_spd = (pdist - lag_pdist)/as.double(difftime(time, lag_time, units = "hours"))/5280)
 routes_categorized <- read_csv(file = "routes_categorized.csv", col_types = "cc")
-bucket_lat <- 364481.35/200
-bucket_lon <- 267203.05/200
+#---
+# Fields you want to query
+fields <- c("des", "spd", "pdist", "lon", "lat", "dly", "origtatripno", "tmstmp")
+# 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$query(query_string)
+# Ensure the columns are coerced to consistent types
+# (Optionally add coercion based on your expected types)
+data <- bind_rows(data) %>%
+mutate(value = as.character(`_value`),
+field = `_field`) %>%
+select(time, rt, pid, vid, value, field)
+results[[i]] <- data
+}
+query_string <- glue('from(bucket: "{bucket}") ',
+'|> range(start: -{days}d) ',
+'|> filter(fn: (r) => r["_measurement"] == "vehicle_data")',
+'|> filter(fn: (r) => r["_field"] == "{field}")')
+data <- influx_connection$query(query_string)
+field <- spd
+field <- "spd"
+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$query(query_string)
+View(data)
+#---
+# Fields you want to query
+fields <- c("des", "spd", "pdist", "lon", "lat", "dly", "origtatripno")
+# Creating an empty list to store results for each field
+results <- vector("list", length(fields))
+# Loop through each field, get data, and coerce types if needed
+for (i in seq_along(fields)) {
+field <- fields[i]
+query_string <- glue('from(bucket: "{bucket}") ',
+'|> range(start: -{days}d) ',
+'|> filter(fn: (r) => r["_measurement"] == "vehicle_data")',
+'|> filter(fn: (r) => r["_field"] == "{field}")')
+data <- influx_connection$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)
+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(origtatripno) %>%
+arrange(time) %>%
+mutate(lag_pdist = lag(pdist),
+lag_time = lag(time)) %>%
+mutate(lag_spd = (pdist - lag_pdist)/as.double(difftime(time, lag_time, units = "hours"))/5280)
+1/364481.35
+bucket_feet <- 200
+bucket_lat <- bucket_feet/364481.35
+bucket_lon <- bucket_feet/267203.05
+bucket_feet <- 200
+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 / 200) * 100) %>%
+mutate(lat_bucket = round(lat / lat_round) * lat_round,
+lon_bucket = round(lon / lon_round) * lon_round) %>%
 group_by(pdist_bucket, rt, des, pid) %>%
 summarise(lat = median(lat, na.rm = TRUE),
 lon = median(lon, na.rm = TRUE),
 spd = median(spd, na.rm = TRUE),
 lag_spd = median(lag_spd, na.rm = TRUE),
 trip_count = length(unique(origtatripno)))
+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(lat_bucket, lon_bucket, rt, des, pid) %>%
+summarise(lat = median(lat, na.rm = TRUE),
+lon = median(lon, na.rm = TRUE),
+spd = median(spd, na.rm = TRUE),
+lag_spd = median(lag_spd, na.rm = TRUE),
+trip_count = length(unique(origtatripno)))
+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(lat_bucket, lon_bucket, rt, des, pid) %>%
+summarise(lat_bucket = median(lat_bucket, na.rm = TRUE),
+lon_bucket = median(lon_bucket, na.rm = TRUE),
+spd = median(spd, na.rm = TRUE),
+lag_spd = median(lag_spd, na.rm = TRUE),
+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)), coords = c("lon_bucket", "lat_bucket"), remove = FALSE)
+metro_summary_sf <- st_as_sf(metro_summary %>% filter(!is.na(lon_bucket)), coords = c("lon_bucket", "lat_bucket"), remove = FALSE)
+segments_sf <- metro_summary_sf %>%
+group_by(rt, pid, des) %>%
+arrange(pid, pdist_bucket) %>%  # Ensure points within each route are sorted if needed
+mutate(
+lead_geom = lead(geometry),
+lead_spd = lead(spd)
+) %>%
+filter(!is.na(lead_geom)) %>%
+# Create a segment for each pair of points
+rowwise() %>%
+mutate(
+segment = st_cast(st_union(geometry, lead_geom), "LINESTRING")
+) %>%
+ungroup() %>%
+as.data.frame() %>%
+select(rt, pid, des, pdist_bucket, spd, segment, lag_spd) %>%
+st_as_sf()
+segments_sf <- metro_summary_sf %>%
+group_by(rt, pid, des) %>%
+arrange(pid, pdist_bucket) %>%  # Ensure points within each route are sorted if needed
+mutate(
+lead_geom = lead(geometry),
+lead_spd = lead(spd)
+) %>%
+filter(!is.na(lead_geom)) %>%
+# Create a segment for each pair of points
+rowwise() %>%
+mutate(
+segment = st_cast(st_union(geometry, lead_geom), "LINESTRING")
+) %>%
+ungroup() %>%
+as.data.frame() %>%
+select(rt, pid, des, lat_bucket, lon_bucket, spd, segment, lag_spd) %>%
+st_as_sf()
+segments_sf <- metro_summary_sf %>%
+group_by(rt, pid, des) %>%
+arrange(vid, time) %>%  # Ensure points within each route are sorted if needed
+mutate(
+lead_geom = lead(geometry),
+lead_spd = lead(spd)
+) %>%
+filter(!is.na(lead_geom)) %>%
+# Create a segment for each pair of points
+rowwise() %>%
+mutate(
+segment = st_cast(st_union(geometry, lead_geom), "LINESTRING")
+) %>%
+ungroup() %>%
+as.data.frame() %>%
+select(rt, pid, des, lat_bucket, lon_bucket, spd, segment, lag_spd) %>%
+st_as_sf()
+segments_sf <- metro_summary_sf %>%
+group_by(rt, pid, des, vid) %>%
+arrange(vid, time) %>%  # Ensure points within each route are sorted if needed
+mutate(
+lead_geom = lead(geometry),
+lead_spd = lead(spd)
+) %>%
+filter(!is.na(lead_geom)) %>%
+# Create a segment for each pair of points
+rowwise() %>%
+mutate(
+segment = st_cast(st_union(geometry, lead_geom), "LINESTRING")
+) %>%
+ungroup() %>%
+as.data.frame() %>%
+select(rt, pid, des, lat_bucket, lon_bucket, spd, segment, lag_spd) %>%
+st_as_sf()
+segments_sf <- metro_summary_sf %>%
+group_by(rt, pid, des, vid) %>%
+arrange(pid, time) %>%  # Ensure points within each route are sorted if needed
+mutate(
+lead_geom = lead(geometry),
+lead_spd = lead(spd)
+) %>%
+filter(!is.na(lead_geom)) %>%
+# Create a segment for each pair of points
+rowwise() %>%
+mutate(
+segment = st_cast(st_union(geometry, lead_geom), "LINESTRING")
+) %>%
+ungroup() %>%
+as.data.frame() %>%
+select(rt, pid, des, lat_bucket, lon_bucket, spd, segment, lag_spd) %>%
+st_as_sf()
+segments_sf <- metro_summary_sf %>%
+group_by(rt, pid, des) %>%
+arrange(pid, time) %>%  # Ensure points within each route are sorted if needed
+mutate(
+lead_geom = lead(geometry),
+lead_spd = lead(spd)
+) %>%
+filter(!is.na(lead_geom)) %>%
+# Create a segment for each pair of points
+rowwise() %>%
+mutate(
+segment = st_cast(st_union(geometry, lead_geom), "LINESTRING")
+) %>%
+ungroup() %>%
+as.data.frame() %>%
+select(rt, pid, des, lat_bucket, lon_bucket, spd, segment, lag_spd) %>%
+st_as_sf()
+segments_sf <- metro_summary_sf %>%
+group_by(rt, pid, des) %>%
+arrange(pid, time) %>%  # Ensure points within each route are sorted if needed
+mutate(
+lead_geom = lead(geometry),
+lead_spd = lead(spd)
+)
+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(lat_bucket, lon_bucket, rt, des, pid) %>%
+summarise(lat_bucket = median(lat_bucket, na.rm = TRUE),
+lon_bucket = median(lon_bucket, na.rm = TRUE),
+spd = median(spd, na.rm = TRUE),
+lag_spd = median(lag_spd, na.rm = TRUE),
+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)
+segments_sf <- metro_summary_sf %>%
+group_by(rt, pid, des) %>%
+arrange(pid, time) %>%  # Ensure points within each route are sorted if needed
+mutate(
+lead_geom = lead(geometry),
+lead_spd = lead(spd)
+)
+View(metro_data_sf)
+# get counts of routes
+route_counts <- metro_data %>% group_by(pid, rt, des) %>% summarise(route_count = length(unique(origtatripno)))
+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(lat_bucket, lon_bucket, rt, des, pid) %>%
+summarise(lat_bucket = median(lat_bucket, na.rm = TRUE),
+lon_bucket = median(lon_bucket, na.rm = TRUE),
+spd = median(spd, na.rm = TRUE),
+lag_spd = median(lag_spd, 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)
+# make charts
+ggplot(data = metro_summary %>% filter(pid %in% c("421", "422")),
+aes(x = pdist,
+y = lag_spd)) +
+geom_point() +
+geom_smooth() +
+facet_grid(paste0(rt, "-", des) ~ .)
+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(origtatripno) %>%
+arrange(time) %>%
+mutate(lag_pdist = lag(pdist),
+lag_time = lag(time)) %>%
+mutate(spd_calc = (pdist - lag_pdist)/as.double(difftime(time, lag_time, units = "hours"))/5280)
+routes_categorized <- read_csv(file = "routes_categorized.csv", col_types = "cc")
+bucket_feet <- 200
+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(lat_bucket, lon_bucket, rt, des, pid) %>%
+summarise(lat_bucket = median(lat_bucket, na.rm = TRUE),
+lon_bucket = median(lon_bucket, na.rm = TRUE),
+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)
+segments_sf <- metro_summary_sf %>%
+group_by(rt, pid) %>%
+arrange(pid, time) %>%  # Ensure points within each route are sorted if needed
+mutate(
+lead_geom = lead(geometry),
+lead_spd = lead(spd)
+) %>%
+filter(!is.na(lead_geom)) %>%
+# Create a segment for each pair of points
+rowwise() %>%
+mutate(
+segment = st_cast(st_union(geometry, lead_geom), "LINESTRING")
+) %>%
+ungroup() %>%
+as.data.frame() %>%
+select(rt, pid, des, lat_bucket, lon_bucket, spd, segment, spd_calc) %>%
+st_as_sf()
+segments_sf <- metro_summary_sf %>%
+group_by(rt, pid)
+segments_sf <- metro_summary_sf %>%
+group_by(rt, pid) %>%
+arrange(pid, time)
+segments_sf <- metro_summary_sf %>%
+group_by(rt, pid) %>%
+arrange(pid, pdist) %>%  # Ensure points within each route are sorted if needed
+mutate(
+lead_geom = lead(geometry),
+lead_spd = lead(spd)
+) %>%
+filter(!is.na(lead_geom)) %>%
+# Create a segment for each pair of points
+rowwise() %>%
+mutate(
+segment = st_cast(st_union(geometry, lead_geom), "LINESTRING")
+) %>%
+ungroup() %>%
+as.data.frame() %>%
+select(rt, pid, des, lat_bucket, lon_bucket, spd, segment, spd_calc) %>%
+st_as_sf()
+# 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% c("421", "422")),
+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(segments_sf %>% filter(pid %in% c("469")) %>% pull(spd_calc), c(0,0.25, 0.5, 0.75, 1))
+# A West
+quantile(segments_sf %>% 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$time))),
+x = NULL,
+y = NULL) +
+theme(axis.text=element_blank(),
+axis.ticks=element_blank(),
+plot.caption = element_text(color = "grey")) +
+geom_sf(data = segments_sf %>% filter(pid %in% route_focus),
+inherit.aes = FALSE,
+aes(color = spd_calc),
+linewidth = 1) +
+scale_color_distiller(palette = "RdYlGn", direction = "reverse", limits = c(0,70), name = "Average speed or segment\n(calculated with locations, not reported speed)")
+ggsave(file = paste0("figures/",
+route,
+".pdf"),
+title = paste0("Metro Route Speed - ", route),
+device = pdf,
+height = 8.5,
+width = 11,
+units = "in",
+create.dir = TRUE)
+}
+library(tidyverse)
+library(influxdbclient)
+library(glue)
+library(ggmap)
+library(sf)
+# parameters needed to make connection to Database
+token <- substr(read_file(file = 'api_keys/influxdb_madison-metro'), 1, 88)
+org <- "e2581d54779b077f"
+bucket <- "madison-metro"
+days <- 1
+influx_connection <- InfluxDBClient$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)
+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(lag_pdist = lag(pdist),
+lag_time = lag(time)) %>%
+mutate(spd_calc = (pdist - lag_pdist)/as.double(difftime(time, lag_time, units = "hours"))/5280)
+routes_categorized <- read_csv(file = "routes_categorized.csv", col_types = "cc")
+bucket_feet <- 200
+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(lat_bucket, lon_bucket, rt, des, pid) %>%
+summarise(lat_bucket = median(lat_bucket, na.rm = TRUE),
+lon_bucket = median(lon_bucket, na.rm = TRUE),
+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)
+segments_sf <- metro_summary_sf %>%
+group_by(rt, pid) %>%
+arrange(pid, pdist) %>%  # Ensure points within each route are sorted if needed
+mutate(
+lead_geom = lead(geometry),
+lead_spd = lead(spd)
+) %>%
+filter(!is.na(lead_geom)) %>%
+# Create a segment for each pair of points
+rowwise() %>%
+mutate(
+segment = st_cast(st_union(geometry, lead_geom), "LINESTRING")
+) %>%
+ungroup() %>%
+as.data.frame() %>%
+select(rt, pid, des, lat_bucket, lon_bucket, spd, segment, spd_calc) %>%
+st_as_sf()
+# 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% c("421", "422")),
+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$lat))
+#get basemap
+basemap <- get_stadiamap(bbox = bbox, zoom = 13, maptype = "stamen_toner_lite")
+# A West
+quantile(segments_sf %>% filter(pid %in% c("469")) %>% pull(spd_calc), c(0,0.25, 0.5, 0.75, 1), na.rm = TRUE)
+for (route in unique(routes_categorized$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 = segments_sf %>% 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)
+}
diff --git a/.gitignore b/.gitignore
index 35a6d9b..5a155c3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,5 @@
 api_keys/*
 figures/*
+.Rhistory
 
 .Rproj.user