edits to analysis

.Rhistory

@@ -1,64 +1,3 @@
-labs(title = "Metro Route Speed",
-subtitle = paste0("averaged between ",
-length(unique(metro_data %>% filter(pid %in% c("422")) %>% pull(origtatripno))),
-" 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) ~ .)
-View(metro_summary)
-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 = n())
-metro_summary <- metro_data %>%
-mutate(pdist_bucket = round(pdist / 500) * 500) %>%
-group_by(pdist_bucket, rt, des, pid, origtatripno) %>%
-summarise(lat = median(lat),
-lon = median(lon),
-spd = median(spd),
-lag_spd = median(lag_spd),
-trip_count = n())
-trip_count = length(unique(origtatripno))
-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)))
-ggmap(basemap) +
-labs(title = "Metro Route Speed",
-subtitle = paste0("averaged between ",
-segments_sf %>% filter(pid %in% c("422")) %>% pull(trip_count))),
-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),
@@ -510,3 +449,64 @@ 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
+metro_summary <- metro_data %>%
+left_join(routes_categorized, by = "pid") %>%
+mutate(lat_bucket = round(lat / 200) * 100) %>%
+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)))