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Augmenting the data: Route summary statistics

We have constructed a dataset that is nearly ready for visualization, route_hod. Let's add in a few more variables that will be useful as cognostics as we anticipate interactively viewing the display.

Cet exercice fait partie du cours

Visualizing Big Data with Trelliscope in R

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Instructions

  • For each route, calculate the total number of rides.
  • For each route, calculate the difference between the mean hourly rides during the work week and the mean hourly rides during the weekend. Note that the variable n already is aggregated to counts per hour, so, for example, the workweek mean is calculated with mean(n[weekday == "workweek"]).
  • For each route, add a variable containing a URL pointing to the route on Google Maps, using the provided make_gmap_url function, providing appropriate arguments from the data.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

library(trelliscopejs)
library(ggplot2)
library(dplyr)

# Function to construct a Google maps URL with cycling directions
make_gmap_url <- function(start_lat, start_lon, end_lat, end_lon) {
  paste0("https://www.google.com/maps/dir/?api=1",
    "&origin=", start_lat, ",", start_lon,
    "&destination=", end_lat, ",", end_lon,
    "&travelmode=bicycling")
}

# Compute tot_rides, weekday_diff, and map_url
route_hod_updated <- route_hod %>% ungroup() %>%
  group_by(start_station_code, end_station_code) %>%
  mutate(
    tot_rides = sum(___),
    weekday_diff = mean(n[weekday == "workweek"]) - ___,
    map_url = ___)
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