@drsimonj here to explain how I used ubeR , an R package for the Uber API, to create this map of my trips over the last couple of years:
The ubeR package, which I first heard about here , is currently available on GitHub. In R, install and load it as follows:
# install.packages("devtools") # Run to install the devtools package if needed devtools::install_github("DataWookie/ubeR") # Install ubeR library(ubeR)For this post I also use many of the tidyverse packages, so install and load this too to follow along:
library(tidyverse) Setting up an appTo use ubeR and the uber API, you’ll need an uber account and to register a new app. In a web browser, log into your uber account and head to this page . Fill in the details. Here’s an example:
Once created, under the Authorization tab, set the Redirect URL to http://localhost:1410/
Further down, under General Scopes , enable “profile” and “history”:
Back under the settings tab, under Authentication , take note of the “Client ID” and “Client Secret”:
In your R session, save these values as two variables:
UBER_CLIENTID <- "<< Your App's Client ID >>" UBER_CLIENTSECRET <- "<< Your App's Client Secret >>"Back in R, run the following:
uber_oauth(UBER_CLIENTID, UBER_CLIENTSECRET)A web page will open asking you to log in to your uber account and permit the app to access your data. It will then say:
Authentication complete. Please close this page and return to R.
We can now access our uber data via the R functions that access the uber API. For example uber_me() returns a vector of information contained in your profile.
me <- uber_me() names(me) #> [1] "email" "first_name" "last_name" "mobile_verified" #> [5] "picture" "promo_code" "rider_id" "uuid" cat("My uber profile name is", me$first_name, me$last_name) #> My uber profile name is Simon Jackson Accessing trip historyThe function uber_history will return a data frame of portions of your trip history. As far as I can tell, you can extract up to 50 trips at a time. You can use uber_history to return one chunk of up to 50 trips. Alternatively, the following will read all of your trips into a data frame:
trips <- data.frame() more_trips <- TRUE off_set <- 0 while(more_trips) { new_trips <- uber_history(50, 50 * off_set) if (is.null(new_trips)) { more_trips <- FALSE } else { trips <- rbind(trips, new_trips) off_set <- off_set + 1 } } trips %>% mutate(longitude = "xx", latitude = "yy") %>% # Just to hide values head #> status distance product_id #> 1 completed 8.557486 2d1d002b-d4d0-4411-98e1-673b244878b2 #> 2 completed 4.037542 2d1d002b-d4d0-4411-98e1-673b244878b2 #> 3 completed 6.808492 2d1d002b-d4d0-4411-98e1-673b244878b2 #> 4 completed 2.939439 2d1d002b-d4d0-4411-98e1-673b244878b2 #> 5 completed 2.296352 2d1d002b-d4d0-4411-98e1-673b244878b2 #> 6 completed 13.008353 893b94af-ca9d-4f0f-9201-6d426cedaa5c #> start_time end_time #> 1 2016-11-25 19:03:43 2016-11-25 19:29:30 #> 2 2016-11-25 15:56:30 2016-11-25 16:22:04 #> 3 2016-11-25 08:18:57 2016-11-25 08:50:18 #> 4 2016-11-20 20:41:20 2016-11-20 20:54:37 #> 5 2016-11-20 18:47:21 2016-11-20 18:57:08 #> 6 2016-11-19 16:04:40 2016-11-19 16:44:33 #> request_id request_time latitude #> 1 53ef8fc0-c9e9-4879-bd72-1a5ccbca5439 2016-11-25 19:00:46 yy #> 2 253bc0b3-2df0-41b0-a1e0-f857b6d8feb4 2016-11-25 15:52:56 yy #> 3 e92525fe-b5c3-42a1-ae16-b7d9047618d6 2016-11-25 08:14:22 yy #> 4 5a57698c-1d1c-460f-91e5-2425efb7adb9 2016-11-20 20:39:43 yy #> 5 0c8edfea-55cc-41fc-8966-be479ac7836a 2016-11-20 18:41:18 yy #> 6 703d4808-5a35-447a-862c-23f683e336ef 2016-11-19 15:57:54 yy #> display_name longitude #> 1 Sydney xx #> 2 Sydney xx #> 3 Sydney xx #> 4 Sydney xx #> 5 Sydney xx #> 6 Melbourne xx # How many trips have I taken? nrow(trips) #> [1] 132 # Time and place of my first trip trips %>% filter(start_time == min(start_time)) %>% select(start_time, display_name) #> start_time display_name #> 1 2014-12-18 05:34:30 Sydney Getting the data into shapeFor the map shown at the beginning, I created two data frames from my trip history:
A data frame of how many trips I’d taken in each city, as well as an estimate of the city longitude and latitude. A data frame of transitions from one city to another, along with their longitudes and latitudes.Calculating the trips per city is pretty straight forward. Given that we’re using a world map, we can average the longitude and latitude for each city to get a good-enough position.
city_trips <- trips %>% group_by(display_name) %>% summarise( n = n(), long = mean(longitude), lat = mean(latitude) ) city_trips #> # A tibble: 9 × 4 #> display_name n long lat #> <chr> <int> <dbl> <dbl> #> 1 Adelaide 1 138.6019 -34.9229 #> 2 Chicago 4 -87.6298 41.8781 #> 3 Dallas 6 -96.7699 32.8030 #> 4 Melbourne 15 144.9700 -37.8100 #> 5 Miami 11 -80.2264 25.7890 #> 6 New Jersey 7 -74.0391 40.7710 #> 7 New Orleans 8 -90.1029 29.9555 #> 8 Philadelphia 3 -75.1638 39.9523 #> 9 Sydney 77 151.2076 -33.8705 City-to-city travel pathsThe data comes with dates and is ordered from most recent to oldest. We can use this to approximately work out which cities I’ve gone from and to.
To get my travel paths, we find all occasions where the city for one trip doesn’t match the city for the next trip. We then use the longitude and latitude from city_trips to create x , y , xend , and yend for the paths that we’ll draw on the map.
travel_paths <- trips %>% # Find the from-to travel paths select(display_name) %>% rename(from = display_name) %>% mutate(to = lag(from)) %>% filter(from != to) %>% filter(!duplicated(.)) %>% # Add coords for from city left_join(select(city_trips, -n), by = c("from" = "display_name")) %>% rename(x = long, y = lat) %>% # Add coords for to city left_join(select(city_trips, -n), by = c("to" = "display_name")) %>% rename(xend = long, yend = lat) travel_paths #> from to x y xend yend #> 1 Melbourne Sydney 144.9700 -37.8100 151.2076 -33.8705 #> 2 Sydney Melbourne 151.2076 -33.8705 144.9700 -37.8100 #> 3 Adelaide Sydney 138.6019 -34.9229 151.2076 -33.8705 #> 4 Sydney Adelaide 151.2076 -33.8705 138.6019 -34.9229 #> 5 Chicago Sydney -87.6298 41.8781 151.2076 -33.8705 #> 6 New Orleans Chicago -90.1029 29.9555 -87.6298 41.8781 #> 7 Miami New Orleans -80.2264 25.7890 -90.1029 29.9555 #> 8 Dallas Miami -96.7699 32.8030 -80.2264 25.7890 #> 9 Sydney Dallas 151.2076 -33.8705 -96.7699 32.8030 #> 10 New Jersey Sydney -74.0391 40.7710 151.2076 -33.8705 #> 11 Philadelphia New Jersey -75.1638 39.9523 -74.0391 40.7710 #> 12 New Jersey Philadelphia -74.0391 40.7710 -75.1638 39.9523 #> 13 Sydney New Jersey 151.2076 -33.8705 -74.0391 40.7710 Plotting on the map (first attempt)The following overlays the data onto the default map with the size of the points for each city determined by the number of trips I’ve taken there:
library(ggrepel) world_map <- map_data(map = "world") ggplot(city_trips, aes(long, lat)) + # Plot map geom_map(data = world_map, map = world_map, aes(map_id = region, x = long, y = lat), fill = "gray93", color = "gray85") + # Plot travel paths geom_curve(data = travel_paths, color = "dodgerblue1", aes(x = x, y = y, xend = xend, yend = yend), arrow = arrow(angle = 20, type = "closed", length = unit(.18, "inches"))) + # Plot city names geom_text_repel(aes(label = display_name), force = 20, color = "black", size = 2.5) + # Plot city points geom_point(aes(size = n), color = "dodgerblue3") + # Adjustments guides(size = "none", color = "none", alpha = "none") + coord_equal() + theme_void()
This is pretty good! However, I live in Australia, and I’ve only used uber internationally in the US.
Plotting on a localized mapTo display my trip information a little more clearly, the following creates a local version of the map:
long_min <- 120 long_max <- 300 local_map <- world_map %>% mutate(long = long + 360, group = group + max(group) + 1) %>% rbind(world_map) local_trips <- city_trips %>% mutate(long = long + 360) %>% rbind(city_trips) local_paths <- travel_paths %>% mutate(x = x + 360, xend = xend + 360) %>% rbind(travel_paths) %>% group_by(from, to) %>% summarise( x = x[between(x, long_min, long_max)], xend = xend[between(xend, long_min, long_max)], y = y[1], yend = yend[1] ) ggplot(local_trips, aes(long, lat)) + geom_map(data = local_map, map = local_map, aes(map_id = region, x = long, y = lat), fill = "gray93", color = "gray85") + geom_curve(data = local_paths, color = "dodgerblue1", aes(x = x, y = y, xend = xend, yend = yend), arrow = arrow(angle = 20, type = "closed", length = unit(.18, "inches"))) + ggrepel::geom_text_repel(aes(label = display_name), force = 10, color = "black") + geom_point(aes(size = n), color = "dodgerblue3") + guides(size = "none", color = "none", alpha = "none") + coord_equal() + scale_x_continuous(limits = c(long_min, long_max)) + scale_y_continuous(limits = c(-50, 60)) + theme_void()
A brief explanation of my trips:
I live in Sydney, Australia, so most of my uber trips are there. I frequently travel to Melbourne to visit family. I often work in Adelaide, but usually hire a car, so don’t use uber much while I’m there. I did a summer internship at ETS in Princeton, so that included my trip through New Jersey and Philadelphia. After finishing my Ph.D. I took a trip around the US, which involved Dallas, Miami, New Orleans, and my inner nerd couldn’t resist a conference in Chicago before coming home.It’s not a complete travel history, but pretty cool to see just through my use of uber!
