Since 2013, I have carefully kept my Google location history, fascinated by the fact that I could, in ten or twenty years from now, retrace every move I made in a day, be it a normal work day, or a holiday trip in Italy. Nathan Yau from Flowingdata.com has made a tutorial on how to plot location points with R, which gave me the idea of plotting my own location history for 4 years.
Load and format data
The first step is to download your file with Google Takeout. You have the choice between JSON and KML, and I chose JSON, which I’m more familiar to work with.
### Load libraries library("jsonlite") library("dplyr") library("ggplot2") library("lubridate")
### Load JSON data raw <- fromJSON("location-history.json")
The import of the JSON files is straightforward with the jsonlite package, although it can take several minutes if you upload several years of location history. The imported data is a list that includes a great number of nested dataframes in the
activities column, which we drop as it won’t be needed.
We actually end up with a very simple dataframe with only 4 columns:
year (computed from the timestamp),
In addition, we can filter the data on a delimited area of latitude and longitude, to focus the map on a specific zone – Paris in this example.
### Clean and format data routes <- raw[][1:4] %>% transmute(time = as.POSIXct(as.numeric(timestampMs) / 1000, origin = "1970-01-01"), year = year(time), latitude = latitudeE7 / 1e+07, longitude = longitudeE7 / 1e+07) %>% filter(year >= 2013, year <= 2016) ### Filter on Paris area zone <- c(48.825, 48.903, 2.250, 2.350) routes <- routes %>% filter(latitude >= zone & latitude <= zone, longitude >= zone & longitude <= zone)
After having tried a number of plot styles, including with maps of Paris, I finally decided that a simple black background lead to the most impactful effect.
### Plot: define theme theme_map <- theme(panel.background = element_rect(fill = "black", colour = "black"), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.title.x = element_blank(), axis.title.y = element_blank(), axis.ticks = element_blank(), legend.position = "none")
Single year in points
This first map is the most basic, with a single year plotted, and each location datapoint plotted as a white dot with 80% transparency. As a results, the most frequented areas stand out: home, office, and home-office journey.
### Plot points only, for a specific year ggplot(data = filter(routes, year == 2015), aes(x = longitude, y = latitude)) + geom_point(alpha = 0.2, size = 0.4, shape = 16, colour = "white") + theme_map
Facets with each year
We can also plot several years in a facet, with a different color for each year. You can see clearly different paths every year, as my home, office, and client’s office have changed between 2013 and 2016.
### Plot points by year ggplot(data = routes, aes(x = longitude, y = latitude, colour = as.factor(year))) + geom_point(alpha = 0.2, size = 0.4, shape = 16) + theme_map + facet_wrap( ~ year)
Finally, the most beautiful effect in my opinon is achieved by drawing lines between each datapoint.
### Plot lines only, for a specific year ggplot(data = filter(routes, year == 2015), aes(x = longitude, y = latitude)) + geom_path(alpha = 0.1, size = 0.4, colour = "white") + theme_map≈