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activatr (pronounced like the word “activator”) is a library for parsing GPX files into a standard format, and then manipulating and visualizing those files.

Getting GPX Files

The process to get a GPX file varies depending on the service you use. In Garmin Connect, you can click the gear menu on an activity and click “Export to GPX”. This package includes sample GPXs as examples.

Parsing GPX Files

Basic parsing of a GPX file is simple: we use the parse_gpx() function and pass it the name of the GPX file.

#> The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
#> which was just loaded, will retire in October 2023.
#> Please refer to R-spatial evolution reports for details, especially
#> It may be desirable to make the sf package available;
#> package maintainers should consider adding sf to Suggests:.
#> The sp package is now running under evolution status 2
#>      (status 2 uses the sf package in place of rgdal)

# Get the running_example.gpx file included with this package.
filename <- system.file(
  package = "activatr"

df <- parse_gpx(filename)

parse_gpx() returns an act_tbl, which has a column for latitude (lat), longitude (lon), elevation (ele, in meters), and time (time).

lat lon ele time
37.80405 -122.4267 17.0 2018-11-03 14:24:45
37.80406 -122.4267 16.8 2018-11-03 14:24:46
37.80408 -122.4266 17.0 2018-11-03 14:24:48
37.80409 -122.4266 17.0 2018-11-03 14:24:49
37.80409 -122.4265 17.2 2018-11-03 14:24:50

activatr also overrides summary() to create a basic one-row tibble summarizing the activity.

Distance Date Time AvgPace MaxPace ElevGain ElevLoss AvgElev Title
9.407317 2018-11-03 14:24:45 4622s (~1.28 hours) 491.319700445454s (~8.19 minutes) 186.462178755299s (~3.11 minutes) 193.9317 259.2122 -24.29198 Sunrise 15K PR (sub-8:00)

For more advanced parsing options, see vignette("parsing").

Analyzing GPX Files

Since this is just a tibble, we can analyze and plot it using usual techniques and libraries. activatr includes a few helpers, like mutate_with_speed(), speed_to_mile_pace() and pace_formatter() to make it easier to analyze pace using these libraries.

df |>
  mutate_with_speed(lead = 10, lag = 10) |>
  mutate(pace = speed_to_mile_pace(speed)) |>
  filter(as.numeric(pace) < 1200) |>
  ggplot() +
  geom_line(aes(x = time, y = as.numeric(pace)), color = "blue") +
  scale_y_reverse(label = pace_formatter) +
  xlab("Time") +
  ylab("Pace (min/mile)")

For more details on those helpers, see vignette("pace").

Visualizing GPX Files

Once we have the data, it’s useful to visualize it. While basic visualizations work as expected with a data frame:

qplot(lon, lat, data = df)

It’s more helpful to overlay this information on a map. To aid in that, get_ggmap_from_df() is a wrapper around ggmap::get_map() that returns a correctly sized and zoomed map, atop which we can visualize our track using ggmap::ggmap().

Let’s see that on its own to start:

We now have a map at the right size to visualize the run. Putting it all together, we can make a nice basic graphic of the run:

ggmap::ggmap(get_ggmap_from_df(df)) +
  theme_void() +
  geom_path(aes(x = lon, y = lat), linewidth = 1, data = df, color = "red")