# Visualizing Activities with activatr

#### 2021-01-11

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 from my Garmin Forerunner 935 as example files for this vignette.

## 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.

library(activatr)

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

df <- parse_gpx(filename)

In its default configuration, parse_gpx will create a row for every GPS point in the file, and pull out the latitude (lat), longitude (lon), elevation (ele, in meters), and time (time) into the tibble:

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

We can also get a summary of the activity:

summary(df)
Distance Date Time AvgPace MaxPace ElevGain ElevLoss AvgElev Title
9.407317 2018-11-03 14:24:45 4622s (~1.28 hours) 491.319700444844s (~8.19 minutes) 186.462178755299s (~3.11 minutes) 188.364 253.4996 -24.29198 Sunrise 15K PR (sub-8:00)

## Visualizing

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

library(ggplot2)
qplot(lon, lat, data=df)

It’s more helpful to overlay this information on a correctly-sized map. To aid in that, get_map_from_df gives us a ggmap object (from the ggmap package), which we can use to visualize our track.

Let’s see that on its own to start:

ggmap::ggmap(get_ggmap_from_df(df)) + theme_void()

The axes show that we now have a ggmap at the right size to visualize the run. So 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), size = 1, data = df, color = "red")