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)
Parsing extension data
If your GPX file contains additional extension information,
activatr can parse that as well. In this case,
running_example.gpx contains heart rate, cadence, and
temperature information. We can parse that by setting
detail = "advanced" in parse_gpx:
df_advanced <- parse_gpx(filename, detail = "advanced")
| 37.80405 |
-122.4267 |
17.0 |
2018-11-03 14:24:45 |
102 |
68 |
| 37.80406 |
-122.4267 |
16.8 |
2018-11-03 14:24:46 |
104 |
73 |
| 37.80408 |
-122.4266 |
17.0 |
2018-11-03 14:24:48 |
107 |
89 |
| 37.80409 |
-122.4266 |
17.0 |
2018-11-03 14:24:49 |
110 |
89 |
| 37.80409 |
-122.4265 |
17.2 |
2018-11-03 14:24:50 |
112 |
89 |
Now we can do plots like heart rate over time, or a distribution of
cadences:
library(ggplot2)
library(dplyr)
ggplot(df_advanced) +
geom_line(aes(x = time, y = hr), color = "red")
ggplot(filter(df_advanced, cad > 80)) +
geom_density(aes(x = cad * 2), fill = "blue", bw = 1)
Sampling datapoints
If you’re parsing many GPX files or GPX files sampled every second,
you often don’t need a “full resolution” view of the activity. The
every argument to parse_gpx allows you to only
sample some points from the GPX, speeding up the parsing:
# Parsing as normal gets all of the rows, but takes longer
full_time <- system.time({
df_full <- parse_gpx(filename)
})
nrow(df_full)
#> [1] 4433
full_time
#> user system elapsed
#> 0.138 0.006 0.145
# Grabbing every hundredth data point runs much faster
sample_time <- system.time({
df_sample <- parse_gpx(filename, every = 100)
})
nrow(df_sample)
#> [1] 44
sample_time
#> user system elapsed
#> 0.023 0.002 0.024