Very simple package which converts
GPX files to sf objects.
library(gpx)
library(sf) ## for print methods of sf objects
# Linking to GEOS 3.7.1, GDAL 2.4.1, PROJ 6.0.0
gpx <- system.file("gpx/city_trail.gpx", package = "gpxsf")
sf <- gpx::gpx_sf( gpx )
sf
# Simple feature collection with 1 feature and 2 fields
# geometry type: LINESTRING
# dimension: XYZM
# bbox: xmin: 144.9356 ymin: -37.84586 xmax: 145.0274 ymax: -37.77897
# z_range: zmin: -1.8 zmax: 54.9
# m_range: mmin: 1499569000 mmax: 1499581000
# epsg (SRID): 4326
# proj4string: +proj=longlat +datum=WGS84 +no_defs
# name type geometry
# 1 Sunday afternoon Capital City trail 1 LINESTRING ZM (144.9998 -37...And that’s it.
However, the real benefit, and the reason I built this package, is so we can plot them in mapdeck’s trip layer
The timestamp can specified as one of
- datetime (default) - the numeric representation of POSIX dates
- counter - every track starts from 0 and counts to the maximum time
- normalise - every track’s time is rescaled to be [0,1]
Here are some examples
sf_time <- gpx_sf( gpx, time = "datetime" )
sf_count <- gpx_sf( gpx, time = "counter" )
sf_norm <- gpx_sf( gpx, time = "normalise" )
d <- getOption("digits")
options(digits = 12)
track_time <- unclass( sf_time[, "geometry"][[1]][[1]] )
head( track_time )
# [,1] [,2] [,3] [,4]
# [1,] 144.999816 -37.845118 21.8 1499569160
# [2,] 144.999838 -37.845079 21.8 1499569161
# [3,] 144.999832 -37.845019 21.4 1499569162
# [4,] 144.999874 -37.845010 21.2 1499569163
# [5,] 144.999931 -37.844929 20.9 1499569164
# [6,] 144.999853 -37.844893 20.7 1499569165
summary( track_time[, 4] )
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 1499569160 1499570978 1499573624 1499574804 1499578474 1499581021
track_count <- unclass( sf_count[, "geometry"][[1]][[1]] )
head( track_count )
# [,1] [,2] [,3] [,4]
# [1,] 144.999816 -37.845118 21.8 0
# [2,] 144.999838 -37.845079 21.8 1
# [3,] 144.999832 -37.845019 21.4 2
# [4,] 144.999874 -37.845010 21.2 3
# [5,] 144.999931 -37.844929 20.9 4
# [6,] 144.999853 -37.844893 20.7 5
summary( track_count[, 4] )
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.00000 1818.00000 4464.00000 5644.41681 9314.00000 11861.00000
track_norm <- unclass( sf_norm[, "geometry"][[1]][[1]] )
head( track_norm )
# [,1] [,2] [,3] [,4]
# [1,] 144.999816 -37.845118 21.8 0.000000000000000
# [2,] 144.999838 -37.845079 21.8 0.000084309923278
# [3,] 144.999832 -37.845019 21.4 0.000168619846556
# [4,] 144.999874 -37.845010 21.2 0.000252929769834
# [5,] 144.999931 -37.844929 20.9 0.000337239693112
# [6,] 144.999853 -37.844893 20.7 0.000421549616390
summary( track_norm[, 4] )
# Min. 1st Qu. Median Mean 3rd Qu. Max.
# 0.000000000 0.153275441 0.376359498 0.475880348 0.785262625 1.000000000
options(digits = d)Oh yes, it’s quite quick too!
This example compares reading 364 gpx files using this library, compared
to reading a single gpx file using library(plotKML)
library(plotKML)
library(microbenchmark)
fp <- path.expand(".../.../")
l <- list.files(fp, patter = "gpx$")
gpx <- paste0(fp, l)
length( gpx )
# 364
microbenchmark::microbenchmark(
gpx = {
sf <- gpx:::rcpp_gpx_to_sf( gpx, time = "default" )
},
kml = {
kml <- plotKML::readGPX( gpx[1] )
},
times = 1
)
# Unit: seconds
# expr min lq mean median uq max neval
# gpx 5.450537 5.450537 5.450537 5.450537 5.450537 5.450537 1
# kml 4.036555 4.036555 4.036555 4.036555 4.036555 4.036555 1