Get your tracks from the Strava API and plot them on Leaflet maps
This article is originally published at https://rcrastinate.blogspot.com/Here is some updated R code from my previous post. It doesn't throw any warnings when importing tracks with and without heart rate information. Also, it is easier to distinguish types of tracks now (e.g., when you want to plot runs and rides separately). Another thing I changed: You get very basic information on the track when you click on it (currently the name of the track and the total length).
Have fun and leave a comment if you have any questions.
options(stringsAsFactors = F)
rm(list=ls())
library(httr)
library(rjson)
library(leaflet)
library(dplyr)
token <- "<your Strava API token>"
# Functions ---------------------------------------------------------------
get.coord.df.from.stream <- function (stream.obj) {
data.frame(lat = sapply(stream.obj[[1]]$data, USE.NAMES = F, FUN = function (x) x[[1]]),
lon = sapply(stream.obj[[1]]$data, USE.NAMES = F, FUN = function (x) x[[2]]))
}
get.stream.from.activity <- function (act.id, token) {
stream <- GET("https://www.strava.com/",
path = paste0("api/v3/activities/", act.id, "/streams/latlng"),
query = list(access_token = token))
content(stream)
}
get.activities2 <- function (token) {
activities <- GET("https://www.strava.com/", path = "api/v3/activities",
query = list(access_token = token, per_page = 200))
activities <- content(activities, "text")
activities <- fromJSON(activities)
res.df <- data.frame()
for (a in activities) {
values <- sapply(c("name", "distance", "moving_time", "elapsed_time", "total_elevation_gain",
"type", "id", "start_date_local",
"location_country", "average_speed", "max_speed", "has_heartrate", "elev_high",
"elev_low", "average_heartrate", "max_heartrate"), FUN = function (x) {
if (is.null(a[[x]])) {
NA } else { a[[x]] }
})
res.df <- rbind(res.df, values)
}
names(res.df) <- c("name", "distance", "moving_time", "elapsed_time", "total_elevation_gain",
"type", "id", "start_date_local",
"location_country", "average_speed", "max_speed", "has_heartrate", "elev_high",
"elev_low", "average_heartrate", "max_heartrate")
res.df
}
get.multiple.streams <- function (act.ids, token) {
res.list <- list()
for (act.id.i in 1:length(act.ids)) {
if (act.id.i %% 5 == 0) cat("Actitivy no.", act.id.i, "of", length(act.ids), "\n")
stream <- get.stream.from.activity(act.ids[act.id.i], token)
coord.df <- get.coord.df.from.stream(stream)
res.list[[length(res.list) + 1]] <- list(act.id = act.ids[act.id.i],
coords = coord.df)
}
res.list
}
activities <- get.activities2(token)
stream.list <- get.multiple.streams(activities$id, token)
# Leaflet -----------------------------------------------------------------
lons.range <- c(9.156572, 9.237580)
lats.range <- c(48.74085, 48.82079)
map <- leaflet() %>%
addProviderTiles("OpenMapSurfer.Grayscale", # nice: CartoDB.Positron, OpenMapSurfer.Grayscale, CartoDB.DarkMatterNoLabels
options = providerTileOptions(noWrap = T)) %>%
fitBounds(lng1 = min(lons.range), lat1 = max(lats.range), lng2 <- max(lons.range), lat2 = min(lats.range))
add.run <- function (act.id, color, act.name, act.dist, strlist = stream.list) {
act.ind <- sapply(stream.list, USE.NAMES = F, FUN = function (x) {
x$act.id == act.id
})
act.from.list <- strlist[act.ind][[1]]
map <<- addPolylines(map, lng = act.from.list$coords$lon,
lat = act.from.list$coords$lat,
color = color, opacity = 1/3, weight = 2,
popup = paste0(act.name, ", ", round(as.numeric(act.dist) / 1000, 2), " km"))
}
# plot all
for (i in 1:nrow(activities)) {
add.run(activities[i, "id"], ifelse(activities[i, "type"] == "Run", "red",
ifelse(activities[i, "type"] == "Ride", "blue", "black")),
activities[i, "name"], activities[i, "distance"])
}
map
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This article is originally published at https://rcrastinate.blogspot.com/
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