bike code

#ALL THIS IS IN TERMINAL

#First, install gpsbabel through some means. I have homebrew, so I did
$ brew install gpsbabel
#Navigated to wherever the gpx files are
$ cd ~/Downloads/activities
#and converted them
$ for i in *.gpx; do gpsbabel -t -i gpx -f $i -o unicsv -F $i.csv; done
#runs enormously fast!
#okay, so how long are all those put together?
$ cat *.gpx.csv | wc -l
#yours is about 750,000 lines, we'll just manually pre-allocate that many below

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#ALL THIS IS IN R

#load files
setwd("~/Downloads/activities")

#this is now 4X faster for me
system.time(
  routes <- (function(){
    file_names <- dir(pattern = "Ride\\.gpx\\.csv$")
    routes <- data.frame(
                          Track=numeric(750000),
                          Latitude=numeric(750000),
                          Longitude=numeric(750000),
                          Altitude=numeric(750000),
                          Date=numeric(750000),
                          Time=numeric(750000)
                        )
    accumulator <- 1
    for(file_name in file_names){
      current_data <- read.csv(file_name, colClasses='character')
      current_data$Track <- gsub(".gpx.csv$", "", file_name)
      routes[accumulator:(nrow(current_data) + accumulator - 1), ] <- current_data[,c('Track', 'Latitude', 'Longitude', 'Altitude','Date','Time'),]
      accumulator <- nrow(current_data) + accumulator
    }
    routes
  })()
)

routes <- routes[complete.cases(routes), ]

as.numeric(routes$Longitude)->routes$Longitude
as.numeric(routes$Latitude)->routes$Latitude
as.factor(routes$Track)->routes$Track

require(ggmap)

baseMap <- get_googlemap(
			center="Forest Park MO",
			maptype="roadmap",
			color="bw", zoom=12)

map <- ggmap(baseMap)+geom_path(aes(x=Longitude, y=Latitude, group=Track), data=routes, alpha=0.1, color="red")

#maps no trouble on my old machine in ~2 minutes
map


##this is no good##
#first stab at a heatmap doesn't really work great for this level of data; and doesn't seem to really go any faster
heatmap <- ggmap(baseMap) +
  stat_bin2d(
    aes(x=Longitude, y=Latitude),
    bins=250,
    alpha=0.8,
    data=routes[c(1:10000),],
  )
heatmap+scale_fill_gradient(low='white',high='red')