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First, let’s load up our data. The data are available in a gist. You can convert your own GPS data to .csv by following the instructions here, using gpsbabel.

```gps <- read.csv("callan.csv",

Next, we can use the function SMA from the package TTR to calculate a moving average of the altitude or elevation data, if we want to smooth out the curve. We can define a constant for the number of data points we want to average to create each moving average value.

If you don't want to convert meters to feet, a metric version of the code is available in the gist (callanMetric.R).

```library(TTR)
movingN <- 5 # define the n for the moving average calculations
gps\$Altitude <- gps\$Altitude * 3.281 # convert m to ft
gps\$SMA <- SMA(gps\$Altitude,
n = movingN)
gps <- gps[movingN:length(gps\$SMA), ] # remove first n-1 points```

Next, we want to calculate the distance of each point. You can skip this step if your dataset already includes distances.

```library(sp)
Dist <- 0
for(i in 2:length(gps\$Longitude)) {
Dist[i] = spDistsN1(as.matrix(gps[i,c("Longitude", "Latitude")]),
c(gps\$Longitude[i-1], gps\$Latitude[i-1]),
longlat = TRUE) / 1.609 # longlat so distances will be in km, then divide to convert to miles
}
gps\$Dist <- Dist

DistTotal <- 0
for(i in 2:length(gps\$Longitude)) {
DistTotal[i] = Dist[i] + DistTotal[i-1]
}
gps\$DistTotal <- DistTotal```

And finally, we can plot our elevation data using geom_ribbons and ggplot:

```library(ggplot2)
ggplot(gps, aes(x = DistTotal)) +
geom_ribbon(aes(ymin = 600, # change this to match your min below
ymax = SMA),
fill = "#1B9E77") + # put your altitude variable here if not using moving averages
labs(x = "Miles",
y = "Elevation") +
scale_y_continuous(limits = c(600,1200)) # change this to limits appropriate for your region```  Elevation profile in ggplot2

Code and data available in a gist.