# Elevation Profiles in R

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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", header = TRUE)

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.

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