(This article was first published on

**R Video tutorial for Spatial Statistics**, and kindly contributed to R-bloggers)For Christmas I decided to treat myself with an Arduino starter kit. I started doing some basic experiments and I quickly found out numerous website that sell every sort of sensor: from temperature and humidity, to air quality.

Long story short, I bought a bunch of sensors to collect spatial data. I have a GPS, accelerometer/magnetometer, barometric pressure, temperature/humidity, UV Index sensor.

Below is the picture of the sensors array, still in a breadboard version. Of course I also had to use an Arduino pro mini 3.3V and an openlog to record the data. Below the breadboard there is a 2200mA lithium battery that provides 3.7V to the Arduino pro mini.

With this system I can collect 19 columns of data: Date, Time, Latitude and Longitude, Speed, Angle, Altitude, Number of Satellites and Quality of the signal, Acceleration in X, Y and Z, Magnetic field in X, Y and Z, Temperature, Humidity, Barometric Pressure, UV Index.

With all these data I can have some fun testing different plotting method on R. In particular I was interested in plotting my data on Google Maps.

These are my results.

First of all I installed the package

**xlsx**, because I have to first import the data into Excel for some preliminary cleaning. Sometimes the satellite sensor loses the connection and I have to delete those entries, or maybe the quality of the signal is poor and the coordinates are not reliable. In all these case I have to delete the entry and I cannot do it in R because of the way the GPS write the coordinates.In fact, the GPS library, created by Adafruit, writes the coordinate in a format similar to this:

4725.43N 831.39E

This is read into a string format in R, so it requires a visual inspection to get rid of bad data.

This format is a combination of degrees and minutes, so it needs to be cleaned up before use. In the example above, the real coordinate in decimal degrees in calculated using the following formula:

47 + 25.13/60 N 8 + 31.39/60 E

Luckily, the fact that R recognises it as a string facilitates this transformation. With the following two lines of code I can transform each entry into a format that I can use:

data$LAT <- as.numeric(substr(paste(data[,”Lat”]),1,2)) + as.numeric(substr(paste(data[,”Lat”]),3,7))/60data$LON <- as.numeric(substr(paste(data[,”Lon”]),1,1)) + as.numeric(substr(paste(data[,”Lon”]),2,6))/60

Then I assign these two columns as coordinate for the file with the sp package and set the projection to WGS84.

Now I can start working on visualising the data. I installed two packages:

**plotGoogleMaps**and**RgoogleMaps**The first is probably the simplest to use and let the user create a javascript page to plot the data from R to google maps through the google maps API. The plot is displayed into the default web browser and it looks extremely good.

With one line of code I can plot the temperature for each spatial point with bubble markers:

plotGoogleMaps(data,zcol=”Temp”)

“Temp” is the name of the column in the SpatialPointsDataFrame that has the temperature data.

By adding an additional line of code I can set the markers as coloured text:

ic=iconlabels(data$Temp, height=12)plotGoogleMaps(data,iconMarker=ic,zcol=”Temp”)

The package RgoogleMaps works differently, because it allows the user to plot google maps as background for static plots. This creates less stunning results but also allows more customisation of the output. It also requires a bit more work.

In this example, I will plot the locations for which I have a data with the road map as background.

I first need to create the map object and for centre the map in the centre of the area I visited, I used the bounding box of my spatial dataset:

box<-bbox(data)Map<-GetMap(center = c(lat =(box[2,1]+box[2,2])/2, lon = (box[1,1]+box[1,2])/2), size = c(640, 640),zoom=16,maptype = c(“roadmap”),RETURNIMAGE = TRUE, GRAYSCALE = FALSE, NEWMAP = TRUE)

Then I need to transform the spatial coordinates into plot coordinates:

tranf=LatLon2XY.centered(Map,data$LAT, data$LON, 16)x=tranf$newXy=tranf$newY

this function creates a new set of coordinates optimised for the zoom of the Map I created above.

At this point I can create the plot with the following two lines:

PlotOnStaticMap(Map)points(x,y,pch=16,col=”red”,cex=0.5)

as you would do with any other plot, you can add points to the google map with the function points.

As I mentioned above, this package creates less appealing results, but allows you to customise the output. In the following example I will compute the heading (the direction I was looking at) using the data from the magnetometer to plot arrows on the map.

I computed the heading with these lines:

Isx = as.integer(data$AccX)Isy = as.integer(data$AccY)heading = (atan2(Isy,Isx) * 180) / piheading[heading<0]=360+heading[heading<0]

I did not tilt compensated the heading because the sensor was almost horizontal all the times. If you need tilt compensation, please look at the following websites for help:

http://theccontinuum.com/2012/09/24/arduino-imu-pitch-roll-from-accelerometer/

With the heading I can plot arrows directed toward my heading with the following loop:

PlotOnStaticMap(Map)for(i in 1:length(heading)){if(heading[i]>=0 & heading[i]<90){arrows(x[i],y[i],x1=x[i]+(10*cos(heading[i])), y1=y[i]+(10*sin(heading[i])),length=0.05, col=”Red”)}if(heading[i]>=90 & heading[i]<180){arrows(x[i],y[i],x1=x[i]+(10*sin(heading[i])), y1=y[i]+(10*cos(heading[i])),length=0.05, col=”Red”)}if(heading[i]>=180 & heading[i]<270){arrows(x[i],y[i],x1=x[i]-(10*cos(heading[i])), y1=y[i]-(10*sin(heading[i])),length=0.05, col=”Red”)}if(heading[i]>=270 & heading[i]<=360){arrows(x[i],y[i],x1=x[i]-(10*sin(heading[i])), y1=y[i]-(10*cos(heading[i])),length=0.05, col=”Red”)}}

I also tested the change of the arrow length according to my speed, using this code:

length=data$Speed.Km.h.*10for(i in 1:length(heading)){if(heading[i]>=0 & heading[i]<90){arrows(x[i],y[i],x1=x[i]+(length[i]*cos(heading[i])), y1=y[i]+(length[i]*sin(heading[i])),length=0.05, col=”Red”)}if(heading[i]>=90 & heading[i]<180){arrows(x[i],y[i],x1=x[i]+(length[i]*sin(heading[i])), y1=y[i]+(length[i]*cos(heading[i])),length=0.05, col=”Red”)}if(heading[i]>=180 & heading[i]<270){arrows(x[i],y[i],x1=x[i]-(length[i]*cos(heading[i])), y1=y[i]-(length[i]*sin(heading[i])),length=0.05, col=”Red”)}if(heading[i]>=270 & heading[i]<=360){arrows(x[i],y[i],x1=x[i]-(length[i]*sin(heading[i])), y1=y[i]-(length[i]*cos(heading[i])),length=0.05, col=”Red”)}}

However, the result is not perfect because in some occasions the GPS recorded a speed of 0 km/h, even though I was pretty sure to be walking.

The arrow plot looks like this:

To

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