# Interactive maps for the web in R

May 15, 2015
By

(This article was first published on R Video tutorial for Spatial Statistics, and kindly contributed to R-bloggers)

Static Maps
In the last post I showed how to download economic data from the World Bank’s website and create choropleth maps in R (Global Economic Maps).
In this post I want to focus more on how to visualize those maps.

Sp Package
Probably the simplest way of plotting choropleth maps in R is the one I showed in the previous post, using the function ssplot(). For example with a call like the following:

`library(sp)spplot(polygons,"CO2",main=paste("CO2 Emissions - Year:",CO2.year),sub="Metric Tons per capita")`

This function takes the object polygons, which is a SpatialPolygonsDataFrame, and in quotations marks the name of the column where to find the values to assign, as colors, to each polygon. This are two basic mandatory elements to call the function. However, we can increase the information in the plot by adding additional elements, such as a title, option main, and a sub title, option sub.

This function creates the following plot:

The color scale is selected automatically and can be changed with a couple of standard functions or using customized color scales. For more information please refer to this page: http://rstudio-pubs-static.s3.amazonaws.com/7202_3145df2a90a44e6e86a0637bc4264f9f.html

Standard Plot
Another way of plotting maps is by using the standard plot() function, which allows us to increase the flexibility of the plot, for example by customizing the position of the color legend.
The flip side is that, since it is the most basic plotting function available and it does not have many built-in options, this function require more lines of code to achieve a good result.
Let’s take a look at the code below:

`library(plotrix)CO2.dat <- na.omit(polygons\$CO2)colorScale <- color.scale(CO2.dat,color.spec="rgb",extremes=c("red","blue"),alpha=0.8) colors.DF <- data.frame(CO2.dat,colorScale)colors.DF <- colors.DF[with(colors.DF, order(colors.DF[,1])), ]colors.DF\$ID <- 1:nrow(colors.DF)breaks <- seq(1,nrow(colors.DF),length.out=10)  jpeg("CO2_Emissions.jpg",7000,5000,res=300)plot(polygons,col=colorScale)title("CO2 Emissions",cex.main=3) legend.pos <- list(x=-28.52392,y=-20.59119)legendg(legend.pos,legend=c(round(colors.DF[colors.DF\$ID %in% round(breaks,0),1],2)),fill=paste(colors.DF[colors.DF\$ID %in% round(breaks,0),2]),bty="n",bg=c("white"),y.intersp=0.75,title="Metric tons per capita",cex=0.8)  dev.off()`

By simpling calling the plot function with the object polygons, R is going to create an image of the country borders with no filling. If we want to add colors to the plot we first need to create a color scale using our data. To do so we can use the function color.scale() in the package plotrix, which I used also in the post regarding the visualization seismic events from USGS (Downloading and Visualizing Seismic Events from USGS ). This function takes a vector, plus the color of the extremes of the color scale, in this case red and blue, and creates a vector of intermediate colors to assign to each element of the data vector.
In this example I first created a vector named CO2.dat, with the values of CO2 for each polygon, excluding NAs. Then I feed it to the color.scale() function.

The next step is the creation of the legend. The first step is the creation of the breaks we are going to need to present the full spectrum of colors used in the plot. For this I first created a data.frame with values and colors and then I subset it into 10 elements, which is the length of the legend.
Now we can submit the rest of the code to create a plot and the legend and save it into the jpeg file below:

Interactive Maps
The maps we created thus far are good for showing our data on papers and allow the reader to have a good understanding of what we are trying to show with them. Clearly these are not the only two methods available to create maps in R, many more are available. In particular ggplot2 now features ways of creating beautiful static maps. For more information please refer to these website and blog posts:
Maps in R
Making Maps in R
Introduction to Spatial Data and ggplot2
Plot maps like a boss
Making Maps with R

In this post however, I would like to focus on ways to move away from static maps and embrace the fact that we are now connected to the web all the times. This allow us to create maps specifically design for the web, which can also be much more easy to read by the general public that is used to them.
These sort of maps are the interactive maps that we see all over the web, for example from Google. These are created in javascript and are extremely powerful. The problem is, we know R and we work on it all the time, but we do not necessarily know how to code in javascript. So how can we create beautiful interactive maps for the web if we cannot code in javascript and HTML?

Luckily for us developers have create packages that allow us to create maps using standard R code but n the form of HTML page that we can upload directly on our website. I will now examine the packages I know and use regularly for plotting choropleth maps.

This package is extremely simple to use and yet capable of creating beautiful maps that can be uploaded easily to our website.
Let’s look at the code below:

`data.poly <- as.data.frame(polygons)data.poly <- data.poly[,c(5,12)]names(data.poly) <- c("Country Name","CO2 emissions (metric tons per capita)") map <- gvisGeoMap(data=data.poly, locationvar = "Country Name", numvar='CO2 emissions (metric tons per capita)',options=list(width='800px',heigth='500px',colors="['0x0000ff', '0xff0000']"))plot(map) print(map,file="Map.html") #http://www.javascripter.net/faq/rgbtohex.htm#To find HEX codes for RGB colors`

The first thing to do is clearly to load the package googleVis. Then we have to transform the SpatialPolygonsDataFrame into a standard data.frame. Since we are interested in plotting only the data related to the CO2 emissions for each country (as far as I know with this package we can plot only one variable for each map), we can subset the data.frame, keeping only the column with the names of each country and the one with the CO2 emissions. Then we need to change the names of these two columns so that the user can readily understand what he is looking at.
Then we can simply use the function gvisGeoMap() to create a choropleth maps using the Google Visualisation API. This function does not read the coordinates from the object, but we need to provide the names of the geo locations to use with the option locationvar, in this case the Google Visualisation API will take the names of the polygons and match them to the geometry of the country. Then we have the option numvar, which takes the name of the column where to find the data for each country. Then we have options, where we can define various customizations available in the Google Visualisation API and provided at this link: GoogleVis API Options
In this case I specified the width and height of the map, plus the two color extremes to use for the color scale.
The result is the plot below:

This is an interactive plot, meaning that if I hover the mouse over a country the map will tell me the name and the amount of CO2 emitted. This map is generated directly from R but it all written in HTML and javascript. We can use the function print(), presented in the snippet above, to save the map into an HTML file that can upload as is on the web.
The map above is accessible from this link:  GoogleVis Map

This is another great package that harness the power of Google’s APIs to create intuitive and fully interactive web maps. The difference between this and the previous package is that here we are going to create interactive maps using the Google Maps API, which is basically the one you use when you look up a place on Google Maps.
Again this API uses javascript to create maps and overlays, such as markers and polygons. However, with this package we can use very simple R code and create stunning HTML pages that we can just upload to our websites and share with friends and colleagues.

Let’s look at the following code:

`library(plotGoogleMaps) polygons.plot <- polygons[,c("CO2","GDP.capita","NAME")]polygons.plot <- polygons.plot[polygons.plot\$NAME!="Antarctica",]names(polygons.plot) <- c("CO2 emissions (metric tons per capita)","GDP per capita (current US\$)","Country Name") #Full Page Mapmap <- plotGoogleMaps(polygons.plot,zoom=4,fitBounds=F,filename="Map_GoogleMaps.html",layerName="Economic Data")  #To add this to an existing HTML pagemap <- plotGoogleMaps(polygons.plot,zoom=2,fitBounds=F,filename="Map_GoogleMaps_small.html",layerName="Economic Data",map="GoogleMap",mapCanvas="Map",map.width="800px",map.height="600px",control.width="200px",control.height="600px")`

Again there is a bit of data preparation to make. Again we need to subset the polygons dataset and keep only the variable we would need for the plot (with this package this is not mandatory, but it is maybe good to avoid large objects). Then we have to exclude Antarctica, otherwise the interactive map will have some problems (you can try and leave it to see what it does and maybe figure out a way to solve it). Then again we change the names of the columns in order to be more informative.

At this point we can use the function plotGoogleMaps() to create web maps in javascript. This function is extremely simple to use, it just takes one argument, which is the data.frame and creates a web map (R opens the browser to show the output). There are clearly ways to customize the output, for example by choosing a level of zoom (in this case the fiBounds option needs to be set to FALSE). We can also set a layerName to show in the legend, which is automatically created by the function.
Finally, because we want to create an HTML file to upload to our website, we can use the option filename to save it.
The result is a full screen map like the one below:

This map is available here: GoogleMaps FullScreen

With this function we also have ways to customize not only the map itself but also the HTML page so that we can later add information to it. In the last line of the code snippet above you can see that I added the following options to the function plotGoogleMaps():

`mapCanvas="Map",map.width="800px",map.height="600px",control.width="200px",control.height="600px"`

These options are intended to modify the aspect of the map on the web page, for example its width and height, and the aspect of the legend and controls, with controls.width and controls.height. We can also add the id of the HTML

element that will contain the final map.
If we have some basic experience with HTML we can then open the file and tweak a bit, for example by shifting map and legend to the center and adding a title and some more info.

This map is available here: GoogleMaps Small

The full code to replicate this experiment is presented below:

`` #Methods to Plot Choropleth Maps in R   load(url("http://www.fabioveronesi.net/Blog/polygons.RData"))      #Standard method   #SP PACKAGE   library(sp)   spplot(polygons,"CO2",main=paste("CO2 Emissions - Year:",CO2.year),sub="Metric Tons per capita")         #PLOT METHOD   library(plotrix)   CO2.dat <- na.omit(polygons\$CO2)   colorScale <- color.scale(CO2.dat,color.spec="rgb",extremes=c("red","blue"),alpha=0.8)      colors.DF <- data.frame(CO2.dat,colorScale)   colors.DF <- colors.DF[with(colors.DF, order(colors.DF[,1])), ]   colors.DF\$ID <- 1:nrow(colors.DF)   breaks <- seq(1,nrow(colors.DF),length.out=10)         jpeg("CO2_Emissions.jpg",7000,5000,res=300)   plot(polygons,col=colorScale)   title("CO2 Emissions",cex.main=3)      legend.pos <- list(x=-28.52392,y=-20.59119)   legendg(legend.pos,legend=c(round(colors.DF[colors.DF\$ID %in% round(breaks,0),1],2)),fill=paste(colors.DF[colors.DF\$ID %in% round(breaks,0),2]),bty="n",bg=c("white"),y.intersp=0.75,title="Metric tons per capita",cex=0.8)       dev.off()                  #INTERACTIVE MAPS   #googleVis PACKAGE   library(googleVis)      data.poly <- as.data.frame(polygons)   data.poly <- data.poly[,c(5,12)]   names(data.poly) <- c("Country Name","CO2 emissions (metric tons per capita)")      map <- gvisGeoMap(data=data.poly, locationvar = "Country Name", numvar='CO2 emissions (metric tons per capita)',options=list(width='800px',heigth='500px',colors="['0x0000ff', '0xff0000']"))   plot(map)      print(map,file="Map.html")      #http://www.javascripter.net/faq/rgbtohex.htm   #To find HEX codes for RGB colors                  #plotGoogleMaps   library(plotGoogleMaps)      polygons.plot <- polygons[,c("CO2","GDP.capita","NAME")]   polygons.plot <- polygons.plot[polygons.plot\$NAME!="Antarctica",]   names(polygons.plot) <- c("CO2 emissions (metric tons per capita)","GDP per capita (current US\$)","Country Name")      #Full Page Map   map <- plotGoogleMaps(polygons.plot,zoom=4,fitBounds=F,filename="Map_GoogleMaps.html",layerName="Economic Data")         #To add this to an existing HTML page   map <- plotGoogleMaps(polygons.plot,zoom=2,fitBounds=F,filename="Map_GoogleMaps_small.html",layerName="Economic Data",map="GoogleMap",mapCanvas="Map",map.width="800px",map.height="600px",control.width="200px",control.height="600px")        ``

R code snippets created by Pretty R at inside-R.org

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