a blog on spatial research and data visualization in R. by paul bidanset © 2013-15

Posts tagged mapping

Creating a TRULY Interactive Map of Craft Breweries in VA Using the leafletR Package (Guest Blog Post by Dr. Keegan Hines)


It’s a good feeling when a great friend who is smarter than you offers to write a blog post, for your blog, that’s better than anything you’ve written so far. Friends, colleagues, people who’ve not yet realized they are at the wrong site: please allow me to introduce to you the awe-inspiring Dr. Keegan Hines. He got his PhD in neuroscience from the University of Texas at Austin in 2014 and is now a data scientist doing some super-secret-James-Bond-machine-learning work for a DoD contractor near D.C. When he is not breathing life into spatially-centered instructional R blogs, he is part of an improv comedy troop, does some consulting work, and serves a mean campfire omelet. Without further adieu…

Microbreweries and Interactive Maps With Leaflet

This is a guest post from Keegan Hines, he’s a neat fella, you can follow him on the internet.

This post is about interactive data visualizations and some powerful new tools that are available to the R community. Moving beyond static data graphics toward interactive experiences allows us to present our audience with much more complex information in an easily digestable way. To empower these interactive graphics, we’re going to utilize tools such as HTML and javascript, technologies that drive the web-based interactive experiences you have every day. But the best part is that we’ll benefit from these technologies without having to learn anything about web development. We’re going to create some amazing things using only R!

As a guiding example, lets return to a previous blog post where Paul visualized the locations of microbreweries in Virginia . In that post, Paul introduced Plotly, a super cool company that allows you to create and deploy interactive graphics on their web-based service. Here, we’re going to do this all ourselves, with help from a new R package called leaflet. So let’s jump right in.

Here’s some boiler-plate stuff. We need to install the package manually from github and then load it.


So first thing, let’s grab some location that we might want to put on a map. I’ll use a function from the ggmap package.

 somePlace <-ggmap::geocode("Washington,DC")      

So we have a dataframe (with one row) and lat/lon coordinates for some arbitrary point in Washington, DC. We’re going to use functions from the leaflet package to generate a map around this point.

leaflet(somePlace) %>% addTiles() %>% addMarkers()

Now we have this draggable, zoomable, interactive map with a single line of R!

A little explanation of what we just did. In case it’s unfamiliar, I’ll first point out that we’re using the forward pipe %>% thing. The forward pipe was introduced in the magrittr package and has now been adopted in lots of places. The idea is that we can pass the output of a function as the input to the next function. This allows us to write code that reads left to right and is more aligned with our logic. For example:

# this is nested and confusing, we have to read it inside-out
# this is sequential and awesome, it reads left to right
    c(1,2,3) %>% sum() %>% sqrt()

So back to leaflet. The first funciton we use is called leaflet() and this returns a base leaflet object, sort of the starting point for everything we might do. We passed our data frame as an argument to leaflet(), and so any later functions that might require data will look to this data frame.

We then sent the output of leaflet() to another function, addTiles(). This is because the output of leaflet() doesn’t have enough visual information to actually create a map – we haven’t provided enough detail yet about what we want. The function addTiles() updates the leaflet object by providing the visual look and feel through different “tiles”. In fact, there’s many different styles of map we could make, just by choosing different tiles. Here’s some examples:

leaflet(somePlace) %>% addProviderTiles("Stamen.Watercolor") %>% addMarkers()

leaflet(somePlace) %>% addProviderTiles("Stamen.Toner") %>% addMarkers()

The full list of available tiles is here.

And so the third function in this simple example is addMarkers(). This function’s purpose is pretty obvious and results in the big blue marker thing on the map. What it does is look through the provided data frame for any columns that are similar to “lat” or “lon” and then plots them. And it’ll do so for every row in the data frame, so it’s effortless to put lots of points on a map, as we’ll see below. There are also a few other other functions that are similar and plot slightly different things. You might be able to guess what addCircles() or addPolyLines() are capable of, but as an example:

leaflet(somePlace) %>%
	addProviderTiles("Stamen.Toner") %>%

So let’s move on to our more interesting example – the breweries. I’ve scraped a list of microbreweries in Virginia and gotten their names, websites, addresses and so. Since I want lat/lon info as well, I’ve used ggmap::geocode() to estimate those as well. The result is a dataframe called ‘breweries’ that has 106 rows and looks like this:

> names(breweries)
[1] "Name"    "Address" "Phone"   "Website" "lat"     "lng"    		

> head(breweries[,c(1,4:6)])
	 Name                         Website                 lat             lng
1  Wolf Hills Brewing Co    www.wolfhillsbrewing.com     36.71231    -81.96560
2  Blue Mountain Brewery www.bluemountainbrewery.com     37.96898    -78.83499
3 Quattro Goomba Brewery       www.goombabrewery.com     38.98597    -77.61748
4   Hops Grill & Brewery          www.hopsonline.com     38.83758    -77.05116
5   Port City Brewing Co     www.portcitybrewing.com     38.80800    -77.10137
6  Loose Shoe Brewing Co    www.looseshoebrewing.com     37.56500    -79.06352

So let’s put em on a map.

 leaflet(breweries) %>% addTiles() %>% addMarkers()

Pretty effortless I’d say! This is great except we don’t know which brewery is which, it’s just anonymous points on a map. We could try to add some text to a map, but remember our goal is to utilize web-based interactivity. So we’re going to take advantage of a click-based popup by inserting the Name column of the data frame.

leaflet(breweries) %>% addTiles() %>% addMarkers(popup=breweries$Name)

And one final trick to make it just a little smoother. We want to add a hyperlink to the website in the popup. Since our data frame has a column for all the websites, we could do this easily in a similar way to what we just did with the Name column. But we can take it a step further. Now I promised you that we don’t need to know any web development stuff in order to make these maps (and we don’t!). But if you happen to have a little side knowledge, you can embed any HTML or javascript that you want. In this case, I’m going to use HTML’s < a > tag for hyperlinks, so that each brewery name actually links out to its website.

popup_style<-paste0("<a href=http://",breweries$Website," target='_blank'>",breweries$Name,"</a>") leaflet(breweries) %>% addTiles() %>% addMarkers(popup=popup_style)

Now we can easily zoom around and explore Virginia’s thriving craft-brew industry! You have to admit that’s a pretty awesome thing we were able to create with just a couple lines of R. And the interactivity allows us to encode a lot information (locations, names, and website of all the breweries) in a simple exerience that any viewer can explore at their own pace. As you might guess, this is just the beginning of what we can do with leaflet, and there’s a great guide at RStudio’s site.

If you’re like me, you’re very excited about incorporating web-based interactivity in your data analyses with R. This general idea of wrapping javascript-based experiences into an easy-to-use R package is something that’s gaining a lot of traction lately. To me, this is one of the most exciting innovations in the R community in the last couple years and is taking off in many exciting directions. If you want to learn more, I’m developing a course for DataSociety entitled “Advanced Visualization With R”. In the course, we’ll explore many of these web-based technologies including Leaflet, rCharts, Shiny and more, so look to sign up later this summer!


Creating an Interactive Map of Craft Breweries in VA Using the plotly R Package

Well folks, another new year’s resolution down the drain. I was initially shooting for a post each month for 2014. More projects came. Plates were full. Plates were emptied. More plates were filled again. I think I will just alter my resolution to 12 posts this year. That’s a fair compromise with myself, right? That’s what we Americans do. Needless to say, it will likely be a busy last week of December for me.

I’m taking a short break from the previous series to share a great data visualization platform I stumbled upon called plotly.There is even an R package that allows you to feed data directly to their site for further analysis and manipulation. Blew my mind and I had to share. Anyway, check out their site for some mesmerizing graphics and data visualization capabilities!

This post is based off of a guest blog post by Matt Sundquist of plotly on Corey Chivers’ blog bayesianbiologist. I tweaked the code only slightly to accommodate my data and I added a geocoding section. Other than that, they are the masterminds.

Alright so with the obvious boom of craft breweries here in Virginia (and well, across the country), I thought I’d be well-received doing a post on two of my favorite things: geographic data visualization and booze.

First off, in order to harness the great powers of plotly, you must register at https://plot.ly/ for your own account. Next, we install the package that will allow us to connect from R to our fresh, new plotly account.


After loading the packages, we can log in to our plotly account straight from R by typing in our respective username and API key (to obtain your API key, log in to plot.ly via your web browser, click Profile > Edit Profile and you will see your API key)

p <- plotly(username="bobdole", key="abcbaseonme")

For my data set of craft brewery locations in Virginia, I queried a data set of current brewery licensees in the state from the Virginia Department of Alcoholic Beverage Control website. I then removed the 'big guys' (sorry, this bud is not for you) and aggregated the count of breweries by city/town and saved as a .csv file. Now we read in our data:

data = read.csv("C:/breww.csv", header=TRUE)

Matt's data already had location coordinates. Since mine only has the respective city/state, I need to geocode it so R will understand how to plot locations on the map. For this I am using the ever-faithful ggmap package.

We named the sheet "data" when we read it in and the column that has the city/state of each brewery is called "City". We can now batch geocode each city. The function geocode() returns and m x 2 matrix, where m is the number of rows of data (cities) and the 2 columns are the latitude (default column name is lat ) and longitude (default column name is lon) of each respective city. We create two new columns in our data set and set them equal to the two columns of the data frame loc we just created.

loc <- geocode(as.character(data$City))

We call the state outlines using the map() function, take its xy coordinates, and assign this as the first trace for plotting the map.

trace1 <- list(x=map("state")$x,

We then create the second trace by extracting the longitude and latitude from our data (assigning as x and y plots, respectively). We specify that the size of the bubbles on the map is based on data$No (i.e. bigger bubble, more breweries), which is the column containing the number of breweries in each respective city.

trace2 <- list(x= data$lon,

Finally, we combine the two traces and send our data to our plotly profile.

response <- p$plotly(trace1,trace2)
url <- response$url
filename <- response$filename

Like magic, running the last code will open your browser and load your fancy new map in the plot.ly interface, ready for you to zoom, crop, and manipulate to your heart's content!

Map Browser Interface

Static shots can also be exported at very high resolutions from the plotly site:

Craft Breweries in Virginia via R & plotly

Maps like this often produce results that may mislead folks. They often just reflect populations, not a higher propensity to consume craft beer - more people in an area (i.e. Richmond, DC, Virginia Beach) results in the capacity and demand for more breweries overall. A 'craft breweries per capita map' would arguably tell a more interesting story. Thanks for reading!

Throw some, throw some STATS on that map…(Part 1)

R is a very powerful and free (and fun) software package that allows you to do, pretty much anything you could ever want. Someone told me that there’s even code that allows you to order pizza (spoiler alert: you actually cannot order pizza using R :( ). But if you’re not hungry, the statistical capabilities are astounding. Some people hate code; their brains shut down and they get sick when they look at it, subsequently falling to the floor restricted to the fetal position. I used to be that guy, but have since stood up, gained composure, sat back down, and developed a passion for statistical programming. I hope to teach the R language with some more intuition in order to keep the faint-of-heart vertical and well.

Alright so for the start in this series, I’m going to lay the foundation for a Baltimore, MD real estate analysis and demonstrate some extremely valuable spatial and statistical functions of R. So without too much blabbing, let’s jump in…

For those of you completely new to R, its interface allows you to download different packages which perform different functions. People use R for so many different data-related reasons, and the inclusion of all or most of the packages would be HUGE, so each one, housed in various servers located around the world, can be downloaded simply. For the first-time use of each package, you’ll need to install it. They will then be on your machine and you will simply load them for each future use.

For the initial map creation, we need to install the following (click Packages->Install Package(s)and holding Ctrl allows you to select multiple ones at a time):


Since these are now installed to our machine, we simply load these packages each session we use them. Loading just ggmap and RgoogleMaps will automatically load the others we just downloaded. With each session, open a script and once you’ve written out your code, highlight it and right-click “Run Line or Selection,” or just press Ctrl -> R. A quick note: unlike other programming languages like SAS and SQL, R is case sensitive.

To load run:


We will specify the object of the map center as CenterOfMap. Anything to the left of “<-” in R is the title and anything to the right are the specified contents of the object. Now for the map we’re using, the shape of Baltimore behaves pretty well, so we can just type within the geocode() command “Baltimore, MD” ( R is smart and that’s all it takes).

CenterOfMap <- geocode("Baltimore, MD")

Not all areas are as symmetrically well behaved as Baltimore, and for other cases, my preferred method of centrally displaying an area's entirety begins with entering the lat/long coordinates of your preferred center. For this, I go to Google Maps, find the area I wish to map, right click on my desired center and click "What's here?" and taking the lat/long coordinates which are then populated in the search bar above. For Baltimore, I'm going to click just north of the harbor.

The code would then look like this:

CenterOfMap <- geocode(" 39.299768,-76.614929")

Now that we told R where the center of our map will be, lets make a map! So remember, left of the "<-" will be our name. I'd say naming the map 'BaltimoreMap' will do.

Baltimore <- get_map(c(lon=CenterOfMap$lon, lat=CenterOfMap$lat),zoom = 12, maptype = "terrain", source = "google")
BaltimoreMap <- ggmap(Baltimore)

Alright, to explain what just happened, getmap() is the command to construct the map perimeters and lay down its foundation. I'm going to retype the code with what will hopefully explain it more intuitively.

get_map(c(lon='The longitude coordinate of the CenterOfMap object we created. The dollar sign shows what follows is part of what is before it, for example ExcelSpreadsheet$ColumnA, lat='The latitude coordinate of the CenterOfMap object we created.', zoom = 'The zoom level of map display. Play around with this and see how it changes moving from say,5 to 25', maptype = 'We assigned "terrain" but there are others to suit your tastes and preferences. Will show more later.', source = 'We assigned "google" but there are other agents who provide types of mapping data')

And the grand unveiling of the first map...

Now that is one good lookin' map. Just a few lines of code, too.

I'll show you some other ways to manipulate it. I like to set the map to black & white often times so the contrast (or lack thereof) of the values later plotted are more defined. I prefer the Easter bunny/night club/glow-in-the-dark type spectrums, and so, I usually plot on the following:

Baltimore <- get_map(c(lon=CenterOfMap$lon, lat=CenterOfMap$lat),zoom = 12, maptype = "toner", source = "stamen")
BaltimoreMap <- ggmap(Baltimore)

We just set the night sky for the meteor shower. Notice that all we did was change maptype from "terrain" to "toner," and source from "google" to "stamen."

A few other examples:

Baltimore <- get_map(c(lon=CenterOfMap$lon, lat=CenterOfMap$lat),zoom = 12,source = "osm")
BaltimoreMap <- ggmap(Baltimore)

This map looks great but it's pretty busy - probably not the best to use if you will be plotting a colorful array of values later.

Here's a fairly standard looking one, similar to Google terrain we covered above.

Baltimore <- get_map(c(lon=CenterOfMap$lon, lat=CenterOfMap$lat),zoom=12)
BaltimoreMap <- ggmap(Baltimore, extent="normal")

And one for the hipsters...

Baltimore <- get_map(c(lon=CenterOfMap$lon, lat=CenterOfMap$lat),zoom = 12, maptype = "watercolor",source = "stamen")
BaltimoreMap <- ggmap(Baltimore)

George Washington and the cartographers of yesteryear would be doing cartwheels if they could see this now. The upcoming installments in this series will cover:

1) Implementing Shapefiles and GIS Data
2) Plotting Statistics and other Relationship Variables on the Maps
3) Analyzing Real Estate Data and Patterns of Residential Crime and Housing Prices

Thanks for reading this!If you have any problems with coding or questions whatsoever, please shoot me an email (pbidanset[@]gmail.com) or leave a comment below and I'll get back to you as my schedule permits (should be quickly). Cheers.

All works on this site (spatioanalytics.com) are subject to copyright (all rights reserved) by Paul Bidanset, 2013-2015. All that is published is my own and does not represent my employers or affiliated institutions.