Slidify my R journey from @matlabulous to rCrimemap

My LondonR Talk

Thanks to Mango Solutions (LondonR organiser), I was given the opportunity last night to talk about my mini project ‘CrimeMap’. Instead of going through all the technical details behind the scenes, I chose to talk the audience through my R journey from a noob to a heavy user. CrimeMap was used as a case study to show how ones can benefit from learning R (or, in some ways, trying to justify the time I spent staring at RStudio IDE last year). The feedback was really great and the talk effectively expanded my network in the data science community so I am really grateful for that! You can find my presentation here.

Before the main event, there was an excellent R-Python workshop by Chris Musselle. The other two interesting presentations were "Dynamic Report Generation" by Kate Hanley and "Customer Clustering for Retail Marketing" by Jon Sedar. Their presentations will soon be made available here.

CrimeMap - A Wonderful Learning Experience

When I first started learning R for real, the goal was very simple - "let's plot something pretty with ggplot2". Well, a lot has changed since then. The more I learned, the more I discovered. It is really hard to summarise the 'R' awesomeness in a few slides due to its diversity. One thing I am absolutely certain is that I made the right move about a year ago to shift from MATLAB to R. Yet, I am keeping my twitter account name @matlabulous just to remind myself that ones should always keep an open mind for new and evolving technology (... and should avoid getting a tattoo of your potential ex-gf/bf's name. On that note, no, I don't have a tattoo.) For more information about the CrimeMap, please see my previous posts here, here and here.

Using Slidify for Professional Presentation

The talk was also the first time I presented something totally unrelated to water engineering. I thought, for a change, let’s try something different. Then I remembered looking at the Slidify slides from Jeff Leek’s Data Analysis course back in Jan-March last year. I thought that would fit perfectly for LondonR because the whole presentation would be coded completely in R. It would be a good reason to learn Slidify too. So I went through the Slidify examples, put some slides together, tweaked the CSS a little bit and then published it to GitHub – a streamline Slidify workflow well thought and designed by Ramnath Vaidyanathan. To me, the results are amazing! So amazing that I am confident to leave PowerPoint and use Slidify for professional presentations in the future.

rMaps + CrimeMap = rCrimemap

Two weeks before the presentation, I wrote an email to Ramnath as I wanted to thank him for Slidify. I told him how I enjoyed using Slidify for the LondonR slides. Out of the blue, Ramnath told me that he had seen my CrimeMap already and he kindly pointed me to this blog post about using Leaflet heat map in rMaps. I thought, OMG, why now? Then I thought, yeah, why not? So I created a new package called ‘rCrimemap’ based on Ramnath’s example and the codes from the CrimeMap project – just in time for the LondonR meeting. At first, I wanted to called the package something different but eventually I chose rCrimemap so it aligns well with Ramnath’s rCharts and rMaps.

Using ‘rCrimemap’

rCrimemap is still raw and experimental. It depends on some new packages such as dplyr, dev version of rCharts and rMaps etc. I have only developed and tested it recently on Linux. Please give it a try if you have a chance. All feedback and suggestions are welcome. Codes are here.

To install it, you will need the RStudio IDE version 0.98.501 or newer and the following packages ...

require(devtools)
install.packages(c("base64enc", "ggmap", "rjson", "dplyr"))
install_github('ramnathv/rCharts@dev')
install_github('ramnathv/rMaps')

After that, install rCrimemap package via ... 

install_github('woobe/rCrimemap')

rCrimemap is basically a big wrapper function. In fact, there is only one function 'rcmap( )' in the package at the moment. (OK, it is obviously an overkill ... but I really wanted to try developing a package.) The function is very similar to the first one I did for CrimeMap prior to the Shiny development. In terms of graphical functionality, it is not as flexible as the CrimeMap yet (for example, CrimeMap can do all these colours and facet). However, it is much more powerful than CrimeMap in the sense that users can move around, zoom in and out like using a real digital map. The colour of the heat map also changes when you zoom in/out. This gives users a much better visibility of where the local crime hot spots are when they zoom in. OK, enough said, let’s go through some example usage …

The arguments of the function 'rcmap( )' are:

1. location: point of interest within England, Wales and Northern Ireland
2. period: a month between Dec 2010 and Jan 2014 (in the format of yyyy-mm)
3. type: category of crime (e.g. "All", "Anti-social behaviour")
4. map_size: the resolution of the map in pixel (e.g. Full HD = c(1920, 1080))
5. provider: the base map provider (e.g. "Nokia.normalDay", "MapQuestOpen.OSM")
6. zoom: zoom level of the map (e.g. I recommend starting with 10 to show all the crimes)

Example 1: “Ball Brothers EC3R 7PP” (LondonR venue since March 2013) during the London riot (Aug 2011). The map can be viewed within RStudio IDE or be exported to a browser. The animation was created outside R (Oh ... what if rCrimemap + animation package? ... I will leave that for later.)

rcmap("Ball Brothers EC3R 7PP", "2011-08", "All", c(1000,1000),"Nokia.normalDay")

Example 2: Manchester in Jan 2014 - using "MapQuestOpen.OSM" as base map instead.

rcmap("Manchester", "2014-01", "All", c(1000,1000), "MapQuestOpen.OSM")

Credits

There you go, enjoy :)

Introducing CrimeMap - A Web App Powered by ShinyApps!

A few months ago I did a mini project using open crime data and R to create crime visualisations. At that time, I was already thinking about a web app using Shiny but I couldn't justify the time to develop the app and then set up a server etc. Not until two weeks ago when I received an invitation to join the alpha testing of ShinyApps.

ShinyApps - A Wonderful Discovery

I went through the ShinyApps' getting started guide. Everything looks pretty straight forward. So I decided to go ahead and moved my codes from the crime visualisation project to a new ShinyApps project. The progress was unexpectedly smooth. Given that I had no web application development experience prior to this mini exercise, I consider this a quick success! All credit goes to the RStudio team for providing these tools and hosting services (especially Tareef Kawaf who kindly answered all my questions). I would summarise the whole process in the following few steps:

1. Sign up for a ShingApps alpha testing account here (you will need a Google account).
2. Install packages shiny and shinyapps (click on the links for installation help).
3. Sign in to my.shinyapps.io, give your ShinyApps account a name ("blenditbayes" in my case) and get your application token/secret.
4. Go through the tutorials, create the ui.R and server.R scripts for your app.
5. Test the app locally using runApp().
6. Once you're happy with the app, apply your token/secret and deploy your app using deployApp().

That's it! I can focus my effort solely on developing the app. The rest has been taken care of and simplified by the deployApp() and other ShinyApps functions.
 

CrimeMap in Action

So here is my first ever web app "CrimeMap" powered by ShinyApps! I will go through the usage briefly in the following sections. I hope you can give it a try and give me some feedback (e.g. what features are missing?) so I can continue to improve it. Your comments will be valuable to the ShinyApps development team too.

URL: http://blenditbayes.shinyapps.io/crimemap/

Basic Usage

First, enter a location of your choice (e.g. London) within England, Wales and Northern Ireland. Select the first month of crime data collection and then the length of the analysis period. After that, click on the "Update" button. My experience is that the graphs should come up within a minute if the length of analysis is less than or equal to 6 months. Depending on the location and the length of analysis, the process might take longer (say a couple minutes). The outputs (at the time of writing) are displayed in three tabs: Data, Crime Map and Trends. The Data tab shows the original crime data records downloaded from the data.police.uk (I may add a feature like "download as CSV in future). The crime map is a density plot of the crime data. Finally, the trends tab shows bar charts of crime records over time in different crime categories. Right click on the map and you can save the image in its original size (1280 x 1280).

Customise the Maps

Continue to scroll down the menu on the left, you will find more settings for the map. Change the facet settings to create map facets according to crime type, categories and month. You can select different Google map format (roadmap, satellite, hybrid or terrain). There are also options to download the Google map image in high resolution (it takes a bit longer), in black & white and at various zoom levels. When you click the "Update" button to refresh the maps, it may take a while to show the new graphs if you have lots of facets.

Fine-tuning the Density Plots

The Density Plot Settings allow user to modify the "behind the scenes" ggplot2 codes. You can modify the layer transparency (alpha range), the number of bins, the width and the colour of the boundary lines as well as the colours of the gradient. My hope is to develop a user-friendly interface that allows users to quickly create maps with their own favourite themes. Surely, ggplot2 is much more powerful than that and has a lot more settings available. What other settings would you like to see here? Please let me know.

Exploring the Trends

Why am I including the trends visualisation? It just happened that I read this article around the same time I received the ShinyApps invitation. As I had already coded something for crime data visualisation, I thought it would be interesting to look at the data myself. So here is a handy tool for you to explore and to visualise the data with a few clicks. I will leave you with your own conclusions.

Feedback Please

As I mentioned above, I am new to web app development and this is my first ever experiment. Please have a go, create a few maps and let me know what can be done better. Thanks in advance!! All the codes are available here.

Visualising Crime Hotspots in England and Wales using {ggmap}

Two weeks ago, I was looking for ways to make pretty maps for my own research project. A quick search led me to some very informative blog posts by Kim Gilbert, David Smith and Max Marchi. Eventually, I Google'd the excellent crime weather map example by David Kahle and decided to stick with the gg-style approach.

Thanks to David Kahle and Hadley Wickham who ramped up that example and subsequently developed the {ggmap} package, making maps in R can be really intuitive and fun!

I wrote a wrapper function that takes a location within England and Wales, downloads crime data around that location over a certain period of time and creates crime weather plots. This blog post discusses the data used, methodology and the wrapper function with some worked examples. The codes are available here.

(Nov-2013 Update: I have updated the codes and created a web app using Shiny and ShinyApps. For more information, please read this new blog post.)

Data

The street-level crime data is one of the 9,000 datasets available from data.gov.uk. The data can be downloaded systematically via the Police API. The latest version of the API no longer requires authentication.

The following URL can be used to obtain crime records at street-level within a one-mile radius of a single point. The parameters required are latitude, longitude and month. The downloaded data is in JSON format which can be converted into R's data format using the {RJSONIO} package.

Example URL: http://data.police.uk/api/crimes-street/all-crime?lat=52.629729&lng=-1.131592&date=2012-04

Methodology

The methodology can be summarised in the following six steps:

1. Obtain latitude and longitude of a user-defined location using ggmap::geocode.
2. Download crime data via the Police API as discussed above.
3. Convert JSON into a list and then a data frame.
4. Download a base map from Google using ggmap::get_googlemap.
5. Covert the base map into a ggplot object using ggmap::ggmap.
6. Add multiple layers on top of the base map using the data frame like a normal ggplot.

For more details, check out the functions in the codes:

• "get.data" and "list2df" for steps 1, 2 and 3
• "visualise.data" for steps 4, 5 and 6

Wrapper and Worked Examples

The wrapper function looks like this ...

crimeplot.wrapper <- function(
  point.of.interest = "London Eye",  ## user-defined location
  period = c("2013-01","2013-02"),  ## period of time in YYYY-MM
  type.map = "roadmap",  ## roadmap, terrain, satellite or hybrid
  type.facet = NA,  ## options: NA, month, category or type
  type.print = NA,  ## options: NA, panel or window
  output.plot = TRUE,  ## print it to a png file?
  output.filename = "temp.png",  ## provide a filename
  output.size = c(700,700)) ## width and height setting                              

... given the location, time period and a few more graphical settings, the wrapper can produce a crime weather map. The following worked examples illustrate the usage.

Example 1 - All crimes around London Eye from Jan-2013 to Apr-2013

Comments:
Here we can see a huge crime hotspt in the Soho district of London - an area full of bars, restaurants, theatres and nightclubs (did I mention Chinatown?)

Codes:
## Define the period
ex1.period <- format(seq(as.Date("2013-01-01"),length=4,by="months"),"%Y-%m")

## Use the wrapper
ex1.plot <- crimeplot.wrapper(point.of.interest = "London Eye",
                              period = ex1.period,
                              type.map = "roadmap",
                              output.filename = "ex1.png",
                              output.size = c(700,700))

Example 2 - Typical crimes and traffic incidents around London Eye from Jan-2013 to Apr-2013

(Note: click on the image to see original image in higher resolution)

Comments:
Now we seperate the data from British Transport Police (BTP) and all other forces (Force) using the facet function in {ggplot}. We can see a traffic black spot on the other side of River Thames.

Codes:

## Define the period
ex2.period <- format(seq(as.Date("2013-01-01"),length=4,by="months"),"%Y-%m")
 
## Use the wrapper
ex2.plot <- crimeplot.wrapper(point.of.interest = "London Eye",
                              period = ex2.period,
                              type.map = "roadmap",
                              type.facet = "type",
                              output.filename = "ex2.png",
                              output.size = c(1400,700))

Example 3 - Monthly crimes in Manchester for the year 2012 on a satellite map

(Note: click on the image to see original image in higher resolution)

Comments:
Using the facet function on "month", we can look at the changes in patterns over time. Looks like there is not much seasonality in Manchester as the crime hotspots remain hot over the year.

Codes:

## Define the period
ex3.period <- format(seq(as.Date("2012-01-01"),length=12,by="months"),"%Y-%m")

## Use the wrapper
ex3.plot <- crimeplot.wrapper(point.of.interest = "Manchester",
                              period = ex3.period,
                              type.map = "satellite",
                              type.facet = "month",
                              output.filename = "ex3.png",
                              output.size = c(1400,1400))

Example 4 - Crimes by categories in Liverpool from Jan-2013 to Apr-2013 on a hybrid map

(Note: click on the image to see original image in higher resolution)

Comments:
Now we separate different categories of crimes. It is interesting to see that only a small part of the city is affected by shoplifting and other theft while burglary, arson and vehicle crimes are very common problems in Liverpool.

Codes:

## Define the period
ex4.period <- format(seq(as.Date("2013-01-01"),length=4,by="months"),"%Y-%m")
 
## Use the wrapper
ex4.plot <- crimeplot.wrapper(point.of.interest = "Liverpool",
                              period = ex4.period,
                              type.map = "hybrid",
                              type.facet = "category",
                              output.filename = "ex4.png",
                              output.size = c(1400,1400))

Further Work

Further work is needed to ...
1. optimise the codes for "list2df" transformation (At the moment it is quite slow. I tried lapply but it didn't give me back the desired data frame format but I know there must be a solution.)
2. better automate the graphical settings for output resolution, font size etc.
3. make it interactive using {Shiny}

(Nov-2013 Update: I have improved (1) using plyr::ldply and done (2 & 3). See this.)

Acknowledgement

I would like to thank Yanchang Zhao for his excellent book titled "R and Data Mining: Examples and Case Studies" which encouraged me to shift from MATLAB to R. All embedded codes were Created by Pretty R at inside-R.org.

Key References

1. David Kahle's original crime map.

2. Police API documentation.