Earlier this week, my company, Lander Analytics, organized our first public Bayesian short course, taught by Andrew Gelman, Bob Carpenter and Daniel Lee.  Needless to say the class sold out very quickly and left a long wait list.  So we will schedule another public training (exactly when tbd) and will make the same course available for private training.

This was the first time we utilized three instructors (as opposed to a main instructor and assistants which we often use for large classes) and it led to an amazing dynamic.  Bob laid the theoretical foundation for Markov chain Monte Carlo (MCMC), explaining both with math and geometry, and discussed the computational considerations of performing simulation draws.  Daniel led the participants through hands-on examples with Stan, covering everything from how to describe a model, to efficient computation to debugging.  Andrew gave his usual, crowd dazzling performance use previous work as case studies of when and how to use Bayesian methods.

It was an intensive three days of training with an incredible amount of information.  Everyone walked away knowing a lot more about Bayes, MCMC and Stan and eager to try out their new skills, and an autographed copy of Andrew’s book, BDA3.

A big help, as always was Daniel Chen who put in so much effort making the class run smoothly from securing the space, physically moving furniture and running all the technology.

On April 24th and 25th Lander Analytics and Work-Bench coorganized the (sold-out) inaugural New York R Conference. It was an amazing weekend of nerding out over R and data, with a little Python and Julia mixed in for good measure. People from all across the R community gathered to see rockstars discuss their latest and greatest efforts.

Highlights include:

Bryan Lewis wowing the crowd (there were literally gasps) with rthreejs implemented with htmlwidgets.

Hilary Parker receiving spontaneous applause in the middle of her talk about reproducible research at Etsy for her explainr, catsplainr and mansplainr packages.

James Powell speaking flawless Mandarin in a talk tangentially about Python.

Vivian Peng also receiving spontaneous applause for her discussion of storytelling with data.

Wes McKinney showing love for data.frames in all languages and sporting an awesome R t-shirt.

Dan Chen using Shiny to study Ebola data.

Andrew Gelman blowing away everyone with his keynote about Bayesian methods with particular applications in politics.

Videos of the talks are available at http://www.rstats.nyc/#speakers with slides being added frequently.

A big thank you to sponsors RStudio, Revolution Analytics, DataKind, Pearson, Brewla Bars and Twillory.

This year we celebrated Mega Pi Day with the date (3/14/15) covering the first four digits of Pi. And of course, we unveiled the Pi Cake at 9:26 to get the next three digits.  This year the cake came from Empire Cakes and was peanut butter flavored.  We even had the bakery put as many digits as would fit around the cake.

A large group from the NYC Data Mafia came out and Scott Wiener of Scott’s Pizza Tours ensured we had the perfect assortment and quantity of pizza.

A look at Pi Cakes from previous years:

The other night I attended a talk about the history of Brooklyn pizza at the Brooklyn Historical Society by Scott Wiener of Scott’s Pizza Tours. Toward the end, a woman stated she had a theory that pizza slice prices stay in rough lockstep with New York City subway fares. Of course, this is a well known relationship that even has its own Wikipedia entry, so Scott referred her to a New York Times article from 1995 that mentioned the phenomenon.

However, he wondered if the preponderance of dollar slice shops has dropped the price of a slice below that of the subway and playfully joked that he wished there was a statistician in the audience.

Naturally, that night I set off to calculate the current price of a slice in New York City using listings from MenuPages. I used R’s XML package to pull the menus for over 1,800 places tagged as “Pizza” in Manhattan, Brooklyn and Queens (there was no data for Staten Island or The Bronx) and find the price of a cheese slice.

After cleaning up the data and doing my best to find prices for just cheese/plain/regular slices I found that the mean price was $2.33 with a standard deviation of$0.52 and a median price of $2.45. The base subway fare is$2.50 but is actually $2.38 after the 5% bonus for putting at least$5 on a MetroCard.

So, even with the proliferation of dollar slice joints, the average slice of pizza ($2.33) lines up pretty nicely with the cost of a subway ride ($2.38).

Taking it a step further, I broke down the price of a slice in Manhattan, Queens and Brooklyn. The vertical lines represented the price of a subway ride with and without the bonus.  We see that the price of a slice in Manhattan is perfectly right there with the subway fare.

MenuPages even broke down Queens Neighborhoods so we can have a more specific plot.

For a d3 bar plot visit http://www.jaredlander.com/plots/PizzaPollPlot.html.

I finally compiled the data from all the pizza polling I’ve been doing at the New York R meetups. The data are available as json at http://www.jaredlander.com/data/PizzaPollData.php.

This is easy enough to plot in R using ggplot2.

require(rjson)
require(plyr)
pizzaJson <- fromJSON(file = "http://jaredlander.com/data/PizzaPollData.php")
pizza <- ldply(pizzaJson, as.data.frame)

##   polla_qid      Answer Votes pollq_id                Question
## 1         2   Excellent     0        2  How was Pizza Mercato?
## 2         2        Good     6        2  How was Pizza Mercato?
## 3         2     Average     4        2  How was Pizza Mercato?
## 4         2        Poor     1        2  How was Pizza Mercato?
## 5         2 Never Again     2        2  How was Pizza Mercato?
## 6         3   Excellent     1        3 How was Maffei's Pizza?
## 1  Pizza Mercato 1.344e+09         13  0.0000
## 2  Pizza Mercato 1.344e+09         13  0.4615
## 3  Pizza Mercato 1.344e+09         13  0.3077
## 4  Pizza Mercato 1.344e+09         13  0.0769
## 5  Pizza Mercato 1.344e+09         13  0.1538
## 6 Maffei's Pizza 1.348e+09          7  0.1429

require(ggplot2)
ggplot(pizza, aes(x = Place, y = Percent, group = Answer, color = Answer)) +
geom_line() + theme(axis.text.x = element_text(angle = 46, hjust = 1), legend.position = "bottom") +
labs(x = "Pizza Place", title = "Pizza Poll Results")


But given this is live data that will change as more polls are added I thought it best to use a plot that automatically updates and is interactive. So this gave me my first chance to need rCharts by Ramnath Vaidyanathan as seen at October’s meetup.

require(rCharts)
pizzaPlot <- nPlot(Percent ~ Place, data = pizza, type = "multiBarChart", group = "Answer")
pizzaPlot$xAxis(axisLabel = "Pizza Place", rotateLabels = -45) pizzaPlot$yAxis(axisLabel = "Percent")
pizzaPlot$chart(reduceXTicks = FALSE) pizzaPlot$print("chart1", include_assets = TRUE)


Unfortunately I cannot figure out how to insert this in WordPress so please see the chart at http://www.jaredlander.com/plots/PizzaPollPlot.html. Or see the badly sized one below.

There are still a lot of things I am learning, including how to use a categorical x-axis natively on linecharts and inserting chart titles. I found a workaround for the categorical x-axis by using tickFormat but that is not pretty. I also would like to find a way to quickly switch between a line chart and a bar chart. Fitting more labels onto the x-axis or perhaps adding a scroll bar would be nice too.

Attending this week’s Strata conference it was easy to see quite how prolific the NYC Data Mafia is when it comes to writing.  Some of the found books:

And, of course, my book will be out soon to join them.

The wonderful people at Gilt are having me teach an introductory course on R this Friday.

The class starts with the very basics such as variable types, vectors, data.frames and matrices.  After that we explore munging data with aggregate, plyr and reshape2.  Once the data is prepared we will use ggplot2 to visualize it and then fit models using lm, glm and decision trees.

Most of the material comes from my upcoming book R for Everyone.

Participants are encouraged to bring computers so they can code along with the live examples.  They should also have R and RStudio preinstalled.

Given the warnings for today’s winter storm, or lack of panic, I thought it would be a good time to plot the NYC evacuation maps using R. Of course these are already available online, provided by the city, but why not build them in R as well?

I obtained the shapefiles from NYC Open Data on February 28th, so it’s possible they are the new shapefiles redrawn after Hurricane Sandy, but I am not certain.

First we need the appropriate packages which are mostly included in maptools, rgeos and ggplot2.

require(maptools) 
## Loading required package: maptools 
## Loading required package: foreign 
## Loading required package: sp 
## Loading required package: lattice 
## Checking rgeos availability: TRUE 
require(rgeos) 
## Loading required package: rgeos 
## Loading required package: stringr 
## Loading required package: plyr 
## rgeos: (SVN revision 348) GEOS runtime version: 3.3.5-CAPI-1.7.5 Polygon ## checking: TRUE 
require(ggplot2) 
## Loading required package: ggplot2 
require(plyr) require(grid) 
## Loading required package: grid 

Then we read in the shape files, fortify them to turn them into a data.frame for easy plotting then join that back into the original data to get zone information.

# read the shape file evac <- readShapeSpatial("../data/Evac_Zones_with_Additions_20121026/Evac_Zones_with_Additions_20121026.shp") # necessary for some of our work gpclibPermit() 
## [1] TRUE 
# create ID variable evac@data\$id <- rownames(evac@data) # fortify the shape file evac.points <- fortify(evac, region = "id") # join in info from data evac.df <- join(evac.points, evac@data, by = "id") # modified data head(evac.df) 
## long lat order hole piece group id Neighbrhd CAT1NNE Shape_Leng ## 1 1003293 239790 1 FALSE 1 0.1 0 <NA> A 9121 ## 2 1003313 239782 2 FALSE 1 0.1 0 <NA> A 9121 ## 3 1003312 239797 3 FALSE 1 0.1 0 <NA> A 9121 ## 4 1003301 240165 4 FALSE 1 0.1 0 <NA> A 9121 ## 5 1003337 240528 5 FALSE 1 0.1 0 <NA> A 9121 ## 6 1003340 240550 6 FALSE 1 0.1 0 <NA> A 9121 ## Shape_Area ## 1 2019091 ## 2 2019091 ## 3 2019091 ## 4 2019091 ## 5 2019091 ## 6 2019091 
# as opposed to the original data head(evac@data) 
## Neighbrhd CAT1NNE Shape_Leng Shape_Area id ## 0 <NA> A 9121 2019091 0 ## 1 <NA> A 12250 54770 1 ## 2 <NA> A 10013 1041886 2 ## 3 <NA> B 11985 3462377 3 ## 4 <NA> B 5816 1515518 4 ## 5 <NA> B 5286 986675 5 

Now, I’ve begun working on a package to make this step, and later ones easier, but it’s far from being close to ready for production. For those who want to see it (and contribute) it is available at https://github.com/jaredlander/mapping. The idea is to make mapping (including faceting!) doable with one or two lines of code.

Now it is time for the plot.

ggplot(evac.df, aes(x = long, y = lat)) + geom_path(aes(group = group)) + geom_polygon(aes(group = group, fill = CAT1NNE)) + list(theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(), axis.text.x = element_blank(), axis.text.y = element_blank(), axis.ticks = element_blank(), panel.background = element_blank())) + coord_equal() + labs(x = NULL, y = NULL) + theme(plot.margin = unit(c(1, 1, 1, 1), "mm")) + scale_fill_discrete("Zone") 

There are clearly a number of things I would change about this plot including filling in the non-evacuation regions, connecting borders and smaller margins. Perhaps some of this can be accomplished by combining this information with another shapefile of the city, but that is beyond today’s code.