mages' blog

Transforming data sets with R is usually the starting point of my data analysis work. Here is a scenario which comes up from time to time: transform subsets of a data frame, based on context given in one or a combination of columns.

As an example I use a data set which shows sales figures by product for a number of years:

df <- data.frame(Product=gl(3,10,labels=c("A","B", "C")), 
                 Year=factor(rep(2002:2011,3)), 
                 Sales=1:30)
head(df)
##   Product Year Sales
## 1       A 2002     1
## 2       A 2003     2
## 3       A 2004     3
## 4       A 2005     4
## 5       A 2006     5
## 6       A 2007     6

I am interested in absolute and relative sales developments by product over time. Hence, I would like to add a column to my data frame that shows the sales figures divided by the total sum of sales in each year, so I can create a chart which looks like this:
There are lots of ways of doing this transformation in R. Here are three approaches using:

• base R with by,
• ddply of the plyr package,
• data.table of the package with the same name.

by

The idea here is to use by to split the data for each year and to apply the transform function to each subset to calculate the share of sales for each product with the following function: fn <- function(x) x/sum(x). Having defined the function fn I can apply it in a by statement, and as its output will be a list, I wrap it into a do.call command to row-bind (rbind) the list elements:

R1 <- do.call("rbind", as.list(
  by(df, df["Year"], transform, Share=fn(Sales))
))

head(R1)
##         Product Year Sales      Share
## 2002.1        A 2002     1 0.03030303
## 2002.11       B 2002    11 0.33333333
## 2002.21       C 2002    21 0.63636364
## 2003.2        A 2003     2 0.05555556
## 2003.12       B 2003    12 0.33333333
## 2003.22       C 2003    22 0.61111111

ddply

Hadely's plyr package provides an elegant wrapper for this job with the ddply function. Again I use the transform function with my self defined fn function:

library(plyr)
R2 <- ddply(df, "Year", transform, Share=fn(Sales))

head(R2)
##   Product Year Sales      Share
## 1       A 2002     1 0.03030303
## 2       B 2002    11 0.33333333
## 3       C 2002    21 0.63636364
## 4       A 2003     2 0.05555556
## 5       B 2003    12 0.33333333
## 6       C 2003    22 0.61111111

data.table

With data.table I have to do a little bit more legwork, in particular I have to think about the indices I need to use. Yet, it is still straight forward:

library(data.table)
## Convert df into a data.table
dt <- data.table(df) 
## Set Year as a key
setkey(dt, "Year") 
## Calculate the sum of sales per year(=key(dt))
X <- dt[, list(SUM=sum(Sales)), by=key(dt)] 
## Join X and dt, both have the same key and
## add the share of sales as an additional column
R3 <- dt[X, list(Sales, Product, Share=Sales/SUM)]

head(R3)
##      Year Sales Product      Share
## [1,] 2002     1       A 0.03030303
## [2,] 2002    11       B 0.33333333
## [3,] 2002    21       C 0.63636364
## [4,] 2003     2       A 0.05555556
## [5,] 2003    12       B 0.33333333
## [6,] 2003    22       C 0.61111111

Although data.table may look cumbersome compared to ddply and by, I will show below that it is actually a lot faster than the two other approaches.

Plotting the results

With any of the three outputs I can create the chart from above with latticeExtra:

library(latticeExtra)
asTheEconomist(
 xyplot(Sales + Share ~ Year, groups=Product, 
  data=R3, t="b", 
  scales=list(relation="free",x=list(rot=45)), 
  auto.key=list(space="top", column=3),
  main="Product information")
)

Comparing performance of by, ddply and data.table

Let me move on to a more real life example with 100 companies, each with 20 products and a 10 year history:

set.seed(1)
df <- data.frame(Company=rep(paste("Company", 1:100),200),
                 Product=gl(20,100,labels=LETTERS[1:20]), 
                 Year=sort(rep(2002:2011,2000)), 
                 Sales=rnorm(20000, 100,10))

I use the same three approaches to calculate the share of sales by product for each year and company, but this time I will measure the execution time on my old iBook G4, running R-2.15.0:

r1 <- system.time(
 R1 <- do.call("rbind", as.list(
   by(df, df[c("Year", "Company")], 
      transform, Share=fn(Sales))
 ))
)

r2 <- system.time(
 R2 <- ddply(df, c("Company", "Year"), 
             transform, Share=fn(Sales))
)

r3 <- system.time({
 dt <- data.table(df)
 setkey(dt, "Year", "Company")
 X <- dt[, list(SUM=sum(Sales)), by=key(dt)]
 R3 <- dt[X, list(Company, Sales, Product, Share=Sales/SUM)]
})

And here are the results:

r1 # by
##  user  system elapsed 
## 13.690   4.178  42.118 
r2 # ddply 
##  user  system elapsed 
## 18.215   6.873  53.061
r3 # data.table 
##  user  system elapsed 
## 0.171   0.036   0.442

It is quite astonishing to see the speed of data.table in comparison to by and ddply, but maybe it shouldn't be surprise that the elegance of ddply comes with a price as well.

Addition (13 June 2012): See also Matt's comments below. I completely missed ave from base R, which is rather simple and quick as well. Additionally his link to a stackoverflow discussion provides further examples and benchmarks.

Finally my session info:

> sessionInfo() # iBook G4 800 MHZ, 640 MB RAM
R version 2.15.0 Patched (2012-06-03 r59505)
Platform: powerpc-apple-darwin8.11.0 (32-bit)

locale:
[1] C

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] latticeExtra_0.6-19 lattice_0.20-6      RColorBrewer_1.0-5 
[4] data.table_1.8.0    plyr_1.7.1         

loaded via a namespace (and not attached):
[1] grid_2.15.0

A new version of googleVis has been released on CRAN and the project site. Version 0.2.16 adds the functionality to plot quarterly and monthly data as a motion chart.

To illustrate the new feature I looked for a quarterly data set and stumbled across the quarterly UK house price data published by Nationwide, a building society. The data is available in a spread sheet format and presents the average house prices and indexed to 100 in Q1 1993 by region in the UK from Q4 1973 to Q1 2012. Unfortunately the data is formated for human eyes rather than for computers, see the screen shot below.

Screen shot of Nationwide's UK house price data in Excel

Never-mind, the XLConnect package by Mirai Solutions does a fabulous job in reading Excel files into R. An advantage of XLConnect, compared to other packages, is that it also works on a Mac, although I had to install the package from source (install.packages("XLConnect", type="source")).

Tonight I will give a talk at the Cambridge R user group about googleVis. Following my good experience with knitr and RStudio to create interactive reports, I thought that I should try to create the slides in the same way as well.

Christopher Gandrud's recent post reminded me of deck.js, a JavaScript library for interactive html slides, which I have used in the past, but as Christopher experienced, it is currently not that straightforward to use with R and knitr.

Thus, I decided to try slidy in combination with knitr and pandoc. And it worked nicely.

I used RStudio again to edit my Rmd-file and knitr to generate the Markdown md-file output. Following this I run pandoc on the command line to convert the md-file into a single slidy html-file:

pandoc -s -S -i -t slidy --mathjax Cambridge_R_googleVis_with_knitr_and_RStudio_May_2012.md -o Cambridge_R_googleVis_with_knitr_and_RStudio_May_2012.html

Et volià, here is the result:

Addition (2 June 2012)

Oh boy, knitr and Markdown are hitting a nail. With slidify by Ramnath Vaidyanathan another project sprung up to ease the creation of web presentations.

John D. Cook gave a great talk about 'Why and how people use R'. The talk resonated with me and highlighted why R is such a great tool for end user computing. A topic which has become increasingly important in the European insurance industry.

John's main point on why people use R is that R gets the job done and I think he is spot on. Of course that's the trouble with R sometimes as well, or to quote Bo again:

"The best thing about R is that it was developed by statisticians.
"The worst thing about R is that it was developed by statisticians."
Bo Cowgill, Google

Last Saturday I met the guys from RStudio at the R in Finance conference in Chicago. I was curious to find out what RStudio could offer. In the past I have used mostly Emacs + ESS for editing R files. Well, and what a surprise it was. JJ, Joe and Josh showed me a preview of version 0.96 of their software, which adds a close integration of Sweave and knitr to RStudio, helping to create dynamic web reports with the new R Markdown and R HTML formats more easily.

Screen shot of RStudio with a knitr file (*.Rmd) in the top left window. Notice also the integrated knitr button.

You probably have come across Sweave in the past, but knitr is a fairly new package by Yihui Xie that brings literate programming to a new level. In particular the markdown approach allows me to create web content really quickly, without worrying to much about layout and R formatting. I begin to wonder if PDF and paper will be replaced by tablets and HTML5 in the future.

Here is a simple example. The knitr source code is available on Github.

Waterfall charts are sometimes quite helpful to illustrate the various moving parts in financial data, in particular when I have positive and negative values like a profit and loss statement (P&L). However, they can be a bit of a pain to produce in Excel. Not so in R, thanks to the waterfall package by James Howard. In combination with the latticeExtra package it is nearly a one-liner to produce a good looking waterfall chart that mimics the look of The Economist:

Example of a waterfall chart in R

library(latticeExtra)
library(waterfall)
data(rasiel) # Example data of the waterfall package
rasiel
#    label          value   subtotal
# 1  Net Sales       150    EBIT
# 2  Expenses       -170    EBIT 
# 3  Interest         18    Net Income
# 4  Gains            10    Net Income
# 5  Taxes            -2    Net Income

asTheEconomist(
               waterfallchart(value ~ label, data=rasiel,
                              groups=subtotal, main="P&L")
               )

Of course you can create a waterfall chart also with ggplot2, the Learning R blog has a post on this topic.

The next Cologne R user group meeting is scheduled for 6 July 2012. All details are available on the new KölnRUG Meetup site. Please sign up if you would like to come along, and notice that there is also pub poll for the after "work" drinks. Notes from the first Cologne R user group meeting are available here.