Transforming subsets of data in R with by, ddply and data.table
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 theplyr
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 rowbind (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 theddply
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 R2.15.0:
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 (20120603 r59505)
Platform: powerpcappledarwin8.11.0 (32bit)
locale:
[1] C
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] latticeExtra_0.619 lattice_0.206 RColorBrewer_1.05
[4] data.table_1.8.0 plyr_1.7.1
loaded via a namespace (and not attached):
[1] grid_2.15.0