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