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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:
1. base R with `by`,
2. `ddply` of the `plyr` package,
3. `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 1=""Company")" language="("Year","][/c], 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.

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 ```