How to pivot a table to make columns fro a variable row values in R

I have a data.frame with the columns: Month, Store and Demand.

Month   Store   Demand
Jan     A   100
Feb     A   150
Mar     A   120
Jan     B   200
Feb     B   230
Mar     B   320

I need to pivot it around to make a new data.frame or array with columns for each month, sto开发者_JAVA技巧re e.g.:

Store   Jan Feb Mar
A       100 150 120
B       200 230 320

Any help is very much appreciated. I have just started with R.

> df <- read.table(textConnection("Month   Store   Demand
+ Jan     A   100
+ Feb     A   150
+ Mar     A   120
+ Jan     B   200
+ Feb     B   230
+ Mar     B   320"), header=TRUE)

So in all likelihood your Month column is a factor with levels sorted alphabetically (EDIT:)

> df$Month <- factor(df$Month, levels= month.abb[1:3])
 # Just changing levels was not correct way to handle the problem. 
 # Need to use within a factor(...) call.
> xtabs(Demand ~ Store+Month, df)
      Month
 Store Jan Feb Mar
     A 100 150 120
     B 200 230 320

A slightly less obvious method (since the 'I' function returns its argument):

> with(df, tapply(Demand, list(Store, Month) , I)  )
  Jan Feb Mar
A 100 150 120
B 200 230 320

Welcome to R.

There are usually many ways to get to the same end using R. Another approach would be to use Hadley's reshape package.

# create the data as explained by @Dwin
df <- read.table(textConnection("Month   Store   Demand
                                Jan     A   100
                                Feb     A   150
                                Mar     A   120
                                Jan     B   200
                                Feb     B   230
                                Mar     B   320"), 
                 header=TRUE)

# load the reshape package from Hadley -- he has created GREAT packages
library(reshape)

# reshape the data from long to wide
cast(df, Store ~ Month)

And for reference, you should check out this great tutorial. http://www.jstatsoft.org/v21/i12/paper

If the data are in dat (and levels set to calendar order), then another base R solution is to use the (incredibly unintuitive) reshape() function:

reshape(dat, v.names = "Demand", idvar = "Store", timevar = "Month", 
        direction = "wide")

which for the snippet of data gives:

> reshape(dat, v.names = "Demand", idvar = "Store", timevar = "Month", 
+         direction = "wide")
  Store Demand.Jan Demand.Feb Demand.Mar
1     A        100        150        120
4     B        200        230        320

The names can easily be cleaned if you want:

> out <- reshape(dat, v.names = "Demand", idvar = "Store", timevar = "Month", 
+                direction = "wide")
> names(out)[-1] <- month.abb[1:3]
> out
  Store Jan Feb Mar
1     A 100 150 120
4     B 200 230 320

(To get the output above, I read the data in in a similar fashion to that shown in @DWin's Answer, and then ran the following:

dat <- transform(dat, Month = factor(Month, levels = month.abb[1:3]))

where dat was what I called the data)