correlation between two data frames in R - r

Question

I have one data frame which has sales values for the time period Oct. 2000 to Dec. 2001 (15 months). Also I have profit values for the same time period as above and I want to find the correlation between these two data frames month wise for these 15 months in R. My data frame sales is:
Month sales
Oct. 2000 24.1
Nov. 2000 23.3
Dec. 2000 43.9
Jan. 2001 53.8
Feb. 2001 74.9
Mar. 2001 25
Apr. 2001 48.5
May. 2001 18
Jun. 2001 68.1
Jul. 2001 78
Aug. 2001 48.8
Sep. 2001 48.9
Oct. 2001 34.3
Nov. 2001 54.1
Dec. 2001 29.3
My second data frame profit is:
period profit
Oct 2000 14.1
Nov 2000 3.3
Dec 2000 13.9
Jan 2001 23.8
Feb 2001 44.9
Mar 2001 15
Apr 2001 58.5
May 2001 18
Jun 2001 58.1
Jul 2001 38
Aug 2001 28.8
Sep 2001 18.9
Oct 2001 24.3
Nov 2001 24.1
Dec 2001 19.3
Now I know that for initial two months I cannot get the correlation as there are not enough values but from Dec 2000 onwards I want to calculate the correlation by taking into consideration the previous months values. So, for Dec. 200 I will consider values of Oct. 2000, Nov. 2000 and Dec. 2000 which will give me 3 sales value and 3 profit values. Similarly for Jan. 2001 I will consider values of Oct. 2000, Nov. 2000 Dec. 2000 and Jan. 2001 thus having 4 sales value and 4 profit value. Thus for every month I will consider previous month values also to calculate the correlation and my output should be something like this:
Month Correlation
Oct. 2000 NA or Empty
Nov. 2000 NA or Empty
Dec. 2000 x
Jan. 2001 y
. .
. .
Dec. 2001 a
I know that in R there is a function cor(sales, profit) but how can I find out the correlation for my scenario?

Answer 1

Make some sample data:
> sales = c(1,4,3,2,3,4,5,6,7,6,7,5)
> profit = c(4,3,2,3,4,5,6,7,7,7,6,5)
> data = data.frame(sales=sales,profit=profit)
> head(data)
sales profit
1 1 4
2 4 3
3 3 2
4 2 3
5 3 4
6 4 5
Here's the beef:
> data$runcor = c(NA,NA,
sapply(3:nrow(data),
function(i){
cor(data$sales[1:i],data$profit[1:i])
}))
> data
sales profit runcor
1 1 4 NA
2 4 3 NA
3 3 2 -0.65465367
4 2 3 -0.63245553
5 3 4 -0.41931393
6 4 5 0.08155909
7 5 6 0.47368421
8 6 7 0.69388867
9 7 7 0.78317543
10 6 7 0.81256816
11 7 6 0.80386072
12 5 5 0.80155885
So now data$runcor[3] is the correlation of the first 3 sales and profit numbers.
Note I call this runcor as its a "running correlation", like a "running sum" which is the sum of all elements so far. This is the correlation of all pairs so far.

Answer 2

Another possibility would be: (if dat1 and dat2 are the initial datasets)
Update
dat1$Month <- gsub("\\.", "", dat1$Month)
datN <- merge(dat1, dat2, sort=FALSE, by.x="Month", by.y="period")
indx <- sequence(3:nrow(datN)) #create index to replicate the rows
indx1 <- cumsum(c(TRUE,diff(indx) <0)) #create another index to group the rows
#calculate the correlation grouped by `indx1`
datN$runcor <- setNames(c(NA, NA,by(datN[indx,-1],
list(indx1), FUN=function(x) cor(x$sales, x$profit) )), NULL)
datN
# Month sales profit runcor
#1 Oct 2000 24.1 14.1 NA
#2 Nov 2000 23.3 3.3 NA
#3 Dec 2000 43.9 13.9 0.5155911
#4 Jan 2001 53.8 23.8 0.8148546
#5 Feb 2001 74.9 44.9 0.9345166
#6 Mar 2001 25.0 15.0 0.9119941
#7 Apr 2001 48.5 58.5 0.7056301
#8 May 2001 18.0 18.0 0.6879528
#9 Jun 2001 68.1 58.1 0.7647177
#10 Jul 2001 78.0 38.0 0.7357748
#11 Aug 2001 48.8 28.8 0.7351366
#12 Sep 2001 48.9 18.9 0.7190413
#13 Oct 2001 34.3 24.3 0.7175138
#14 Nov 2001 54.1 24.1 0.7041889
#15 Dec 2001 29.3 19.3 0.7094334