Split and Diff function in R - r

Question

I have a data frame called data. I am splitting the data using split function by an attribute called KEY.
data <- split(data, data$KEY);
After splitting the dataframe by KEY, what we get is data for individual firms. dataframe data had the data for all the firms in the universe. After the split, each individual split has two columns, year and sales. For each split, I have to calculate incremental sales corresponding to each year. For instance, if we have data 2002 - 10, 2003 - 12, 2004 - 15, 2005 - 20. What I am interested in getting would be 2003-2, 2004 -3, 2005 - 5, for each split.
I have written a function, called mod_sale, to perform the job mentioned:
data[with(data, order(year)),];
sale_data <- diff(data$SALE);
data <- data[-1,];
data$SALE <- sale_data;
return(data)
Currently, I am using for loop:
for(key in names(data)){
a <- try(mod_sale(data[[key]]))
if(class(a) == "try-error") next;
mod_data <- rbind(mod_data,a)};
I think there is some way, I can use sapply (and may be plyr too). Can someone help me with improving this R code? Not sure how sapply code would go.
sapply(data, mod_sale)
Any help would be appreciated. Thanks.
Edit:
Here is a data example:
a <- data.frame();
key <- c(1,1,1,1,2,2,2,2,2,3,3,3);
sales <- c(12,12,15,8,3,6,3,9,9,12,3,7);
year <- c(2002,2003,2004,2005,2001,2002,2003,2004,2005,2003,2004,2005);
ovar <- runif(12,5.0,7.5);
a <- data.frame(key,sales,year,ovar)
In the resultant data.frame, I am expecting incremental sales rather than real sales. Obviously, we will lose 3 data points for 3 key; one for each starting year, as we are taking difference. So there will be three less rows in the resultant data.frame, which would have columns key,diff(sales),year, and ovar.

Answer 1

This is what I would have done:
a$diffsales <- ave( a$sales, a$key, FUN=function(x) c(NA, diff(x) ) )
a
key sales year ovar diffsales
1 1 12 2002 6.845177 NA
2 1 12 2003 6.328153 0
3 1 15 2004 6.872669 3
4 1 8 2005 6.098920 -7
5 2 3 2001 7.154824 NA
6 2 6 2002 6.110810 3
7 2 3 2003 5.906624 -3
8 2 9 2004 5.214369 6
9 2 9 2005 5.818218 0
10 3 12 2003 5.354354 NA
11 3 3 2004 6.728992 -9
12 3 7 2005 7.412213 4

Answer 2

I appreciate the attempt to display what you'd tried. Thank you.
In the future, try to provide a small example, like this:
df <- data.frame(year = 2001:2010,
sale = sample(20,10))
df <- rbind(df,df,df)
df$key <- rep(letters[1:3],each = 10)
That makes it much clearer what your data look like, and it makes it very easy for people trying to answer. The easier you make it for us, the faster+better answers you'll get.
I'd recommend sorting before splitting:
#Sort first (already sorted, but you get the idea)
df <- df[order(df$key,df$year),]
df_split <- split(df,df$key)
You don't actually want to use sapply. (Try it and see.) You just want lapply:
out <- lapply(df_split,function(x) {x$sale_diff <- c(NA,diff(x$sale)); x[-1,]})
You'd put it all together again using:
do.call(rbind,out)
You're right, plyr or data.table could also do this. I'll leave those examples to others.

Answer 3

Using data.table:
library(data.table)
dt = data.table(a)
dt[, sale_diff := c(NA, diff(sales)), by = key]
dt
# key sales year ovar sale_diff
# 1: 1 12 2002 7.416857 NA
# 2: 1 12 2003 5.625818 0
# 3: 1 15 2004 5.018934 3
# 4: 1 8 2005 6.671986 -7
# 5: 2 3 2001 6.242739 NA
# 6: 2 6 2002 6.297763 3
# 7: 2 3 2003 6.482124 -3
# 8: 2 9 2004 6.724256 6
# 9: 2 9 2005 5.071265 0
#10: 3 12 2003 6.136681 NA
#11: 3 3 2004 6.974392 -9
#12: 3 7 2005 6.517553 4