R Sorting Data Frame by Date - r

Question

I'm working on a R data.frame which is made of stocks'dividends per year (I've got 60 stocks in columns and the usual calendar in rows). When a dividend is paid, I've got the figure and otherwise there is a NA.
Basically , here is how my Data.frame looks like
BARC LN BARN SE BAS GY BATS LN
1999-01-01 0.26 NA NA
1999-01-02 NA 0.56 0.35 NA
1999-01-03 NA NA NA NA
2000-01-04 NA NA 0.40 NA
1999-01-05 0.23 0.28 NA NA
2001-01-06 NA NA NA NA
2001-01-07 0.85 NA 0.15 NA
I would like to get the amount of dividend paid per year for each stock in order to compute the dividend yield ratio and finally get a Data;frame like the one below :
BARC LN BARN SE BAS GY BATS LN
1999 NA NA NA NA
2000 NA NA NA NA
2001 NA NA NA NA
How can i do that?

Answer 1

So, assuming your data is in a data.frame like the one you've posted above called div:
div <- structure(list(barc.ln = c(0.26, NA, NA, NA, 0.23, NA, 0.85),
barn.se = c(NA, 0.56, NA, NA, 0.28, NA, NA), bas.gy = c(NA,
0.35, NA, 0.4, NA, NA, 0.15), bats.ln = c(NA, NA, NA, NA,
NA, NA, NA)), .Names = c("barc.ln", "barn.se", "bas.gy",
"bats.ln"), row.names = c("1999-01-01", "1999-01-02", "1999-01-03",
"2000-01-04", "1999-01-05", "2001-01-06", "2001-01-07"), class = "data.frame")
just as you've done you can extract the years from your row.names:
div$years <- as.POSIXlt(row.names(div))$year + 1900
The plyr and reshape2 packages work well here and I think make the code particularly clear. Specifically, I'll use melt to make the data long and then ddply to split into groups and sum the dividends:
library(plyr)
library(reshape2)
div.melt <- melt(div, id.vars='years')
div.sum <- ddply(div.melt,
.(years, variable),
summarise,
dividend = sum(value, na.rm=TRUE))
> div.sum
years variable dividend
1 1999 barc.ln 0.49
2 1999 barn.se 0.84
3 1999 bas.gy 0.35
4 1999 bats.ln 0.00
5 2000 barc.ln 0.00
6 2000 barn.se 0.00
7 2000 bas.gy 0.40
8 2000 bats.ln 0.00
9 2001 barc.ln 0.85
10 2001 barn.se 0.00
11 2001 bas.gy 0.15
12 2001 bats.ln 0.00
>
you can then use another function from reshape2 called cast to format your data "wide":
> dcast(div.sum, years ~ variable, value.var='dividend')
years barc.ln barn.se bas.gy bats.ln
1 1999 0.49 0.84 0.35 0
2 2000 0.00 0.00 0.40 0
3 2001 0.85 0.00 0.15 0
>

Answer 2

I think you can do this pretty easily using by(). Here's how I did it. I've put each block, along with an explanation below each block.
dividends <- data.frame(barc_ln=c(0.26,NA,NA,NA,0.23,NA,0.85),
barn_se=c(NA,0.56,NA,NA,0.28,NA,NA),
bas_gy=c(NA,0.35,NA,0.40,NA,NA,0.15),
bats_ln=c(NA,NA,NA,NA,NA,NA,NA),
row.names=c("1999-01-01","1999-01-02","1999-01-03","2000-01-04","1999-01-05","2001-01-06","2001-01-07"))
This just creates the original data frame you gave.
dividends[,"dates"] <- as.Date(row.names(dividends))
dividends <- dividends[order(dividends[,"dates"]),]
dividends[,"year"] <- format(dividends$dates,"%Y")
This takes the row name dates, and then turns them into a new column ("dates") in the data frame. Then, we order the data frame (not necessarily required, but I find it to be more intuitive) by date and extract the year (as a character, mind you) using format.
div_output <- data.frame(row.names=unique(dividends$year))
Next, I create the output data frame that will receive the data. I use the unique() function on the year variable to get the unique vector of years. They're already ordered (one advantage of ordering the data frame).
for(x in 1:4) {
div_output[,x] <- by(dividends[,x],INDICES=dividends$year,FUN=sum,na.rm=TRUE)
}
names(div_output) <- names(dividends)[1:4]
Using a simple loop, we just go through each of the columns and apply the by() function. The variable is the column, the indices are the year, and we just use the sum function. I tag on na.rm=TRUE so that instead of NAs, you get actual data.
print(div_output)
barc_ln barn_se bas_gy bats_ln
1999 0.49 0.84 0.35 0
2000 0.00 0.00 0.40 0
2001 0.85 0.00 0.15 0
And there's the output I get.