I am trying to transform my data.frame by calculating the log-differences of each column
and controlling for the rows id. So basically I like to calculate the growth rates for each id's variable.
So here is a random df with an id column, a time period colum p and three variable columns:
df <- data.frame (id = c("a","a","a","c","c","d","d","d","d","d"),
p = c(1,2,3,1,2,1,2,3,4,5),
var1 = rnorm(10, 5),
var2 = rnorm(10, 5),
var3 = rnorm(10, 5)
)
df
id p var1 var2 var3
1 a 1 5.375797 4.110324 5.773473
2 a 2 4.574700 6.541862 6.116153
3 a 3 3.029428 4.931924 5.631847
4 c 1 5.375855 4.181034 5.756510
5 c 2 5.067131 6.053009 6.746442
6 d 1 3.846438 4.515268 6.920389
7 d 2 4.910792 5.525340 4.625942
8 d 3 6.410238 5.138040 7.404533
9 d 4 4.637469 3.522542 3.661668
10 d 5 5.519138 4.599829 5.566892
Now I have written a function which does exactly what I want BUT I had to take a detour which is possibly unnecessary and can be removed. However, somehow I am not able to locate
the shortcut.
Here is the function and the output for the posted data frame:
fct.logDiff <- function (df) {
df.log <- dlply (df, "code", function(x) data.frame (p = x$p, log(x[, -c(1,2)])))
list.nalog <- llply (df.log, function(x) data.frame (p = x$p, rbind(NA, sapply(x[,-1], diff))))
ldply (list.nalog, data.frame)
}
fct.logDiff(df)
id p var1 var2 var3
1 a 1 NA NA NA
2 a 2 -0.16136569 0.46472004 0.05765945
3 a 3 -0.41216720 -0.28249264 -0.08249587
4 c 1 NA NA NA
5 c 2 -0.05914281 0.36999681 0.15868378
6 d 1 NA NA NA
7 d 2 0.24428771 0.20188025 -0.40279188
8 d 3 0.26646102 -0.07267311 0.47041227
9 d 4 -0.32372771 -0.37748866 -0.70417351
10 d 5 0.17405309 0.26683625 0.41891802
The trouble is due to the added NA-rows. I don't want to collapse the frame and reduce it, which would be automatically done by the diff() function. So I had 10 rows in my original frame and am keeping the same amount of rows after the transformation. In order to keep the same length I had to add some NAs. I have taken a detour by transforming the data.frame into a list, add the NAs, and afterwards transform the list back into a data.frame. That looks tedious.
Any ideas to avoid the data.frame-list-data.frame class transformation and optimize the function?
