I've got a dataframe containing rates for 'live' treatments and rates for 'killed' treatments. I'd like to subtract the killed treatments from the live ones:
df <- data.frame(id1=gl(2, 3, labels=c("a", "b")),
id2=rep(gl(3, 1, labels=c("live1", "live2", "killed")), 2),
y=c(10, 10, 1, 12, 12, 2),
otherFactor = gl(3, 2))
I'd like to subtract the values of y for which id2=="killed" from all the other values of y, separated by the levels of id1, while preserving otherFactor. I would end up with
id1 id2 y otherFactor
a live1 9 1
a live2 9 1
b live1 10 2
b live2 10 3
This almost works:
df_minusKill <- ddply(df, .(id1), function(x) x$y[x$id2!="killed"] - x$y[x$id2=="killed"])
names(df_minusKill) <- c("id1", "live1", "live2")
df_minusKill_melt <- melt(df_minusKill, measure.var=c("live1", "live2"))
except that you lose the values of otherFactor. Maybe I could use merge to put the values of otherFactor back in, but in reality I have about a dozen "otherFactor" columns, so it would be less cumbersome to just keep them in there automatically.
df2 <- ddply(df, .(id1), transform, y = y-y[id2=="killed"])
df2[-which(df2$id2=="killed"),]
id1 id2 y otherFactor
1 a live1 9 1
2 a live2 9 1
4 b live1 10 2
5 b live2 10 3
The by function can process sections of dataframe separately by factors (or you could use lapply(split(df , ...)):
> by(df, df$id1, FUN= function(x) x[['y']]-x[ x$id2=="killed", "y"] )
df$id1: a
[1] 9 9 0
---------------------------------------------------------------------------
df$id1: b
[1] 10 10 0
> unlist( by(df, df$id1, FUN= function(x) x[['y']]-x[ x$id2=="killed", "y"] ) )
a1 a2 a3 b1 b2 b3
9 9 0 10 10 0
You could assign this to a column in df and subset out the rows with id2 not equal to 'killing'.
来源：https://stackoverflow.com/questions/11196567/easiest-way-to-subtract-associated-with-one-factor-level-from-values-associated