Looking to learn function writing. I have data laid out in the following (eg):
Genus Species Wing Tail
A X 10.5 20.3
A Y 10.7 20.7
B XX 15.2 22.5
B XY 15.5 24
I calculate variance for a given trait using the equation:
sqrt(max(Wing) - min (Wing))
which I sum for all traits.
So I can write the following function so sum variance for the total data set:
variance<- function(data){
t <- sqrt(max(Tail)-min(Tail))
w <- sqrt(max(Wing)-min(Wing))
x <- sum(t,w)
x
}
But I can'twork out how to generate a response to give me an output where this result is dependant on the Genus. So i'm looking to generate an output like:
Genus A Genus B
2.345 3.456
I am going to give a new name to your function because it's just wrong to call it "variance". I hope you can overlook that. We can work on a dataframe object
dput(dfrm)
structure(list(Genus = structure(c(1L, 1L, 2L, 2L), .Label = c("A",
"B"), class = "factor"), Species = structure(c(1L, 4L, 2L, 3L
), .Label = c("X", "XX", "XY", "Y"), class = "factor"), Wing = c(10.5,
10.7, 15.2, 15.5), Tail = c(20.3, 20.7, 22.5, 24)), .Names = c("Genus",
"Species", "Wing", "Tail"), class = "data.frame", row.names = c(NA,
-4L))
dev2<- function(df){
t <- sqrt(max(df[["Tail"]])-min(df[["Tail"]]))
w <- sqrt(max(df[["Wing"]])-min(df[["Wing"]]))
x <- sum(t,w)
x
}
Now use it to work on the full dataframe, using the split-lapply strategy, which passes sections of the original dataframe determined by the Genus values to the dev2 function
lapply( split(dfrm, list(dfrm$Genus)), FUN = dev2)
$A
[1] 1.079669
$B
[1] 1.772467
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