R help - Dec 2015 - column means dropping column minimum

Suppose I have something like the following dataframe:

samp1 <- c(60,50,20,90)
samp2 <- c(60,60,90,58)
samp3 <- c(25,65,65,90)

test <- data.frame(samp1,samp2,samp3)

I want to calculate column means. Easy enough, if I want to use all the 
data within each column:


print(colMeans(test),na.rm = TRUE)


However, I'm danged if I can figure out how to do the same thing after 
dropping the minimum value for each column. For example, column 1 in the 
dataframe test consists of 60, 50,20,90. I want to calculate the mean 
over (60,50,90), dropping the minimum value (20). Figuring out what the 
minimum value is in a single column is easy, but I can't figure out how 
to arm-twist colMeans into 'applying itself' to the elements of a column
greater than the minimum, for each column in turn. I've tried 
permutations of select, subset etc., to no avail. Only thing I can think 
of is to (i) find the minimum in a column, (ii) change it to NA, and 
then (iii) tell colMeans to na.rm = TRUE):

test2 <- test

for (i in 1:ncol(test)) { test2[which.min(test[,i]),i]==NA}

print(test2)

print(colMeans(test2),na.rm = TRUE)


While this works, seems awfully 'clunky' -- is there a better way?

Thanks in advance...

There is a bug in your code: it is not
> for (i in 1:ncol(test)) { test2[which.min(test[,i]),i]==NA}
but

for (i in 1:ncos(test)) {
  test2[which.min(test[, i]), i] <- NA
}

Otherwise, a solution would be to create your own function to compute the mean
of a vector without the smallest value:

meanNoMin <- function (x, na.rm = FALSE)
  mean(sort(x)[-1], na.rm = na.rm)

? and then, to apply it to all columns of your data frame:

sapply(test, meanNoMin, na.rm = TRUE)

If you need faster code, you may want to look at Rcpp and a C++ version of the
previous code, but it is much more work.
Best,

Philippe Grosjean

> On 08 Dec 2015, at 15:29, Evan Cooch <evan.cooch at gmail.com> wrote:
> 
> Suppose I have something like the following dataframe:
> 
> samp1 <- c(60,50,20,90)
> samp2 <- c(60,60,90,58)
> samp3 <- c(25,65,65,90)
> 
> test <- data.frame(samp1,samp2,samp3)
> 
> I want to calculate column means. Easy enough, if I want to use all the
data within each column:
> 
> 
> print(colMeans(test),na.rm = TRUE)
> 
> 
> However, I'm danged if I can figure out how to do the same thing after
dropping the minimum value for each column. For example, column 1 in the
dataframe test consists of 60, 50,20,90. I want to calculate the mean over
(60,50,90), dropping the minimum value (20). Figuring out what the minimum value
is in a single column is easy, but I can't figure out how to arm-twist
colMeans into 'applying itself' to the elements of a column greater than
the minimum, for each column in turn. I've tried permutations of select,
subset etc., to no avail. Only thing I can think of is to (i) find the minimum
in a column, (ii) change it to NA, and then (iii) tell colMeans to na.rm =
TRUE):
> 
> test2 <- test
> 
> for (i in 1:ncol(test)) { test2[which.min(test[,i]),i]==NA}
> 
> print(test2)
> 
> print(colMeans(test2),na.rm = TRUE)
> 
> 
> While this works, seems awfully 'clunky' -- is there a better way?
> 
> Thanks in advance...
> 
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