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I am trying to calculate the mean of each row in my data frame. There are zeros in each row and I want to exclude these from calculation. I do not want to remove the entire row but only the zeros and calculate the mean of remaing values in each row. If row has all zero values than result should be Zero.
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Anyway, if you compile your protein matrix correctly, there should not be any missing values originating from technical causes in there at all, and in that case, you should definitely take 0-values into account when calculating a mean or median.
Calculate the mean of rows of a data frame in R To create a data frame in R, use the data. frame() function. To calculate the mean of rows of the data frame, use the rowMeans() function.
Select a blank cell and type this formula =COUNTIF(A1:H8,0) into it, and press Enter key, now all the zero cells excluding blank cells are counted out. Tip: In the above formula, A1:H8 is the data range you want to count the zeros from, you can change it as you need.
Once the first row is given, we can just add the total rows in the range and subtract 1 to get the last row number. For a very large number of ranges, we can use the INDEX function instead of the MIN function. The formula will be =ROW(INDEX(range,1,1))+ROWS(range)-1.
How about
nzmean <- function(x) {
if (all(x==0)) 0 else mean(x[x!=0])
}
apply(mydata,1,nzmean)
?
It occurs to me that it might be marginally faster to do
nzmean <- function(x) {
zvals <- x==0
if (all(zvals)) 0 else mean(x[!zvals])
}
i.e. try to avoid doing the comparison of x with zero twice.
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