Suppose you want to select column 1, 3 and 4 only.
### you don't need the cbind(...) here
Data1 <- data.frame(I1 = 3, I2 = c(0, 3:1, 2:5, NA),
I3 = c(1:4, NA, 5:2), I4 = 2, I5 = 3)
Data1$mean_134 <- rowMeans(Data1[, c(1, 3:4)], na.rm = TRUE)
Data1
Bill Venables.
-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]
On Behalf Of aaront
Sent: Monday, 26 November 2007 6:02 AM
To: r-help@r-project.org
Subject: [R] rowMean, specify subset of columns within Dataframe?
I would like to calculate the mean of tree leader increment growth over
5
years (I1 through I5) where each tree is a row and each row has 5
columns.
So far I have achieved this using rowMeans when all columns are numeric
type
and used in the calculation:
Data1 <- data.frame(cbind(I1 = 3, I2 = c(0,3:1, 2:5,NA), I3
=c(1:4,NA,5:2),I4=2,I5=3))
Data1
Data1$mean_5 <- rowMeans(Data1, na.rm =T)
Data1
My real dataset has many columns including several Factor type. Is it
possible to specify a range of columns within a data frame using
rowMeans
dims= (say where there is a one
Factor column called Species leading I1 to I5, and one Factor column
called
Moisture following, so 7 columns total) , or do I either need to extract
those columns to a new data frame, calculate means, and reattach to the
original data frame, or use a different function such as apply?
Unfortunately I am fairly new to R and have a difficult time with some
terminology and concepts in R Help.
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and provide commented, minimal, self-contained, reproducible code.
