First of all, I assume that you call features the variables and not the samples/observations. In this case, you could do something like the following by creating a biplot function that shows everything in one plot. In this example, I am using the iris data. Before the example, please note that the basic idea when … Read more
I posted my answer even though another answer has already been accepted; the accepted answer relies on a deprecated function; additionally, this deprecated function is based on Singular Value Decomposition (SVD), which (although perfectly valid) is the much more memory- and processor-intensive of the two general techniques for calculating PCA. This is particularly relevant here … Read more
When logical vectors are used for indexing, they are recycled so this gets you odd columns or odd rows calld[ c(TRUE,FALSE), ] # rows calld[ , c(TRUE,FALSE) ] #columns Even rows or columns: calld[ !c(TRUE,FALSE), ] # rows calld[ , !c(TRUE,FALSE) ] #columns Every third column: calld[ , c(TRUE,FALSE, FALSE) ] #columns 1,4,7 , …. … Read more