rows

In the example below I have a dataset containing two experiments F1 and F2. A classification is performed based on F1 signal, and both F1 and F2 values are ordered accordingly. In this diagram, each facet has the same dimension although the number of rows is not the same ( e.g class #7 contains only few elements compare to the other classes). I would like to modify the code to force row height to be the same across facets (facets would thus have various blank space below). Any hints would be greatly appreciated. Thank you library(ggplot2) library(reshape2) set.seed(123) # let's create a fake

I have an array a as follows: a = [ 1 2; 3 4; 1 2 ]; I want to delete all rows appearing more than once in a and get c : c = [ 3 4 ]; Please note that this is not the same operation as keeping unique rows, since I don't want rows that had duplicates to appear at all. How can I accomplish this? The third output of unique gives you the index of the unique row in the original array. You can use this with accumarray to count the number of occurrences, which can be used to select rows that occur only once. For example: A = [1 2; 3 4; 1 2]; [uniquerow, ~, rowidx] = unique(A, 'rows'); noccurrences =

set.seed(8) df <- data.frame( A=sample(c(1:3), 10, replace=T), B=sample(c(1:3), 10, replace=T), C=sample(c(1:3), 10, replace=T), D=sample(c(1:3), 10, replace=T), E=sample(c(1:3), 10, replace=T), F=sample(c(1:3), 10, replace=T)) Would like to pass a subset of columns into a dplyr mutate() and make a row-wise calculation, for instance cor() to get correlation between column A-C and D-F, but cannot figure out how. Found SO inspiration here , here and here , but nevertheless failed to produce an acceptable code. For instance, I tried this: require(plyr) require(dplyr) df %>% rowwise() %>% mutate(c