I notice that you mention a function %like% in your current approach. I don’t know if that’s a reference to the %like% from “data.table”, but if it is, you can definitely use it as follows. Note that the object does not have to be a data.table (but also remember that subsetting approaches for data.frames and … Read more
Logic index: d<-d[!(d$A==”B” & d$E==0),]
In Python 3, you can use dict.items() to get a set-like view of the dict items. You can then use the <= operator to test if one view is a “subset” of the other: d1.items() <= d2.items() In Python 2.7, use the dict.viewitems() to do the same: d1.viewitems() <= d2.viewitems() In Python 2.6 and below … Read more
bool isSubset = !t2.Except(t1).Any();
To select rows according to one ‘cell_type’ (e.g. ‘hesc’), use ==: expr[expr$cell_type == “hesc”, ] To select rows according to two or more different ‘cell_type’, (e.g. either ‘hesc’ or ‘bj fibroblast’), use %in%: expr[expr$cell_type %in% c(“hesc”, “bj fibroblast”), ]
I assume X and Y are arrays? If so, there’s a very simple way to do this: x = [1, 1, 2, 4] y = [1, 2, 2, 2] # intersection x & y # => [1, 2] # union x | y # => [1, 2, 4] # difference x – y # => … Read more
You can use the ! operator to basically make any TRUE FALSE and every FALSE TRUE. so: D2 = subset(D1, !(V1 %in% c(‘B’,’N’,’T’))) EDIT: You can also make an operator yourself: ‘%!in%’ <- function(x,y)!(‘%in%'(x,y)) c(1,3,11)%!in%1:10 [1] FALSE FALSE TRUE
You should use either indexing or the subset function. For example : R> df <- data.frame(x=1:5, y=2:6, z=3:7, u=4:8) R> df x y z u 1 1 2 3 4 2 2 3 4 5 3 3 4 5 6 4 4 5 6 7 5 5 6 7 8 Then you can use the … Read more
This question was answered in well in the comments by @James, pointing to an excellent explanation by Hadley Wickham of the dangers of subset (and functions like it) [here]. Go read it! It’s a somewhat long read, so it may be helpful to record here the example that Hadley uses that most directly addresses the … Read more