summarization

This question already has an answer here: How to select the row with the maximum value in each group 10 answers How to select the rows with maximum values in each group with dplyr? [duplicate] 6 answers a <- c(rep(1:2,3)) b <- c("A","A","B","B","B","B") df <- data.frame(a,b) > str(b) chr [1:6] "A" "A" "B" "B" "B" "B" a b 1 1 A 2 2 A 3 1 B 4 2 B 5 1 B 6 2 B I want to group by variable a and return the most frequent value of b My desired result would look like a b 1 1 B 2 2 B In dplyr it would be something like df %>% group_by(a) %>% summarize (b = most.frequent(b)) I mentioned dplyr only to

I have data of the following format: gen = function () sample.int(10, replace = TRUE) x = data.frame(A = gen(), C = gen(), G = gen(), T = gen()) I would now like to attach, to each row, the total sum of all the elements in the row (my actual function is more complex but sum illustrates the problem). Without dplyr, I’d write cbind(x, Sum = apply(x, 1, sum)) Resulting in: A C G T Sum 1 3 1 6 9 19 2 3 4 3 3 13 3 3 1 10 5 19 4 7 2 1 6 16 … But it seems surprisingly hard to do this with dplyr. I’ve tried x %>% rowwise() %>% mutate(Sum = sum(A : T)) But the result is not the sum of the columns of

I have two data.tables. I would like to count the number of rows matching a combination of a table in another table. I have checked the data.table documentation but I have not found my answer. I am using data.table 1.9.2. DT1 <- data.table(a=c(3,2), b=c(8,3)) DT2 <- data.table(w=c(3,3,3,2,3), x=c(8,8,8,3,7), z=c(2,6,7,2,2)) DT1 # a b # 1: 3 8 # 2: 2 3 DT2 # w x z # 1: 3 8 2 # 2: 3 8 6 # 3: 3 8 7 # 4: 2 3 2 # 5: 3 7 2 Now I would like to count the number of (3, 8) pairs and (2, 3) pairs in DT2. setkey(DT2, w, x) nrow(DT2[J(3, 8), nomatch=0]) # [1] 3 ## OK ! nrow(DT2[J(2, 3), nomatch=0]) # [1] 1