tidyr

I would appreciate any help to create a function that allows me to create categories of one variable using the order of a set of other variables values. Specifically, I want a function that: creates category E1 of the variable variable the first time that each combination of values of the variables A , B , and ID appears in the dataset. creates category E2 of the variable variable the second time that each combination of values of the variables A , B , and ID appears in the dataset. creates category E3 of the variable variable the third time that each combination of values of the variables A ,

I have a dataframe containing multiple observations from the control and the experimental cohorts with replicates for each subject. Here is an example of my dataframe: subject cohort replicate val1 val2 A control 1 10 0.1 A control 2 15 0.3 A experim 1 40 0.7 A experim 2 45 0.9 B control 1 5 0.3 B experim 1 30 0.0 C control 1 50 0.5 C experim 1 NA 1.0 I'd like to pair each control observation with its corresponding experimental one for each value to calculate the ratio between the pairs. The desired output would look something like this: subject replicate ratio_val1 ratio_val2 A 1 4 7 A 2 3 3

For the dataset mtcars2 mtcars2 = mtcars mtcars2 = mtcars2 %>% mutate(cyl9=cyl, disp9=disp, gear2=gear) I want to get a new column which is the sum of multiple columns, by using regular expressions to capture the pattern. This is a solution, however this is done by hard-coding select(mtcars2, cyl9) + select(mtcars2, disp9) + select(mtcars2, gear2) I tried something like this but it gives me a number instead of a vector mtcars2 %>% select(matches("[0-9]")) %>% sum Please dplyr solutions only, since i need to apply these functions to a sql table later on. Thanks! Update.. I need the solution to

library(tidyverse) df <- tibble(x1 = c("A", "A", "A", "B", "B", "B"), x2 = c(NA, 8, NA, NA, NA, 5), x3 = c(3, 6, 5, 9, 1, 9)) #> # A tibble: 6 x 3 #> x1 x2 x3 #> <chr> <dbl> <dbl> #> 1 A NA 3 #> 2 A 8 NA #> 3 A NA 5 #> 4 B NA 9 #> 5 B NA 1 #> 6 B 5 9 I have groups 'A' and 'B' shown in column x1 . I need the 'NA' values in columns x2 and x3 to populate only from values within the same group, in the updown direction. That's simple enough, here's the code: df %>% group_by(x1) %>% fill(c(x2, x3), .direction = "updown") #> # A tibble: 6 x 3 #> x1 x2 x3 #> <chr> <dbl> <dbl> #> 1 A 8 3 #> 2 A 8 5 #>