What is the equivalent of mutate_at (dplyr) in data.table?

Question

I am trying to move some of my slower processes in dplyr to using data.table, however can not seem to find an efficient way of using a "mutate_at" type approach in data.table. Especially, when it comes to naming the new variables created & applying more than 1 function to multiple columns.

Below I use mutate_at to apply 2 different functions to 2 different columns with associated naming + using a group by statement. I want to be able to replicate this easily in data.table.

library(tibble)
library(zoo)

Data = tibble(A = rep(c(1,2),50),
              B = 1:100,
              C = 101:200)

Data %>% 
    group_by(A) %>% 
    mutate_at(vars(B,C), funs(Roll.Mean.Week = 7 * rollapply(., width = 7, mean, align = "right", fill = 0, na.rm = T, partial = T),
                              Roll.Mean.Two.Week = 7 * rollapply(., width = 14, mean, align = "right", fill = 0, na.rm = T, partial = T))) %>% 
    ungroup()

Answer 1

And also from Apply multiple functions to multiple columns in data.table

f <- function(x) lapply(c(Roll.Mean.Week=7L, Roll.Mean.Two.Week=14L),
    function(n) rollapplyr(x, width=n, mean, fill=0L, na.rm=TRUE, partial=TRUE))
DT[, unlist(lapply(.SD, f), recursive=FALSE), A, .SDcols=B:C]

output:

     A B._Roll.Mean.Week B._Roll.Mean.Two.Week C._Roll.Mean.Week C._Roll.Mean.Two.Week
  1: 1                 1                     1               101                   101
  2: 1                 2                     2               102                   102
  3: 1                 3                     3               103                   103
  4: 1                 4                     4               104                   104
  5: 1                 5                     5               105                   105
  6: 1                 6                     6               106                   106
  7: 1                 7                     7               107                   107
  8: 1                 9                     8               109                   108
  9: 1                11                     9               111                   109
 10: 1                13                    10               113                   110
 [trunc...]

Answer 2

With data.table, we can specify the columns of interest in .SDcols, loop through the .SD with lapply and apply the function of interest. Here, the funcion rollapply is repeated with only change in width parameter. So, it may be better to create a function to avoid repeating the whole arguments. Also, while applying the function (f1), the output can be kept in a list, later unlist with recursive = FALSE and assign (:=) to columns of interest

library(data.table)
library(zoo)
nm1 <- c("B", "C")
nm2 <- paste0(nm1, "_Roll.Mean.Week")
nm3 <- paste0(nm1, "_Roll.Mean.Two.Week")
f1 <- function(x, width) rollapply(x, width = width, mean,
        align = "right", fill = 0, na.rm = TRUE, partial = TRUE)
setDT(Data)[, c(nm2, nm3) := unlist(lapply(.SD, function(x)
  list(f1(x, 7), f1(x, 14))), recursive = FALSE), by = A, .SDcols = nm1]
head(Data)
#   A B   C B_Roll.Mean.Week C_Roll.Mean.Week B_Roll.Mean.Two.Week C_Roll.Mean.Two.Week
#1: 1 1 101                1                1                  101                  101
#2: 2 2 102                2                2                  102                  102
#3: 1 3 103                2                2                  102                  102
#4: 2 4 104                3                3                  103                  103
#5: 1 5 105                3                3                  103                  103
#6: 2 6 106                4                4                  104                  104

---

Note that funs is deprecated in tidyverse and in its place, can use list(~ or just ~

Data %>% 
    group_by(A) %>% 
    mutate_at(vars(B,C), list(Roll.Mean.Week =  ~f1(., 7),
                              Roll.Mean.Two.Week = ~ f1(., 14)))%>% 
    ungroup()