Efficient group-by processing functions for data.table written with Rcpp.

See:

• https://chataignon.com/dtutils.html
• https://chataignon.com/na_fill.html

Replace NA by previous or next value, optionally by group

na_fill_by(dt, var = NULL, by = NULL, type = 1L, inplace = FALSE)

a list with item for each var

When type = 1 , LOCF = Last Observation Carried Forward. Ex : c(NA, 1, NA, NA, 2, NA, 3, NA) gives c(NA, 1, 1, 1, 2, 2, 3, 3) When type = 2 , NOCB = Next Observation Carried Backward. Ex : c(NA, 1, NA, NA, 2, NA, 3, NA) gives c(1, 1, 2, 2, 2, 3, 3, NA) When type = 3 , LOCF then NOCB. Ex : c(NA, 1, NA, NA, 2, NA, 3, NA) gives c(1, 1, 1, 1, 2, 2, 3, 3) .

Replace NA by fill value

na_replace(dt, var = NULL, fill, inplace = FALSE)

Returns a logical TRUE for first row of each group

Returns a logical TRUE for last row of each group

Create a sequence 1...N by group

Case_when like function with multiple constant values

case_when_mult(...)

 library(data.table)
 dt <- data.table(a = c(1:5, NA, 7:10), b=rnorm(10), c=letters[1:10])
 dt[, c("w", "x") :=
 case_when_mult(
 a %% 4 == 0 ~ list("p", 1.5),
 a %% 2 == 0 ~ list("m", 4) ,
 T ~ list("i", 6)
 )
 ]
 print(dt)

Returns first non NA value for var by group

Returns lag or lead values for var by group

Returns weighted sum for var by group

Returns TRUE if all boolean are TRUE by group

Returns TRUE if any boolean is TRUE by group