I have a data.table like this:
date a b c d e f
2008 1 5 3 6 9 8
2008 3 6 2 1 5 8
2008 2 8 3 4 3 0
2009 5 5 3 6 9 8
2009 3 3 2 2 5 5
2010 2 8 3 7 7
You can use Map
to apply a different n
to each column:
cols <- setdiff(names(DT), "date")
DT[, (cols) := Map(shift, .SD, seq_along(.SD) - 1L, fill = 0), .SDcols = cols]
> DT
date a b c d e f
1: 2008 1 0 0 0 0 0
2: 2008 3 5 0 0 0 0
3: 2008 2 6 3 0 0 0
4: 2009 5 8 2 6 0 0
5: 2009 3 5 3 1 9 0
6: 2010 2 3 3 4 5 8
Here is a tidyverse
solution. Approach is to remove the date
column, then use purrr::map2_dfc
and dplyr::lag
to apply the correct shifts. map2
allows you to iterate along each column along with a vector of the correct shifts for each column, and then _dfc
just makes sure it binds the columns back together into a data frame. The last line is just to add the date column back on the left hand side.
library(tidyverse)
tbl <- read_table2(
"date a b c d e f
2008 1 5 3 6 9 8
2008 3 6 2 1 5 8
2008 2 8 3 4 3 0
2009 5 5 3 6 9 8
2009 3 3 2 2 5 5
2010 2 8 3 7 7 0"
)
tbl %>%
select(-date) %>%
map2_dfc(.x = ., .y = 1:ncol(.), ~ lag(.x, n = .y - 1, default = 0)) %>%
bind_cols(date = tbl$date, .)
#> # A tibble: 6 x 7
#> date a b c d e f
#> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2008 1 0 0 0 0 0
#> 2 2008 3 5.00 0 0 0 0
#> 3 2008 2 6.00 3.00 0 0 0
#> 4 2009 5 8.00 2.00 6.00 0 0
#> 5 2009 3 5.00 3.00 1.00 9.00 0
#> 6 2010 2 3.00 3.00 4.00 5.00 8.00
Created on 2018-02-23 by the reprex package (v0.2.0).
doing it manually with shift, which shifts a column (either leading or lagging for upward or downward) n units, padding with whatever you want:
library(data.table)
DT <- data.table(date=c(2008, 2008, 2008, 2009, 2009, 2010), a=c(1,3,2,5,3,2),b=c(5,6,8,5,3,8),c=c(3,2,3,3,2,3),d=c(6,1,4,6,2,7),e=c(9,5,3,9,5,7),f=c(8,8,0,8,5,0))
DT[,b := shift(b, n=1, type="lag", fill=0)]
DT[,c := shift(c, n=2, type="lag", fill=0)]
DT[,d := shift(d, n=3, type="lag", fill=0)]
DT[,e := shift(e, n=4, type="lag", fill=0)]
DT[,f := shift(f, n=5, type="lag", fill=0)]