How to create a lag variable within each group?
Using package dplyr:
library(dplyr)
data <-
data %>%
group_by(groups) %>%
mutate(lag.value = dplyr::lag(value, n = 1, default = NA))
gives
> data
Source: local data table [7 x 4]
Groups: groups
time groups value lag.value
1 1 a 0.07614866 NA
2 2 a -0.02784712 0.07614866
3 3 a 1.88612245 -0.02784712
4 1 b 0.26526825 NA
5 2 b 1.23820506 0.26526825
6 3 b 0.09276648 1.23820506
7 4 b -0.09253594 0.09276648
As noted by @BrianD, this implicitly assumes that value is sorted by group already. If not, either sort it by group, or use the order_by argument in lag. Also note that due to an existing issue with some versions of dplyr, for safety, arguments and the namespace should be explicitly given.
You could do this within data.table
library(data.table)
data[, lag.value:=c(NA, value[-.N]), by=groups]
data
# time groups value lag.value
#1: 1 a 0.02779005 NA
#2: 2 a 0.88029938 0.02779005
#3: 3 a -1.69514201 0.88029938
#4: 1 b -1.27560288 NA
#5: 2 b -0.65976434 -1.27560288
#6: 3 b -1.37804943 -0.65976434
#7: 4 b 0.12041778 -1.37804943
For multiple columns:
nm1 <- grep("^value", colnames(data), value=TRUE)
nm2 <- paste("lag", nm1, sep=".")
data[, (nm2):=lapply(.SD, function(x) c(NA, x[-.N])), by=groups, .SDcols=nm1]
data
# time groups value value1 value2 lag.value lag.value1
#1: 1 b -0.6264538 0.7383247 1.12493092 NA NA
#2: 2 b 0.1836433 0.5757814 -0.04493361 -0.6264538 0.7383247
#3: 3 b -0.8356286 -0.3053884 -0.01619026 0.1836433 0.5757814
#4: 1 a 1.5952808 1.5117812 0.94383621 NA NA
#5: 2 a 0.3295078 0.3898432 0.82122120 1.5952808 1.5117812
#6: 3 a -0.8204684 -0.6212406 0.59390132 0.3295078 0.3898432
#7: 4 a 0.4874291 -2.2146999 0.91897737 -0.8204684 -0.6212406
# lag.value2
#1: NA
#2: 1.12493092
#3: -0.04493361
#4: NA
#5: 0.94383621
#6: 0.82122120
#7: 0.59390132
Update
From data.table versions >= v1.9.5, we can use shift with type as lag or lead. By default, the type is lag.
data[, (nm2) := shift(.SD), by=groups, .SDcols=nm1]
# time groups value value1 value2 lag.value lag.value1
#1: 1 b -0.6264538 0.7383247 1.12493092 NA NA
#2: 2 b 0.1836433 0.5757814 -0.04493361 -0.6264538 0.7383247
#3: 3 b -0.8356286 -0.3053884 -0.01619026 0.1836433 0.5757814
#4: 1 a 1.5952808 1.5117812 0.94383621 NA NA
#5: 2 a 0.3295078 0.3898432 0.82122120 1.5952808 1.5117812
#6: 3 a -0.8204684 -0.6212406 0.59390132 0.3295078 0.3898432
#7: 4 a 0.4874291 -2.2146999 0.91897737 -0.8204684 -0.6212406
# lag.value2
#1: NA
#2: 1.12493092
#3: -0.04493361
#4: NA
#5: 0.94383621
#6: 0.82122120
#7: 0.59390132
If you need the reverse, use type=lead
nm3 <- paste("lead", nm1, sep=".")
Using the original dataset
data[, (nm3) := shift(.SD, type='lead'), by = groups, .SDcols=nm1]
# time groups value value1 value2 lead.value lead.value1
#1: 1 b -0.6264538 0.7383247 1.12493092 0.1836433 0.5757814
#2: 2 b 0.1836433 0.5757814 -0.04493361 -0.8356286 -0.3053884
#3: 3 b -0.8356286 -0.3053884 -0.01619026 NA NA
#4: 1 a 1.5952808 1.5117812 0.94383621 0.3295078 0.3898432
#5: 2 a 0.3295078 0.3898432 0.82122120 -0.8204684 -0.6212406
#6: 3 a -0.8204684 -0.6212406 0.59390132 0.4874291 -2.2146999
#7: 4 a 0.4874291 -2.2146999 0.91897737 NA NA
# lead.value2
#1: -0.04493361
#2: -0.01619026
#3: NA
#4: 0.82122120
#5: 0.59390132
#6: 0.91897737
#7: NA
data
set.seed(1)
data <- data.table(time =c(1:3,1:4),groups = c(rep(c("b","a"),c(3,4))),
value = rnorm(7), value1=rnorm(7), value2=rnorm(7))