How to create a lag variable within each group?
I have a data.table:
set.seed(1)
data <- data.table(time = c(1:3, 1:4),
groups = c(rep(c("b", "a"), c(3, 4))),
value = rnorm(7))
data
# groups time value
# 1: b 1 -0.6264538
# 2: b 2 0.1836433
# 3: b 3 -0.8356286
# 4: a 1 1.5952808
# 5: a 2 0.3295078
# 6: a 3 -0.8204684
# 7: a 4 0.4874291
I want to compute a lagged version of the "value" column, within each level of "groups".
The result should look like
# groups time value lag.value
# 1 a 1 1.5952808 NA
# 2 a 2 0.3295078 1.5952808
# 3 a 3 -0.8204684 0.3295078
# 4 a 4 0.4874291 -0.8204684
# 5 b 1 -0.6264538 NA
# 6 b 2 0.1836433 -0.6264538
# 7 b 3 -0.8356286 0.1836433
I have tried to use lag directly:
data$lag.value <- lag(data$value)
...which clearly wouldn't work.
I have also tried:
unlist(tapply(data$value, data$groups, lag))
a1 a2 a3 a4 b1 b2 b3
NA -0.1162932 0.4420753 2.1505440 NA 0.5894583 -0.2890288
Which is almost what I want. However the vector generated is ordered differently from the ordering in the data.table which is problematic.
What is the most efficient way to do this in base R, plyr, dplyr, and data.table?
Answer
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))