Generate Group-Id from Integer Sequences
seqid.Rdseqid can be used to group sequences of integers in a vector, e.g. seqid(c(1:3, 5:7)) becomes c(rep(1,3), rep(2,3)). It also supports increments > 1, unordered sequences, and missing values in the sequence.
Some applications are to facilitate identification of, and grouped operations on, (irregular) time series and panels.
Arguments
- x
a factor or integer vector. Numeric vectors will be converted to integer i.e. rounded downwards.
- o
an (optional) integer ordering vector specifying the order by which to pass through
x.- del
integer. The integer deliminating two consecutive points in a sequence.
del = 1letsseqidtrack sequences of the formc(1,2,3,..),del = 2tracks sequencesc(1,3,5,..)etc.- start
integer. The starting value of the resulting sequence id. Default is starting from 1.
- na.skip
logical.
TRUEskips missing values in the sequence. The default behavior is skipping such thatseqid(c(1, NA, 2))is regarded as one sequence and coded asc(1, NA, 1).- skip.seq
logical. If
na.skip = TRUE, this changes the behavior such that missing values are viewed as part of the sequence, i.e.seqid(c(1, NA, 3))is regarded as one sequence and coded asc(1, NA, 1).- check.o
logical. Programmers option:
FALSEprevents checking that each element ofois in the range[1, length(x)], it only checks the length ofo. This gives some extra speed, but will terminate R if any element ofois too large or too small.
Details
seqid was created primarily as a workaround to deal with problems of computing lagged values, differences and growth rates on irregularly spaced time series and panels before collapse version 1.5.0 (#26). Now flag, fdiff and fgrowth natively support irregular data so this workaround is superfluous, except for iterated differencing which is not yet supported with irregular data.
The theory of the workaround was to express an irregular time series or panel series as a regular panel series with a group-id created such that the time-periods within each group are consecutive. seqid makes this very easy: For an irregular panel with some gaps or repeated values in the time variable, an appropriate id variable can be generated using settransform(data, newid = seqid(time, radixorder(id, time))). Lags can then be computed using L(data, 1, ~newid, ~time) etc.
In general, for any regularly spaced panel the identity given by identical(groupid(id, order(id, time)), seqid(time, order(id, time))) should hold.
For the opposite operation of creating a new time-variable that is consecutive in each group, see data.table::rowid.
Value
An integer vector of class 'qG'. See qG.
Examples
## This creates an irregularly spaced panel, with a gap in time for id = 2
data <- data.frame(id = rep(1:3, each = 4),
time = c(1:4, 1:2, 4:5, 1:4),
value = rnorm(12))
data
#> id time value
#> 1 1 1 -1.540203133
#> 2 1 2 -2.131035606
#> 3 1 3 0.377222256
#> 4 1 4 1.693972475
#> 5 2 1 0.512538179
#> 6 2 2 -1.414468126
#> 7 2 4 -0.007331919
#> 8 2 5 -1.392200673
#> 9 3 1 -1.007205635
#> 10 3 2 -1.300076492
#> 11 3 3 0.749182687
#> 12 3 4 1.687936283
## This gave a gaps in time error previous to collapse 1.5.0
L(data, 1, value ~ id, ~time)
#> id time L1.value
#> 1 1 1 NA
#> 2 1 2 -1.540203133
#> 3 1 3 -2.131035606
#> 4 1 4 0.377222256
#> 5 2 1 NA
#> 6 2 2 0.512538179
#> 7 2 4 NA
#> 8 2 5 -0.007331919
#> 9 3 1 NA
#> 10 3 2 -1.007205635
#> 11 3 3 -1.300076492
#> 12 3 4 0.749182687
## Generating new id variable (here seqid(time) would suffice as data is sorted)
settransform(data, newid = seqid(time, order(id, time)))
data
#> id time value newid
#> 1 1 1 -1.540203133 1
#> 2 1 2 -2.131035606 1
#> 3 1 3 0.377222256 1
#> 4 1 4 1.693972475 1
#> 5 2 1 0.512538179 2
#> 6 2 2 -1.414468126 2
#> 7 2 4 -0.007331919 3
#> 8 2 5 -1.392200673 3
#> 9 3 1 -1.007205635 4
#> 10 3 2 -1.300076492 4
#> 11 3 3 0.749182687 4
#> 12 3 4 1.687936283 4
## Lag the panel this way
L(data, 1, value ~ newid, ~time)
#> newid time L1.value
#> 1 1 1 NA
#> 2 1 2 -1.540203133
#> 3 1 3 -2.131035606
#> 4 1 4 0.377222256
#> 5 2 1 NA
#> 6 2 2 0.512538179
#> 7 3 4 NA
#> 8 3 5 -0.007331919
#> 9 4 1 NA
#> 10 4 2 -1.007205635
#> 11 4 3 -1.300076492
#> 12 4 4 0.749182687
## A different possibility: Creating a consecutive time variable
settransform(data, newtime = data.table::rowid(id))
data
#> id time value newid newtime
#> 1 1 1 -1.540203133 1 1
#> 2 1 2 -2.131035606 1 2
#> 3 1 3 0.377222256 1 3
#> 4 1 4 1.693972475 1 4
#> 5 2 1 0.512538179 2 1
#> 6 2 2 -1.414468126 2 2
#> 7 2 4 -0.007331919 3 3
#> 8 2 5 -1.392200673 3 4
#> 9 3 1 -1.007205635 4 1
#> 10 3 2 -1.300076492 4 2
#> 11 3 3 0.749182687 4 3
#> 12 3 4 1.687936283 4 4
L(data, 1, value ~ id, ~newtime)
#> id newtime L1.value
#> 1 1 1 NA
#> 2 1 2 -1.540203133
#> 3 1 3 -2.131035606
#> 4 1 4 0.377222256
#> 5 2 1 NA
#> 6 2 2 0.512538179
#> 7 2 3 -1.414468126
#> 8 2 4 -0.007331919
#> 9 3 1 NA
#> 10 3 2 -1.007205635
#> 11 3 3 -1.300076492
#> 12 3 4 0.749182687
## With sorted data, the time variable can also just be omitted..
L(data, 1, value ~ id)
#> id L1.value
#> 1 1 NA
#> 2 1 -1.540203133
#> 3 1 -2.131035606
#> 4 1 0.377222256
#> 5 2 NA
#> 6 2 0.512538179
#> 7 2 -1.414468126
#> 8 2 -0.007331919
#> 9 3 NA
#> 10 3 -1.007205635
#> 11 3 -1.300076492
#> 12 3 0.749182687