data.table: Extension of `data.frame`

shift

R Documentation

Fast lead/lag for vectors and lists

Description

lead or lag vectors, lists, data.frames or data.tables implemented in C for speed.

bit64::integer64 is also supported.

Usage

shift(x, n=1L, fill, type=c("lag", "lead", "shift", "cyclic"), give.names=FALSE)

Arguments

`x`	A vector, list, data.frame or data.table.
`n`	integer vector denoting the offset by which to lead or lag the input. To create multiple lead/lag vectors, provide multiple values to `n`; negative values of `n` will "flip" the value of `type`, i.e., `n=-1` and `type='lead'` is the same as `n=1` and `type='lag'`.
`fill`	default is `NA`. Value to use for padding when the window goes beyond the input length.
`type`	default is `"lag"` (look "backwards"). The other possible values `"lead"` (look "forwards"), `"shift"` (behave same as `"lag"` except given names) and `"cyclic"` where pushed out values are re-introduced at the front/back.
`give.names`	default is `FALSE` which returns an unnamed list. When `TRUE`, names are automatically generated corresponding to `type` and `n`. If answer is an atomic vector, then the argument is ignored.

Details

shift accepts vectors, lists, data.frames or data.tables. It always returns a list except when the input is a vector and length(n) == 1 in which case a vector is returned, for convenience. This is so that it can be used conveniently within data.table's syntax. For example, DT[, (cols) := shift(.SD, 1L), by=id] would lag every column of .SD by 1 for each group and DT[, newcol := colA + shift(colB)] would assign the sum of two vectors to newcol.

Argument n allows multiple values. For example, DT[, (cols) := shift(.SD, 1:2), by=id] would lag every column of .SD by 1 and 2 for each group. If .SD contained four columns, the first two elements of the list would correspond to lag=1 and lag=2 for the first column of .SD, the next two for second column of .SD and so on. Please see examples for more.

shift is designed mainly for use in data.tables along with := or set. Therefore, it returns an unnamed list by default as assigning names for each group over and over can be quite time consuming with many groups. It may be useful to set names automatically in other cases, which can be done by setting give.names to TRUE.

Note that when using shift with a list, it should be a list of lists rather than a flattened list. The function was not designed to handle flattened lists directly. This also applies to the use of list columns in a data.table. For example, DT = data.table(x=as.list(1:4)) is a data.table with four rows. Applying DT[, shift(x)] now lags every entry individually, rather than shifting the full columns like DT[, shift(as.integer(x))] does. Using DT = data.table(x=list(1:4)) creates a data.table with one row. Now DT[, shift(x)] returns a data.table with four rows where x is lagged. To get a shifted data.table with the same number of rows, wrap the shift function in list or dot, e.g., DT[, .(shift(x))].

Value

A list containing the lead/lag of input x.

Examples

# on vectors, returns a vector as long as length(n) == 1, #1127
x = 1:5
# lag with n=1 and pad with NA (returns vector)
shift(x, n=1, fill=NA, type="lag")
# lag with n=1 and 2, and pad with 0 (returns list)
shift(x, n=1:2, fill=0, type="lag")
# getting a window by using positive and negative n:
shift(x, n = -1:1)
shift(x, n = -1:1, type = "shift", give.names = TRUE)
# cyclic shift where pad uses pushed out values
shift(x, n = -1:1, type = "cyclic")

# on data.tables
DT = data.table(year=2010:2014, v1=runif(5), v2=1:5, v3=letters[1:5])
# lag columns 'v1,v2,v3' DT by 1 and fill with 0
cols = c("v1","v2","v3")
anscols = paste("lead", cols, sep="_")
DT[, (anscols) := shift(.SD, 1, 0, "lead"), .SDcols=cols]

# return a new data.table instead of updating
# with names automatically set
DT = data.table(year=2010:2014, v1=runif(5), v2=1:5, v3=letters[1:5])
DT[, shift(.SD, 1:2, NA, "lead", TRUE), .SDcols=2:4]

# lag/lead in the right order
DT = data.table(year=2010:2014, v1=runif(5), v2=1:5, v3=letters[1:5])
DT = DT[sample(nrow(DT))]
# add lag=1 for columns 'v1,v2,v3' in increasing order of 'year'
cols = c("v1","v2","v3")
anscols = paste("lag", cols, sep="_")
DT[order(year), (cols) := shift(.SD, 1, type="lag"), .SDcols=cols]
DT[order(year)]

# while grouping
DT = data.table(year=rep(2010:2011, each=3), v1=1:6)
DT[, c("lag1", "lag2") := shift(.SD, 1:2), by=year]

# on lists
ll = list(1:3, letters[4:1], runif(2))
shift(ll, 1, type="lead")
shift(ll, 1, type="lead", give.names=TRUE)
shift(ll, 1:2, type="lead")

# fill using first or last by group
DT = data.table(x=1:6, g=rep(1:2, each=3))
DT[ , shift(x, fill=x[1L]), by=g]
DT[ , shift(x, fill=x[.N], type="lead"), by=g]

Rdatatable/data.table documentation built on Dec. 21, 2024, 3:08 a.m.