all.equal.data.table: Equality Test Between Two Data Tables
In data.table: Extension of `data.frame`

all.equal

R Documentation

Equality Test Between Two Data Tables

Description

Convenient test of data equality between data.table objects. Performs some factor level stripping.

Usage

  ## S3 method for class 'data.table'
all.equal(target, current, trim.levels=TRUE, check.attributes=TRUE,
    ignore.col.order=FALSE, ignore.row.order=FALSE, tolerance=sqrt(.Machine$double.eps),
    ...)

Arguments

`target`, `current`	`data.table`s to compare. If `current` is not a `data.table`, but `check.attributes` is FALSE, it will be coerced to one via as.data.table.
`trim.levels`	A logical indicating whether or not to remove all unused levels in columns that are factors before running equality check. It effect only when `check.attributes` is TRUE and `ignore.row.order` is FALSE.
`check.attributes`	A logical indicating whether or not to check attributes, will apply not only to data.table but also attributes of the columns. It will skip `c("row.names",".internal.selfref")` data.table attributes.
`ignore.col.order`	A logical indicating whether or not to ignore columns order in `data.table`.
`ignore.row.order`	A logical indicating whether or not to ignore rows order in `data.table`. This option requires datasets to use data types on which join can be made, so no support for list, complex, raw, but still supports integer64.
`tolerance`	A numeric value used when comparing numeric columns, by default `sqrt(.Machine$double.eps)`. Unless non-default value provided it will be forced to `0` if used together with `ignore.row.order` and duplicate rows detected or factor columns present.
`...`	Passed down to internal call of `all.equal`.

Details

For efficiency data.table method will exit on detected non-equality issues, unlike most all.equal methods which process equality checks further. Besides that fact it also handles the most time consuming case of ignore.row.order = TRUE very efficiently.

Value

Either TRUE or a vector of mode "character" describing the differences between target and current.

Examples

dt1 <- data.table(A = letters[1:10], X = 1:10, key = "A")
dt2 <- data.table(A = letters[5:14], Y = 1:10, key = "A")
isTRUE(all.equal(dt1, dt1))
is.character(all.equal(dt1, dt2))

# ignore.col.order
x <- copy(dt1)
y <- dt1[, .(X, A)]
all.equal(x, y)
all.equal(x, y, ignore.col.order = TRUE)

# ignore.row.order
x <- setkeyv(copy(dt1), NULL)
y <- dt1[sample(nrow(dt1))]
all.equal(x, y)
all.equal(x, y, ignore.row.order = TRUE)

# check.attributes
x = copy(dt1)
y = setkeyv(copy(dt1), NULL)
all.equal(x, y)
all.equal(x, y, check.attributes = FALSE)
x = data.table(1L)
y = 1L
all.equal(x, y)
all.equal(x, y, check.attributes = FALSE)

# trim.levels
x <- data.table(A = factor(letters[1:10])[1:4]) # 10 levels
y <- data.table(A = factor(letters[1:5])[1:4]) # 5 levels
all.equal(x, y, trim.levels = FALSE)
all.equal(x, y, trim.levels = FALSE, check.attributes = FALSE)
all.equal(x, y)

data.table documentation built on Oct. 10, 2024, 5:07 p.m.