| merge {data.table} | R Documentation |
Fast merge of two data.tables.
This merge method for data.table behaves very similarly to that
of data.frames with one major exception: By default,
the columns used to merge the data.tables are the shared key columns
rather than the shared columns with the same names. Set the by, or by.x,
by.y arguments explicitly to override this default.
## S3 method for class 'data.table'
merge(x, y, by = NULL, by.x = NULL, by.y = NULL,
all = FALSE, all.x = all, all.y = all, sort = TRUE, suffixes = c(".x", ".y"),
allow.cartesian=getOption("datatable.allow.cartesian"), # default FALSE
...)
x, y |
|
by |
A vector of shared column names in |
by.x, by.y |
Vectors of column names in |
all |
logical; |
all.x |
logical; if |
all.y |
logical; analogous to |
sort |
logical. If |
suffixes |
A |
allow.cartesian |
See |
... |
Not used at this time. |
merge is a generic function in base R. It dispatches to either the
merge.data.frame method or merge.data.table method depending on the class of its first argument.
In versions < v1.9.6, if the specified columns in by was not the key (or head of the key) of x or y, then a copy is first rekeyed prior to performing the merge. This was less performant and memory inefficient.
In version v1.9.4 secondary keys was implemented. In v1.9.6, the concept of secondary keys has been
extended to merge. No deep copies are made anymore and therefore very performant and memory efficient. Also there is better control for providing the columns to merge on with the help of newly implemented by.x and by.y arguments.
For a more data.table-centric way of merging two data.tables, see [.data.table; e.g., x[y, ...]. See FAQ 1.12 for a detailed comparison of merge and x[y, ...].
Merges on numeric columns: Columns of numeric types (i.e., double) have their last two bytes rounded off while computing order, by defalult, to avoid any unexpected behaviour due to limitations in representing floating point numbers precisely. For large numbers (integers > 2^31), we recommend using bit64::integer64. Have a look at setNumericRounding to learn more.
A new data.table based on the merged data tables, sorted by the
columns set (or inferred for) the by argument.
data.table, [.data.table,
merge.data.frame
(dt1 <- data.table(A = letters[1:10], X = 1:10, key = "A"))
(dt2 <- data.table(A = letters[5:14], Y = 1:10, key = "A"))
merge(dt1, dt2)
merge(dt1, dt2, all = TRUE)
(dt1 <- data.table(A = letters[rep(1:3, 2)], X = 1:6, key = "A"))
(dt2 <- data.table(A = letters[rep(2:4, 2)], Y = 6:1, key = "A"))
merge(dt1, dt2, allow.cartesian=TRUE)
(dt1 <- data.table(A = c(rep(1L, 5), 2L), B = letters[rep(1:3, 2)], X = 1:6, key = "A,B"))
(dt2 <- data.table(A = c(rep(1L, 5), 2L), B = letters[rep(2:4, 2)], Y = 6:1, key = "A,B"))
merge(dt1, dt2)
merge(dt1, dt2, by="B", allow.cartesian=TRUE)
# test it more:
d1 <- data.table(a=rep(1:2,each=3), b=1:6, key="a,b")
d2 <- data.table(a=0:1, bb=10:11, key="a")
d3 <- data.table(a=0:1, key="a")
d4 <- data.table(a=0:1, b=0:1, key="a,b")
merge(d1, d2)
merge(d2, d1)
merge(d1, d2, all=TRUE)
merge(d2, d1, all=TRUE)
merge(d3, d1)
merge(d1, d3)
merge(d1, d3, all=TRUE)
merge(d3, d1, all=TRUE)
merge(d1, d4)
merge(d1, d4, by="a", suffixes=c(".d1", ".d4"))
merge(d4, d1)
merge(d1, d4, all=TRUE)
merge(d4, d1, all=TRUE)
# new feature, no need to set keys anymore
set.seed(1L)
d1 <- data.table(a=sample(rep(1:3,each=2)), z=1:6)
d2 <- data.table(a=2:0, z=10:12)
merge(d1, d2, by="a")
merge(d1, d2, by="a", all=TRUE)
# new feature, using by.x and by.y arguments
setnames(d2, "a", "b")
merge(d1, d2, by.x="a", by.y="b")
merge(d1, d2, by.x="a", by.y="b", all=TRUE)
merge(d2, d1, by.x="b", by.y="a")