Merge two data frames by common columns or row names, or do other versions of database join operations.
merge(x, y, …)# S3 method for default
merge(x, y, …)
# S3 method for data.frame
merge(x, y, by = intersect(names(x), names(y)),
by.x = by, by.y = by, all = FALSE, all.x = all, all.y = all,
sort = TRUE, suffixes = c(".x",".y"), no.dups = TRUE,
incomparables = NULL, …)
data frames, or objects to be coerced to one.
specifications of the columns used for merging. See ‘Details’.
logical; all = L is shorthand for all.x = L and
all.y = L, where L is either TRUE or
FALSE.
logical; if TRUE, then extra rows will be added to
the output, one for each row in x that has no matching row in
y. These rows will have NAs in those columns that are
usually filled with values from y. The default is
FALSE, so that only rows with data from both x and
y are included in the output.
logical; analogous to all.x.
logical. Should the result be sorted on the by
columns?
a character vector of length 2 specifying the suffixes
to be used for making unique the names of columns in the result
which are not used for merging (appearing in by etc).
logical indicating that suffixes are appended in
more cases to avoid duplicated column names in the result. This
was implicitly false before R version 3.5.0.
values which cannot be matched. See
match. This is intended to be used for merging on one
column, so these are incomparable values of that column.
arguments to be passed to or from methods.
A data frame. The rows are by default lexicographically sorted on the
common columns, but for sort = FALSE are in an unspecified order.
The columns are the common columns followed by the
remaining columns in x and then those in y. If the
matching involved row names, an extra character column called
Row.names is added at the left, and in all cases the result has
‘automatic’ row names.
merge is a generic function whose principal method is for data
frames: the default method coerces its arguments to data frames and
calls the "data.frame" method.
By default the data frames are merged on the columns with names they
both have, but separate specifications of the columns can be given by
by.x and by.y. The rows in the two data frames that
match on the specified columns are extracted, and joined together. If
there is more than one match, all possible matches contribute one row
each. For the precise meaning of ‘match’, see
match.
Columns to merge on can be specified by name, number or by a logical
vector: the name "row.names" or the number 0 specifies
the row names. If specified by name it must correspond uniquely to a
named column in the input.
If by or both by.x and by.y are of length 0 (a
length zero vector or NULL), the result, r, is the
Cartesian product of x and y, i.e.,
dim(r) = c(nrow(x)*nrow(y), ncol(x) + ncol(y)).
If all.x is true, all the non matching cases of x are
appended to the result as well, with NA filled in the
corresponding columns of y; analogously for all.y.
If the columns in the data frames not used in merging have any common
names, these have suffixes (".x" and ".y" by
default) appended to try to make the names of the result unique. If
this is not possible, an error is thrown.
If a by.x column name matches one of y, and if
no.dups is true (as by default), the y version gets suffixed as
well, avoiding duplicate column names in the result.
The complexity of the algorithm used is proportional to the length of the answer.
In SQL database terminology, the default value of all = FALSE
gives a natural join, a special case of an inner
join. Specifying all.x = TRUE gives a left (outer)
join, all.y = TRUE a right (outer) join, and both
(all = TRUE) a (full) outer join. DBMSes do not match
NULL records, equivalent to incomparables = NA in R.
data.frame,
by,
cbind.
dendrogram for a class which has a merge method.
# NOT RUN {
authors <- data.frame(
## I(*) : use character columns of names to get sensible sort order
surname = I(c("Tukey", "Venables", "Tierney", "Ripley", "McNeil")),
nationality = c("US", "Australia", "US", "UK", "Australia"),
deceased = c("yes", rep("no", 4)))
authorN <- within(authors, { name <- surname; rm(surname) })
books <- data.frame(
name = I(c("Tukey", "Venables", "Tierney",
"Ripley", "Ripley", "McNeil", "R Core")),
title = c("Exploratory Data Analysis",
"Modern Applied Statistics ...",
"LISP-STAT",
"Spatial Statistics", "Stochastic Simulation",
"Interactive Data Analysis",
"An Introduction to R"),
other.author = c(NA, "Ripley", NA, NA, NA, NA,
"Venables & Smith"))
(m0 <- merge(authorN, books))
(m1 <- merge(authors, books, by.x = "surname", by.y = "name"))
m2 <- merge(books, authors, by.x = "name", by.y = "surname")
stopifnot(exprs = {
identical(m0, m2[, names(m0)])
as.character(m1[, 1]) == as.character(m2[, 1])
all.equal(m1[, -1], m2[, -1][ names(m1)[-1] ])
identical(dim(merge(m1, m2, by = NULL)),
c(nrow(m1)*nrow(m2), ncol(m1)+ncol(m2)))
})
## "R core" is missing from authors and appears only here :
merge(authors, books, by.x = "surname", by.y = "name", all = TRUE)
## example of using 'incomparables'
x <- data.frame(k1 = c(NA,NA,3,4,5), k2 = c(1,NA,NA,4,5), data = 1:5)
y <- data.frame(k1 = c(NA,2,NA,4,5), k2 = c(NA,NA,3,4,5), data = 1:5)
merge(x, y, by = c("k1","k2")) # NA's match
merge(x, y, by = "k1") # NA's match, so 6 rows
merge(x, y, by = "k2", incomparables = NA) # 2 rows
# }
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