datawizard (version 0.13.0)

data_merge: Merge (join) two data frames, or a list of data frames

Description

Merge (join) two data frames, or a list of data frames. However, unlike base R's merge(), data_merge() offers a few more methods to join data frames, and it does not drop data frame nor column attributes.

Usage

data_merge(x, ...)
data_join(x, ...)
# S3 method for data.frame
data_merge(x, y, join = "left", by = NULL, id = NULL, verbose = TRUE, ...)
# S3 method for list
data_merge(x, join = "left", by = NULL, id = NULL, verbose = TRUE, ...)

Value

A merged data frame.

Arguments

x, y

A data frame to merge. x may also be a list of data frames that will be merged. Note that the list-method has no y argument.

...

Not used.

join

Character vector, indicating the method of joining the data frames. Can be "full", "left" (default), "right", "inner", "anti", "semi" or "bind". See details below.

by

Specifications of the columns used for merging.

id

Optional name for ID column that will be created to indicate the source data frames for appended rows. Only applies if join = "bind".

verbose

Toggle warnings.

Merging data frames

Merging data frames is performed by adding rows (cases), columns (variables) or both from the source data frame (y) to the target data frame (x). This usually requires one or more variables which are included in both data frames and that are used for merging, typically indicated with the by argument. When by contains a variable present in both data frames, cases are matched and filtered by identical values of by in x and y.

Left- and right-joins

Left- and right joins usually don't add new rows (cases), but only new columns (variables) for existing cases in x. For join = "left" or join = "right" to work, by must indicate one or more columns that are included in both data frames. For join = "left", if by is an identifier variable, which is included in both x and y, all variables from y are copied to x, but only those cases from y that have matching values in their identifier variable in x (i.e. all cases in x that are also found in y get the related values from the new columns in y). If there is no match between identifiers in x and y, the copied variable from y will get a NA value for this particular case. Other variables that occur both in x and y, but are not used as identifiers (with by), will be renamed to avoid multiple identical variable names. Cases in y where values from the identifier have no match in x's identifier are removed. join = "right" works in a similar way as join = "left", just that only cases from x that have matching values in their identifier variable in y are chosen.

In base R, these are equivalent to merge(x, y, all.x = TRUE) and merge(x, y, all.y = TRUE).

Full joins

Full joins copy all cases from y to x. For matching cases in both data frames, values for new variables are copied from y to x. For cases in y not present in x, these will be added as new rows to x. Thus, full joins not only add new columns (variables), but also might add new rows (cases).

In base R, this is equivalent to merge(x, y, all = TRUE).

Inner joins

Inner joins merge two data frames, however, only those rows (cases) are kept that are present in both data frames. Thus, inner joins usually add new columns (variables), but also remove rows (cases) that only occur in one data frame.

In base R, this is equivalent to merge(x, y).

Binds

join = "bind" row-binds the complete second data frame y to x. Unlike simple rbind(), which requires the same columns for both data frames, join = "bind" will bind shared columns from y to x, and add new columns from y to x.

See Also

• Functions to rename stuff: data_rename(), data_rename_rows(), data_addprefix(), data_addsuffix()

• Functions to reorder or remove columns: data_reorder(), data_relocate(), data_remove()

• Functions to reshape, pivot or rotate data frames: data_to_long(), data_to_wide(), data_rotate()

• Functions to recode data: rescale(), reverse(), categorize(), recode_values(), slide()

• Functions to standardize, normalize, rank-transform: center(), standardize(), normalize(), ranktransform(), winsorize()

• Split and merge data frames: data_partition(), data_merge()

• Functions to find or select columns: data_select(), extract_column_names()

• Functions to filter rows: data_match(), data_filter()

Examples

x <- data.frame(a = 1:3, b = c("a", "b", "c"), c = 5:7, id = 1:3)
y <- data.frame(c = 6:8, d = c("f", "g", "h"), e = 100:102, id = 2:4)

x
y

# "by" will default to all shared columns, i.e. "c" and "id". new columns
# "d" and "e" will be copied from "y" to "x", but there are only two cases
# in "x" that have the same values for "c" and "id" in "y". only those cases
# have values in the copied columns, the other case gets "NA".
data_merge(x, y, join = "left")

# we change the id-value here
x <- data.frame(a = 1:3, b = c("a", "b", "c"), c = 5:7, id = 1:3)
y <- data.frame(c = 6:8, d = c("f", "g", "h"), e = 100:102, id = 3:5)

x
y

# no cases in "y" have the same matching "c" and "id" as in "x", thus
# copied variables from "y" to "x" copy no values, all get NA.
data_merge(x, y, join = "left")

# one case in "y" has a match in "id" with "x", thus values for this
# case from the remaining variables in "y" are copied to "x", all other
# values (cases) in those remaining variables get NA
data_merge(x, y, join = "left", by = "id")

data(mtcars)
x <- mtcars[1:5, 1:3]
y <- mtcars[28:32, 4:6]

# add ID common column
x$id <- 1:5
y$id <- 3:7

# left-join, add new variables and copy values from y to x,
# where "id" values match
data_merge(x, y)

# right-join, add new variables and copy values from x to y,
# where "id" values match
data_merge(x, y, join = "right")

# full-join
data_merge(x, y, join = "full")


data(mtcars)
x <- mtcars[1:5, 1:3]
y <- mtcars[28:32, c(1, 4:5)]

# add ID common column
x$id <- 1:5
y$id <- 3:7

# left-join, no matching rows (because columns "id" and "disp" are used)
# new variables get all NA values
data_merge(x, y)

# one common value in "mpg", so one row from y is copied to x
data_merge(x, y, by = "mpg")

# only keep rows with matching values in by-column
data_merge(x, y, join = "semi", by = "mpg")

# only keep rows with non-matching values in by-column
data_merge(x, y, join = "anti", by = "mpg")

# merge list of data frames. can be of different rows
x <- mtcars[1:5, 1:3]
y <- mtcars[28:31, 3:5]
z <- mtcars[11:18, c(1, 3:4, 6:8)]
x$id <- 1:5
y$id <- 4:7
z$id <- 3:10
data_merge(list(x, y, z), join = "bind", by = "id", id = "source")