In R you use the merge function to combine data frames. This powerful function tries to identify columns or rows that are common between the two different data frames. How to use merge to find the intersection of data The simplest form of merge finds the. These two options can be used to retain certain rows of your input data tables, even when no match is found for the merging. With the following R codes, we can keep all rows of our first input data frame (i.e. Merge ( data1, data2, by = 'id', all.x = TRUE) # Keep all rows of x-data.

  1. R Merge Datasets
  2. Merge Datasets In R Studio
  3. Combine Datasets R
Source: R/join.r

These are generic functions that dispatch to individual tbl methods - see themethod documentation for details of individual data sources. x andy should usually be from the same data source, but if copy isTRUE, y will automatically be copied to the same source as x.


x, y

tbls to join


a character vector of variables to join by. If NULL, thedefault, *_join() will do a natural join, using all variables withcommon names across the two tables. A message lists the variables sothat you can check they're right (to suppress the message, simplyexplicitly list the variables that you want to join).

To join by different variables on x and y use a named vector.For example, by = c('a' = 'b') will match x.a toy.b.


If x and y are not from the same data source,and copy is TRUE, then y will be copied into thesame src as x. This allows you to join tables across srcs, butit is a potentially expensive operation so you must opt into it.


If there are non-joined duplicate variables in x andy, these suffixes will be added to the output to disambiguate them.Should be a character vector of length 2.


other parameters passed onto methods, for instance, na_matchesto control how NA values are matched. See join.tbl_df for more.


If TRUE the by columns are kept in the nesting joins.


the name of the list column nesting joins create. If NULL the name of y is used.

Join types

Currently dplyr supports four types of mutating joins, two types of filtering joins, anda nesting join.

R Merge Datasets

Mutating joins combine variables from the two data.frames:


return all rows from x where there are matchingvalues in y, and all columns from x and y. If there are multiple matchesbetween x and y, all combination of the matches are returned.

R Merge Datasets

return all rows from x, and all columns from xand y. Rows in x with no match in y will have NA values in the newcolumns. If there are multiple matches between x and y, all combinationsof the matches are returned.


return all rows from y, and all columns from xand y. Rows in y with no match in x will have NA values in the newcolumns. If there are multiple matches between x and y, all combinationsof the matches are returned.


return all rows and all columns from both x and y.Where there are not matching values, returns NA for the one missing.

Merge Datasets In R Studio

Filtering joins keep cases from the left-hand data.frame:


return all rows from x where there are matchingvalues in y, keeping just columns from x. A semi join differs from an inner join because an inner join will returnone row of x for each matching row of y, where a semijoin will never duplicate rows of x.


return all rows from x where there are notmatching values in y, keeping just columns from x.

Nesting joins create a list column of data.frames:


Combine Datasets R

return all rows and all columns from x. Adds alist column of tibbles. Each tibble contains all the rows from ythat match that row of x. When there is no match, the list column isa 0-row tibble with the same column names and types as y. nest_join() is the most fundamental join since you can recreate the other joins from it.An inner_join() is a nest_join() plus an tidyr::unnest(), and left_join() is anest_join() plus an unnest(.drop = FALSE).A semi_join() is a nest_join() plus a filter() where you check that every element of data hasat least one row, and an anti_join() is a nest_join() plus a filter() where you check every element has zero rows.


Groups are ignored for the purpose of joining, but the result preservesthe grouping of x.


Matching Variables

When merging files it is generally easier to use the original variables rather than the ID variables. For example, it is not possible to merge the household and women’s files using HHID and CASEID because CASEID has three extra characters identifying the women’s line number. The files can be more easily merged using variables HV001 with V001 and HV002 with V002.
The following reference table shows the variables required to match different files. In the rows, the base files are listed. In the columns, the secondary files along with the variables to be used as keys or matching variables are listed. In the cells intersecting the rows and columns, variables from the base files used to match the secondary file are listed.

This table shows that household variables can be appended to women, men and children. Women variables can be appended to their children. They also can be appended to men, to create couples. Notice that there is no relationship between children and men because children come from the birth history, which is asked to women.
With software that requires the variables that are used for merging to have the same name in both files it will be necessary to either rename or to create copies of the matching variables in one file to match the names in the other file being used. For example, to match the household data to the women's data, first rename HV001 to V001 and HV002 to V002, or create a copy of HV001 in V001 and a copy of HV002 in V002 in the household data before merging.

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