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.

Merge cell values into a single cell by rows or columns When a single column header is split across cells, merge the cells with mergerows or mergecols. 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. Data frames to combine. Each argument can either be a data frame, a list that could be a data frame, or a list of data frames. When row-binding, columns are matched by name, and any missing columns will be filled with NA. When column-binding, rows are matched by position, so all data frames must have the same number of rows.

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)).

Also notice only rows with matching ids in both data # frames are retained. In database terminology this is known as an INNER JOIN. # Only those records with matching 'by' variables are joined. # If we wanted to merge all rows regardless of match, we use the argument # all=TRUE. It is FALSE by default. This creates an OUTER JOIN. Basic Application of merge Function in R. First, we need to create some data frames that we can.

Combine Rows In Excel Based On Data

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.

RowsCombine two rows in rRows

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.

The merge function in R allows you to combine two data frames, much like the join function that is used in SQL to combine data tables. Merge, however, does not allow for more than two data frames to be joined at once, requiring several lines of code to join multiple data frames.

This post explains the methodology behind merging multiple data frames in one line of code using base R. We will be using the Reduce function, part of Funprog in base R v3.4.3. Funprog contains a suite of higher order functions which provide simple alternatives to laborious, long winded coding solutions.

The merge function

As described, merge is essentially the “join” of the R world. Whilst this post is not about the fine workings of merge, I will give a brief introduction.

Merge takes two data frames, x and y, and combines them based on one or more shared columns. Rows are combined where the data of these shared columns are equal, meaning we can combine columns from different data frames that refer to the same piece of data. For instance, take the following two data frames:

It is clear that the two data frames are referring to the same characters, however it may be more useful to us if the two were combined into a single data frame. This is where merge comes in. Merge takes the following structure:

Here, we are looking to combine the height and gender data frames where the character columns are equal. To continue the SQL analogy, x is the left-hand table, y is the right-hand table, and merge is the LEFT JOIN operation. The “by” component is our “ON” clause. For example:

Running this merge function gives us the following output:

This is the result we were expecting, but what if we introduce a third data frame?

Sadly, merge does not allow us to simply add our eyeColour data frame as a third input (we only have x and y parameters available). That’s where Reduce comes in.

The Reduce function

Reduce takes a function and sequentially applies it to a given list of inputs, in our case a list of data frames. For example, imagine we have a function f which accepts two arguments, and a list of objects (a, b, c). Then Reduce(x, list(a, b, c)) would perform the following action:

f(a, f(b, c))

where the function x is first applied to data frames b and c, and is then applied to data frame a and the output of the first application of x. This allows us to avoid running and saving x(b, c), like this:

R merge by row names

Applying Reduce to merge

In merge we have an example of a function that performs an action on two inputs. Reduce takes two parameters; f which stands for function and x which represents a vector. Reduce will sequentially apply the function f to the list x.

In our example, the function that we want to apply is merge, and the vector which we want to apply it to is a list of our data frames. First off, let’s try the following:

Perfect! But what if we wanted to specify the parameters within our merge function call? Well, we could define our own function which merges two data frames with specified parameters:

Merge rows in r dplyr

R Merge Multiple Data Frames

Here, we have specified our f as a custom function, which takes two parameters and applies the merge function to them. Within this custom function, we have specified our by parameter, which may be necessary for longer or more complex uses of Reduce.

Merge Rows Report Builder

Further reading

The function that we passed to Reduce is known in the world of functional programming as a lambda function, or an anonymous function; a single use function that is not named and saved. Functional programming is a principle around which R is built, and can provide many smart and elegant ways to achieve things that would otherwise require large amounts of coding. We may explore more of the functional programming features of R in future blog posts, however for now the following link provides a nice overview of the most used techniques:

by Jon Willis

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