Stack dataframes in r

Stack dataframes in r DEFAULT


stack: Stack or Unstack Vectors from a Data Frame or List


Stacking vectors concatenates multiple vectors into a single vector along with a factor indicating where each observation originated. Unstacking reverses this operation.


stack(x, …) # S3 method for default stack(x, drop=FALSE, …) # S3 method for data.frame stack(x, select, drop=FALSE, …)

unstack(x, …) # S3 method for default unstack(x, form, …) # S3 method for data.frame unstack(x, form, …)



a list or data frame to be stacked or unstacked.


an expression, indicating which variable(s) to select from a data frame.


a two-sided formula whose left side evaluates to the vector to be unstacked and whose right side evaluates to the indicator of the groups to create. Defaults to in the data frame method for .


Whether to drop the unused levels from the “ind” column of the return value.

further arguments passed to or from other methods.


produces a list of columns according to the formula . If all the columns have the same length, the resulting list is coerced to a data frame.

produces a data frame with two columns:


the result of concatenating the selected vectors in .


a factor indicating from which vector in the observation originated.


The function is used to transform data available as separate columns in a data frame or list into a single column that can be used in an analysis of variance model or other linear model. The function reverses this operation.

Note that applies to vectors (as determined by ): non-vector columns (e.g., factors) will be ignored with a warning. Where vectors of different types are selected they are concatenated by whose help page explains how the type of the result is chosen.

These functions are generic: the supplied methods handle data frames and objects coercible to lists by .

See Also



# NOT RUN { require(stats) formula(PlantGrowth) # check the default formula pg <- unstack(PlantGrowth) # unstack according to this formula pg stack(pg) # now put it back together stack(pg, select = -ctrl) # omitting one vector # }
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We’re going to walk through how to merge or combine data frames in R.

This article continues the examples started in our data frame tutorial . We’re using the ChickWeight data frame example which is included in the standard R distribution. You can easily get to this by typing: data(ChickWeight) in the R console. This data frame captures the weight of chickens that were fed different diets over a period of 21 days. If you can imagine someone walking around a research farm with a clipboard for an agricultural experiment, you’ve got the right idea….

This series has a couple of parts – feel free to skip ahead to the most relevant parts.

This section of our tutorial is going to deal with how to combine data frames in R. This allows us to take multiple data frames with a different character vector count and combine them into one merged data frame without even needing the dplyr package. There are many ways to combine multiple dataframes, from the rbind function to left outer join to logical vector combinations, but to minimize your work and ensure that every key column and variable from your multiple datasets are combined correctly, there are three main techniques we are going to look at:

  • cbind() – combining the columns of two data frames side-by-side
  • rbind() – stacking two data frames on top of each other, appending one to the other
  • merge() – joining two data frames using a common column

Using cbind() to merge two R data frames

We will start with the cbind() R function . This a simple way to join multiple datasets in R where the rows are in the same order and the number of records are the same. This means we don’t have any remaining columns out of place after merging multiple data frames because the left data frame and the right data frame have the same number of key variable names and no na value in the specified columns. We can select each data file and not have to create a new column at all when merging datasets with this method.

Syntax is straightforward – we’re going to use two imaginary data frames here, chicken and eggs:

The final result of this operation is the two data frames appended side by side. It is recommended but not required that the two data frames have the same number of rows. In the event one data frame is shorter than the other, R will recycle the values of the smaller data frame to fill the missing space.

Now, if you need to do a more complicated merge, read below. We will discuss how to merge data frames by multiple columns, set up complex joins to handle missing values, and merge using fields with different row names. Our base case assumes you’re dealing with the same variables with minimal drama from things such as missing values.

Using rbind() to merge two R data frames

We’ve encountered rbind() before, when appending rows to a data frame. This function stacks the two data frames on top of each other, appending the second data frame to the first.

For this function to operate, both data frames need to have the same number of columns and the same column names.

Using Merge to join Two Data Frames by A Common Field

This is one of the more common applications of merging two different but related data frames. We covered a simple version of this already in our example of setting buckets and flags, where we used R code to set the value of a flag. But we usually need to integrate a much larger amount of data.

For this example, assume we have a large data frame containing a detailed nutritional analysis of each diet formula, assembled via laboratory testing each sample for a variety of nutritional components, vitamins, and minerals. We want to append this to our weights data frame using the diet id as a common key.

Implementing more complicated merges

The merge operation will return a data frame that contains all records which can be matched between the two datasets. If you wanted to join a data frame on two fields, perhaps based on a daily analysis of what the chicks are fed, you could set up something like the following:

This would match the records using the two fields.

When it comes to seeing what records are returned from the merge, you have options beyond the default criteria (the equivalent of an SQL inner join, returning only records which match both data frames). You can specify an additional parameter ‘all’ which controls which records are returned.

  • Outer Join – returns all records including no-matches (in either direction)
  • Left / Right Join – returns all records in on data frame and matching records in the other (specify left dataframe or right dataframe)
  • Cross Join – returns all possible combination of the rows in the two data frames; this is the infamous Cartesian join in SQL

The R code implementation of these additional joins:

Finally, in the event the two columns you want to merge on have different names, this can be addressed by adjusting your ‘by’ parameter to handle each one separately. Sample code looks like:

Collectively, these options for merging an R dataframe replicate the core of SQL’s join function. You can use subset selection and other operations to implement filters as needed.

For complicated merge efforts, we recommend you abstract the r code into a repeatable merge function and generate a new data frame. This makes your project easier to troubleshoot.

Common Errors & Missing Value Situations

Two common issues with managing a merge.

First, there is the problem of using a field with missing value records for the join column. This breaks any simple join algorithm (such as used by the merge command). You’re going to need to look at using alternative fields as your join key, potentially combining several items in the data set as a lookup table. For example, you may accept a matching row based on one of several specified columns in the dataset to do the merge.

The next issue is that of duplicate column names. This trips you up in syntax references; be sure to indicate which table you are referring to.

In the event you need to review another section of the tutorial…

Topic: r merge data frames, r merge by index


Combine Two Data Frames in R

Created: November-27, 2020 | Updated: July-18, 2021

  1. Use to Combine Two Data Frames in R
  2. Use the Package
  3. Combine Big Data Frames in R

When manipulating data with R code, we often face the need to combine two data frames into one. This tutorial will see a few methods to efficiently combine two data frames in R.

Suppose you have two data frames, and , with some matching columns. For example:

And you need to combine them into one resulting data frame, called , for example. Such data frames could be like these:

R data frame example

Use to Combine Two Data Frames in R

The function combines data structures, such as data frames, vectors, or matrices, by rows. Its name stands for row-bind.

When using to combine two data frames, both data frames need to have the same columns. Therefore, in the previous example, you need to add the column to the data frame . This can be done by executing this command:

Now the data frame should look like this:

R data frame example

Now you can use to combine the and data frames into the new data frame by executing this command:


Use the Package

If you don’t want to write an extra line of code or add fictitious columns to one of the data frames just to be able to use , you can install the package and then simply use:

It populates the data frame with the combination of and .

Combine Big Data Frames in R

The previous examples work fine with small data frames with a few rows and 2 or 3 columns. But when you need to merge big data sets with a lot of rows and an arbitrary number of columns, it could be better to write a function that does the job faster, like the following:

This function begins by comparing the column names in the data frames and then adding the necessary columns to make them equal. Finally, it uses the function to combine the rows and return the result. To call the function, you use:

The complete example code is as below.



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