# R sweep Function | 3 Example Codes (Matrix Operation with MARGIN & STATS)

**Basic R Syntax:**

sweep(x = data, MARGIN = 1, STATS = 1, FUN = "+") |

The sweep R function applies an operation (e.g. + or -) to a data matrix by row or by column.

The following parameters have to be specified within the sweep function:

**x**: Typically a matrix.**MARGIN**: Specifies typically whether the operation should be applied by row or by column. MARGIN = 1 operates by row; MARGIN = 2 operates by column.**STATS**: Specifies usually the value that should be used for the operation (e.g. the value that should be added or subtracted).**FUN**: The operation that should be carried out (e.g. + or -).

Further (not necessarily needed) arguments can be specified for sweep in R. Type ?sweep into your RStudio console to learn more.

However, let’s not waste too much time and let’s dive strictly into the examples…

## Example 1: Sweep Matrix in R

Let’s start with a simple example. Consider the following data matrix:

data <- matrix(0, nrow = 6, ncol = 4) # Create example matrix data # Print matrix to R console |

**Table 1: Example Matrix for the Application of sweep in R.**

We have created a matrix with 4 columns and 6 rows; All values are zero.

Now, let’s apply the R sweep command to this example matrix:

data_ex1 <- sweep(x = data, MARGIN = 1, STATS = 5, FUN = "+") # Apply sweep in R data_ex1 # Print example 1 to console |

**Table 2: Example Matrix After Simple Application of sweep in R.**

As you can see, all zeros where replaced by 5. But why? Let’s go through the code step by step:

**x**: Our example matrix, which is called*data*.**MARGIN**: We want to apply the operation by row. Therefore, we set*MARGIN = 1*.**STATS**: In our operation, we want to use*the value 5*for each data cell.**FUN**: We want to apply the operation*plus*.

To explain it as simple as possible: With the previous code, we added the value 5 to each of our data cells.

Sounds stupid? Let’s move on to a more realistic example…

## Example 2: Apply sweep() with Complex Specification of STATS

For the next example, I’m going to use the same example data set as in Example 1 (our matrix with zeros). However, this time I’m going to specify the **STATS argument in a more complex way**. The rest of the code is kept as in Example 1:

data_ex2 <- sweep(x = data, MARGIN = 1, # Sweep with Complex STATS STATS = c(1, 3, 0, 2, 10, 5), FUN = "+") data_ex2 # Print example 2 to console |

**Table 3: Example Matrix after Applying sweep with Complex STATS Specification.**

All rows are different. So what happened this time?!

By using a vector with six different numbers for the STATS argument (i.e. *c(1, 3, 0, 2, 10, 5)*), we can use a different value for each of our six rows. We added the value 1 to each cell of the first row, 3 to each cell of the second row, 0 to each cell of the third and so on…

So, what about the MARGIN argument? You guessed it – That’s what I’m going to show you now…

## Example 3: The MARGIN Argument of the Sweep R Function

For the third example, I’m keeping the code exactly as in Example 2, but this time I’m going to change the specification of MARGIN:

data_ex3 <- sweep(x = data, MARGIN = 2, # Change MARGIN Argument to 2 STATS = c(1, 3, 0, 2, 10, 5), FUN = "+") |

Oh gosh, what happened?!!

*Warning message:
In sweep(x = data, MARGIN = 2, STATS = c(1, 3, 0, 2, 10, 5), FUN = “+”) :
STATS is longer than the extent of ‘dim(x)[MARGIN]’*

Let’s have a look at the output:

`data_ex3 # Print example 3 to RStudio` |

**Table 4: Warning: STATS is longer than the extent of ‘dim(x)[MARGIN]’.**

As you can see, we received a valid output. However, the operation recycled across the end of each row. Usually you should try to avoid this by specifying the length of STATS equal to the number of rows/columns.

Let’s do this:

data_ex3_b <- sweep(x = data, MARGIN = 2, # Change length of STATS STATS = c(1, 3, 0, 2), FUN = "+") data_ex3_b # Print example 3b to RStudio |

**Table 5: Fitting Length of STATS Argument.**

After deleting the last two values of our STATS specification (i.e. 10 and 5), the output data is ordered well.

Looks much better!

## Video Explanation: sweep in R

More examples? I know, the sweep function is not easy to understand. Have a look at the following video of the R programming Library YouTube channel:

## Further Reading