# R pairs & ggpairs Plot Functions | 5 Example Codes (Color, Labels, Panels & by Group)

**Basic R Syntax:**

pairs(data) |

pairs(data)

The pairs R function **returns a plot matrix, consisting of scatterplots for each variable-combination of a data frame**. The basic R syntax for the pairs command is shown above.

In the following tutorial, I’ll explain in **five examples** how to use the pairs function in R.

If you want to learn more about the pairs function, keep reading…

## Example 1: Basic Application of pairs() in R

I’m going to start with a very basic application of the pairs R function. Let’s first create some random data for this example:

set.seed(525354) # Set seed for reproducibility N <- 1000 # Sample size of 1000 x1 <- rnorm(N) # Create variable x2 <- x1 + rnorm(N, 0, 3) # Create correlated variable x3 <- 2 * x1 - x2 + rnorm(N, 0, 2) # Create another correlated variable data <- data.frame(x1, x2, x3) # Combine all variables to data.frame |

set.seed(525354) # Set seed for reproducibility N <- 1000 # Sample size of 1000 x1 <- rnorm(N) # Create variable x2 <- x1 + rnorm(N, 0, 3) # Create correlated variable x3 <- 2 * x1 - x2 + rnorm(N, 0, 2) # Create another correlated variable data <- data.frame(x1, x2, x3) # Combine all variables to data.frame

Our example data contains three numeric variables and 1,000 rows.

Now, let’s apply the pairs function in R:

pairs(data) # Apply pairs function |

pairs(data) # Apply pairs function

**Figure 1: Basic pairs() R Plot.**

As you can see, we are able to produce a relatively complex matrix of scatterplots with only one line of code. So, what does this pairs plot actually contain?

- The diagonal shows the names of the three numeric variables of our example data.
- The other cells of the plot matrix show a scatterplot (i.e. correlation plot) of each variable combination of our data frame. The middle graphic in the first row illustrates the correlation between x1 & x2; The right graph in the first row illustrates the correlation between x1 & x3; The left figure in the second row illustrates the correlation between x1 & x2 once more and so on…

In this first example, I have shown you the most basic usage of pairs in R. Let’s modify the options of the function a little bit…

## Example 2: Selecting Variables of pairs Plot

Often, you will only be interested in the correlations of a few of your variables. Fortunately, this can be done easily by specifying a formula within the pairs command:

pairs(~ x1 + x2 + x3, data = data) # Produces same plot as in Example 1 |

pairs(~ x1 + x2 + x3, data = data) # Produces same plot as in Example 1

With the code above, we can create exactly the same plot as in Example 1. However, we can simply remove the variables from the formula, for which we don’t want to produce a scatterplot:

pairs(~ x1 + x3, data = data) # Leave out one variable |

pairs(~ x1 + x3, data = data) # Leave out one variable

**Figure 2: Pairs Plot with Selection of Variables.**

In this example, I deleted x2 from the formula, leading to a plot matrix that contains only the scatterplots of x1 and x3.

More modifications? Let’s do it!

## Example 3: Modify Color, Shape of Points, Labels & Title

In this example, I’m going to modify many different things:

pairs(data[ , 1:3], col = "red", # Change color pch = 18, # Change shape of points labels = c("var1", "var2", "var3"), # Change labels of diagonal main = "This is a nice pairs plot in R") # Add a main title |

pairs(data[ , 1:3], col = "red", # Change color pch = 18, # Change shape of points labels = c("var1", "var2", "var3"), # Change labels of diagonal main = "This is a nice pairs plot in R") # Add a main title

**Figure 3: R Pairs Plot with Manual Color, Shape of Points, Labels, and Main Title.**

The modified pairs plot has a different color, diamonds instead of points, user-defined labels, and our own main title. For even more options, have a look at the help documentation of pairs by typing ?pairs to the RStudio console.

## Example 4: Modify pairs R Plot by Group

Quite often you will have different subsets or subgroups in your data. Let’s add a group indicator (three groups 1, 2 & 3) to our example data to simulate such a situation:

group <- NA group[data$x1 < - 0.5] <- 1 group[data$x1 >= - 0.5 & data$x1 <= 0.5] <- 2 group[data$x1 > 0.5] <- 3 |

group <- NA group[data$x1 < - 0.5] <- 1 group[data$x1 >= - 0.5 & data$x1 <= 0.5] <- 2 group[data$x1 > 0.5] <- 3

Now, let’s apply the pairs function again, but this time dependent on the group variable:

pairs(data[ , 1:3], col = c("red", "cornflowerblue", "purple")[group], # Change color by group pch = c(8, 18, 1)[group], # Change points by group labels = c("var1", "var2", "var3"), main = "This is an even nicer pairs plot in R") |

pairs(data[ , 1:3], col = c("red", "cornflowerblue", "purple")[group], # Change color by group pch = c(8, 18, 1)[group], # Change points by group labels = c("var1", "var2", "var3"), main = "This is an even nicer pairs plot in R")

**Figure 4: pairs() Plot with Color & Points by Group.**

As you can see in Figure 4, we colored the plots and changed the shape of our data points according to our groups.

You need even more options? No problem, let’s move on…

## Example 5: ggpairs R Function [ggplot2 & GGally]

So far, we have only used the pairs function that comes together with the base installation of R. However, the ggplot2 and GGally packages provide an even more advanced pairs function, which is called ggpairs(). Let’s install and load the packages:

install.packages("ggplot2") # Packages need to be installed only once install.packages("GGally") library("ggplot2") # Load ggplot2 package library("GGally") # Load GGally package |

install.packages("ggplot2") # Packages need to be installed only once install.packages("GGally") library("ggplot2") # Load ggplot2 package library("GGally") # Load GGally package

The basic application of ggpairs is similar to the pairs function of base R. You simply have to write the following R code:

ggpairs(data) # Apply ggpairs function |

ggpairs(data) # Apply ggpairs function

**Figure 5: ggpairs R Plot via ggplot2 & GGally packages.**

Main difference to the pairs function of base R: The diagonal consists of the densities of the three variables and the upper panels consist of the correlation coefficients between the variables.

Even better than pairs of base R, isn’t it? However, there is even more to explore. In case, you want to know more about the R ggpairs function, I can recommend the following YouTube video of the channel Dragonfly Statistics:

## Further Reading

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## 8 Comments. Leave new

Hello Joachim, thanks for all your effort, this site is very helpful!

While trying to practice the pairs function along with grouping (specially example 4), I keep getting this error message:

Error in axis(side = side, at = at, labels = labels, …) :

invalid value specified for graphical parameter “pch”

Can you please help explaining the issue?

Thanks so much

Asadi

Hi Asadi,

Thank you for the very nice feedback!

This error message typically occurs when the number of pch values is not the same as the number of groups.

In Example 4 we added this line to the code:

With

, we specified three different pch values for our three different groups.

If I would change the number of pch values (e.g.

), I would get the same error message as you.

Thank you so much for your quick feedback, this is helpful!

Cheers 🙂

I’m glad that it helped 🙂

Hi Joachim,

Great article. I’m running pairs() to correlate HVAC runtimes with power usage. I have set col=month where month is a factor that represents the month the data came from. Is there any way to either control the color for each month or plot a key in the base R version of pairs in this circumstance ?

Hi Kevin,

Thank you very much for your comment. If I understand your problem correctly, Example 4 of this tutorial is what you are looking for. Your month variable would be the “group” variable that I have created in the example. Let me know whether you were able to fix your problem.

Regards,

Joachim

Thanks Joachim,

That worked – I saw your approach earlier, but thought the group had to be numeric. Of course, factors work just as well.

Regards

Kevin

Nice to hear 🙂