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Want to see how some of your variables relate to many others? Here’s an example of just this:

```
library(tidyr)
library(ggplot2)
mtcars %>%
gather(-mpg, -hp, -cyl, key = "var", value = "value") %>%
ggplot(aes(x = value, y = mpg, color = hp, shape = factor(cyl))) +
geom_point() +
facet_wrap(~ var, scales = "free") +
theme_bw()
```

This plot shows a separate scatter plot panel for each of many variables against `mpg`

; all points are coloured by `hp`

, and the shapes refer to `cyl`

.

Let’s break it down.

## Some previous advice

This post is an extension of a previous one that appears here: https://drsimonj.svbtle.com/quick-plot-of-all-variables.

In that prior post, I explained a method for plotting the univariate distributions of many numeric variables in a data frame. This post does something very similar, but with a few tweaks that produce a very useful result. So, in general, I’ll skip over a few minor parts that appear in the previous post (e.g., how to use `purrr::keep()`

if you want only variables of a particular type).

## Tidying our data

As in the previous post, I’ll mention that you might be interested in using something like a `for`

loop to create each plot. Personally, however, I think this is a messy way to do it. Instead, we’ll make use of the `facet_wrap()`

function in the `ggplot2`

package, but doing so requires some careful data prep. Thus, assuming our data frame has all the variables we’re interested in, the first step is to get our data into a tidy form that is suitable for plotting.

We’ll do this using `gather()`

from the `tidyr`

package. In the previous post, we gathered all of our variables as follows (using `mtcars`

as our example data set):

```
library(tidyr)
mtcars %>% gather() %>% head()
#> key value
#> 1 mpg 21.0
#> 2 mpg 21.0
#> 3 mpg 22.8
#> 4 mpg 21.4
#> 5 mpg 18.7
#> 6 mpg 18.1
```

This gives us a `key`

column with the variable names and a `value`

column with their corresponding values. This works well if we only want to plot each variable by itself (e.g., to get univariate information).

However, here we’re interested in visualising multivariate information, with a particular focus on one or two variables. We’ll start with the bivariate case. Within `gather()`

, we’ll first drop our variable of interest (say `mpg`

) as follows:

```
mtcars %>% gather(-mpg, key = "var", value = "value") %>% head()
#> mpg var value
#> 1 21.0 cyl 6
#> 2 21.0 cyl 6
#> 3 22.8 cyl 4
#> 4 21.4 cyl 6
#> 5 18.7 cyl 8
#> 6 18.1 cyl 6
```

We now have an `mpg`

column with the values of `mpg`

repeated for each variable in the `var`

column. The `value`

column contains the values corresponding to the variable in the `var`

column. This simple extension is how we can use `gather()`

to get our data into shape.

## Creating the plot

We now move to the `ggplot2`

package in much the same way we did in the previous post. We want a scatter plot of `mpg`

with each variable in the `var`

column, whose values are in the `value`

column. Creating a scatter plot is handled by `ggplot()`

and `geom_point()`

. Getting a separate panel for each variable is handled by `facet_wrap()`

. We also want the scales for each panel to be “free”. Otherwise, `ggplot`

will constrain them all the be equal, which doesn’t make sense for plotting different variables. For a clean look, let’s also add `theme_bw()`

.

```
mtcars %>%
gather(-mpg, key = "var", value = "value") %>%
ggplot(aes(x = value, y = mpg)) +
geom_point() +
facet_wrap(~ var, scales = "free") +
theme_bw()
```

We now have a scatter plot of every variable against `mpg`

. Let’s see what else we can do.

## Extracting more than one variable

We can layer other variables into these plots. For example, say we want to colour the points based on `hp`

. To do this, we also drop `hp`

within `gather()`

, and then include it appropriately in the plotting stage:

```
mtcars %>%
gather(-mpg, -hp, key = "var", value = "value") %>%
head()
#> mpg hp var value
#> 1 21.0 110 cyl 6
#> 2 21.0 110 cyl 6
#> 3 22.8 93 cyl 4
#> 4 21.4 110 cyl 6
#> 5 18.7 175 cyl 8
#> 6 18.1 105 cyl 6
mtcars %>%
gather(-mpg, -hp, key = "var", value = "value") %>%
ggplot(aes(x = value, y = mpg, color = hp)) +
geom_point() +
facet_wrap(~ var, scales = "free") +
theme_bw()
```

Let’s go crazy and change the point shape by `cyl`

:

```
mtcars %>%
gather(-mpg, -hp, -cyl, key = "var", value = "value") %>%
head()
#> mpg cyl hp var value
#> 1 21.0 6 110 disp 160
#> 2 21.0 6 110 disp 160
#> 3 22.8 4 93 disp 108
#> 4 21.4 6 110 disp 258
#> 5 18.7 8 175 disp 360
#> 6 18.1 6 105 disp 225
mtcars %>%
gather(-mpg, -hp, -cyl, key = "var", value = "value") %>%
ggplot(aes(x = value, y = mpg, color = hp, shape = factor(cyl))) +
geom_point() +
facet_wrap(~ var, scales = "free") +
theme_bw()
```

## Perks of ggplot2

If you’re familiar with `ggplot2`

, you can go to town. For example, let’s add loess lines with `stat_smooth()`

:

```
mtcars %>%
gather(-mpg, key = "var", value = "value") %>%
ggplot(aes(x = value, y = mpg)) +
geom_point() +
stat_smooth() +
facet_wrap(~ var, scales = "free") +
theme_bw()
```

The options are nearly endless at this point, so I’ll stop here.

## Sign off

Thanks for reading and I hope this was useful for you.

For updates of recent blog posts, follow @drsimonj on Twitter, or email me at [email protected] to get in touch.

If you’d like the code that produced this blog, check out the blogR GitHub repository.

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