**blogR**, and kindly contributed to R-bloggers)

@drsimonj here with a quick share on making great use of the secondary y axis with ggplot2 – super helpful if you’re plotting groups of time series!

Here’s an example of what I want to show you how to create (pay attention to the numbers of the right):

## Setup

To setup we’ll need the tidyverse package and the `Orange`

data set that comes with R. This tracks the circumference growth of five orange trees over time.

```
library(tidyverse)
d <- Orange
head(d)
#> Grouped Data: circumference ~ age | Tree
#> Tree age circumference
#> 1 1 118 30
#> 2 1 484 58
#> 3 1 664 87
#> 4 1 1004 115
#> 5 1 1231 120
#> 6 1 1372 142
```

## Template code

To create the basic case where the numbers appear at the end of your time series lines, your code might look something like this:

```
# You have a data set with:
# - GROUP colum
# - X colum (say time)
# - Y column (the values of interest)
DATA_SET
# Create a vector of the last (furthest right) y-axis values for each group
DATA_SET_ENDS <- DATA_SET %>%
group_by(GROUP) %>%
top_n(1, X) %>%
pull(Y)
# Create plot with `sec.axis`
ggplot(DATA_SET, aes(X, Y, color = GROUP)) +
geom_line() +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(sec.axis = sec_axis(~ ., breaks = DATA_SET_ENDS))
```

## Let’s see it!

Let’s break it down a bit. We already have our data set where the group colum is `Tree`

, the X value is `age`

, and the Y value is `circumference`

.

So first get a vector of the last (furthest right) values for each group:

```
d_ends <- d %>%
group_by(Tree) %>%
top_n(1, age) %>%
pull(circumference)
d_ends
#> [1] 145 203 140 214 177
```

Next, let’s set up the basic plot without the numbers to see how each layer adds up.

```
ggplot(d, aes(age, circumference, color = Tree)) +
geom_line()
```

Now we can use `scale_y_*`

, with the argument `sec.axis`

to create a second axis on the right, with numbers to be displayed at `breaks`

, defined by our vector of line ends:

```
ggplot(d, aes(age, circumference, color = Tree)) +
geom_line() +
scale_y_continuous(sec.axis = sec_axis(~ ., breaks = d_ends))
```

This is a great start, The only major addition I suggest is expanding the margins of the x-axis so the gap disappears. You do this with `scale_x_*`

and the `expand`

argument:

```
ggplot(d, aes(age, circumference, color = Tree)) +
geom_line() +
scale_y_continuous(sec.axis = sec_axis(~ ., breaks = d_ends)) +
scale_x_continuous(expand = c(0, 0))
```

## Polishing it up

Like it? Here’s the code to recreate the first polished plot:

```
library(tidyverse)
d <- Orange %>%
as_tibble()
d_ends <- d %>%
group_by(Tree) %>%
top_n(1, age) %>%
pull(circumference)
d %>%
ggplot(aes(age, circumference, color = Tree)) +
geom_line(size = 2, alpha = .8) +
theme_minimal() +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(sec.axis = sec_axis(~ ., breaks = d_ends)) +
ggtitle("Orange trees getting bigger with age",
subtitle = "Based on the Orange data set in R") +
labs(x = "Days old", y = "Circumference (mm)", caption = "Plot by @drsimonj")
```

## 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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**blogR**.

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