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## Introduction

This blog post is the follow-up on part I on programming with ggplot2. If you have not read the first post of the series, I strongly recommend doing so before continuing with this second part, otherwise it might prove difficult to follow.

Having developed a scalable approach to column-wise and data type-dependent visualization, we will continue to customize our plots. Specifically, the focus of this post is how we can use a log-transformed x-axis with nice breakpoints for continuous data. If you don’t like the idea of having a non-linear scale, don’t stop reading here. The principles developed below can be generalized well to customize the plots regarding other aspects in which the customization depends on the data itself.

## The problem

Recall from part one that we ended up with the following code to produce graphs for two different data types in our data frame with four columns.

Our goal is to alter the x-axis from a linear to a log-transformed scale to make better use of the space in the plot.

## A fist solution

At first glance, the solution to the problem seems easy. Similarly to the first post of this series, we can create a new function scale_x_adapt which returns a continuous scale and a discrete scale otherwise. Then, we could pass the transform argument via ... to scale_x_continuous and integrate it with our current framework.

This seems fine, except for the fact that the break ticks are not really chosen wisely. There are various ways to go about that:

• Resort to functionality from existing packages like trans_breaks (from the scales package), annotation_logticks (ggplot2) and others.
• Create your own function that returns pretty breaks.

We go for the second option because it is a slightly more general approach and I was not able to find a solution that pleased me for our specific case.

## A second solution

We need to change the way the breaks are created within scale_x_adapt. To produce appropriate breaks, we need to know the maximum and the minimum of the data we are dealing with (that is, the column that lapply currently passes over) and then create a sequence between the minimum and the maximum with some function. Recall that in part 1 we used a function current_class that does something similar to what we want. It gets the class of the current data. Hence, we can expand this function to get any property from our current data (and give the function a more general name).

Note the new argument f, which allows us to fetch a wider range of properties from the current data, not just the class, as current_class did.

This is key for every customization that depends on the input data, because this function can now get us virtually any information out of the data we could possibly want. In our case, we are interested in the minimum and maximum values for the current batch of data. As a finer detail, also note that current_class called class and returned the first value, since objects can have multiple classes and we were only interested in the first one (otherwise we could not do the logical comparison with %in%). We now return all elements that f returns, since we can always perform the subset outside the function current_property, and this makes the function more flexibile.

Next, we need to create a function that, given a range, computes some nice break values we can pass to the breaks argument of scale_x_continuous. This task is independent of the rest of the framework we are developing here. One function that does something that is close to what we want is the following.

Let me break these lines into pieces.

• The basic idea is to create a sequence of breaks between the minimum and the maximum value of the current batch of data using seq.
• Let us assume we want break points that are equi-distant on the log scale. Since our plot is going to be on a logarithmic x-axis, we need to create a linear sequence between log(start) and log(end) and transform it with exp so we end up with breaks that have the same distance on the logarithmic scale It becomes evident that the solution presented above is suitable for a log-transformed axis, but if you choose another transformation, e.g. the square root- transformation, you need to adapt the function.
• We want to round the values depending on their absolute value. For example, the values for carat (which are in the range of 0.2 to 5) should be rounded to one decimal point, whereas the values of price (ranging up to 18’000) should be rounded to thousands or tens of thousands. So note that log10(10) is one, log10(100) = 2 and log10(0.1) = -1 etc, which is exactly what we need. In other words, we make the rounding dependent on the log of the difference between the maximum and the minimum of the input data for each plot.
• A constant correction is added so it is possible to manually adjust the rounding from more to less digits.

Finally, we can put it all together:

## Conclusion

In this blog post, we wanted to further customize our plots created in the first post of the series. We introduced a new function, scale_x_adapt that returns a predefined scale for a given data type. It can be integrated with our framework similarly to geom_hist_or_bar. We created a more general version of current_class, current_property which takes a function as an argument and allows us to evaluate this function on the current data column. In our example, this is helpful because using current_property(min) and current_property(max), we found out the range of the column we are processing and hence can construct nice breakpoints with calc_log_breaks that then get used in scale_x_adapt. current_property is a key function in the framework developed here since it can extract any information from the batch of data we are processing within lapply.