Adjusting the Y-Axis Range in ggplot2: A Guide to Scaling and Limits

ggplot: y-axis range after scaling

Introduction

In this article, we will discuss the challenges of adjusting the y-axis range in a ggplot2 graph when the data has been previously scaled. We’ll cover the necessary steps and concepts to achieve the desired result.

Understanding ggplot2’s Scaling Mechanism

ggplot2 is an R package for creating high-quality statistical graphics. One of its key features is the ability to scale numeric axes, allowing us to control what values are displayed on the x- and y-axes. The scaling mechanism in ggplot2 is based on a combination of position adjustments and the use of aesthetics.

When we create a graph with ggplot2, we can adjust the scales using various functions like scale_x_continuous(), scale_y_continuous(), and others. These functions are used to customize the appearance of the axes.

However, there’s an important distinction between setting limits for the y-axis and scaling it. Setting limits (lim) only sets the range of values displayed on the axis, whereas scaling adjusts the actual units of measurement.

The Problem with Rescaled Data

When we rescale our data using a transformation (like scale_x_discrete(limits = c("r", "+", "1","2","3","4","5"))), we’re essentially adjusting the values displayed on the axis. However, this doesn’t change the actual units of measurement.

The issue arises when we try to adjust the y-axis range. If we use ylim(), it will overwrite the scale_y_discrete() function, which is not what we want.

Scales and Limits

So, how do we adjust the y-axis range without overwriting the scaling mechanism? We need to understand the difference between scales and limits.

A scale defines a set of aesthetic mappings that are used to transform data into visual representations. When we use scale_y_discrete(), it sets up a discrete mapping from numeric values to categorical labels (like our “r”, “+”, etc.).

On the other hand, limits (lim) simply define the range of values displayed on the axis.

Solution: Using Scale Functions with Care

To adjust the y-axis range without overwriting the scaling mechanism, we need to use the scale functions in a specific way.

One approach is to create multiple scales and switch between them using coord_cartesian(). This allows us to define different scales for different parts of the graph.

Here’s an example:

ggplot(DichteM, aes(UT, Mittelwert.von.Solidagoscale) )+
  geom_point() +
  ggtitle("Solidago") +
  theme(plot.title = element_text(hjust = 0.5, size = 12)) +
  stat_smooth(method = "lm", formula = "y~x", se=FALSE, size = 0.1) +
  coord_cartesian(xlim(c(0,15))) + # x-axis limit
  scale_y_continuous(limits = c(-10, 30), labels = function(x) as.character(round(x))) + # y-axis limits and labels

In this example, we create two separate scales: one for the x-axis using coord_cartesian() and another for the y-axis using scale_y_continuous(). This allows us to adjust both axes independently.

Another Approach: Using `stat_summary`

Another approach is to use stat_summary with a function that adjusts the y-axis limits. Here’s an example:

ggplot(DichteM, aes(UT, Mittelwert.von.Solidagoscale) )+
  geom_point() +
  ggtitle("Solidago") +
  theme(plot.title = element_text(hjust = 0.5, size = 12)) +
  stat_smooth(method = "lm", formula = "y~x", se=FALSE, size = 0.1) +
  stat_summary(fun = function(x) c(min(x), max(x)), geom="point", aes(y=..value..)) + # calculate min and max values
  scale_y_continuous(limits = c(-10, 30), labels = function(x) as.character(round(x))) + # y-axis limits and labels

In this example, we use stat_summary() to calculate the minimum and maximum values of the data, which we then use to adjust the y-axis limits.

Conclusion

Adjusting the y-axis range in a ggplot2 graph when the data has been previously scaled can be challenging. However, by understanding the difference between scales and limits, using scale functions with care, or employing stat_summary functions, we can achieve our desired result.

Remember to experiment with different approaches and adjust your code accordingly to find the solution that works best for your specific use case.

Last modified on 2024-08-28