Boosting Simulation Plot Readability: Making Fonts Larger

Nov 17, 2025 by Alex Johnson 58 views

Hey there! Ever found yourself squinting at those simulation plots, wishing the text was just a little bit bigger? I know I have! In this article, we're diving into a practical issue: making the font size in simulation plots more readable, specifically within the context of the epiestim_overdispesion project. We'll explore the problem, the potential solutions, and the considerations that come with adjusting plot aesthetics. This is crucial for anyone working with simulations, as clear visualization is key to understanding and interpreting your results. Let's make those plots pop!

The Challenge: Small Fonts in Simulation Plots

So, what's the deal? Why are the labels and titles in our simulation plots looking so tiny compared to the case study plots? It's a common problem in data visualization. Different datasets and simulation setups often require adjustments to plot parameters to achieve optimal readability. In this specific scenario, the issue stems from the default settings used to generate the plots. The default font sizes might be suitable for the case study but become a hindrance when dealing with simulation results. Small fonts can make it difficult to discern labels, titles, and axis ticks, leading to misinterpretations and frustration. Understanding this is the first step; then we will find a solution to it.

We need to find a way to increase the font size without making the plots look cluttered or overwhelming. This balance is critical, and we will explore the best way to get there. The goal is to make the plots accessible and understandable at a glance.

The Problem in Detail

Let's be more specific. The issue centers around the font sizes used for titles, axis labels, and potentially even the text within the plots, such as legends or annotations. When these elements are too small, they become hard to read, especially on screens with lower resolutions or for individuals with visual impairments. This is a common usability issue in data visualization. The problem is compounded when plots are included in reports or presentations, where the audience may not be viewing the plot at its original size. The consequences of unclear plots are real, including misunderstandings, wasted time, and potentially incorrect conclusions.

The Solution: Adjusting Plot Parameters

The good news is that adjusting the font size in your plots is typically a straightforward process, often involving modifying parameters within your plotting code. In this project, the key lies in identifying the specific code responsible for generating the plots and modifying the relevant arguments. This likely involves changing parameters related to font size for the title, axis labels, and other textual elements.

Where to Start: Code Snippet Analysis

The starting point is the provided link to the _targets.R file within the epiestim_overdispesion repository. This file likely contains the code that generates the simulation plots. Here is the link again: https://github.com/kit-health-statistics/epiestim_overdispesion/blob/97997c97637b3c1486d6d99ca496824b925a7924/_targets.R#L53. By examining this code, we can pinpoint the exact lines that control the plot's visual aspects.

Specifically, look for the following:

Plotting functions: Identify the functions used to create the plots (e.g., ggplot2 in R or similar libraries in other languages).
Font size arguments: Find the arguments that control font sizes. These might be named something like title_size, axis_text_size, or similar.
Default values: Note the default values currently used for these parameters. This will guide you in making appropriate adjustments.

Implementing the Change

Once you've located the relevant code, the process is simple:

Modify the font size arguments: Increase the values of the font size parameters. For example, if the default axis_text_size is 10, try increasing it to 12 or 14. Experiment with different values to find the optimal balance between readability and plot aesthetics.
Test the changes: Rerun the code to generate the plots with the new font sizes. Check how the plots look and whether the text is more readable.
Iterate and refine: If the initial changes don't produce the desired results, adjust the font size parameters further until you achieve the desired outcome. Remember to consider all text elements in the plot, not just titles and axis labels.