This is a small portfolio showcasing the figures produced for Challenges of dehydration result in a behavioral shift in invasive toads. This project studied the behavior patterns of invasive and native toad species in response to dehydration. Figure 1 is an example. To see all figures and legends, check this page.
The portfolio can be used as a real-life example teaching tool to cover how publication quality figures can be assembled in R. The examples go from basic elements of The Grammar of Graphics such as layers of aesthetic mapping and geoms, to more advanced concepts such as the structure of graphic objects (grobs).
Below is an in-depth explanation of the code structured as an introduction to graphing in R.
In this lesson, we'll explore how to create a detailed plot using ggplot2 in R. We'll start with the basics and gradually introduce more advanced concepts.
We'll be working with a dataset named All, which contains information about different populations and their movement latency under various conditions.
MovementLatency <- ggplot(data = All, mapping = aes(x = population, y = Movementlatency))ggplot(): This function initializes a ggplot object. It's the foundation upon which we'll build our plot.
data = All: Specifies the dataset we're using.
mapping = aes(x = population, y = Movementlatency): This is an aesthetic mapping. It tells ggplot what variables to use on the x and y axes.
geom_boxplot(mapping = aes(fill = population), outlier.shape = NA) +
geom_point(mapping = aes(shape = population), color = "#737373", position = position_jitter(width = 0.1), size = 2)geom_boxplot(): **Adds a boxplot to the graph. The fill aesthetic is mapped to the population variable, meaning different populations will have different colors.
outlier.shape = NA: **This removes outliers from the boxplot.
geom_point(): **Adds points to the graph. Each point represents an observation in the dataset.
color = "#737373": **Sets the color of the points.
position = position_jitter(width = 0.1): **Adds a bit of random noise to the position of the points to prevent overlap.
size = 2: **Sets the size of the points.
theme_classic(base_size = Size) +
labs(x = "Population", y = "Latency to start moving (s)") +
theme_bw(base_size = Size) +
theme(panel.background = element_rect(fill = NA),
panel.grid = element_blank(),
strip.background = element_rect(fill = "white"))theme_classic(): **Applies a classic theme to the plot.
labs(): **Used to customize or add labels to the plot.
theme_bw(): **Applies a black-and-white theme.
theme(): **Further customization of the plot's appearance.
facet_grid(cols = vars(Condition)) +
scale_y_continuous(breaks = c(0, 150, 300, 450, 600, 750), limits = c(0, 750)) +
scale_shape_manual(values = c(15, 17)) +
scale_fill_manual(values = c("#cfcfcf", "#ffffff")) +
guides(shape = FALSE, fill = FALSE)facet_grid(): **Splits the plot into multiple plots based on the Condition variable.
scale_y_continuous(): **Customizes the y-axis scale.
scale_shape_manual() and scale_fill_manual(): **Manually set the shapes and fills used in the plot.
guides(): **Controls the display of legends.
gridTitle <- ggplotGrob(MovementLatency)
grid.newpage()
grid.draw(gridTitle)
ggsave("Fig1_MovementLatency.pdf", gridTitle, width = 5, height = 4)ggplotGrob(): **Converts the ggplot object to a grob (graphical object) which allows for more detailed customization.
grid.newpage() and grid.draw(): **Functions from the grid package used to draw the plot.
ggsave(): **Saves the plot to a file.
With ggplot2, you can create detailed and customized plots in R. By understanding the logic behind elements, layers, geoms, facets, scales, and themes, you can craft a wide variety of visualizations tailored to your data.
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