Summary  
This chapter explains how to apply and customize ggplot2 themes to control non-data plot elements (such as background, fonts, and grid lines) and how to export the resulting plots to image files using `ggsave()`.  

General domain of usage  
Data visualization and reporting

After you have learned how to build and customize a wide range of plots in ggplot2, you may want to ensure your visualizations have a consistent, professional look and are ready to be shared outside of R. ggplot2 offers powerful tools for styling your plots with themes, as well as functions for exporting your work to image files. A **theme** in ggplot2 controls the non-data elements of your plot, such as background color, grid lines, font, and legend appearance. ggplot2 provides several built-in themes like `theme_minimal()`, `theme_classic()`, and `theme_dark()`, each offering a distinct style. You can apply these themes directly to your plot, and further customize specific elements using the `theme()` function. Customizations might include changing the font size of axis titles, adjusting legend placement, or modifying background colors. By combining built-in themes with targeted adjustments, you can create plots that match your desired aesthetic or publication requirements.

library(ggplot2)

# Create a basic scatter plot
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(color = "steelblue", size = 3) +
  labs(title = "MPG vs Weight", x = "Weight", y = "Miles per Gallon")

# Apply a built-in theme and customize text
p + 
  theme_minimal() +
  theme(
    plot.title = element_text(size = 18, face = "bold", color = "navy"),
    axis.title = element_text(size = 14),
    axis.text = element_text(color = "gray20")
  )

Once your plot looks the way you want, you can export it for use in presentations, reports, or publications. The `ggsave()` function makes it easy to save your current plot to a file. By default, `ggsave()` will save the last plot you displayed, but you can also specify which plot to save by passing it as the first argument. You can set the filename, file type (such as PNG, JPEG, PDF, or SVG), and image dimensions. For example, to save a plot as a PNG file, you might use `ggsave("myplot.png")`. Adjust the `width` and `height` arguments to control the size of the exported image. This allows you to share high-quality graphics with collaborators or include them in documents and slides.

library(testthat)

source("user_code.R")
test_that("plot uses theme_classic", {
    # Check if my_plot exists and is a ggplot object
    expect_true(exists("my_plot"), info = "my_plot object should exist.")
    expect_true(inherits(my_plot, "ggplot"), info = "my_plot should be a ggplot object.")
    # Check if theme_classic is in the plot's theme
    th <- my_plot$theme
    # theme_classic sets panel.border to element_blank() and axis.line to element_line()
    expect_true(!is.null(th$panel.border) && inherits(th$panel.border, "element_blank"),
        info = "theme_classic should set panel.border to element_blank()."
    )
    expect_true(!is.null(th$axis.line) && inherits(th$axis.line, "element_line"),
        info = "theme_classic should set axis.line to element_line()."
    )
})

test_that("plot title is customized", {
    expect_true(exists("my_plot"), info = "my_plot object should exist.")
    th <- my_plot$theme
    expect_true(!is.null(th$plot.title), info = "plot.title should be customized.")
    expect_equal(th$plot.title$size, 16, info = "plot.title size should be 16.")
    expect_equal(th$plot.title$face, "bold", info = "plot.title face should be bold.")
    expect_equal(th$plot.title$colour, "darkred", info = "plot.title color should be darkred.")
})

test_that("axis titles are customized", {
    expect_true(exists("my_plot"), info = "my_plot object should exist.")
    th <- my_plot$theme
    expect_true(!is.null(th$axis.title), info = "axis.title should be customized.")
    expect_equal(th$axis.title$size, 12, info = "axis.title size should be 12.")
    expect_equal(th$axis.title$colour, "black", info = "axis.title color should be black.")
})


test_main.R

Master the art of data visualization in R using the powerful ggplot2 package. This course guides you from the basics of plotting to advanced visualization techniques, enabling you to create insightful and visually appealing graphics for data analysis and presentation.

Comprehensive exploration of information visualization in R using ggplot2, from foundational concepts to advanced techniques.

Themes and Exporting Plots

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