ggplot2 Plots in R Programming
Understanding the graphical system’s fundamental philosophy and making use of qplot(), a key utility function intended for quick visualization, are the main goals of the Introduction to ggplot2 course. One notable package that Hadley Wickham contributed to was the ggplot2 package. In addition to the Lattice package, ggplot2 offers advanced graphics capabilities, producing layered representations that are thought to be more flexible and attractive than those generated by simple R Programming charting routines. This package is regarded as a potent tool for R data visualization. Library(“ggplot2”) loads ggplot2 into R before use. Install.packages(“ggplot2”) downloads and installs the package.
Understanding the Grammar of Graphics Philosophy
It is the Grammar of Graphics (GoG) that provides the “gg” in ggplot2. First presented by Leland Wilkinson in 2005, the GoG is a precise and formal framework for characterizing, designing, and building data charts. By dissecting a statistical visual into its basic, layered components, this approach goes beyond just labeling particular chart types (such as “scatterplots” or “histograms”).
Following this kind of thought helps ggplot2 accomplish a number of important objectives:
Standardization: In statistical analysis and graphical representation, standardization includes scaling a variable or dataset to allow comparison or match modeling assumptions. In the normal distribution, a random variable $X$ with a mean of $\mu$ and a standard deviation of $\sigma$ can be standardized to a new random variable $Z = (X – \mu) / \sigma$, resulting in the standard normal distribution $Z \sim N(0, 1)$.
A standard distribution is often used to compare normally distributed random variables on the same probability scale. Variables are often rescaled, or standardized, in statistical modeling because the standard error formula estimates sample mean variability. The Grammar of Graphics (GoG), which underpins ggplot2, standardizes plot generation by offering a formal, consistent procedure [GoG in previous response].
Streamlining: According to the Grammar of Graphics (GoG) concept, the ggplot2 package simplifies complex data visualization tasks [GoG in previous response]. Defining and manipulating layers in ggplot2 simplifies operations like aesthetic mapping-based legend generation and integration [GoG in previous response]. No need to manually separate data subsets and plot them with custom legends, ggplot2 automatically segments data and applies visual cues (such color, shape, or size) based on a variable in the dataset [GoG in previous response].
Automation makes “less fiddly” chores easier to manage while generating complex graphics [GoG in previous response]. Beyond visuals, producing lightning-fast vectorized R code streamlines programs and simulations, outperforming those created by normal R users.
Layered Construction: Layered composition is central to the Grammar of Graphics (GoG) theory of ggplot2 [GoG in previous response]. This technique views plots as objects produced by explicitly defining and manipulating layers [GoG in previous response]. Unlike standard R charting, which is sequential and dynamic, this versatility is striking. The addition operator (+) is used to add specific geoms (geometric modifiers) to an original plot object [GoG in previous response].
Geoms like geom_point(), geom_line(), and geom_bar() are layered functions that define and adapt the graphic’s visual features, such as point shape and size, line type, color or fill, based on data or explicit specifications [GoG in previous response]. Because ggplot2 plots are immutable objects, users can save a plot and add geom layers to customize and enhance the visual output before final presentation [GoG in previous response].
Ggplot2’s design makes it a versatile and effective tool. In contrast to typical R graphics, which consider the graphics device as an active canvas on which features are added one after the other, ggplot2 plots are saved as immutable objects. Compared to standard R methods, this object-oriented aspect can be a big advantage because it lets the user store a plot object and edit or improve it before it is explicitly displayed.
The graphic is displayed when the object’s print method is used for an action that necessitates viewing the plot (such as entering the object name at the prompt). The package provides qplot(), a wrapper for fast visualizations, in addition to ggplot(), the core function that embodies the GoG.
Creating a “Quick Plot” with qplot()
In the ggplot2 package, the qplot() function is the fundamental charting tool. An easy wrapper for creating different charts with a standard input scheme. It is similar to the fundamental R plot() function to make it easy to use, especially in the R console.
The sort of graph that is produced while using qplot depends on the input:
Scatterplots (Two Variables): When given two equal-length vectors, qplot() constructs a scatterplot. The first and second vectors determine the x and y axes. The link between two variables can be seen with the use of scatterplots. When two vectors are supplied by default, the point geom is implicitly used.
Histograms (One Variable): Using a single vector, qplot() creates a histogram. To show a variable’s distribution, a histogram shows data points’ frequencies in bins. These intervals can be set in size using the optional argument binwidth. It’s noteworthy that ggplot2 asks users to enter a suitable binwidth number; if none is supplied, the function defaults to 30 bins and outputs a warning message suggesting a change.
Density Plots: qplot generates a density plot, or smoothed histogram, in place of the default histogram when a user defines the geometric modifier as geom = “density” and provides only one variable.
Main (plot title), xlab (horizontal axis label), and ylab (vertical axis label) are examples of common plot arguments that qplot can receive and are the same as those used in base R.
Aesthetic Mapping with Geoms
Although qplot() is capable of producing simple plots in an instant, its true strength is in its ability to integrate with the GoG layer system, especially through the use of aesthetic mapping and geoms (geometric modifiers).
Geoms and Layers: The Geoms and Layers Functions known as geoms (such as geom_point(), geom_line(), and geom_bar()) are applied to the original plot object by use of the addition operator (+). The following basic graphical properties can be defined and customized by the user using geoms. One may use qplot(foo, bar) + geom_line(), for instance, to link points with lines. If a plot object (like myqplot) is kept, the user can add various geom layers often to vary the visual result without redefining the data plot.
Aesthetic Mapping: Beautiful Charting Using aesthetic mapping, ggplot2 can automatically assign visual qualities like color, shape, and size to a continuous or categorical variable in the dataset. The user can use qplot settings to map variables to aesthetic features when the data is in a frame (data = mtcars). The factorized cylinder count can be mapped to both color and size using a single command, for example, qplot(wt, mpg, data = mtcars, size = factor(cyl), color = factor(cyl) to visualize the relationship between weight (wt) and miles per gallon (mpg) while differentiating points by the number of cylinders (cyl).
When compared to isolating and plotting subsets separately in base R Programming, this method significantly reduces the labor needed by automating the segmentation of data and the application of corresponding visual signals. Additionally, ggplot2 incorporates and automatically creates a suitable legend that makes reference to these aesthetically pleasing maps.
The mapping=aes(…) argument must be used within the specific geom function to define new, localized aesthetics in advanced use cases where a geom layer needs to ignore or override the default aesthetic mapping set in the initial qplot call.