ggplot: Introduction

What is ggplot2?

ggplot2 is a popular data visualization package in the programming language R. It is built on the philosophy of the Grammar of Graphics, which emphasizes a layered approach to creating graphics by combining data, aesthetics, and geometric objects. ggplot2 provides a flexible and powerful framework for creating a wide range of visualizations, including scatter plots, bar charts, line graphs, histograms, and more.

Getting Started

To use ggplot, you first have to install it via the function install.packages(). Afterwards, you can call the package with library(ggplot2).

install.packages("ggplot2")
library(ggplot2)

Loading your Data

Let's assume your data is stored as a .csv file. There are two basic ways of reading csv files: read.table() and read.csv(). The default settings of both functions differ slightly. If you created a RStudio project beforehand (see "Setting up the environment"), then your working directory will be automatically setup for you, and you can refer to your .csv file relative to your working directory. Otherwise, you may need to use an absolute path to your file. The whole function could look like this, assuming your file contains a header row and the values are separated by a semicolon:

data <- "read.table("data/ngs.csv", sep=";", header=TRUE)

We do not have to define column names for a file with a header row as R will automatically name the columns after their header value. If the data file does not have a header, we can define column names with colnames(data) and assign a vector of column names to it: colnames(data) <- c("gene", "expression") Alternatively, you can create your data frame directly in R. We do not recommend this however, as it is usually more convenient to use .csv files.

Calling ggplot

The basic building block of a ggplot2 plot is the "ggplot" object, which represents the overall plot structure and the dataset to be visualized. The ggplot object is created using the ggplot() function:

p <- ggplot()

We earlier assigned our NGS data to the variable "data", so we specify data as our first argument in ggplot():

p <- ggplot(data)

The Building Blocks of a Plot

To plot a graph in ggplot2, you need to familiarize yourself with the 7 elements that are used to build all kinds of plots. ggplot2 constructs plots by layering these grammatical elements on top of each other, almost like a pyramid:

Element:	What it does:
Data	Is the data that you want to visualize
Aesthetics	Used to map (or link) information from your dataset to the visible part of a graph
Geometries	The way that the visible part of the data is presented on a plot, e.g. scatter plot, line plots, histogramms, etc.
Themes	All things in a plot that are not part of the actual data, such as labels, fonts, background, titles, legends, etc.
Statistics	Statistical transformation of the data for better visualisation
Coordinates	Provides axes and gridlines for the data to be mapped to a graphic plane
Facets	Breaks up subsets of data to display them on separate planes

Using these 7 elements, it is possible to create any graph you can think of. However, for a simple plot you only need the first three elements: data, aesthetics and geometries. The four other elements can be used additionally to create even more complex graphs.

info

The key components for a plot in ggplot2 are the data, aesthetics and the geometry! Without these components, R will not be able to produce a plot!

Below is a code template that includes all seven elements. The individual parts of the code will be addressed in the upcoming chapters:

ggplot(<DATA>, aes(<MAPPINGS>), stat = <STAT>, position =<POSITION>) + 
    <GEOM_FUNCTION> +
    <COORDINATE_FUNCTION> +
    <THEME_FUNCTION> +
    <FACET_FUNCTION>

It is useful to look up the ggplot cheat sheet, which has all important information summarized together!