ggplot2 A cool way for creating plots in R Maria Novosolov

Your scientific graphic options

Why not only Excel? Excel is relatively limited in its support of scientific graphing It’s options provide limited control over the output Limited selection of graph types Limited number of data points that can be plotted

Why ggplot2? Helps creating good looking graphs for publishing The package format allows manipulation on every step of the way Create new graphics tailored for your problem Easy to create complex plots

The idea behind ggplot2 Combining all the good and leaving out all the bad of all R packages for plotting

Some examples

Attention!! It doesn’t suggest what graphics you should use to answer the questions you are interested in

The basics of ggplot2 Works as a layer by layer system Based on the Grammar of Graphics (Wilkinson, 2005) The plots can be edited after they are ready

How it works Two options of working with the package 1. Easy – using qplot() (=quick plot) 2. Complicated – using ggplot

qplot Basically very similar to the function plot() in R The first two arguments to qplot() are x and y An optional data argument (recommended) Exp: qplot(x, y, data = mydata)

qplot You can change color, size, shape and transparency (all of this is called aesthetics) This can either be set to the default aesthetics or manually using the function I(). You can decide on the type of the plot (scatterplot, box-plot, histogram) by using “geom” function.

ggplot

Works by creating the plot layer by layer A layer is composed of four parts: data and aesthetic mapping a statistical transformation (stat) a geometric object (geom) and a position adjustment.

How it works A plot is made up of multiple layers. A layer consists of data, a set of mappings between variables and aesthetics, a geometric object and a statistical transformation. Scales control the details of the mapping. All components are independent and reusable.

Layers Iteratively update a plot Change a single feature at a time Think about the high level aspects of the plot in isolation Instead of choosing a static type of plot, create new types of plots on the fly Cure against immobility Developers can easily develop new layers without affecting other layers

Lets dive into the syntax

Anatomy of a plot 1. Data – the data name that you want to present 2. Aesthetics – the visualization of the plot 3. Geom – geometric objects. Represent what you see in the plot: points, lines, polygon etc. 4. Themes – control presentation of non- data elements 5. Stat – statistical transformations 6. Scale – intersect between the data and the aesthetics.

Anatomy of a plot 7. Coord – coordinate system. describes how data coordinates are mapped to the plane of the graphic. 8. Position adjustments 9. Facet - describes how to break up the data into subsets and how to display those subsets as small multiples.

Anatomy of a plot 1. Data * 2. Aesthetic mapping * 3. Geometric Object * 4. Statistical transformation 5. Scales 6. Facets 7. Themes 8. Position adjustments * Mandatory layers

1. Data In ggplot2, we always explicitly specify the data qplot(displ, hwy, data = mpg)

2. Aesthetic Mapping In ggplot land aesthetic means "something you can see“ Includes: Position (i.e., on the x and y axes) Color ("outside" color) Fill ("inside" color) Shape (of points) Line type Size Aesthetic mappings are set with the aes() function

3. Geometric Object A geom can only display certain aesthetics A plot must have at least one geom; there is no upper limit

Example ggplot(data=mpg, aes(x=cty, y=hwy)) + geom_point() Aesthetic Mappings The data.frame to plot What geom to use in plotting

4. Statistical Transformations Each geom has a default statistic, but these can be changed Some plot types (such as scatterplots) do not require transformations–each point is plotted at x and y coordinates equal to the original value Other plots, such as boxplots, histograms, prediction lines etc. require statistical transformations

5. Scales Control mapping from data to aesthetic attributes One scale per aesthetic

5. Scales In ggplot2 scales include: position color and fill size shape line type Modified with scale_ _

5. Scales Common Scale Arguments: name: the first argument gives the axis or legend title limits: the minimum and maximum of the scale breaks: the points along the scale where labels should appear labels: the labels that appear at each break

5. Scales Partial combination matrix of available scales

6. Faceting Lay out multiple plots on a page Split data into subsets Plot subsets into different panels

6. Faceting Faceting is ggplot2 parlance for small multiples The idea is to create separate graphs for subsets of data ggplot2 offers two functions for creating small multiples: facet_wrap() : define subsets as the levels of a single grouping variable facet_grid() : define subsets as the crossing of two grouping variables Facilitates comparison among plots, not just of geoms within a plot

Facet Types 2D grid of panels: 1D ribbon of panels wrapped into 2D:

Faceting Formula

Scales in Facets

7. Themes The ggplot2 theme system handles non-data plot elements such as Axis labels Plot background Facet label background Legend appearance Specific theme elements can be overridden using theme()

8. Position adjustments Tweak positioning of geometric objects Avoid overlaps

Exp: position_jitter Avoid overplotting by jittering points ggplot(overplotted, aes(x,y)) + geom_point(position=position_jitter (w=0.1, h=0.1))

ggplot Creating a plot -Adding data -Mapping aesthetics Adding layers* * In order to start seeing the plot you need to first declare a geom Exp: p <- ggplot(diamonds, aes(carat, price, colour = cut)) Exp: p <- p + layer(geom = "point")

Summary Allows creating high quality plots Many options Many blogs and webpages explaining how to do different plots in ggplot Disadvantage – takes time to learn the grammar There is a book: ggplot2: Elegant Graphics for Data Analysis

Useful links content/uploads/2011/09/handout_ggplot2. pdf content/uploads/2011/09/handout_ggplot2. pdf