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#R plot rename x lab how to#

So ?theme is the first place you want to look at if you want to change the look and feel of any component.

face, sets the font face (“plain”, “italic”, “bold”, “alic”)Ībove example covers some of the frequently used theme modifications and the actual list is too long.

hjust, controls the horizontal spacing.

vjust, controls the vertical spacing between title (or label) and plot.

5), # X axis text = element_text( size= 10)) # Y axis text Size= 15), # X axis title = element_text( size= 15), # Y axis title = element_text( size= 10, Hjust= 0.5), # subtitle plot.caption= element_text( size= 15), # caption = element_text( vjust= 10, Lineheight= 1.2), # title plot.subtitle= element_text( size= 15, Gg + theme( plot.title= element_text( size= 20, The axis text can be rotated by changing the angle. Since the plot and axis titles are textual components, element_text() is used to modify them.īelow, I have changed the size, color, face and line-height. The theme() function accepts one of the four element_type() functions mentioned above as arguments. Therefore, it can be modified using the theme() function. Plot and axis titles and the axis text are part of the plot’s theme. Let’s discuss a number of tasks related to changing the plot output, starting with modifying the title and axis texts. More on this follows in upcoming discussion.

element_blank(): Turns off displaying the theme item.

element_rect(): Modifies rectangle components such as plot and panel background.

element_line(): Likewise element_line() is use to modify line based components such as the axis lines, major and minor grid lines, etc.element_text(): Since the title, subtitle and captions are textual items, element_text() function is used to set it.The arguments passed to theme() components require to be set using special element_type() functions. Gg <- ggplot(midwest, aes( x=area, y=poptotal)) + geom_point( aes( col=state, size=popdensity)) + geom_smooth( method= "loess", se=F) + xlim( c( 0, 0.1)) + ylim( c( 0, 500000)) + labs( title= "Area Vs Population", y= "Population", x= "Area", caption= "Source: midwest") # midwest <- read.csv("") # bkup data source # Add plot components. Type ?theme in the R console and see for yourself. Most of the requirements related to look and feel can be achieved using the theme() function. The below plot has the essential components such as the title, axis labels and legend setup nicely.

We have done something similar in the previous ggplot2 tutorial already. The point’s color and size vary based on state (categorical) and popdensity (continuous) columns respectively. Let’s begin with a scatterplot of Population against Area from midwest dataset.

Inheritance Structure of Theme Components.

#R plot rename x lab how to#

How to Removing Major and Minor Grid, Border, Axis Title, Text and Ticks.Modifying Plot Background, Major and Minor Axis.

Faceting: Draw multiple plots within one figure.How to Adding Custom Annotation Anywhere inside Plot.How to Adding Text and Label around the Points.How to Style the Legend Title, Text and Key.How to Change Legend Labels and Point Color.Put together, it provides a fairly comprehensive list of how to accomplish your plot customization tasks in detail. In this tutorial, I discuss how to customize the looks of the 6 most important aesthetics of a plot. Part 3: Top 50 ggplot2 Visualizations - The Master List, applies what was learnt in part 1 and 2 to construct other types of ggplots such as bar charts, boxplots etc. Part 2: Customizing the Look and Feel, is about more advanced customization like manipulating legend, annotations, multiplots with faceting and custom layouts Part 1: Introduction to ggplot2, covers the basic knowledge about constructing simple ggplots and modifying the components and aesthetics. This tutorial is primarily geared towards those having some basic knowledge of the R programming language and want to make complex and nice looking charts with R ggplot2. This is part 2 of a 3-part tutorial on ggplot2, an aesthetically pleasing (and very popular) graphics framework in R. The Complete ggplot2 Tutorial - Part 2 | How To Customize ggplot2 (Full R code)