Category Archives: ggplot2

Colors in ggplot

Posted on December 18, 2013 by Brad Horn

There are number of ways to control the default colors in ggplot.

The HCL Color Wheel

ggplot simplifies color choice with its default color selection, which are based on a “color wheel.” The result is a well balanced graphic that doesn’t draw too much attention to any one color. ggplot uses the HCL color wheel and the hue_pal() function from the scales package. Specifically, if there are two colors, then they will be selected from opposite points on the circle; if there are three colors, they will be 120° apart on the color circle; and so on. This ensure that discrete data has maximum contrast as a function of the number variables present.

Read more... (202 words, 2 images, estimated 48 secs reading time)

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Legends (ggplot)

Posted on January 1, 2014 by Brad Horn

Legends are a key component of data visualization. ggplot format controls are defined below.

Data

The diamonds data that ships with ggplot.

The Default Legend

The following example presents the default legend to be cusotmized.

def <- ggplot(diamonds, aes(cut, price)) + geom_boxplot(aes(fill = factor(cut))) +
  labs(title = "Diamonds Data", x = "Cut", y = "Price (USD)")

1 2	def <- ggplot(diamonds, aes(cut, price)) + geom_boxplot(aes(fill = factor(cut))) + labs(title = "Diamonds Data", x = "Cut", y = "Price (USD)")

Click to enlarge

Removing the Legend

Legends are created for different aesthetics, such as fill, color/colour, linetype, shape, etc. Each aesthetic has a scale function than can be called to remove the legend:

Read more... (299 words, 10 images, estimated 1:12 mins reading time)

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Line Graphs (ggplot)

Posted on November 23, 2013 by Brad Horn

ggplot line graph example are provided to illustrate different format options.

Data

The Orange data frame ships with R and depicts the growth of fruit trees as a function of age (days). The data object has 35 rows and 3 columns in long data format.

Basic Line Plot Syntax

library(ggplot2)

# Plot skeleton
p <- ggplot(Orange, aes(age, circumference, group=Tree))
# Plot1: Basic line chart of circumference vs. age
p + geom_line()

library(ggplot2)

# Plot skeleton

p <- ggplot(Orange, aes(age, circumference, group=Tree))

# Plot1: Basic line chart of circumference vs. age

p + geom_line()

Line Plot Aesthetics

Line chart aesthetics control selected x and y data, color (by name), linetype and size, transparency or alpha level. Line symbols or points are added using geom_point().

Read more... (179 words, 5 images, estimated 43 secs reading time)

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Multiple Plots (ggplot)

Posted on November 23, 2013 by Brad Horn

***The* multiplot() *Function***

Winston Chang’s R Graphical Cookbook provides a useful function to simplify the creation of layouts with multiple plots. The function accepts ggplot objects as inputs.

# Multiplot creates a layout structure for multiple plots in one
# window. The function accepts ggplot objects via "..." or as a
# list of objects. Use input "cols" to define the number of
# columns in the plot layout. Alternatively, input "layout" to
# define the row/col matrix of plot elements. If "layout" is
# present, "cols" is ignored. For example, given a layout defined
# by matrix(c(1,2,3,3), nrow=2, byrow=TRUE), then plot #1 goes in
# the upper left, #2 goes in the upper right, and #3 will cross
# the entire bottom row, utilizing both columns.

multiplot <- function(..., plotlist = NULL, file, cols = 1, layout = NULL) {
   # launch the grid graphical system
   require(grid)

   # make a list from the "..." arguments and/or plotlist
   plots <- c(list(...), plotlist)
   numPlots = length(plots)

   # if layout = NULL, then use 'cols' to determine layout
   if (is.null(layout)) {
      # make the panel using ncol; nrow is calculated from ncol
      layout <- matrix(seq(1, cols * ceiling(numPlots/cols)), ncol = cols, nrow = ceiling(numPlots/cols))
   }

   if (numPlots==1) {
      print(plots[[1]])
   } else {
      # set up the page
      grid.newpage()
      pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))
      # put each plot, in the correct location
      for (i in 1:numPlots) {
         # get the i,j matrix position of the subplot
         matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
         print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row, layout.pos.col = matchidx$col))
      }
   }
}

# Multiplot creates a layout structure for multiple plots in one

# window. The function accepts ggplot objects via "..." or as a

# list of objects. Use input "cols" to define the number of

# columns in the plot layout. Alternatively, input "layout" to

# define the row/col matrix of plot elements. If "layout" is

# present, "cols" is ignored. For example, given a layout defined

# by matrix(c(1,2,3,3), nrow=2, byrow=TRUE), then plot #1 goes in

# the upper left, #2 goes in the upper right, and #3 will cross

# the entire bottom row, utilizing both columns.

multiplot <- function(..., plotlist = NULL, file, cols = 1, layout = NULL) {

# launch the grid graphical system

require(grid)

# make a list from the "..." arguments and/or plotlist

plots <- c(list(...), plotlist)

numPlots = length(plots)

# if layout = NULL, then use 'cols' to determine layout

if (is.null(layout)) {

# make the panel using ncol; nrow is calculated from ncol

layout <- matrix(seq(1, cols * ceiling(numPlots/cols)), ncol = cols, nrow = ceiling(numPlots/cols))

}

if (numPlots==1) {

print(plots[[1]])

} else {

# set up the page

grid.newpage()

pushViewport(viewport(layout = grid.layout(nrow(layout), ncol(layout))))

# put each plot, in the correct location

for (i in 1:numPlots) {

# get the i,j matrix position of the subplot

matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))

print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row, layout.pos.col = matchidx$col))

}

Use of the function is straightforward. First, multiplot() needs to be sourced and available in memory. Then the plots need to be coded with variable assignments to create plot objects. Finally, multiplot() is used to call the plot objects for placement in the predefined plot layout.

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Other Geoms (ggplot)

Posted on November 23, 2013 by Brad Horn

All geoms that ship with ggplot2 are listed for below. See HTML help in R for detailed argument structures and examples.

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R Graphics (ggplot2)

Posted on October 17, 2013 by Brad Horn

The flexibility of R graphics using the package ggplot is illustrated through a series of structured examples.

The Grammar of Graphics
Building Layered Plots
Scatter Plots (ggplot)
Bar Graphs (ggplot)
Line Graphs (ggplot)
Boxplots (ggplot)
Error Bars (ggplot)
Facetting (ggplot)
Titles (ggplot)
Axes (ggplot)
Legends (ggplot)
Other Geoms (ggplot)
Multiple Plots (ggplot)
Themes (ggplot)

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Scatter Plots (ggplot)

Posted on November 23, 2013 by Brad Horn

Scatter plots in ggplot are simple to construct and can utilize many format options.

Data

The mtcars data frame ships with R and was extracted from the 1974 US Magazine Motor Trend. The data compares fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

Basic Scatter Plot Syntax

library(ggplot2)

# Plot skeleton
p <- ggplot(mtcars, aes(wt, mpg))
# Plot1: basic scatter plot of car mpg vs. weight
p + geom_point()

library(ggplot2)

# Plot skeleton

p <- ggplot(mtcars, aes(wt, mpg))

# Plot1: basic scatter plot of car mpg vs. weight

p + geom_point()

Aesthetics

Scatter plot aesthetics are used to control selected x and y data, color (by name), point shape (1 thru 24), alpha level (or transparency), point size, and point fill.

Read more... (204 words, 20 images, estimated 49 secs reading time)

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Text (ggplot)

Posted on November 23, 2013 by Brad Horn

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The Grammar of Graphics

Posted on October 27, 2013 by Brad Horn

Comparison of Graphic Tools

When it comes to producing graphics in R, there are basically three choices:

Base graphics which ships with R,
the lattice package extension, and
the ggplot2 package extension.

Base graphics was described extensively in the previous few chapters, and is the preferred choice for creating highly customized charts, like the polar windrose plot below, where flexibility and control over all graph objects is essential:

Read more... (366 words, 2 images, estimated 1:28 mins reading time)

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Themes (ggplot)

Posted on December 18, 2013 by Brad Horn

Quick Intro

Themes and theme elements control the non-data components of the plot. The use, of predefined themes and custom themes in ggplot are described below.

ggplot provides two built-in format themes. theme_grey() is the default theme and has a grey plot background. theme_bw() is a simple black and white theme. ggplot also provides functions to modify theme elements or to create new themes.

Read more... (307 words, 3 images, estimated 1:14 mins reading time)

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Titles (ggplot)

Posted on November 23, 2013 by Brad Horn

Options to control titles in ggplot are described below.

Chapter Content

Data
Basic Plot: No Title Aesthetics
Title Aesthetics

Data

The diamonds dataset that ships with ggplot.

Basic Plot: No Titles

# Plot skeleton
p0 <- ggplot(diamonds, aes(depth)) + xlim(58, 68)
# Plot1: Histogram with facetting
p1 <- p + geom_histogram(aes(y = ..density..), binwidth = 0.1, colour = "dark orange", fill = "orange") +
   facet_grid(cut ~ .)
# Print graph object
p1

# Plot skeleton

p0 <- ggplot(diamonds, aes(depth)) + xlim(58, 68)

# Plot1: Histogram with facetting

p1 <- p + geom_histogram(aes(y = ..density..), binwidth = 0.1, colour = "dark orange", fill = "orange") +

facet_grid(cut ~ .)

# Print graph object

Title Aesthetics

# Plot2: Main title with line break
p1 + labs(title = "Diamond Data\nDepth versus Cut")

# Plot3: Main title with alternative line break and font formats
p1 + labs(title = expression(atop(bold("Diamond Data"), 
                             atop(italic("Depth versus Cut"), "")))) +
theme(plot.title = element_text(size = 25))

# Plot3: x and y axis title
p1 + labs(x = "Diamond Depth", y = "Frequency %")

# Plot4: x any y axis titles with text and title formats
p1 + labs(x = "Diamond Depth (mm)", y = "Frequency (%)") +
   theme(axis.title.y = element_text(size = 14, vjust = 0.25, face = "bold"),
         axis.text.y = element_text(size = 11, color = "black"),
         axis.title.x = element_text(size = 14, vjust = -0.25, face = "bold"),
         axis.text.x = element_text(size = 11, color = "black", angle = -90),
         strip.text.y = element_text(size=12, face="bold", color="white"),
         strip.background = element_rect(color="black", fill="skyblue4"),
         panel.border = element_rect(fill = NA, colour="navy"))

# Plot2: Main title with line break

p1 + labs(title = "Diamond Data\nDepth versus Cut")

# Plot3: Main title with alternative line break and font formats

p1 + labs(title = expression(atop(bold("Diamond Data"),

atop(italic("Depth versus Cut"), "")))) +

theme(plot.title = element_text(size = 25))

# Plot3: x and y axis title

p1 + labs(x = "Diamond Depth", y = "Frequency %")

# Plot4: x any y axis titles with text and title formats

p1 + labs(x = "Diamond Depth (mm)", y = "Frequency (%)") +

theme(axis.title.y = element_text(size = 14, vjust = 0.25, face = "bold"),

axis.text.y = element_text(size = 11, color = "black"),

axis.title.x = element_text(size = 14, vjust = -0.25, face = "bold"),

axis.text.x = element_text(size = 11, color = "black", angle = -90),

strip.text.y = element_text(size=12, face="bold", color="white"),

strip.background = element_rect(color="black", fill="skyblue4"),

panel.border = element_rect(fill = NA, colour="navy"))

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Axes (ggplot)

Posted on November 23, 2013 by Brad Horn

Data

A simple sequence of numbers is provided to populate the base graph.

library(ggplot2)

# Define data
sensors <- factor(paste0("loc", 1:10), levels=paste0("loc", 1:10))
df1 <- data.frame(loc = sensors,
                  x = 100/seq(100, 1000, by = 100),
                  y = 100/seq(1000, 100, by = -100))
# Convert from wide to long format
df1 <- melt(df1, id.vars = "loc")

# Base graph; no axes formats
p <- ggplot(df1, aes(loc, value, group=variable, fill=variable)) +
        geom_bar(stat="identity", color="green") +
        scale_fill_manual(values=c("grey35", "grey65"))

library(ggplot2)

# Define data

sensors <- factor(paste0("loc", 1:10), levels=paste0("loc", 1:10))

df1 <- data.frame(loc = sensors,

x = 100/seq(100, 1000, by = 100),

y = 100/seq(1000, 100, by = -100))

# Convert from wide to long format

df1 <- melt(df1, id.vars = "loc")

# Base graph; no axes formats

p <- ggplot(df1, aes(loc, value, group=variable, fill=variable)) +

geom_bar(stat="identity", color="green") +

scale_fill_manual(values=c("grey35", "grey65"))

Click to enlarge

Axes Text Formats

The following examples modify axes text formats with colors and bold font so they are easier to see. The second chart benefits from axis text being rotated 90 degrees.

# Change axes text font and color
p <- p + theme(axis.text.x = element_text(face="bold", color="blue", size=12),
               axis.text.y = element_text(face="bold", color="red", size=12))
print(p)

# Change x-axis angle
p + theme(axis.text.x = element_text(angle = -90, vjust = 0.5))

# Change axes text font and color

p <- p + theme(axis.text.x = element_text(face="bold", color="blue", size=12),

axis.text.y = element_text(face="bold", color="red", size=12))

print(p)

# Change x-axis angle

p + theme(axis.text.x = element_text(angle = -90, vjust = 0.5))

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Layered Plots

Posted on November 22, 2013 by Brad Horn

Layered plots are one way to achieve new insight and actionable intelligence when working with complex data. ggplot is well suited for layered plots.

Data Pre-Processing

To make graphs with ggplot(), the data must be in a data frame and in “long” (as opposed to wide) format. Converting between “wide” and “long” data formats is facilitated with the reshape2 package. Specifically, the melt() function converts wide to long format, and the cast() function converts long to wide format. The following code block presents examples of the two data formats.

Read more... (393 words, 3 images, estimated 1:34 mins reading time)

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Bar Graphs (ggplot)

Posted on November 23, 2013 by Brad Horn

Data

Basic Bar Graph Syntax

A basic plot skeleton is created by declaring the data, aesthetic mappings. The additional stat argument is set equal to “bin” (the default) to obtain counts of the data observations and “identity” and to plot observed values.

library(ggplot)

# Plot skeleton
c <- ggplot(mtcars, aes(factor(cyl)))

# The argument stat="bin" is the default and is not specified
# Plot1: Basic bar chart of car count by cylinder
c + geom_bar()

library(ggplot)

# Plot skeleton

c <- ggplot(mtcars, aes(factor(cyl)))

# The argument stat="bin" is the default and is not specified

# Plot1: Basic bar chart of car count by cylinder

c + geom_bar()

Read more... (235 words, 13 images, estimated 56 secs reading time)

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Boxplots (ggplot)

Posted on November 23, 2013 by Brad Horn

Data

diamonds is a dataset that ships with ggplot2 with observations from almost 54,000 diamonds. Data slicing is possible by price, carat, cut, color, clarity, size, depth and table width. Boxplots are ideally suited for visualizing data variability.

Basic Boxplot Syntax

library(ggplot)

# Plot Skeleton
p <- ggplot(diamonds, aes(x=factor(color), y=carat))

# Boxplot of diamond carat as a function of diamond color
p + geom_boxplot()

library(ggplot)

# Plot Skeleton

p <- ggplot(diamonds, aes(x=factor(color), y=carat))

# Boxplot of diamond carat as a function of diamond color

p + geom_boxplot()

Boxplot Aesthetics

Boxplot aesthetics define the x input data and have several argument parameters that control box attributes: ymin,lower quartile, middle bar statistic, upper quartile, and ymax. Additional arguments include color, fill, linetype, shape, size, weight and alpha transparency level.

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Category Archives: ggplot2

The HCL Color Wheel

Data

The Default Legend

Removing the Legend

Data

Basic Line Plot Syntax

Line Plot Aesthetics

The multiplot() Function

Data

Basic Scatter Plot Syntax

Aesthetics

Comparison of Graphic Tools

Quick Intro

Chapter Content

Data

Basic Plot: No Titles

Title Aesthetics

Data

Axes Text Formats

Data Pre-Processing

Data

Basic Bar Graph Syntax

Data

Basic Boxplot Syntax

Boxplot Aesthetics

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***The* multiplot() *Function***