Category Archives: R Graphics

Trellis Graphs

Posted on October 17, 2013 by Brad Horn

Permanent link to this post (5 words, 1 image, estimated 1 secs reading time)

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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.

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R Graphics: Color Ramp Functions

Posted on December 18, 2013 by Brad Horn

Color ramp functions in R simplify color selection and assignment. Visual reference examples are provided to illustrate their use and flexibility.

Color Ramp: rainbow()

The rainbow() ramp is is the standard light spectrum with red, green and blue as its base. The functional form is rainbow(n, s=1, v=1, start=0, end=max(1, n-1)/n, alpha=1). The s and v values correspond to saturation and value from the HCL color scheme (described below). The parameters start and end can be used to specify particular subranges of hues. The following values can be used when generating such a subrange: red = 0, yellow = 1/6, green = 2/6, cyan = 3/6, blue = 4/6 and magenta =5/6.

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R Graphics Gallery

Posted on October 19, 2013 by Brad Horn

An R graphics gallery has been created with simple scripts for long-term reference and to expedite data visualization.

Simple Line Plot

Click to enlarge

x <- 1:10
y <- c(4, 3, 2, 5, 6, 7, 1, 8, 9, 7)
plot(x, y, 
     type = "b",
     main = "Simple Plot",
     xlab = "X-axis Title",
     ylab = "Y-axis Title",
     col = "red3")

x <- 1:10

y <- c(4, 3, 2, 5, 6, 7, 1, 8, 9, 7)

plot(x, y,

type = "b",

main = "Simple Plot",

xlab = "X-axis Title",

ylab = "Y-axis Title",

col = "red3")

Bar Plot of Vector

Click to enlarge

# Bar plot of vector
set.seed(1)
y.vec <- sample(-10:10,10, rep = TRUE)
barplot(y.vec,
        col = "steelblue4",
        names = LETTERS[1:10],
         ylim=c(-10,10))
abline(h = 0)

# Bar plot of vector

set.seed(1)

y.vec <- sample(-10:10,10, rep = TRUE)

barplot(y.vec,

col = "steelblue4",

names = LETTERS[1:10],

ylim=c(-10,10))

abline(h = 0)

Bar Plot of Matrix: #1

Click to enlarge

# Bar plot of matrix: #1
set.seed(10)
y.mat <- matrix(sample(1:10, 60, rep = TRUE), nrow = 6)
barplot(y.mat, 
        col = colorRampPalette(c("steelblue4", "green"))(6),
        names=LETTERS[1:10],
        ylim=c(0, 50))
abline(h = 0)

# Bar plot of matrix: #1

set.seed(10)

y.mat <- matrix(sample(1:10, 60, rep = TRUE), nrow = 6)

barplot(y.mat,

col = colorRampPalette(c("steelblue4", "green"))(6),

names=LETTERS[1:10],

ylim=c(0, 50))

abline(h = 0)

Bar Plot of Matrix: #2

Click to enlarge

# Bar plot of matrix: #2
barplot(y.mat,
        col=colorRampPalette(c("steelblue4", "green"))(6),
        names=LETTERS[1:10],
        horiz = TRUE,
        legend = paste("Sensor", 1:6),
        xlim=c(0,50))
abline(v = 0)

# Bar plot of matrix: #2

barplot(y.mat,

col=colorRampPalette(c("steelblue4", "green"))(6),

names=LETTERS[1:10],

horiz = TRUE,

legend = paste("Sensor", 1:6),

xlim=c(0,50))

abline(v = 0)

Read more... (258 words, 17 images, estimated 1:02 mins reading time)

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R Graphics: Plot Parameters

Posted on October 17, 2013 by Brad Horn

High level plot commands open a graphics device. High level plot elements add objects. R plot parameters ensure actual control over the graphics device.

R Plot Parameters

All high level plotting functions have arguments which can be used to customize the plot. barplot(), for example, has arguments to control bar width, styles, etc. High level functions also take the optional “three dots” argument, which allows for argument sharing. The most common arguments shared are low level graph parameters from the par() function.

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R Graphics: Heat Colors

Posted on December 18, 2013 by Brad Horn

Heat colors in R utilize basic color ramp functions. Simple example scripts with basic control options follow.

Heat Colors

The heat.colors() function creates a ramp of contiguous colors clustered around the color orange in the red spectrum of the RGB scale. The function has the form heat.colors(num_colors, alpha=value).

Color Ramp Example

# Configure graphical device
par(mai = c(0.3, 0, 0.3, 0), bg = "grey85")
h.grid <- layout(matrix(c(1, 2, 3, 4, 5), nrow = 5))
h.count <- c(3, 6, 12, 24, 48)
# Plot creation
for(i in 5:1){
   h.ramp <- heat.colors(h.count[i])
   barplot(rep(2, 
           h.count[i]), 
           axes = FALSE, 
           space = 0, 
           col = h.ramp, 
           main = paste0("n = ", h.count[i]))
}
# Reset graphical device to default values
par(op)

# Configure graphical device

par(mai = c(0.3, 0, 0.3, 0), bg = "grey85")

h.grid <- layout(matrix(c(1, 2, 3, 4, 5), nrow = 5))

h.count <- c(3, 6, 12, 24, 48)

# Plot creation

for(i in 5:1){

h.ramp <- heat.colors(h.count[i])

barplot(rep(2,

h.count[i]),

axes = FALSE,

space = 0,

col = h.ramp,

main = paste0("n = ", h.count[i]))

}

# Reset graphical device to default values

par(op)

Alpha Transparency

Heat color transparency is controlled with the optional alpha argument. The default value is alpha=1.

Read more... (71 words, 2 images, estimated 17 secs reading time)

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R Graphics: Grey Scales

Posted on December 18, 2013 by Brad Horn

Grey scales in R can be used effectively in data visualization. Several simple functions can be used to great effect.

Creating Gray Scales

The gray.colors() function creates a vector of interpolated values that represent evenly-spaced gray colors.

The function has the form gray.colors(num_colors, start = value, end = value, gamma = value). End and start define the endpoints of the range of grays, with 0 = black and 1 = white being the extreme values. (By default, start = 0.3 and end = 0.9.) gamma is an optional argument which controls luminance.

Read more... (141 words, 2 images, estimated 34 secs reading time)

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R Graphics: High Level Commands

Posted on October 13, 2013 by Brad Horn

High level commands and plotting functions in R set up a co-ordinate system. No plotting is done inside a graphics device until at least one high level function has established the co-ordinate system. High level commands include plot type and plot elements. Together, they provide users with direct control of all graph objects and support the creation of highly customized data visualizations.

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R Graphics: Multi-Graph Layouts

Posted on October 17, 2013 by Brad Horn

***The* layout() *Function***

The ability to manage multiple plots in one graphical device or window is a key capability to enhance data visualization and analysis.

The layout() function in base R is the most straightforward method to divide a graphical device into rows and columns. The function requires an input matrix definition. Column-widths and the row-heights can be defined using additional input arguments. layout.show() can then be used to see multi-graph layouts and how the graphical device is being split.

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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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R Color Tables: Download

Posted on December 18, 2013 by Brad Horn

The following page provides R color tables by name and hexadecimal code. The tables can be downloaded for local reference or recreated with R code provided.

R Color Tables: By Name

A pdf file for the color name table can be downloaded here” R Colors By Name (771 downloads)

Click to enlarge

Read more... (93 words, 2 images, estimated 22 secs reading time)

Posted in R Colors, R Graphics | 1 Comment

Color Palettes in R

Posted on December 16, 2013 by Brad Horn

The following list details all color palettes and color ramp functions in R. Detailed color tables are created for long-term reference and to aide code scripting. The objective is to simplify the delivery of publication quality graphics.

Permanent link to this post (53 words, estimated 13 secs reading time)

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Base Graphics in R

Posted on September 25, 2013 by Brad Horn

R graphics reference material is supplied on the high-level commands and the low level parameters that control full flexibility of R’s graphical environment. Subchapters are organized as follows:

Back | Next

Permanent link to this post (45 words, estimated 11 secs reading time)

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RColorBrewer Palettes

Posted on December 18, 2013 by Brad Horn

Color Groups

RColorBrewer is an R packages that uses the work from http://colorbrewer2.org/ to help you choose sensible colour schemes for figures in R. The colors are split into three group, sequential, diverging, and qualitative.

Sequential – Light colours for low data, dark for high data
Diverging – Light colours for mid-range data, low and high contrasting dark colours
Qualitative – Colours designed to give maximum visual difference between classes

Read more... (129 words, 2 images, estimated 31 secs reading time)

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Term Structure of Crude Oil 2018

Posted on January 30, 2019 by Brad Horn

An animation showing the term structure of NYMEX crude oil. For source code, go here.

Permanent link to this post (17 words, 1 image, estimated 4 secs reading time)

Posted in Animation, Data, Economics, R Graphics | Comments Off

Category Archives: R Graphics

Trellis Graphs

Layered Plots

Data Pre-Processing

R Graphics: Color Ramp Functions

Color Ramp: rainbow()

R Graphics Gallery

Simple Line Plot

Bar Plot of Vector

Bar Plot of Matrix: #1

Bar Plot of Matrix: #2

R Graphics: Plot Parameters

R Plot Parameters

R Graphics: Heat Colors

Heat Colors

Color Ramp Example

Alpha Transparency

R Graphics: Grey Scales

Creating Gray Scales

R Graphics: High Level Commands

R Graphics: Multi-Graph Layouts

***The* layout() *Function***

Multiple Plots (ggplot)

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

R Color Tables: Download

R Color Tables: By Name

Color Palettes in R

Base Graphics in R

RColorBrewer Palettes

Color Groups

Term Structure of Crude Oil 2018

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Category Archives: R Graphics

Data Pre-Processing

Color Ramp: rainbow()

Simple Line Plot

Bar Plot of Vector

Bar Plot of Matrix: #1

Bar Plot of Matrix: #2

R Plot Parameters

Heat Colors

Color Ramp Example

Alpha Transparency

Creating Gray Scales

The layout() Function

The multiplot() Function

R Color Tables: By Name

Color Groups

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Today in Energy

Tags

Archives

R-Bloggers

***The* layout() *Function***

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