Session1

A. R ENVIRONMENT

1. Working Directory

getwd() # print the current working directory

## [1] "C:/Users/noviyantisagala/OneDrive - Bina Nusantara University/WORK/Teaching Material/even21-22/DataMining&Visualization/Lab"

ls() # list the objects in the current workspace

## character(0)

#setwd(mydirectory) # change to mydirectory
setwd("C:/Users/noviyantisagala/Documents")

2. Previous Commands

history() # display last 25 commands history(max.show=Inf) # display all previous commands loadhistory(file=“myfile”) # default is “.Rhistory”

3. Save your command history

savehistory(file=“myfile”) # default is “.Rhistory”

B. Plotting Systems

1. The Base Plotting System

The base plotting system is the original plotting system for R. The basic model is sometimes referred to as the “artist’s palette” model. The idea is you start with blank canvas and build up from there.

In more R-specific terms, you typically start with plot function (or similar plot creating function) to initiate a plot and then annotate the plot with various annotation functions (text, lines, points, axis)

The base plotting system is often the most convenient plotting system to use because it mirrors how we sometimes think of building plots and analyzing data. If we don’t have a completely well-formed idea of how we want to look at some data, often we’ll start by “throwing some data on the page” and then slowly add more information to it as our thought process evolves.

The core plotting and graphics engine in R is encapsulated in the following packages:

• graphics: contains plotting functions for the “base” graphing systems, including plot, hist, boxplot and many others.

• grDevices: contains all the code implementing the various graphics devices, including X11, PDF, PostScript, PNG, etc.

The grDevices package contains the functionality for sending plots to various output devices. The graphics package contains the code for actually constructing and annotating plots.

## Create the plot / draw canvas
with(cars, plot(speed, dist))

The downside of the base plotting system is that it’s difficult to describe or translate a plot to others because there’s no clear graphical language or grammar that can be used to communicate what you’ve done. The only real way to describe what you’ve done in a base plot is to just list the series of commands/functions that you’ve executed, which is not a particularly compact way of communicating things. This is one problem that the ggplot2 package attempts to address.

2. Base Graphics

Base graphics are used most commonly and are a very powerful system for creating data graphics. There are two phases to creating a base plot:

1. Initializing a new plot
2. Annotating (adding to) an existing plot

Calling plot(x, y) or hist(x) will launch a graphics device (if one is not already open) and draw a new plot on the device. If the arguments to plot are not of some special class, then the default method for plot is called; this function has many arguments, letting you set the title, x axis label, y axis label, etc.

The base graphics system has many global parameters that can set and tweaked. These parameters are documented in ?par and are used to control the global behavior of plots, such as the margins, axis orientation, and other details. It wouldn’t hurt to try to memorize at least part of this help page!

Another typical base plot is constructed with the following code.

data(cars)

## Create the plot / draw canvas
with(cars, plot(speed, dist))

## Add annotation
title("Speed vs. Stopping distance")

Base plot with title

3. Simple Base Graphics

Histogram

Here is an example of a simple histogram made using the hist() function in the graphics package. If you run this code and your graphics window is not already open, it should open once you call the hist() function.

library(datasets)

## Draw a new plot on the screen device
hist(airquality$Ozone)  

Ozone levels in New York City

Boxplot

Boxplots can be made in R using the boxplot() function, which takes as its first argument a formula. The formula has form of y-axis ~ x-axis. Anytime you see a ~ in R, it’s a formula. Here, we are plotting ozone levels in New York by month, and the right hand side of the ~ indicate the month variable. However, we first have to transform the month variable in to a factor before we can pass it to boxplot(), or else boxplot() will treat the month variable as continuous.

airquality <- transform(airquality, Month = factor(Month))
boxplot(Ozone ~ Month, airquality, xlab = "Month", ylab = "Ozone (ppb)")

Ozone levels by month in New York City

Each boxplot shows the median, 25th and 75th percentiles of the data (the “box”), as well as +/- 1.5 times the interquartile range (IQR) of the data (the “whiskers”). Any data points beyond 1.5 times the IQR of the data are indicated separately with circles.

In this case the monthly boxplots show some interesting features. First, the levels of ozone tend to be highest in July and August. Second, the variability of ozone is also highest in July and August. This phenomenon is common with environmental data where the mean and the variance are often related to each other.

Scatterplot

Here is a simple scatterplot made with the plot() function.

with(airquality, plot(Wind, Ozone))

Scatterplot of wind and ozone in New York City

Generally, the plot() function takes two vectors of numbers: one for the x-axis coordinates and one for the y-axis coordinates. However, plot() is what’s called a generic function in R, which means its behavior can change depending on what kinds of data are passed to the function.

One thing to note here is that although we did not provide labels for the x- and the y-axis, labels were automatically created from the names of the variables (i.e. “Wind” and “Ozone”). This can be useful when you are making plots quickly, but it demands that you have useful descriptive names for the your variables and R objects.

4. Some Important Base Graphics Parameters

Many base plotting functions share a set of global parameters. Here are a few key ones:

• pch: the plotting symbol (default is open circle)
• lty: the line type (default is solid line), can be dashed, dotted, etc.
• lwd: the line width, specified as an integer multiple
• col: the plotting color, specified as a number, string, or hex code; the colors() function gives you a vector of colors by name
• xlab: character string for the x-axis label
• ylab: character string for the y-axis label

Base Plotting Functions

The most basic base plotting function is plot(). The plot() function makes a scatterplot, or other type of plot depending on the class of the object being plotted. Calling plot() will draw a plot on the screen device (and open the screen device if not already open). After that, annotation functions can be called to add to the already-made plot.

Some key annotation functions are

• lines: add lines to a plot, given a vector of x values and a corresponding vector of y values (or a 2-column matrix); this function just connects the dots
• points: add points to a plot
• text: add text labels to a plot using specified x, y coordinates
• title: add annotations to x, y axis labels, title, subtitle, outer margin
• mtext: add arbitrary text to the margins (inner or outer) of the plot
• axis: adding axis ticks/labels

1. Add and customize titles
2. Add text (Text characteristics)
3. Add and customize legends
4. Change point shapes
5. Change line types
6. Change colors

1. Add Titles

Plot Titles Plot titles can be specified either directly to the plotting functions during the plot creation or by using the title() function (to add titles on an existing plot).

We make the plot with the plot() function and then add a title to the top of the plot with the title() function.

library(datasets)

## Make the initial plot
with(airquality, plot(Wind, Ozone))

## Add a title
title(main = "Ozone and Wind in New York City")  

Base plot with annotation

# Add titles
barplot(c(2,5), main="Main title",
        xlab="X axis title",
        ylab="Y axis title",
        sub="Sub-title",
        col.main="red", col.lab="blue", col.sub="black")

# Increase the size of titles
barplot(c(2,5), main="Main title",
        xlab="X axis title",
        ylab="Y axis title",
        sub="Sub-title",
        cex.main=2, cex.lab=1.7, cex.sub=1.2)

2. Add Text(Text characteristics)

Graphic parameters are also used to specify text size, font, and style.

set.seed(1)

# Generate sample data
x <- rnorm(500)
y <- x + rnorm(500)

plot(x, y, main = "My title", sub = "Subtitle",
     cex.main = 2,   # Title size
     cex.sub = 1.5,  # Subtitle size
     cex.lab = 3,    # X-axis and Y-axis labels size
     cex.axis = 0.5) # Axis labels size

You can set this argument to 1 for plain text, 2 to bold (default), 3 italic and 4 for bold italic text. This argumento won’t modify the title style.

plot(x, y, font = 2, main = "Bold") # Bold

plot(x, y, font = 3, main = "Italics") # Italics

plot(x, y, font = 4, main = "Bold italics") # Bold italics

You can also specify the style of each of the texts of the plot with the font.main, font.sub, font.axis and font.lab arguments.

plot(x, y,
     main = "My title",
     sub = "Subtitle",
     font.main = 1, # Title font style
     font.sub  = 2, # Subtitle font style
     font.axis = 3, # Axis tick labels font style
     font.lab  = 4) # Font style of X and Y axis labels

On the one hand, the mtext function in R allows you to add text to all sides of the plot box. There are 12 combinations (3 on each side of the box, as left, center and right align). You just need to change the side and adj to obtain the combination you need.

On the other, the text function allows you to add text or formulas inside the plot at some position setting the coordinates. In the following code block some examples are shown for both functions.

mtext does not support rotation, only horizontal adjustment with las = 1 for the vertical axis and vertical adjustment with las = 3 for the X-axis. If you need to rotate the text you can use text function with srt argument instead.

mtext()

line, to set the margin line where to set the text. Default value is 0. adj, to adjust the text in the reading direction from 0 to 1 (default value is 0.5).

plot(x, y, main = "Main title", cex = 2, col = "blue")

#---------------
# mtext function
#---------------

# Bottom-center
mtext("Bottom text", side = 1)

# Left-center
mtext("Left text", side = 2)

# Top-center
mtext("Top text", side = 3)

# Right-center
mtext("Right text", side = 4)


# Bottom-left
mtext("Bottom-left text", side = 1, adj = 0)

# Top-right
mtext("Top-right text", side = 3, adj = 1)


# Top with separation
mtext("Top higher text", side = 3, line = 2.5)

text()

plot(x, y, main = "Main title", cex = 2, col = "blue")
#--------------
# Text function
#--------------

# Add text at coordinates (-2, 2)
text(-2, 2, "More text")

# Rotate 45 degrees
text(3,2, label = "Text annotation",
     srt = 45) # Rotation


# Split the text in several lines
text(3, -2,
     label = "Text\n annotation") # Split text

3. Add Legends

The legend() function can be used. A simplified format is :

x and y : the co-ordinates to be used for the legend. Keywords can also be used for x : “bottomright”, “bottom”, “bottomleft”, “left”, “topleft”, “top”, “topright”, “right” and “center”. legend : the text of the legend col : colors of lines and points beside the text for legends

legend(x, y=NULL, legend, col)

# Generate some data
x<-1:10; y1=x*x; y2=2*y1
# First line plot
plot(x, y1, type="b", pch=19, col="red", xlab="x", ylab="y")
# Add a second line
lines(x, y2, pch=18, col="blue", type="b", lty=2)
# Add legends
legend("topleft", legend=c("Line 1", "Line 2"),
       col=c("red", "blue"), lty=1:2, cex=0.8)

4. Change Point Shape

Point symbols can be changed using the argument pch.

The following arguments can be used to change the color and the size of the points :

col : color (code or name) to use for the points bg : the background (or fill) color for the open plot symbols. It can be used only when pch = 21:25. cex : the size of pch symbols lwd : the line width for the plotting symbols

x<-c(2.2, 3, 3.8, 4.5, 7, 8.5, 6.7, 5.5)
y<-c(4, 5.5, 4.5, 9, 11, 15.2, 13.3, 10.5)
# Change plotting symbol using pch
plot(x, y, pch = 19, col="blue")

plot(x, y, pch = 18, col="red")

plot(x, y, pch = 24, cex=2, col="blue", bg="red", lwd=2)

5. Change Line Type

Line types can be changed using the graphical parameter lty. line type (lty) can be specified using either text (“blank”, “solid”, “dashed”, “dotted”, “dotdash”, “longdash”, “twodash”) or number (0, 1, 2, 3, 4, 5, 6). Note that lty = “solid” is identical to lty=1.

x=1:10; y=x*x
plot(x, y, type="l") # Solid line (by default)

plot(x, y, type="l", lty="dashed")# Use dashed line type

plot(x, y, type="l", lty="dashed", lwd=3)# Change line width

6. Change Colors

1. Built-in color names in R
2. Specifying colors by hexadecimal code
3. Using RColorBrewer palettes
4. Colors can be specified by names (e.g col=red) or with hexadecimal code (e.gcol =“#FFCC00”).

a. Built-in color names in R

# Generate a plot of color names which R knows about.
#++++++++++++++++++++++++++++++++++++++++++++
# cl : a vector of colors to plots
# bg: background of the plot
# rot: text rotation angle
#usage=showCols(bg="gray33")
showCols <- function(cl=colors(), bg = "grey",
                     cex = 0.75, rot = 30) {
    m <- ceiling(sqrt(n <-length(cl)))
    length(cl) <- m*m; cm <- matrix(cl, m)
    require("grid")
    grid.newpage(); vp <- viewport(w = .92, h = .92)
    grid.rect(gp=gpar(fill=bg))
    grid.text(cm, x = col(cm)/m, y = rev(row(cm))/m, rot = rot,
              vp=vp, gp=gpar(cex = cex, col = cm))
  }

To view all the built-in color names which R knows about (n = 657), use the following R code :

showCols(cl= colors(), bg="gray33", rot=30, cex=0.75)

## Loading required package: grid

# The first sixty color names
showCols(bg="gray20",cl=colors()[1:60], rot=30, cex=0.9)

# Barplot using color names
barplot(c(2,5), col=c("chartreuse", "blue4"))

2. Specifying colors by hexadecimal code

Barplot using hexadecimal color code

Source : http://www.visibone.com

barplot(c(2,5), col=c("#009999", "#0000FF"))

3. Using RColorBrewer palettes

#install.packages("RColorBrewer")
library("RColorBrewer")
display.brewer.all()

There are 3 types of palettes : sequential, diverging, and qualitative.

• Sequential palettes are suited to ordered data that progress from low to high (gradient). The palettes names are : Blues, BuGn, BuPu, GnBu, Greens, Greys, Oranges, OrRd, PuBu, PuBuGn, PuRd, Purples, RdPu, Reds, YlGn, YlGnBu YlOrBr, YlOrRd.

• Diverging palettes put equal emphasis on mid-range critical values and extremes at both ends of the data range. The diverging palettes are : BrBG, PiYG, PRGn, PuOr, RdBu, RdGy, RdYlBu, RdYlGn, Spectral

• Qualitative palettes are best suited to representing nominal or categorical data. They not imply magnitude differences between groups. The palettes names are : Accent, Dark2, Paired, Pastel1, Pastel2, Set1, Set2, Set3

4. Colors can be specified by names (e.g col=red) or with hexadecimal code (e.gcol = “#FFCC00”).

# use color names
barplot(c(2,5), col=c("blue", "red"))

# use hexadecimal color code
barplot(c(2,5), col=c("#009999", "#0000FF"))

C. Box Plots

D. Scatter Plots

E. Bar Plots

F. Line Plots

G. Pie Charts

H. Multiple Plots

References

Paul Murrell (2011). R Graphics, CRC Press.

Hadley Wickham (2009). ggplot2, Springer.

Deepayan Sarkar (2008). Lattice: Multivariate Data Visualization with R, Springer.

https://r-coder.com/