getwd() # print the current working directory
## [1] "E:/Binus University/Semester 2/Data Mining and Visualization/Lab"
#ls() # list the objects in the current workspace
#setwd(mydirectory) # change to mydirectory
#setwd("E:/Binus University")
#history()
#loadhistory(file="Lab_Session1.Rhistory")
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”
The base plotting system is the original 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))
library(datasets)
## Draw a new plot on the screen device
hist(airquality$Ozone)
airquality <- transform(airquality, Month = factor(Month))
boxplot(Ozone ~ Month, airquality, xlab = "Months", ylab = "Ozone (ppb)")
with(airquality, plot(Wind, Ozone))
library(datasets)
## Make the initial plot
with(airquality, plot(Wind, Ozone))
## Add a title
title(main = "Ozone and Wind in New York City")
# Add titles
barplot(c(2,5), main = "Main title",
xlab="X axis title",
ylab="Y axis title",
sub="Sub-title",
col.main="red", col.lab="blue", col.sub="black")
# Increase the size of titles
barplot(c(2,5), main = "Main title",
xlab="X axis title",
ylab="Y axis title",
sub="Sub-title",
cex.main=2, cex.lab=1.7, cex.sub=1.2)
set.seed(1)
# Generate sample data
#x <- rnorm(500)
#y <- x + rnorm(500)
x <- airquality$Ozone
y <- airquality$Wind
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
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
plot(x, y,
main = "My title",
sub = "Subtitle",
font.main = 1, # Title font style
font.sub = 2, # Subtitle dont style
font.axis = 3, # Axis tick labels font style
font.lab = 4) # font style of X and Y axis labels
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)