Intro to R
Stat 430
Fall 2011
Outline
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Installation
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Graphics
Learning a new language
Grammar
Vocabulary
Installing R
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The main website for R is
http://www.r-project.org/
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Follow the link to CRAN (Comprehensive R
Archive Network), pick a server close to you (try
http://streaming.stat.iastate.edu/CRAN/ ), and
download R for your platform
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The newest version of R is 2.13.2
R is updated biannually (in Oct and APril)
Start R
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Double-click the icon
or type R in your command-line
environment
Learning a
new
language
Learning a language
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Grammar / Syntax
Vocabulary
“Thinking in that language”
Grammar
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ma
Lik
the e
ma
tic
Basic algebra is the same
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but 2*x not 2x, 2^p instead of 2p
Applying a function is similar
Making a variable, use <- instead of =
Everything in R is a vector
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Index a vector using [ ]
s
Examples
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x=2/3
a = 2(x + 3)2
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•y
• ∑y
• 2y
• f(y, 2)
y = (1 2 3 5)T
1
You try
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x = (4 1 3 9)T
y = (1 2 3 5)T
d = √(x 2 - y 2)
• 2(d
1
+ d4)
Vocabulary
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What verbs (=functions) do you need to know?
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Loading data
Accessing parts of things
Statistical summaries & models
Graphical summaries
...
Reference card: PDF
Loading data
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Import data with:
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read.csv() for csv files
(and use file.choose() to help find your file)
Save from excel as csv files
Stored in a data.frame
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a list of variables with the same length
Your turn
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Download flights-train data
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Use head(flights) to check it worked
Load it into R
(use flights <- read.csv(file.choose())
Examining variables
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a
head(a)
summary(a)
str(a)
dim(a)
What do we have?
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A data.frame = a list of variables of the
same length (but may be different types)
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Has row and column names
Extracting bits of a
data.frame
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x$variable
x[, “variable”]
x[rows, columns]
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x[1:5, 2:3]
x[c(1,5,6), c(“Day.of.Week”,“X.capital”)]
x$variable[rows]
Statistical summaries
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mean, median, min, max, range
sd, var, cor
table
Your turn
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Compare mean and median of ArrDelay.
How can we interpret the difference?
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Does day of the week have an impact on
the number of landings? Use the command
table to find out
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Do delays depend on weekday? How could
we find out?
Packages
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One of the great advantages of R is that it
encourages development of ‘packages’
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i.e. modules with additional functionality
made by users for users
Plotting package
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install.packages("ggplot2")
See http://had.co.nz/ggplot2/ for more info
Your turn
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load ggplot2 into your workspace:
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Plot arrival delay in a histogram:
qplot(ArrDelay, data=flights)
library(ggplot2)
Histograms
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Divide data into bins
Count number of observations in each bin
Histograms
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qplot(ArrDelay, data=flights,
geom="histogram")
qplot(ArrDelay, data=flights,
geom="histogram", binwidth=10)
qplot(ArrDelay, data=flights,
geom="histogram", binwidth=60)
qplot(ArrDelay, data=flights,
geom="histogram", binwidth=1)
Interpreting Histograms
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Big Pattern: Shape of the data
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peaked vs flat,
skew vs symmetric
Small Pattern:
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location/number of modes
gaps (or areas of low density)
Investigating relationships
Variables are
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both continuous: use scatterplot
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continuous and discrete: use multiple
boxplots
qplot(X,Y, data=flights)
qplot(factor(X),Y, data=flights, geom="boxplot")
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both discrete: ?
Your turn
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Explore relationships between arrival delay
and other variables
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Are there any interesting patterns?
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What does alpha=I(0.25) do?
Continuous vs discrete
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If we use a scatterplot, there is a lot of
overplotting
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Some solutions:
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jitter points randomly so they don’t
overlap
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summarise the distribution using
boxplots or histograms