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An Introduction to R

R

Yihui Xie

Department of Statistics, Iowa State University
Econ workshops on R

Nov 8, 2011

Outline

  • What is R
  • Computing
    • grammar
    • data manipulation
    • simulation
    • modelling
  • Graphics
    • base graphics
    • ggplot2
  • Other useful stuff
    • debugging
    • 3D plots, animations, interactive graphics

Ready?

PART I: Basics and computation

Nov 8, 2011

R is the language for statistical computing and graphics (and more)

Examples of using R for statistics

More examples

Comparison with other languages

  • oriented to statistics (mean(), var(), lm(), glm(), rnorm(), boxplot(), ...)
  • heavily vectorized (very important! can avoid loops in many, many cases)
  • object-oriented, but in different forms (no obj.method(), but often method(obj))
  • unbeatable number of packages (~3400 now)
  • free (in both senses of beer and freedom)

Setting up

  • Download: http://cran.r-project.org (you may choose the Iowa State mirror)
    • three major platforms (Win, Linux, Mac)
  • Editor? RStudio may be a good choice (alternatives: Notepad++/NppToR, Emacs/ESS, Eclipse/StatET, ...)
    • anything but Notepad under Windows

Getting help

use the question mark ? to read documentation

e.g. try ?lm for help on linear models

or help.start() for a view on the whole package

Install add-on packages

## use install.packages(), e.g.
install.packages('animation')

You will be asked to choose a mirror.

Too many packages?? Use the task view: http://cran.r-project.org/web/views/ (e.g. Econometrics)

R is still being actively updated

no easy way to update R with a menu or command; have to uninstall old version and install new version manually

easy to update packages: update.packages()

Grammar: simple calculations

  • assignment by <- or = (experts recommend the former but I do not believe them)
  • ?Arithmetic (e.g. x+y, x %% y)
  • indexing by []
x <- 10
y <- 15:3

1 + 2
11 %% 2
11 %/% 2
3^4
log(10)

y[4]
y[-1]  # what do negative indices mean?

Grammar: functions

  • function_name(arguments)
z = rnorm(100)
fivenum  # what are the arguments of this function?

fivenum(z)

fivenum(x = z, na.rm = TRUE)

fivenum(z, TRUE)

Grammar: conditions and loops

## the if-else statement
if (TRUE) {
  print(1)
} else {
  print(2)
}

## a for-loop (the other type is while-loop)
s = 0; x = c(4, 2, 6)
for (i in 1:3) {
  s = s + x[i]
}
s
## the above loop is the most stupid thing to do in R

Work with data

  • a series of functions like read.table(), read.csv(), ...
  • can work with databases too (need add-on packages like RODBC, RMySQL, ...)
## the tips data
tips = read.csv('http://dicook.public.iastate.edu/Army/tips.csv')

str(tips)  # structure of an object; one of the most useful function in R

summary(tips)
mean(tips$bill)  # index by $name
var(tips[, 'tip'])  # index by character name
table(tips[, 4])  # index by column number

hist(tips$bill)
boxplot(tip~sex, data=tips)
plot(tip~bill, data=tips, col=as.integer(tips$sex))

Other types of data objects

  • vector
  • matrix, array
  • list (very flexible; perhaps second most widely used)
  • ...

Simulation

you know the process, but the input is random

A family of functions for distributions: dfun(), pfun(), qfun() and rfun()

For example, rnorm()

A simulation of fire flames

  library(animation)
  demo('fire', ask = FALSE)  # an application of image()

Another example with more scientific flavor

Q: How many times do we need to flip the coin until we get a sequence of HTH and HTT respectively? (For example, for the sequence HHTH, the number for HTH to appear is 4, and in THTHTT, the number for HTT is 6.)

A: https://yihui.org/en/2009/08/counterintuitive-results-in-flipping-coins/

Modelling: linear regression

Take the tips data for example

fit1 = lm(tip ~ bill, data = tips)
summary(fit1)

You get

Call:
lm(formula = tip ~ bill, data = tips)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1982 -0.5652 -0.0974  0.4863  3.7434 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 0.920270   0.159735   5.761 2.53e-08 ***
bill        0.105025   0.007365  14.260  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 1.022 on 242 degrees of freedom
Multiple R-squared: 0.4566,        Adjusted R-squared: 0.4544 
F-statistic: 203.4 on 1 and 242 DF,  p-value: < 2.2e-16

Modelling: the formula

The formula is an important component of many R functions for modelling.

fit2 = lm(tip ~ bill + sex, data = tips)  # two variables

fit3 = lm(tip ~ bill + 0, data = tips)  # without intercept

## you try summary() on them

PART II: Graphics and more

Nov 15, 2011

Before we get started, let's see if you can install the gWidgetsRGtk2 package:

install.packages('gWidgetsRGtk2')
library(gWidgetsRGtk2)
## follow instructions in case of errors

Two graphics systems in R

  • base graphics
    • the graphics package
    • once drawn, no way to modify it again (have to redraw everything)
    • functions to draw points, lines, polygons, ... like other languages
    • many built-in types of plots (histogram, boxplot, bar chart, ...)
  • grid graphics
    • the grid package
    • more object-oriented: graphical elements are objects
    • can be modified without explicitly redraw the whole plot
    • more like an infrastructure package (no built-in plot types)

Add-on packages for graphics

There are many add-on packages based on the two systems; see the Graphics task view on CRAN for an overview: http://cran.r-project.org/web/views/Graphics.html

  • lattice: Trellis plots
    • sub-plots conditional on categorical variables
    • shipped with R (no need to install; just library(lattice))
  • ggplot2: Grammar of Graphics

Examples of base graphics

Last week we mentioned the tips data.

tips = read.csv('http://dicook.public.iastate.edu/Army/tips.csv')
str(tips)

## scatter plot: positive correlation with a 'constraint'
plot(tip ~ bill, data = tips)
## what is the problem with R's default choice of point shapes?

## plot() is a very 'tricky' function in R; details later

hist(tips$tip, main = 'histogram of tips')
## you see nothing except a right-skewed distribution

hist(tips$tip, breaks = 30)  # more bins

hist(tips$tip, breaks = 100) # what do you see now?

20-30 years ago, the research on choosing the histogram binwidth was extremely hot in statistics, but... who cares?

A trivial example of interactive graphics

We can change the binwidth interactively in R via many tools; one possibility is to build a GUI

## I prefer gWidgetsRGtk2, but I do not know if you can install it
## gWidgetstcltk is easier to install
if (!require('gWidgetstcltk')) install.packages('gWidgetstcltk')
library(gWidgetstcltk)  # or library(gWidgetsRGtk2)
options(guiToolkit = 'tcltk')  # or options(guiToolkit = 'RGtk2')

x = tips$tip
gslider(from = 1, to = 100, by = 1,
        container = gwindow('Change the number of bins'),
        handler = function(h, ...) {
            hist(x, breaks = seq(min(x), max(x),
                    length = svalue(h$obj)))
        })

## many many other GUI elements to use

Play with colors

There are many color models in R, like rgb(), hsv(), ... And there are built-in color names, e.g. 'red', 'purple'. Here is an example of rgb()

rgb(1, 0, 0)  # red (hexidecimal representation)
rgb(1, 1, 0)  # yellow

## an interactive example
g = ggroup(horizontal = FALSE, container = gwindow('Color Mixer'))
x = c(0,0,0)  # red, green, blue
for(i in 1:3) {
    gslider(from = 0, to = 1, by = 0.05, action = i,
            handler = function(h, ...) {
                x[h$action] <<- svalue(h$obj)
                par(bg = rgb(x[1], x[2], x[3]), mar = rep(0, 4))
                plot.new()
            }, container = g)
}

colors()  # all names you can use
plot(rnorm(30), pch = 19, col = sample(colors(), 30), cex = 2)

Other plots in base graphics system

Old-fashioned but many goodies...

  • open help.start() and take a look at the graphics package
  • all you need to learn about base graphics is there
  • many types of plots of interest: contour(), filled.contour(), fourfoldplot(), mosaicplot(), pairs(), smoothScatter(), stripchart(), sunflowerplot(), symbols()

Some comments

Graphics are not as easy as you might have imagined

  • avoid pie charts (why?)
  • avoid 3D plots (what?!)
    • unless they are interactive (e.g. the rgl package)
    • an alternative is the contour plot
  • consider color-blind people
  • The Elements of Graphing Data (William S Cleveland)
    • order of precision (length good; angle bad; ...)

trend of vertical difference between two curves

Luke, use the source!

Only the source code is real.

x = seq(.1, 10, .1)
plot(x, 1/x, type = 'l', lwd = 2)
lines(x, 1/x + 2, col = 'red', lwd = 2)

http://youtu.be/2LTsvOHq3xc

Life is short, use ggplot2!

Why ggplot2?

  • you have to wrestle with gory details in base graphics
    • yes, it is flexible
    • but you have to take care of everything
    • point symbols, colors, line types, line width, legend, ...
  • common tasks in graphics
    • color the points according to the sex variable
    • different point symbols denote the smoker variable
    • darker points denote larger parties (the size variable)
    • add a smoothing/regression line on a scatter plot
    • ...

Simple ggplot2 examples

We still use the tips data here.

library(ggplot2)
## different colors denote the sex variable
qplot(bill, tip, data = tips, color = sex)

## point symbols
qplot(bill, tip, data = tips, shape = smoker)

## you can manipulate ggplot2 objects
p = qplot(bill, tip, data = tips, color = size)
p

## do not like the color scheme? change it
p + scale_colour_gradient2(low="white", high="blue")

## faceting
qplot(tip, data = tips, facets = time ~ day)

p + geom_smooth() # smoothing line

More examples in ggplot2 website

Unlike most of R packages, ggplot2 has its own website of documentation, which is a rich source of examples.

Other packages on graphics

As mentioned before, there are many other packages based on the two graphics systems.

  • animation: a gallery of statistical animations and tools to export animations
  • rgl: interactive 3D plots
  • iplots: interactive statistical graphics
  • rggobi: connect R with GGobi (a standalone software package for interactive stat graphics)
## be prepared
install.packages(c('animation', 'rgl', 'iplots'))

The animation package

The idea is simple:

  ## rotate the word 'Animation'
  for (i in 1:360) {
    plot(1, ann = FALSE, type = "n", axes = FALSE)
    text(1, 1, "Animation", 
      srt = i, col = rainbow(360)[i], cex = 7 * i/360)
    Sys.sleep(0.01)
  }

animation examples

library(animation)

?brownian.motion

?quincunx

?grad.desc

## export to an HTML page
?saveHTML

## want more hilarious examples? try
demo('busybees')

demo('CLEvsLAL')

rgl and iplots

Play with statistical graphics.

## use your mouse (drag or wheel up/down)
library(rgl)
demo('rgl')

## an artificial dataset
library(animation)
demo('pollen')

## linked plots
library(iplots)
ibar(tips$sex)
ihist(tips$tip)

http://vimeo.com/30173477

formatR: Computing on the Code

  • R can compute on its own code: parse() and deparse()
  • the formatR package uses the side effect of them to reformat R code
  • you give me 1+1 and I return you 1 + 1 (what the heck is the difference?)

well, compare

for(k in 1:10){j=cos(sin(k)*k^2)+3;print(j-5)}

to

for (k in 1:10) {
    j = cos(sin(k) * k^2) + 3
    print(j - 5)
}

Debugging

The function debug can be used to debug a function.

f = function(x) {
    m = length(x)
    x[is.na(x)] = mean(x, na.rm = TRUE)  # impute by mean
    s = sum(x^2)  # sum of squares
    s
}
f(c(1, NA, 2))

## begin to debug the function now
debug(f)

f(c(1, NA, 2))

undebug(f)

Thanks!