R for Perl Hackers

http://meis.github.io/talks/R-for-Perl-Hackers

Ladies and gentlemen, welcome to the

DISCLAIMER

R?

R is a programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. Polls, surveys of data miners, and studies of scholarly literature databases show that R's popularity has increased substantially in recent years.

R is an implementation of the S programming language combined with lexical scoping semantics inspired by Scheme. S was created by John Chambers while at Bell Labs. There are some important differences, but much of the code written for S runs unaltered.

R was created by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand, and is currently developed by the R Development Core Team, of which Chambers is a member. R is named partly after the first names of the first two R authors and partly as a play on the name of S.

R is a GNU project. The source code for the R software environment is written primarily in C, Fortran, and R. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems. R uses a command line interface; there are also several graphical front-ends for it.

Wikipedia

Environment

  • sudo apt-get install r-base; R
  • Optional IDE (RStudio)
  • Help: "help(something)" or "?something"
  • Example data: data()
  • Plotting included

R Language

  • Impure functional
  • Interpreted
  • Weakly typed
  • Lexically scoped
  • Everything is an object
  • x <- 'lerele'
  • 'larala' -> y

Data Types

  • Atomic classes: numeric, logical, character, integer, complex
  • Missing values
  • Vectors and Matrices
  • Factors
  • Data frames

Data Types: Missing Values

  • NA
  • Nan (0/0)

Data Types: Vectors

Vectors contain elements of the same type

> x <- c(1,2,3,4)
> x
[1] 1 2 3 4

> y <- 1:32
> y
 [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
[26] 26 27 28 29 30 31 32

Data Types: Vectors

Vector arithmetics

> x <- 1:10
> x * 2 
 [1]  2  4  6  8 10 12 14 16 18 20

Data Types: Vectors

Vector arithmetics

> 10:1 + 1:10

 [1] 11 11 11 11 11 11 11 11 11 11

Data Types: Vectors

Vector arithmetics

> c(1,2,3,4) + c(0,10)
 [1] 1 12 3 14

Data Types: Vectors

Vector slicing

> x <- 1:10
> x > 3
 [1] FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE  TRUE
> x[x > 3]
[1]  4  5  6  7  8  9 10

Data Types: Vectors

Vector access

> x <- (1:10)

> x[3]
[1] 3

> x[83]
[1] NA

> x[4] <- 3
> x
 [1]  1  2  3  3  5  6  7  8  9 10

Data Types: Matrices

Multi-dimension vectors

> m <- 1:10 

> m
[1] 1 2 3 4 5 6 7 8 9 10 

> dim(m) <- c(2, 5)

> m
     [,1] [,2] [,3] [,4] [,5]
[1,]    1    3    5    7    9
[2,]    2    4    6    8   10

Data Types: Factors

Represent categories ("labeled lists")

> x <- factor(c("yes", "yes", "no", "yes", "no")) 

> x
[1] yes yes no yes no
Levels: no yes

> table(x) 
x
no yes 
 2   3

> unclass(x)
[1] 2 2 1 2 1
attr(,"levels")
[1] "no"  "yes"

Data Types: Data frames

List where every element of the list have a name and the same length

> x <- data.frame(foo = 1:4, bar = c(T, T, F, F)) 
> x
  foo   bar
1   1  TRUE
2   2  TRUE
3   3 FALSE
4   4 FALSE

> nrow(x)
[1] 4

> ncol(x)
[1] 2

Data Types: Data frames

> x <- data.frame(foo = 1:4, bar = c(T, T, F, F)) 

> x$foo
[1] 1 2 3 4

> x[,'foo']
[1] 1 2 3 4

> x[1,'foo']
[1] 1

> x[1,]
  foo  bar
1   1 TRUE

Data Types: Data frames

> x <- data.frame(foo = 1:10, bar = 100:109)

> x[5:6,]
  foo bar
5   5 104
6   6 105

> x[5:6,'bar']
[1] 104 105

Objects

attributes()

  • names, dimnames
  • dimensions (e.g. matrices, arrays)
  • class
  • length
  • Attributes

> Marc$promise('MoreOnObjects')

Flow Control

if-else 
> w <- 3
> if  (w < 5) {
        d <- 2
    } else if (w < 10) {
        d <- 10
    } else {
        d <- 50
    }
> d
2

Flow Control

for 
x <- c("a", "b", "c", "d")

for(i in 1:4) {
    print(x[i])
}

for(i in seq_along(x)) {
    print(x[i])
}

for(letter in x) {
    print(letter)
}

for(i in 1:4) print(x[i])

Flow Control

while 
z <- 5

while(z >= 3 && z <= 10) {
    print(z)
    coin <- rbinom(1, 1, 0.5)

    if(coin == 1) {  ## random walk
      z <- z + 1
    } else {
      z <- z - 1
    }
}

Flow Control

repeat, next, return

Functions

> x = function() { print('hi') }

> x
function() { print('hi') }

> x()
[1] "hi"

Functions

Signatures!!!

> args(lm)
function (formula, data, subset, weights, na.action, method = "qr", 
    model = TRUE, x = FALSE, y = FALSE, qr = TRUE, singular.ok = TRUE, 
    contrasts = NULL, offset, ...)

Functions

Idiom: ... first

> args(paste)
function (..., sep = " ", collapse = NULL)

> paste("a", "b", sep = ":")
[1] "a:b"

> paste("a", "b", se = ":")
[1] "a b :"

Functions

Scope

 f1 <- function(x) {
  f2 <- function(y) {
    x <- y
  }
} 
 f1 <- function(x) {
  f2 <- function(y) {
    x <<- y
  }
}

Plotting

plot, boxplot, hist, pie...

Plotting

> data(women)
> plot(women)

Plotting

use ggplot2

Files?

Executing scripts: 
m@x:~$ Rscript 
Usage: /path/to/Rscript [--options] [-e expr [-e expr2 ...] | file] [args]
Loading scripts: 
source('scriptname.R')
Loading data: 
> HousePrice <- read.table("houses.data")
> HouseData <- read.csv("houses.csv")

Working With Data

TONS of Analysis and Statitical functions

Working With Data

Taking a look: 
> data(women)
> dim(women)
[1] 15  2
> names(women)
[1] "height" "weight"

Working With Data

Taking a look: 
> head(women)
  height weight
1     58    115
2     59    117
3     60    120
4     61    123
5     62    126
6     63    129
> tail(women)
   height weight
10     67    142
11     68    146
12     69    150
13     70    154
14     71    159
15     72    164

Working With Data

Taking a look: 
> summary(women)
     height         weight     
 Min.   :58.0   Min.   :115.0  
 1st Qu.:61.5   1st Qu.:124.5  
 Median :65.0   Median :135.0  
 Mean   :65.0   Mean   :136.7  
 3rd Qu.:68.5   3rd Qu.:148.0  
 Max.   :72.0   Max.   :164.0

> str(women)
'data.frame': 15 obs. of  2 variables:
 $ height: num  58 59 60 61 62 63 64 65 66 67 ...
 $ weight: num  115 117 120 123 126 129 132 135 139 142 ...

Working With Data

Distributions

  • rnorm: generate random Normal variates with a given mean and standard deviation
  • dnorm: evaluate the Normal probability density (with a given mean/SD) at a point (or vector of points)
  • pnorm: evaluate the cumulative distribution function for a Normal distribution
  • rpois: generate random Poisson variates with a given rate

*norm, *pois, *binom, *gamma, ...

Working With Data

apply and family 
# create a matrix of 10 rows x 2 columns
> m <- matrix(c(1:10, 11:20), nrow = 10, ncol = 2)

# mean of the rows
> apply(m, 1, mean)
 [1]  6  7  8  9 10 11 12 13 14 15

# mean of the columns
> apply(m, 2, mean)
[1]  5.5 15.5

# divide all values by 2
> apply(m, 1:2, function(x) x/2)
      [,1] [,2]
 [1,]  0.5  5.5
 [2,]  1.0  6.0
 [3,]  1.5  6.5
 [4,]  2.0  7.0
 [5,]  2.5  7.5
 [6,]  3.0  8.0
 [7,]  3.5  8.5
 [8,]  4.0  9.0
 [9,]  4.5  9.5
[10,]  5.0 10.0

see also lapply, tapply, sapply...

Packages

  • Standard packages

  • CRAN!!

    • > 6000 packages
    • Graphics, Database Drivers, Machine Learning, Genetics, NLP, Spatial...
    • swirl

More?

http://cran.r-project.org/manuals.html
http://swirlstats.com/

quit()

Thanks

Roger D. Peng - Examples from https://github.com/rdpeng/courses (CC-NC-SA)