Introduction to R programming language
Karel Vaculík
Tuesday, February 17, 2015
Motivation: R example
library(RWeka) library(partykit)
weather <- read.csvC'weather.csv") model <- J48(play ~ ., data = weather) plot(model)
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Introduction
What is R
► free software environment and programming language primarily designed for statistical computing and graphics
► object-oriented, multi-paradigm, interpreted language (console, scripts)
► can be downloaded from http://www.r-project.org/
► RStudio IDE: http://www.rstudio.com/ (recommended!)
R is useful for
► easy manipulation with files
► various operations on vectors, matrices and data frames
► statistical computing / data analysis / machine learning / data mining
► graphical output
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Introduction
Some other advantages
► multi-platform
► allows integration of C, C++, .. . code for higher performance
► a lot of packages available (CRAN, Bioconductor, .. .)
► available in many tools (SAS, SPSS, Matlab, RapidMiner, .. .)
Some drawbacks
by default, objects must be stored in physical memory (however, there are some solutions)
► authors of packages are responsible for the state of the packages
► not ideal for all purposes
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R basics
► prompt >
► commands separated by a new line or ;
► help as ?<command> or help.search("string_to_be_found") or Fl key
► # comments begin with a hashtag
► libraryO for loading packages
► paths are relative to current working directory, use functions getwd and setwd to get it or change it
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R basics
Variables
► no declaration
► assignment:
x <- 5
names can contain . (dot), e.g.: y.l <- 10
Data types
R has five atomic classes:
► character (strings)
► numeric (real numbers) integer
► complex
► logical (possible values: TRUE, T, FALSE, F)
xl <- "Hello everyone!" x2 <- 5.4 x3 <- 20L x4 <- 1 + li x5 <- TRUE
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Data types
Vectors
► basic data type
► all elements of the same atomic class
► creation by enumeration:
x <- 5
y <- c(l,1,2,3,5,8)
p <- c("one", "two", "three")
r <- c(T, F, TRUE, FALSE)
r
## [1]    TRUE FALSE   TRUE FALSE The first element has index IN!
In output: [i] means that the following element has index i
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Data types
► creation by range specification zl <- 1:10
zl <- seq(l, 10, 1)    # from, to, by zl
##    [1]    1   2   3   4   5   6   7   8   9 10
z2 <- 5:-5
z2 <- seq(5, -5, -1)
z2
##    [1]    5   4   3   2   1   0 -1 -2 -3 -4 -5
► length of a vector: length(z2)
## [1] 11
Data types
► creation of an empty vector:
a <- numeric(0) length(a)
## [1] 0
b <- numeric(5) b
## [1] 0 0 0 0 0
► similarly by functions character, logical, etc.
Data types
Lists
► can contain elements of different types, example:
listl <- listC'a", FALSE, 4)
list2 <- list(l, listl)    # nested list
list2
## [[1]]
## [1] 1 ##
## [[2]]
## [[2]][[1]]
## [1] "a" ##
## [[2]][[2]]
## [1] FALSE ##
## [[2]][[3]]
## [1] 4
Data types
Matrices
► all elements of the same atomic class
► function matrix(data = NA, nrow = 1, ncol = 1, byrow = FALSE, dimnames = NULL)
ml <- matrixd: 8, nrow = 2, ncol = 4) ml
## [1,] ## [2,]
[,1]   [,2]   [,3] [,4]
13 5 7 2       4       6 8
m2 < m2
matrix(0, nrow = 2, ncol = 2)
## [1,] ## [2,]
[,1] [,2]
0 0 0 0
1
Data types
Data frames
► each column contains elements of only one class
► function data, frame ()
first =1:3
second = c("a",  "b", "cd")
d <- data.frame(first, second)
d
##		first	second
##	1	1	a
##	2	2	b
##	3	3	cd
Data types
Factors
► for categorical (nominal) data
► internally represented by numbers
used by many functions by default, e.g. read.csvO, data, frame()
char.vector <- cC'male",  "female",  "female", "male")
f <- factor(char.vector)
f
## [1] male     female female male ## Levels: female male
levels(f)
## [1]  "female" "male"
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Operators
► mostly element-wise (help: ?Arithmetic): 1 + 1
## [1] 2
0(1,1,2) - c(3,5,8)
## [1] -2 -4 -6 0(1,1,2) * 3
## [1] 3 3 6
► character concatenation:
paste(c("You",  "are",  "welcome!"), collapse = " ")
## [1]  "You are welcome!"
Operators
► dot product: c(2,3,5) 1*1 0(1,2,3)
## [,1] ## [1,] 23
► matrix multiplication: a <- matrix(l:4, 2, 2)
## [,1] [,2]
## [1,] 1 3 ## [2,]       2 4
a 1*1 a
## [,1] [,2]
## [1,] 7 15
## [2,] 10 22
Operators
► relational and logical operators:
y <- c(l,1,2,3,5,8) y > 1
## [1] FALSE FALSE TRUE TRUE TRUE TRUE !(y > 1)
## [1] TRUE TRUE FALSE FALSE FALSE FALSE y > 1 & y < 8     # AND
## [1] FALSE FALSE TRUE TRUE TRUE FALSE y == 2 I y == 5     # OR
## [1] FALSE FALSE   TRUE FALSE   TRUE FALSE
Operators
► matching operator:
y <- c(l,1,2,3,5,8) y t/.int/. 2:5
## [1] FALSE FALSE   TRUE   TRUE   TRUE FALSE
Control Structures
► if-else:
x <- 2
if (x > 4) {
print("YES") } else {
print("NO")
}
## [1] "NO
Control Structures
► for loop:
for(i in c("a",  "b",  "c")) { print(i)
}
## [1] "a ## [1] "b ## [1] "c
► useful functions: seq, seq_along, seq_len
Control Structures
► while loop: x <- 1
while(x < 5) { print(x) x <- x+1
}
## [1] 1
## [1] 2
## [1] 3
## [1] 4
Control Structures
► repeat loop:
x <- 1 repeat {
if (x == 3) { x <- x+1
next       # continue
}
if (x > 5) { break
}
print(x) x <- x+1
}
## [1] 1
## [1] 2 ## [1] 4 ## [1] 5
User-defined functions
► example:
myFunc <- function(x, y = 3) { x + y
}
myFunc(2)
## [1] 5 myFunc(2, 5)
## [1] 7
■= -0<\O
Data indexing
► indexing vectors y <- c(l,1,2,3,5,8)
y[3]
## [1] 2
y[c(F, F, T, T, F, F)]
## [1] 2 3 y[2:4]
## [1]  12 3 y[-c(2,3)]
## [1]  13 5 8
Data indexing
y <- c(l,1,2,3,5,8)
y[y >= 2]      # returns values
## [1] 2 3 5 8
which(y >= 2)    # returns indices
## [1] 3 4 5 6 which(c(T,F,T))
## [1]  1 3
Data indexing
► indexing data frames
d <- data.frame(first = 4:6, second d
## first	second
## 1 4	a
##2 5	b
##3 6	c
d[2,2]	
## [1] b	
## Levels:	a b c
d[2, 1:2] # also: d[2, ]
## first second ##2        5 b
Data indexing
d <- data.frame(first = 1:3, second = c("a", "b", "c")) d$first
## [1]  12 3 d[[l]]
## [1]  12 3 d[2, "first"]
## [1] 2
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ile reading and writing
► read.csv, read.table, write.csv, write.table
weather <- read.csvC'weather.csv") weather[1:3, ]
## outlook temperature humidity windy play ## 1       sunny 85 85 false no
## 2       sunny 80 90   true no
## 3 overcast 83 86 false yes
► readLines and writeLines functions
► XML package
► xlsx package
► foreign package (SAS, SPSS, Weka, Octave, .. .)
Data inspection
► inspecting data frames
weather <- read.csv("weather.csv")
head(weather)	# see also	tail function	
##      outlook temperature		humidity windy	play
## 1 sunny	85	85 false	no
## 2 sunny	80	90 true	no
## 3 overcast	83	86 false	yes
## 4 rainy	70	96 false	yes
## 5 rainy	68	80 false	yes
## 6 rainy	65	70 true	no
4 (5 ►   4 S ►   4 S ►
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Data inspection
summary(weather)
## outlook
## overcast:4
## rainy     :5
## sunny     :5 ## ## ##
str(weather)
## 'data.frame' :
## $ outlook
## $ temperature
## $ humidity
## $ windy
## $ play
temperature
Min.	64	00
1st Qu.	69	25
Median	72	00
Mean	73	57
3rd Qu.	78	75
Max.	85	00
humidity
Min.	65	00
1st Qu.	71	25
Median	82	50
Mean	81	64
3rd Qu.	90	00
Max.	96	00
windy play false:8 no :5 true :6     yes:9
14 obs. of   5 variables: Factor w/ 3 levels "overcast","rainy",..: 3 3 int   85 80 83 70 68 65 64 72 69 75 ... int   85 90 86 96 80 70 65 95 70 80 ... Factor w/ 2 levels "false","true":  12 111 Factor w/ 2 levels "no","yes":  112 2 2 12
ata inspection
table(weather$outlook) ##
## overcast rainy sunny ##455
table(weather$outlook, weather$play) ##
## no yes
## overcast 0 4 ## rainy 2 3 ##     sunny        3 2
Data inspection
dim(weather)
## [1] 14 5 nrow(weather)
## [1] 14 ncol(weather)
## [1] 5
# dimension
# number of rows
# number of columns
Missing values
► special value NA; for each atomic class a <- c(l,2,3,NA,5,NA)
► checking for NA values: is.na(a)
## [1] FALSE FALSE FALSE   TRUE FALSE TRUE
Plots
plot(x = weather$temperature, y = weather$humidity)
wea1her$1empera1ure
Plots
plot(x = weather$temperature, y = weather$humidity, col = weather$play, pch = 20, cex = 2, xlab = "temperature", ylab = "humidity", main = "Weather data")
legend("topright", levels(weather$play), col =1:2, pch = 20)
Weather data
m Ol		• • • •				• no • yes
Si ~~ m co				• •		•
o co		•	•			
lo _				■		
o _	•	•	•			
in CD	•					
	1 65	1 70	1 75	1 80		1 85
			temperature	< □ ► < fi? ►	4	=
Plots
random_values <- rnorm(1000, mean =25, sd = 10) head(random_valTies)
## [1] 17.90690 28.69438 18.28820 31.42117 30.80640 34.86196 hist(random_values)
Histogram of random_values
20 40 60
randomvalues
Plots
plot(1:20, random_values[l:20], type = "1")
R example - once again
library(RWeka) library(partykit)
weather <- read.csvC'weather.csv") model <- J48(play ~ ., data = weather) plot(model)
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Other resources
R in general
► Quick-R tutorials: http://www.statmethods.net/
► RDocumentation: http://www.rdocumentation.org/
Machine learning / data mining
► Wikibook Data Mining Algorithms In R: https://en.wikibooks.org/wiki/Category: Data_Mining_Algorithms_In_R
► RDataMining: http://www.rdatamining.com/
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