Lecture 2

23/02/2020

R System Basics

Package system

R is based on a powerful packaging system
Almost everything is packaged as a separate library of functions
Some packages are delivered by default at installation
- base, boot, compiler, datasets, …
Others, you have to install
- dplyr, ggplot2, tidyr, …
To install a package (i.e. dplyr):

install.packages("dplyr")
To load an installed package:

library(dplyr)

Installed packages

To get a list of all installed packages on the system

installed.packages()

or

library()
Check if a package can be updated

old.packages()
Update old packages

update.packages()
All of these can also be done from RStudio interface

RStudio Package Management

RStudio Package Management

Setting a seed

You can set a seed to ensure reproducebility of results.

set.seed(1024)
runif(5, 0, 1)

## [1] 0.21808916 0.98763424 0.34846189 0.38104699 0.02098596

set.seed(1023)
runif(5, 0, 1)

## [1] 0.2493580 0.2529563 0.9496766 0.4891691 0.4063322

set.seed(1024)
runif(5, 0, 1)

## [1] 0.21808916 0.98763424 0.34846189 0.38104699 0.02098596

The same seed always produces the same random numbers.

Interaction with the R Console

4 + 3 / 5^2

## [1] 4.12

The first line is the input command
The second line is the output
Notice the [1] at the beginning of the output
In R, almost everything is a vector (array) of something
Here, the output is a vector of numbers and the first item is 4.12
Beware!!! R vectors start with index 1

Interaction with the R Console

rnorm(10, mean = 4, sd = 2)

##  [1] 5.347261 3.059237 4.444793 7.473732 3.772683 2.175777 6.118450
##  [8] 3.959706 3.423982 3.934415

mean(sample(1:10, 5))

## [1] 6.6

Interaction with the R Console

Plots are drawn in a different window, (or tab in RStudio)

plot(x = sample(1:10,5), y = sample(1:10,5), main = "Five random points", 
     xlab = "X values", ylab = "Y values")

Primitive Data Types

numeric, character, logical
- also, integer and complex
Assign a value to a variable with <-

age <- 10

name <- "Ali"

registered <- FALSE
You can re-assign on the fly

age <- 10

age <- "ten"

Printing an Object’s Value

From the console just type the name of the object

age <- 10
age

## [1] 10

Within a script, preferred method is

print(age)

## [1] 10

cat(age)

## 10

Printing an Object’s Value

print and cat are different at EOL (end of line)

x <- 5
y <- 10
print(x)
print(y)

## [1] 5
## [1] 10

cat(x)
cat(y)

## 510

List and remove variables and objects

x <- 5
y <- 10
ls()

## [1] "x" "y"

rm(x)   # ... or, rm("x")
ls()

## [1] "y"

Functions

R Functions

Functions are also objects

foo <- function()
{
   print("Hello World!")
}

foo()

## [1] "Hello World!"

R Functions

Parameters can be provided

foo <- function(name)
{
   cat("Hello", name, "\n")
}

foo("Ali")

## Hello Ali

Why not use print()?
- print does not allow comma concatenation

R Functions

Parameters can be initiated

foo <- function(name = "Hasan")
{
   cat("Hello", name, "\n")
}

foo()

## Hello Hasan

foo("Ali")

## Hello Ali

R Functions

Parameters can be called by name

foo <- function(name = "Hasan", surname = "Kaya")
{
   cat("Hello", name, surname, "\n")
}

foo(surname = "Topal")

## Hello Hasan Topal

R Functions

If called by name, order is not important

foo <- function(name = "Hasan", gender = "M")
{
   if (gender == "M")
      cat("Hello", "Mr.", name, "\n")
   else
      cat("Hello", "Ms.", name, "\n")
}

foo(name = "Ali", gender = "M")

## Hello Mr. Ali

foo(gender = "F", name = "Ayşe")

## Hello Ms. Ayşe

R Functions

Some useful functions

max(1:10)

## [1] 10

min(1:10)

## [1] 1

mean(1:10)

## [1] 5.5

R Functions

Some useful functions

sd(1:10)

## [1] 3.02765

length(1:10)

## [1] 10

range(1:10)

## [1]  1 10

R Functions

Is a function name already used?

exists("max")

## [1] TRUE

exists("burkay")

## [1] FALSE

Vectors

A vector is a list of things of same type
Create one with the c() function

names <- c("Ali", "Ayşe", "Burak")
names

## [1] "Ali"   "Ayşe"  "Burak"

ages <- c(12, 24, 36)
ages

## [1] 12 24 36

married <- c(F, T, T)
married

## [1] FALSE  TRUE  TRUE

Vectors - Useful functions

length(names)

## [1] 3

mode(names)

## [1] "character"

Vectors - Coercion

ages <- c(12, 24, 36, "forty")
ages

## [1] "12"    "24"    "36"    "forty"

mode(ages)

## [1] "character"

mixed <- c(T, 12, "test")
mixed

## [1] "TRUE" "12"   "test"

Missing Values

A missing value is represented by an NA

ages <- c(12, 24, 36, NA)
ages

## [1] 12 24 36 NA

Be careful with computations if NA exists

mean(ages)

## [1] NA

mean(ages, na.rm = T)

## [1] 24

Vector indexing

Use brackets vec[i] to get element i of vec

ages

## [1] 12 24 36 NA

ages[1]

## [1] 12

ages[4]

## [1] NA

Vector indexing

ages[4] <- 20
ages

## [1] 12 24 36 20

Subsetting

ages

## [1] 12 24 36 20

ages[1:3]

## [1] 12 24 36

ages[-1]

## [1] 24 36 20

ages[-1:-3]

## [1] 20

Subsetting

ages

## [1] 12 24 36 20

ages[c(1,3)]

## [1] 12 36

ages[c(-2,-4)]

## [1] 12 36

Batch assignments

ages <- 1:10
ages

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

ages[1:5] <- 20
ages

##  [1] 20 20 20 20 20  6  7  8  9 10

Dynamic resizing

ages <- c(12, 24, 36)
ages

## [1] 12 24 36

ages[4] <- 48
ages

## [1] 12 24 36 48

ages[6] <- 72
ages

## [1] 12 24 36 48 NA 72

Empty vectors

ages <- vector()
ages

## logical(0)

ages[1] <- 24
ages

## [1] 24

Empty vectors

You can create vectors of any size, but they will be initialized by default values:

ages <- numeric()
ages

## numeric(0)

ages <- numeric(10)
ages

##  [1] 0 0 0 0 0 0 0 0 0 0

gender <- character(10)
gender

##  [1] "" "" "" "" "" "" "" "" "" ""

married <- logical(10)
married

##  [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

Vector concatenation

You can concatenate vectors with c():

x <- c(1, 2, 3)
x <- c(x, 4)
x

## [1] 1 2 3 4

x <- c(x, x)
x

## [1] 1 2 3 4 1 2 3 4

Vectorization

R supports many operations and functions to work on vectors
Scalars will be automatically converted to vectors

x <- 1:10
y <- x + 1
y

##  [1]  2  3  4  5  6  7  8  9 10 11

y <- x^2
y

##  [1]   1   4   9  16  25  36  49  64  81 100

Vectorization

x <- -10:10
y <- 2*x^2 - 4*x + 1
plot(x, y)

Vectorization - Recycling

x <- 1:10
y <- 10:1
x + y

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

y <- 1:5
x
x + y

##  [1]  1  2  3  4  5  6  7  8  9 10
##  [1]  2  4  6  8 10  7  9 11 13 15

y <- 1:3
x
x + y

## Warning in x + y: longer object length is not a multiple of shorter object
## length

##  [1]  1  2  3  4  5  6  7  8  9 10
##  [1]  2  4  6  5  7  9  8 10 12 11

Factors

gender <- c("m", "f", "f", "f", "m")
gender

## [1] "m" "f" "f" "f" "m"

gender <- factor(gender)
gender

## [1] m f f f m
## Levels: f m

gender[3]

## [1] f
## Levels: f m

gender[4] == "f"

## [1] TRUE

Factors

gender

## [1] m f f f m
## Levels: f m

gender[4] <- "m"
gender

## [1] m f f m m
## Levels: f m

gender[4] <- "o"

## Warning in `[<-.factor`(`*tmp*`, 4, value = "o"): invalid factor level, NA
## generated

gender

## [1] m    f    f    <NA> m   
## Levels: f m

Factors - Forced levels

gender <- factor(c("m", "f", "f", "f", "m"), levels = c("f", "m", "o"))
gender

## [1] m f f f m
## Levels: f m o

gender[4] <- "o"
gender

## [1] m f f o m
## Levels: f m o

Factors - Useful functions

levels(gender)

## [1] "f" "m" "o"

length(gender)

## [1] 5

table(gender)

## gender
## f m o 
## 2 2 1

Cross tabulation

hair <- factor(c("Red", "Brown", "Brown", "Black", "Yellow", "Yellow", "Black", "Brown"))
eye <- factor(c("Brown", "Black", "Brown", "Green", "Black", "Brown", "Green", "Black"))
table(hair, eye)

##         eye
## hair     Black Brown Green
##   Black      0     0     2
##   Brown      2     1     0
##   Red        0     1     0
##   Yellow     1     1     0

Cross tabulation

t <- table(hair, eye)
margin.table(t, 1)

## hair
##  Black  Brown    Red Yellow 
##      2      3      1      2

margin.table(t, 2)

## eye
## Black Brown Green 
##     3     3     2

Cross tabulation

t <- table(hair, eye)
prop.table(t, 1)

##         eye
## hair         Black     Brown     Green
##   Black  0.0000000 0.0000000 1.0000000
##   Brown  0.6666667 0.3333333 0.0000000
##   Red    0.0000000 1.0000000 0.0000000
##   Yellow 0.5000000 0.5000000 0.0000000

prop.table(t, 2)

##         eye
## hair         Black     Brown     Green
##   Black  0.0000000 0.0000000 1.0000000
##   Brown  0.6666667 0.3333333 0.0000000
##   Red    0.0000000 0.3333333 0.0000000
##   Yellow 0.3333333 0.3333333 0.0000000

prop.table(t)

##         eye
## hair     Black Brown Green
##   Black  0.000 0.000 0.250
##   Brown  0.250 0.125 0.000
##   Red    0.000 0.125 0.000
##   Yellow 0.125 0.125 0.000

Sequences

seq(1, 30, 1)

##  [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] 24 25 26 27 28 29 30

seq(1, 30, 5)

## [1]  1  6 11 16 21 26

seq(-1, 1, 0.1)

##  [1] -1.0 -0.9 -0.8 -0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1  0.0  0.1  0.2  0.3
## [15]  0.4  0.5  0.6  0.7  0.8  0.9  1.0

seq(1, -1, -0.2)

##  [1]  1.0  0.8  0.6  0.4  0.2  0.0 -0.2 -0.4 -0.6 -0.8 -1.0

Sequences

seq(1, 30, length=6)

## [1]  1.0  6.8 12.6 18.4 24.2 30.0

seq(from = 10, length = 5, by = 3)

## [1] 10 13 16 19 22

Repetitive sequences

rep(1, 10)

##  [1] 1 1 1 1 1 1 1 1 1 1

rep(1:2, 5)

##  [1] 1 2 1 2 1 2 1 2 1 2

rep(1:2, each = 5)

##  [1] 1 1 1 1 1 2 2 2 2 2

Useful Tricks

Generating factors

gl(3, 5)

##  [1] 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3
## Levels: 1 2 3

gl(3, 5, labels = c("Red", "Green", "Blue"))

##  [1] Red   Red   Red   Red   Red   Green Green Green Green Green Blue 
## [12] Blue  Blue  Blue  Blue 
## Levels: Red Green Blue

Other generators

rnorm(10, 1, 2)

##  [1]  4.21060332  3.89519151  0.74339555 -0.07785289  1.78317210
##  [6]  2.75843405 -0.64946418  2.46575285 -0.32982902  1.72177110

rpois(10, 3)

##  [1] 5 4 2 3 5 3 2 3 5 2

rbinom(10, 3, 0.3)

##  [1] 1 2 0 2 0 0 1 0 2 0

runif(10, -10, 10)

##  [1] -4.968684 -3.225994 -7.735908  5.628258  6.942104  3.860872  4.541266
##  [8] -2.912779 -4.304087 -6.685231

More subsetting

x <- rpois(10, 3)
x

##  [1] 1 3 0 2 4 2 2 3 2 2

x > 3

##  [1] FALSE FALSE FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE

x[x > 3]

## [1] 4

which(x > 3)

## [1] 5

More subsetting

x <- rpois(10, 3)
x

##  [1] 6 3 4 5 2 2 2 2 2 3

x > 3

##  [1]  TRUE FALSE  TRUE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE

x[x > 3]

## [1] 6 4 5

which(x > 3)

## [1] 1 3 4

More subsetting

##  [1] 6 3 4 5 2 2 2 2 2 3

x[x > 3] <- 3
x

##  [1] 3 3 3 3 2 2 2 2 2 3

Binary comparison

x <- c(F, F, T, T)
y <- c(F, T, T, F)
x & y

## [1] FALSE FALSE  TRUE FALSE

x | y

## [1] FALSE  TRUE  TRUE  TRUE

x && y

## [1] FALSE

x || y

## [1] FALSE

Named indexes

ages <- c(12, 24, 36)
names(ages) <- c("Ali", "Ayşe", "Burak")
ages

##   Ali  Ayşe Burak 
##    12    24    36

ages["Burak"]

## Burak 
##    36

ages[c("Ali", "Burak")]

##   Ali Burak 
##    12    36

Named indexes

ages <- c(Ali = 12, Ayşe = 24, Burak = 36)
ages

##   Ali  Ayşe Burak 
##    12    24    36

Matrices and Arrays

Matrices

m <- c(1, 2, 3, 4, 5, 6, 7, 8)
dim(m)

## NULL

dim(m) <- c(2, 4)
m

##      [,1] [,2] [,3] [,4]
## [1,]    1    3    5    7
## [2,]    2    4    6    8

m <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8), 2, 4)
m

##      [,1] [,2] [,3] [,4]
## [1,]    1    3    5    7
## [2,]    2    4    6    8

Matrices - By row

m <- matrix(c(1, 2, 3, 4, 5, 6, 7, 8), 2, 4, byrow = T)
m

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]    5    6    7    8

Matrices - Indexing

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]    5    6    7    8

m[2, 3]

## [1] 7

m[2,]

## [1] 5 6 7 8

m[,3]

## [1] 3 7

Matrices - Indexing

##      [,1] [,2] [,3] [,4]
## [1,]    1    2    3    4
## [2,]    5    6    7    8

m[2, -2]

## [1] 5 7 8

m[1:2, 2:3]

##      [,1] [,2]
## [1,]    2    3
## [2,]    6    7

Matrices - Forced matrix output

m[2,]

## [1] 5 6 7 8

m[2, , drop = F]

##      [,1] [,2] [,3] [,4]
## [1,]    5    6    7    8

Matrices - Binding

m <- matrix(1:9, 3, 3)
v <- c(10, 11, 12)
rbind(m, v)

##   [,1] [,2] [,3]
##      1    4    7
##      2    5    8
##      3    6    9
## v   10   11   12

cbind(m, v)

##             v
## [1,] 1 4 7 10
## [2,] 2 5 8 11
## [3,] 3 6 9 12

Matrices - Naming

m <- matrix(1:9, 3, 3)
colnames(m) <- c("A", "B", "C")
rownames(m) <- c("x", "y", "z")
m

##   A B C
## x 1 4 7
## y 2 5 8
## z 3 6 9

m["y", "C"]

## [1] 8

Arrays

Arrays are matrices with higher dimensions
- Don’t confuse with C and Java type arrays

a <- array(1:24, dim = c(4, 3, 2))
a

## , , 1
## 
##      [,1] [,2] [,3]
## [1,]    1    5    9
## [2,]    2    6   10
## [3,]    3    7   11
## [4,]    4    8   12
## 
## , , 2
## 
##      [,1] [,2] [,3]
## [1,]   13   17   21
## [2,]   14   18   22
## [3,]   15   19   23
## [4,]   16   20   24

Recycling in Matrices and Arrays

m <- matrix(1, 3, 3)
m

##      [,1] [,2] [,3]
## [1,]    1    1    1
## [2,]    1    1    1
## [3,]    1    1    1

m <- matrix(1:3, 3, 3)
m

##      [,1] [,2] [,3]
## [1,]    1    1    1
## [2,]    2    2    2
## [3,]    3    3    3

Matrix operations

m1 <- matrix(1:9, 3, 3)
m2 <- matrix(2, 3, 3)
m1 + m2

##      [,1] [,2] [,3]
## [1,]    3    6    9
## [2,]    4    7   10
## [3,]    5    8   11

m1 * m2

##      [,1] [,2] [,3]
## [1,]    2    8   14
## [2,]    4   10   16
## [3,]    6   12   18

m1 %*% m2  # Matrix multiplication

##      [,1] [,2] [,3]
## [1,]   24   24   24
## [2,]   30   30   30
## [3,]   36   36   36

Lists

Lists allow you to package different types of objects

my.lst <- list(stud.id=34453,
               stud.name="Ali",
               stud.marks=c(14.3,12,15,19))
my.lst

## $stud.id
## [1] 34453
## 
## $stud.name
## [1] "Ali"
## 
## $stud.marks
## [1] 14.3 12.0 15.0 19.0

Lists

my.lst

## $stud.id
## [1] 34453
## 
## $stud.name
## [1] "Ali"
## 
## $stud.marks
## [1] 14.3 12.0 15.0 19.0

my.lst[1]

## $stud.id
## [1] 34453

my.lst[[1]]

## [1] 34453

Lists

my.lst$stud.id          # same as my.lst[[1]]

## [1] 34453

my.lst["stud.id"]       # same as my.lst[1]

## $stud.id
## [1] 34453

my.lst[["stud.id"]]     # same as my.lst[[1]]

## [1] 34453

Lists - tricks with names

l <- list(names = c("Ali", "Ayşe", "Burak"), 
          ages = c(12, 24, 36),
          registered = c(T, F, F))
l$names

## [1] "Ali"   "Ayşe"  "Burak"

l$nam

## [1] "Ali"   "Ayşe"  "Burak"

l$r

## [1]  TRUE FALSE FALSE

Lists - tricks with names

l <- list(names = c("Ali", "Ayşe", "Burak"), 
          ages = c(12, 24, 36),
          agents = c(2, 1, 2),
          registered = c(T, F, F))
l$age

## NULL

l$agen

## [1] 2 1 2

Lists - tricks with names

l <- list(names = c("Ali", "Ayşe", "Burak"), 
          ages = c(12, 24, 36),
          registered = c(T, F, F))
names(l)

## [1] "names"      "ages"       "registered"

names(l)[2] <- "age"
l

## $names
## [1] "Ali"   "Ayşe"  "Burak"
## 
## $age
## [1] 12 24 36
## 
## $registered
## [1]  TRUE FALSE FALSE

Lists - adding to a list

l <- list(names = c("Ali", "Ayşe", "Burak"), 
          ages = c(12, 24, 36),
          registered = c(T, F, F))
l$agents <- c(2, 1, 2)
l

## $names
## [1] "Ali"   "Ayşe"  "Burak"
## 
## $ages
## [1] 12 24 36
## 
## $registered
## [1]  TRUE FALSE FALSE
## 
## $agents
## [1] 2 1 2

Lists - removing from a list

l <- list(names = c("Ali", "Ayşe", "Burak"), 
          ages = c(12, 24, 36),
          registered = c(T, F, F))
l$ages <- NULL
l

## $names
## [1] "Ali"   "Ayşe"  "Burak"
## 
## $registered
## [1]  TRUE FALSE FALSE

Lists - removing from a list

l <- list(names = c("Ali", "Ayşe", "Burak"), 
          ages = c(12, 24, 36),
          registered = c(T, F, F))
l <- l[-2]
l

## $names
## [1] "Ali"   "Ayşe"  "Burak"
## 
## $registered
## [1]  TRUE FALSE FALSE

Lists - concatenation

l1 <- list(1, "a", T)
l2 <- list("red", 1.72)
l <- c(l1, l2)
l

## [[1]]
## [1] 1
## 
## [[2]]
## [1] "a"
## 
## [[3]]
## [1] TRUE
## 
## [[4]]
## [1] "red"
## 
## [[5]]
## [1] 1.72