Introduction

• vector: basic data structure in R
• one dimensional array of the same data type
• three data types: numeric, character, logical
• c() function to combine elements to a vector

Numeric, character, logical

• character: elements are specified in quotes: "hello"
• logical: keywords TRUE or FALSE
• numeric: regular numbers

Numeric, character, logical

c(3+4, 2^3, sqrt(123), exp(2))

[1]  7.000000  8.000000 11.090537  7.389056

c(TRUE, FALSE, FALSE)

[1]  TRUE FALSE FALSE

c("hi", "by")

[1] "hi" "by"

Length of a vector

• vectors always have a defined length

m <- seq(1, 12)
length(m)

[1] 12

Creation

• the c() function: c(1, 2, 12, 3)
• the rep() function: rep("A", times = 4)
• the seq() function seq(from = 1, to = 4, by = 0.25)

Sequences

• c() to combine values to a vector of the same data type.
• rep() to repeat the values of a given vector
• seq() to generate sequences with fixed length
• x:y short for seq(x, y, by = 1)

Casting and testing

• using the functions as.numeric(), is.numeric(), as.character(), is.character(), … one can test and cast an object from one type to another

as.numeric("4")

[1] 4

is.logical(0)

[1] FALSE

is.logical(TRUE)

[1] TRUE

Names of a vector

month <- 1:12
month

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

names(month) <- c("Jan", "Feb", "Mär", "Apr", "Mai", "Jun", "Jul", "Aug", "Sep", "Okt", "Nov", "Dez")
month

Jan Feb Mär Apr Mai Jun Jul Aug Sep Okt Nov Dez 
  1   2   3   4   5   6   7   8   9  10  11  12 

Random numbers

• rnorm() random numbers from the normal distribution
• rbinom() - random numbers from the binomial distribution
• runif() - uniform distribution
• rt() - t distribution
• sample() - sample values with or without replacement from a vector

Overview

• Numeric operators: \(numeric \times numeric = numeric\)
• Relational operators: \(numeric \times numeric = logical\)
• Logical operators: \(logical \times logical = logical\)
• Assignment Operator <-: \(name \times value = object\)

Numeric operators: \(numeric \times numeric = numeric\)

3^2

[1] 9

-3+5

[1] 2

3*7

[1] 21

9/3

[1] 3

Relational operators: \(numeric \times numeric = logical\)

4 < 5

[1] TRUE

3 != 4

[1] TRUE

5 == 2

[1] FALSE

"a" < "b" 

[1] TRUE

"hallo" == 10

[1] FALSE

Logical operators: \(logical \times logical = logical\)

TRUE | FALSE

[1] TRUE

TRUE & FALSE

[1] FALSE

!TRUE

[1] FALSE

Assignment Operator \(name \times value = object\)

y <- 10
x <- 2
x

[1] 2

Vectorization

• Most operators and many functions work for vectors
• This is usefull for fast calculations and checking on large vectors

numbers <- c(1, 2, 5, -100, 23, 43)
sqrt(numbers)

Warning in sqrt(numbers): NaNs produced

[1] 1.000000 1.414214 2.236068      NaN 4.795832 6.557439

numbers < 0

[1] FALSE FALSE FALSE  TRUE FALSE FALSE

Missing values

• very common when working with real datasets
• Keyword: NA

Examples

val <- c(3, 5, 2, NA)
sum(val)

[1] NA

sum(val, na.rm = T)

[1] 10

is.na(val)

[1] FALSE FALSE FALSE  TRUE

sum(is.na(val))

[1] 1