R Structure

• Simple calculator

5*5

## [1] 25

• Built-in function

mean(c(5,7,9))

## [1] 7

• Write your own function

set.seed(1234)
anomalies <- function(x)  {
   anom <-  (x-mean(x))/sd(x)
   return(anom)
   }
print(rnorm(5))

## [1] -1.2070657  0.2774292  1.0844412 -2.3456977  0.4291247

anomalies(rnorm(5))

## [1]  1.7239706 -0.3012748 -0.2486045 -0.2819967 -0.8920945

R Structure

• Usage of Packages

#install.packages("ggplot2") #Install the package on local
library(ggplot2)          #Tells R to load the package   
ggplot(cars, aes(x=speed,y=dist))+geom_point()+theme_bw()

Vector

Is the basic data structure in R and is made of element of the same type character,logical, integer or numeric.
You can create a vector in different way:

#Empty vector with a specified length
vector()

## logical(0)

vector("numeric",length=3)

## [1] 0 0 0

#Vectors with specific content
x <- c(5,6,3.2)
x <- c(1L,2L)
x <- c(TRUE,FALSE)
x <- c("Hello","World")

Vector

Vector as any kind of data structure can be investigated with several functions.

length(x)

## [1] 2

class(x)

## [1] "character"

str(x)

##  chr [1:2] "Hello" "World"

One may add element to an already existing vector or obtain a vector from a sequence of number

x <- seq(0:10)
x <- 0:10

x <- c(15.2,x)

Matrix

Matrices are an extension of numeric or character vectors. They are basically multi-dimensional vector.

m <- matrix(nrow = 3,ncol = 2)
print(m)

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

dim(m)

## [1] 3 2

class(m)

## [1] "matrix"

Matrix

#filled column-wise
m <- matrix(data = seq(1,30,2),nrow = 5,ncol = 3)
print(m)

##      [,1] [,2] [,3]
## [1,]    1   11   21
## [2,]    3   13   23
## [3,]    5   15   25
## [4,]    7   17   27
## [5,]    9   19   29

typeof(m)

## [1] "double"

n <- matrix(data = 1:2,nrow = 2,ncol = 3)

Matrix

rbind(m,n)

##      [,1] [,2] [,3]
## [1,]    1   11   21
## [2,]    3   13   23
## [3,]    5   15   25
## [4,]    7   17   27
## [5,]    9   19   29
## [6,]    1    1    1
## [7,]    2    2    2

cbind(t(m),t(n))

##      [,1] [,2] [,3] [,4] [,5] [,6] [,7]
## [1,]    1    3    5    7    9    1    2
## [2,]   11   13   15   17   19    1    2
## [3,]   21   23   25   27   29    1    2

Indexing

Selecting a subset of a structure.

By position

x[1]

x[-3]

x[2:4]

x[-(2:4)]

x[c(1,6)]

By value

x[x == 2]

x[x < 0]

x[x %in%  seq(1,5,1)]

Indexing

By Name

head(iris)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

head(iris[,"Species"])

## [1] setosa setosa setosa setosa setosa setosa
## Levels: setosa versicolor virginica

Lists

Lists allocate every mode of data, each element can be of a different type.
List can be created in multiple way:

x <- list("Hi",15.2,TRUE,5)
x

## [[1]]
## [1] "Hi"
## 
## [[2]]
## [1] 15.2
## 
## [[3]]
## [1] TRUE
## 
## [[4]]
## [1] 5

x <- vector("list",length = 3)
x

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

Lists

# List can have names for each element
x <- list(flowers=iris,numbers= rnorm(10,5,2),colors=c("red","yellow"))
names(x)

## [1] "flowers" "numbers" "colors"

names(x)[2] <- "norm numbers"
head(x$flowers)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

print(x[2:3])

## $`norm numbers`
##  [1] 4.779429 3.977981 3.177609 3.325657 9.831670 5.268176 4.018628
##  [8] 4.118904 5.919179 3.612560
## 
## $colors
## [1] "red"    "yellow"

List are helpful when writing functions. It allows you to return multiple object in a “tricky” way.

Data frame

It’s the basic data structure for tabular data. Data frame is a rectangular list, all columns have the same length and as for the list they can host every kind of data.

df <- data.frame(initials = rep("LC",5), height=runif(5,min = 1.50,max = 2),weight=rnorm(5,mean = 70,sd = 5))

df

##   initials   height   weight
## 1       LC 1.536890 70.04930
## 2       LC 1.654843 73.39136
## 3       LC 1.858636 75.14782
## 4       LC 1.752273 61.35236
## 5       LC 1.576499 58.97826

df$initials

## [1] LC LC LC LC LC
## Levels: LC

df[['initials']]

## [1] LC LC LC LC LC
## Levels: LC

Data frame

The indexing rules apply the same to dataframe structure

df[,1]

## [1] LC LC LC LC LC
## Levels: LC

df[3:5,2]

## [1] 1.858636 1.752273 1.576499

df[2,"height"]

## [1] 1.654843

Control statements

For loop is one of the control statements in R programming that executes a set of statements in a loop for a specific number of times, as per the vector provided to it.

for (variable in vector) {
  Something happen
}

A while loop is one of the control statements in R programming which executes a set of statements in a loop until the condition (the Boolean expression) evaluates to TRUE.

while (condition) {
  Something happen
}

Control statements

Consists of a Boolean expression (condition) and a set of statements (do something). If the condition is satisfied, the set of statements is executed otherwise the statements after the end of the if are executed

if (condition) {
  Do something
}else {
  Do something different
}

It is possible to write your own function, which is stored in R and can be ‘called’ in your scripts

function_name <- function(var){
  Do something special
  return(something special)
}

Example

i <- 0
while (i < 5) {
  print(i)
  i=i+1
}

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

if (i == 0) {
  my_name = "Luigi"
}else {
  print("I don't have a name")
}

## [1] "I don't have a name"

cyl <- c()
for (j in 1:4) {
  cyl[j] <- mtcars[j,"cyl"]
}
print(cyl)

## [1] 6 6 4 6