Hits: 20

# R Vector

A vector is a collection of elements, all the same type (similar to an array in other programming languages but more versatile).

When using R, you will frequently encounter the four basic vector types viz. logical, character, integer and double (often called numeric).

## Create a vector

In R, there are several ways to create a new vector; the simplest is to use the `c()`

function.

```
# integer vector
c(1, 2, 3, 4, 5, 6)
[1] 1 2 3 4 5 6
# double vector
c(1*pi, 2*pi, 3*pi, 4*pi)
[1] 3.141593 6.283185 9.424778 12.566371
# character vector
c("red", "green", "blue")
[1] "red" "green" "blue"
# logical vector
c(TRUE, FALSE, TRUE, FALSE)
[1] TRUE FALSE TRUE FALSE
```

As a vector contains elements of the same type, if you try to combine different type of elements, the `c()`

function converts (coerces) them into a single type.

```
# numerics are converted to characters
v <- c(1, 2, 3, "a", "b", "c")
v
[1] "1" "2" "3" "a" "b" "c"
# logical are turned to numerics
v <- c(1, 2, 3, TRUE, FALSE)
v
[1] 1 2 3 1 0
```

You can check the type of a vector using `typeof()`

function.

## Create a Sequence

You can also create a vector using:

### The : Operator

You can generate an equally spaced sequence of numbers, by using the `:`

sequence operator.

```
# sequence of numbers from 1 to 10
1:10
[1] 1 2 3 4 5 6 7 8 9 10
```

### The seq() Function

The `seq()`

function works the same as the `:`

operator, except you can specify a different increment (step size).

```
# sequence of numbers from 1 to 10 with increment of 2
seq(from=1,to=10,by=2)
[1] 1 3 5 7 9
```

### The rep() Function

With `rep()`

function you can generate a sequence by simply repeating certain values.

```
# repeat a value 6 times
rep(x=1,times=6)
[1] 1 1 1 1 1 1
# repeat a vector 3 times
rep(x=c(1,2,3),times=3)
[1] 1 2 3 1 2 3 1 2 3
```

## Change the Vector Type

By using the `as.vector()`

function, you can change the vector type.

```
# Turn numerical vector to character
v <- c(0, 1, 2, 3, 4, 5)
as.vector(v, mode="character")
[1] "0" "1" "2" "3" "4" "5"
```

```
# Turn numerical vector to logical
v <- c(0, 1, 2, TRUE, FALSE)
as.vector(v, mode="logical")
[1] FALSE TRUE TRUE TRUE FALSE
```

## Naming a Vector

Each element of a vector can have a name. It allows you to access individual elements by names. You can give a name to the vector element with the `names()`

function.

```
v <- c("Apple", "Banana", "Cherry")
names(v) <- c("A", "B", "C")
v
A B C
"Apple" "Banana" "Cherry"
```

You can also give a name to the vector element while creating a vector.

```
v <- c("A"="Apple", "B"="Banana", "C"="Cherry")
v
A B C
"Apple" "Banana" "Cherry"
```

## Subsetting Vectors

There are several ways to subset a vector (extract a value from the vector). You can do this by combining square brackets `[]`

with:

- Positive integers
- Negative integers
- Logical values
- Names

### Subsetting with Positive Integers

Subsetting with positive integers returns the elements at the specified positions. Note that vector positioning starts from 1.

```
v <- c("a","b","c","d","e","f")
# select 3rd element
v[3]
[1] "c"
# select 5th element
v[5]
[1] "e"
```

You can select multiple elements at once by using a vector of indexes.

```
v <- c("a","b","c","d","e","f")
# select elements from index 2 to 5
v[2:5]
[1] "b" "c" "d" "e"
# select elements from index 1 to 6 by increment 2
v[seq(from=1,to=6,by=2)]
[1] "a" "c" "e"
```

The indexing vector need not be a simple sequence. You can select elements anywhere within the vector.

```
v <- c("a","b","c","d","e","f")
# select 1st, 3rd, 5th and 6th element
v[c(1,3,5,6)]
[1] "a" "c" "e" "f"
```

### Subsetting by Negative Integer

Subsetting with negative integers will omit the elements at the specified positions.

```
v <- c("a","b","c","d","e","f")
# omit first element
v[-1]
[1] "b" "c" "d" "e" "f"
# omit elements from index 2 to 5
v[-2:-5]
[1] "a" "f"
# omit 1st, 3rd and 5th element
v[c(-1,-3,-5)]
[1] "b" "d" "f"
```

### Subsetting by Logical Values

Subsetting with logical values will return the elements where the corresponding logical value is TRUE.

```
v <- c(1,2,3,4,5,6)
v[c(TRUE,FALSE,TRUE,FALSE,TRUE,FALSE)]
[1] 1 3 5
```

You can also use a logical vector to select elements based on a condition.

```
# select even elements
v <- c(1,2,3,4,5,6,7,8,9)
v[v %% 2 == 0]
[1] 2 4 6 8
# skip values from 4 to 7
v <- c(1,2,3,4,5,6,7,8,9)
v[v < 4 | v > 7]
[1] 1 2 3 8 9
```

### Subsetting by Names

Subsetting with names will return the elements having the matching names.

```
v <- c("A"="Apple", "B"="Banana", "C"="Cherry")
v
A B C
"Apple" "Banana" "Cherry"
# select element 'A'
v["A"]
A
"Apple"
# select element 'B'
v["B"]
B
"Banana"
```

## Modify Vector Elements

Modifying a vector element is pretty straightforward. You use the `[]`

to access the element, and simply assign a new value.

```
v <- c("a","b","c","d","e","f")
v[3] <- 1
v
[1] "a" "b" "1" "d" "e" "f"
```

You can also modify more than one element at once.

```
v <- c("a","b","c","d","e","f")
v[1:3] <- c(1,2,3)
v
[1] "1" "2" "3" "d" "e" "f"
```

## Add Elements to a Vector

The `c()`

function can also be used to add elements to a vector.

```
v <- c(1,2,3)
# Add a single value to v
v <- c(v,4)
v
[1] 1 2 3 4
# Append an entire vector to v
w <- c(5,6,7,8)
v <- c(v,w)
v
[1] 1 2 3 4 5 6 7 8
```

## Insert Element into a Vector

To insert an element into the middle of a vector, use `append()`

function.

```
# Insert 99 after 5th element
append(1:10, 99, after=5)
[1] 1 2 3 4 5 99 6 7 8 9 10
```

```
# Insert 99 at the start
append(1:10, 99, after=0)
[1] 99 1 2 3 4 5 6 7 8 9 10
```

## Combine Multiple Vectors

If the arguments to the `c()`

function are vectors, it flattens them and combines them into one single vector.

```
# Combine three vectors
v1 <- c(1, 2, 3)
v2 <- c(4, 5, 6)
v3 <- c(7, 8, 9)
c(v1, v2, v3)
[1] 1 2 3 4 5 6 7 8 9
```

```
# Combine a vector and a sequence
v <- c(1, 2, 3)
c(v, 4:9)
[1] 1 2 3 4 5 6 7 8 9
```

## Vector Arithmetic

Vector operations are one of R’s great strengths. All the basic arithmetic operators can be performed on pairs of vectors. Each operation is performed in an element-by-element manner.

```
v1 <- c(11,12,13,14,15)
v2 <- c(1,2,3,4,5)
# addition
v1 + v2
[1] 12 14 16 18 20
# subtraction
v1 - v2
[1] 10 10 10 10 10
# multiplication
v1 * v2
[1] 11 24 39 56 75
# division
v1 / v2
[1] 11.000000 6.000000 4.333333 3.500000 3.000000
# exponents
v1 ^ v2
[1] 11 144 2197 38416 759375
```

If one operand is a vector and the other is a scalar, then the operation is performed between every vector element and the scalar.

```
# Arithmetic operations on a vector and a scalar
v <- c(1,2,3,4,5)
# addition
v + 2
[1] 3 4 5 6 7
# subtraction
v - 2
[1] -1 0 1 2 3
# multiplication
v * 2
[1] 2 4 6 8 10
# division
v / 2
[1] 0.5 1.0 1.5 2.0 2.5
# exponents
v ^ 2
[1] 1 4 9 16 25
```

You can also apply a function on each element of a vector.

```
v <- c(1,2,3,4,5)
sqrt(v)
[1] 1.000000 1.414214 1.732051 2.000000 2.236068
log(v)
[1] 0.0000000 0.6931472 1.0986123 1.3862944 1.6094379
```

## The Recycling Rule

Arithmetic operations are performed in an element-by-element manner. That works well when both vectors have the same length. But, when the vectors have **unequal lengths**, R’s Recycling Rule kicks in.

If we apply arithmetic operations to two vectors of unequal length, then the elements of the shorter vector are recycled to match the longest vector.

```
long <- c(1,2,3,4,5,6)
short <- c(1,2,3)
long + short
[1] 2 4 6 5 7 9
```

Here, the elements of `long`

and `short`

are added together starting from the first element of both vectors. When R reaches the end of the `short`

vector, it starts again at the first element of `short`

and continues until it reaches the last element of the `long`

vector.

## Sort a Vector

You can sort a vector using the `sort()`

function. You can also specify the order in which you want to sort a vector.

```
# Sort an integer vector
v <- c(2,7,3,6,1,5,9)
sort(v,decreasing=FALSE)
[1] 1 2 3 5 6 7 9
sort(v,decreasing=TRUE)
[1] 9 7 6 5 3 2 1
```

```
# Sort a character vector
v <- c("f","c","g","a","d","e","b")
sort(v,decreasing=FALSE)
[1] "a" "b" "c" "d" "e" "f" "g"
sort(v,decreasing=TRUE)
[1] "g" "f" "e" "d" "c" "b" "a"
```

## Find a Vector Length

To find the total number of elements in a vector, use `length()`

function.

```
v <- c(1,2,3,4,5)
length(v)
[1] 5
```

## Calculate Basic Statistics

You can calculate basic statistics by using below simple R functions.

Statistic | Function |

mean | mean(x) |

median | median(x) |

standard deviation | sd(x) |

variance | var(x) |

correlation | cor(x, y) |

covariance | cov(x, y) |

These functions take a vector of numbers as an argument and return the calculated statistic.

```
v <- c(1,2,3,4,5,6)
# mean
mean(v)
[1] 3.5
# median
median(v)
[1] 3.5
# standard deviation
sd(v)
[1] 1.870829
# variance
var(v)
[1] 3.5
# correlation
cor(v, 2:7)
[1] 1
# covariance
cov(v, 2:7)
[1] 3.5
```

# Python Example for Beginners

## Two Machine Learning Fields

There are two sides to machine learning:

**Practical Machine Learning:**This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.**Theoretical Machine Learning**: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

**Data Science Resources: Data Science Recipes and Applied Machine Learning Recipes**

**Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!**

Latest end-to-end Learn by Coding Recipes in Project-Based Learning:

**Applied Statistics with R for Beginners and Business Professionals**

**Data Science and Machine Learning Projects in Python: Tabular Data Analytics**

**Data Science and Machine Learning Projects in R: Tabular Data Analytics**

**Python Machine Learning & Data Science Recipes: Learn by Coding**

**R Machine Learning & Data Science Recipes: Learn by Coding**

**Comparing Different Machine Learning Algorithms in Python for Classification (FREE)**

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.