# Data Type
A **data type**, in programming, is a classification that specifies which type of value a variable has and what type of mathematical, relational, or logical operations can be applied to it without causing an error.
There are mainly 5 data types in R:
1. **Vector**
2. **Matrix**
3. **Array**
4. **List**
5. **Data Frame**
### 1. Vector
A vector is a sequence of data elements of the **same basic type**. The are 5 classes of vectors.
> In R vectors are denoted by `c()`.
1. **Logical**
Ex: True or False
2. **Integer**: The whole number values.
Ex: 1, 2, 5, 100, 20L, 15L, etc.
3. **Numeric**: Both whole numbers and decimal values.
Ex: 4, 3.1416, 0.534, etc.
4. **Complex**
Ex: 3+4i, 5+2i, etc.
5. **Character:** Needs to be enclosed between single or double quotes.
Ex: "M", "We", "Someone", etc.
{% hint style="info" %}
"We can use the **`L`** suffix to qualify any number with the intent of making it an explicit integer"
{% endhint %}
{% hint style="info" %}
To check the type/class of a vector use `class(vectorName)`.
{% endhint %}
**Code Example:**
```r
VactorName <- c("We", "love", "R", "programming")
```
{% hint style="warning" %}
Do not use more than one class of vector in a single vector.
{% endhint %}
### 2. Matrix
Matrix is the R object in which the elements are arranged in a two-dimensional rectangular layout.
{% tabs %}
{% tab title="Sytax" %}
```r
matrix(data, nrow, ncol, byrow, dimnames)
```
**Syntax Breakdown:**
* **data**: is the input vector which becomes the data elements of the matrix.
* **nrow**: is the number of rows to be created.
* **ncol**: is the number of columns to be created.
* **byrow**: is a logical clue. If TRUE then the input vector elements will be arranged by row.
* **dimnames**: are the names assigned to the rows and columns.
{% endtab %}
{% tab title="Code Example" %}
```r
# By default byrow = FALSE
matr <- matrix(c(5:29),5,5)
# When byrow = TRUE
matr <- matrix(c(5:29),5,5, byrow = TRUE)
```
Code Breakdown:
* `c(5:29)`: is the **data**, which creates a sequence of numbers from 5 to 29.
* Then by `5,5` we denote to create a matrix with **5x5** dimension.
{% endtab %}
{% endtabs %}
\
**Code Example Output:**
Example of a matrix
### **3. Array**
Arrays are the R data objects which can store data in more than two dimensions.
{% tabs %}
{% tab title="Syntax" %}
```r
array(data, dim, dimnames)
```
{% endtab %}
{% tab title="Code Example" %}
```r
arr <- array(c(0:15), dim = c(4,4,2,2))
```
**Code Breakdown:**
* `c(0:15)`: is the **data**, which creates a sequence of numbers from 0 to 15
* `c(4,4,2,2):` is the dimension (dim), `4,4` denotes a matrix of **4x4** dimension, then `2,2` denotes an array of **2x2** dimensions with that matrix.
{% endtab %}
{% endtabs %}
### 4. List
Lists are the R objects that contain elements of different types like - numbers, strings, vectors, and other lists inside them.
{% hint style="info" %}
In simple words, a list can contain more than one type of data.
{% endhint %}
{% tabs %}
{% tab title="Syntax" %}
```
listName <- list(data)
```
{% endtab %}
{% tab title="Code Example" %}
```r
# Let's create some vectors with different Data type
# Integer
vctr1 <- c(1:5)
# Character
vctr2 <- c("I", "love", "R", "programming")
# Logical
vctr3 <- c(TRUE, FALSE)
# Create a list
aList <- list(vctr1, vctr2, vctr3)
# View the list
aList
```
{% endtab %}
{% endtabs %}
**Code Output:**
### 4. DataFrame
A dataframe is a table or two-dimensional array-like structure in which each column contains values of one variable and each row contains one set of values from each column.
{% tabs %}
{% tab title="Syntax" %}
```r
data.frame(data, row.names = NULL, stringsAsFactors = FALSE)
```
**Syntax Breakdown:**
* **data:** can be a matrix, table, etc.
* `row.names = NULL`: Whether you want to specify a column name that will be used as row names. Unless specified (NULL) row names will be integer numbers.
* `stringsAsFactors = FALSE`: If TRUE, the columns with character values will be considered as a factor.
{% endtab %}
{% tab title="Code Example" %}
```r
# Let's create some vector
vctr1 <- c(1:5)
vctr2 <- c("Rahim", "Karim", "Jodu", "Modu", "Neymar")
vctr3 <- c(14,16,78,23,24)
# Now, use these vectors to create a dataframe
df <- data.frame(vctr1, vctr2, vctr3)
# View the dataframe
df
```
{% endtab %}
{% endtabs %}
**Code Output:**
{% hint style="info" %}
R contains a number of preloaded datasets. You can load and use them instantly.
* To view the built-in datasets in R, use the following command: `data()`
* To load these datasets in your **RStudio** environment use `data(datasetName)`.
{% endhint %}
To learn more about dataframe and their manipulation view the following page:
{% content-ref url="../dataframe" %}
[dataframe](https://ar-riyaz.gitbook.io/r-for-bioinformatics/dataframe)
{% endcontent-ref %}
### Sources of the contents on this page:
1. [R for Data Science Full Course | Data Science Training | Edureka](https://youtu.be/ckdHNu4kfL0)
