🥳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

Source: http://venus.ifca.unican.es/Rintro/dataStruct.html

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.

"We can use the L suffix to qualify any number with the intent of making it an explicit integer"

To check the type/class of a vector use class(vectorName).

Code Example:

VactorName <- c("We", "love", "R", "programming")

Do not use more than one class of vector in a single vector.

2. Matrix

Matrix is the R object in which the elements are arranged in a two-dimensional rectangular layout.

Sytax

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.

Code Example

# 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.

Code Example Output:

Example of a matrix

3. Array

Arrays are the R data objects which can store data in more than two dimensions.

Syntax

array(data, dim, dimnames)

Code Example

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.

Source: https://www.neonscience.org/resources/learning-hub/tutorials/hsi-hdf5-r

4. List

Lists are the R objects that contain elements of different types like - numbers, strings, vectors, and other lists inside them.

In simple words, a list can contain more than one type of data.

Syntax

listName <- list(data)

Code Example

# 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

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.

Syntax

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.

Code Example

# 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

Code Output:

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).

To learn more about dataframe and their manipulation view the following page:

⚽DataFrame

Sources of the contents on this page:

1. R for Data Science Full Course | Data Science Training | Edureka