# How to Manually Enter Raw Data in R?

In this article, we will discuss how to manually enter raw data in the R Programming Language.

In the R Language, we work with loads of different datasets by importing them through a variety of file formats. But Sometimes we need to enter our own raw data in the form of a character vector, a data frame, or a matrix. There are multiple methods to enter the raw data manually in the R Language.

## Enter data as a vector

To enter data as a vector in the R Language, we use the combine function i.e. c(). The c() function is a generic function that combines its arguments to form a vector. All arguments are coerced to a common type. To create a numeric vector we pass numbers as arguments to the c() function. To create a character vector we pass the strings or characters as arguments to the c() function.

Syntax:sample_vector <- c( data1, data2, ….. , datan )

where: data1, data2…:determines the numeric values that comprise the vector.

**Example: **Demonstrating basic character and numeric vectors.

## R

`# create numeric vector` `numeric <- ` `c` `(1,2,3,4,5)` ` ` `# create character vector` `character <- ` `c` `(` `"geeks"` `, ` `"for"` `, ` `"geeks"` `)` ` ` `# print vectors and their class` `print` `(` `"Character vector:"` `)` `character` `print` `(` `"Class:"` `)` `class` `(character)` `print` `(` `"Numeric vector:"` `)` `numeric` `print` `(` `"Class:"` `)` `class` `(numeric)` |

**Output:**

Character vector: "geeks" "for" "geeks" Class: "character" Numeric vector: 1 2 3 4 5 Class: "numeric"

## Enter data as a data frame

To enter data as a data frame in the R Language, we use the data.frame() function. The data.frame() function creates data frames that are tightly coupled collections of variables. These data frames are widely used as the fundamental data structure in the R Language. A single data frame can contain different vectors of different classes together thus it becomes one data structure for all the needs.

Syntax:data_frame <- data.frame( column_name1 = vector1, column_name2 = vector2 )

where,

column_name1, column_name2:determines the name for columns in data framevector1, vector2:determines the data vector that contain data values for data frame columns.

**Example: **Basic data frame that contains one numeric vector and one character vector.

## R

`# create data frame` `data_frame <- ` `data.frame` `( id = ` `c` `(1,2,3),` ` ` `name = ` `c` `(` `"geeks"` `, ` `"for"` `, ` ` ` `"geeks"` `) )` ` ` `# print dataframe, summary and its class ` `print` `(` `"Data Frame:"` `)` `data_frame` `print` `(` `"Class:"` `)` `class` `(data_frame)` `print` `(` `"Summary:"` `)` `summary` `(data_frame)` |

**Output:**

Data Frame: id name 1 1 geeks 2 2 for 3 3 geeks Class: "data.frame" Summary: id name Min. :1.0 Length:3 1st Qu.:1.5 Class :character Median :2.0 Mode :character Mean :2.0 3rd Qu.:2.5 Max. :3.0

## Enter data as a matrix

To enter data as a matrix in the R Language, we create all the columns of the matrix as a vector and then use the column binding function that is cbind() to merge them together into a matrix. The cbind() function is a merge function that combines two data frames or vectors with the same number of rows into a single data frame.

Syntax:mat <- cbind( col1, col2 )

where, col1, col2:determines the column vectors that are to be merged to form a matrix.

**Example:**

Here, is a basic 3X3 matrix in the R Language made using the cbind() function.

## R

`# create 3 column vectors with 3` `# rows each for a 3X3 matrix` `col1 <- ` `c` `(1,2,3)` `col2 <- ` `c` `(4,5,6)` `col3 <- ` `c` `(7,8,9)` ` ` `# merge three column vectors into a matrix` `mat <- ` `cbind` `(col1, col2, col3)` ` ` `# print matrix, its class and summary` `print` `(` `"Matrix:"` `)` `mat` `print` `(` `"Class:"` `)` `class` `(mat)` `print` `(` `"Summary:"` `)` `summary` `(mat)` |

**Output:**

Matrix: col1 col2 col3 [1,] 1 4 7 [2,] 2 5 8 [3,] 3 6 9 Class: "matrix" "array" Summary: col1 col2 col3 Min. :1.0 Min. :4.0 Min. :7.0 1st Qu.:1.5 1st Qu.:4.5 1st Qu.:7.5 Median :2.0 Median :5.0 Median :8.0 Mean :2.0 Mean :5.0 Mean :8.0 3rd Qu.:2.5 3rd Qu.:5.5 3rd Qu.:8.5 Max. :3.0 Max. :6.0 Max. :9.0