This is a short guide on how to with data structures in R programming language.

The data structures in R are five and are Vector, Matrix, Array, Data Frame and List . Lets see those data structures one by one:

It can be defined with the combine function c() in the following way

`v1 <- c(1,2,3,4,5)`

`mode(v1)`

`## [1] "numeric"`

The class of the vector v1 is numeric. By declaring another vector v2 note that it will automatically transform the numbers in characters so that all the elements belong to the same class.

`v2<- c(1,2,3,4,"five")`

`mode(v2)`

`## [1] "character"`

. You declare a matrix by the matrix() function and by specifying the number of elements and number of rows and columns

```
m <- matrix(1:20, 4, 5)
m
```

```
## [,1] [,2] [,3] [,4] [,5]
## [1,] 1 5 9 13 17
## [2,] 2 6 10 14 18
## [3,] 3 7 11 15 19
## [4,] 4 8 12 16 20
```

You access a row or a column of the matrix by indicating the number of row or the column. In this example the code provides access to the row 2 and to the column 3 of the matrix m.

`m[2,]`

`## [1] 2 6 10 14 18`

`m[,3]`

`## [1] 9 10 11 12`

Arrays in R can have more that two dimensions. You can immagine an array as a Rubics cube. IN this example the array have three dimensions just like the rubiks cube.

.

```
x <- c("A1", "A2", "A3")
y <- c("B1", "B2", "B3")
z <- c("C1", "C2", "C3")
data <- array(1:27, c(3, 3, 3), dimnames=list(x, y, z))
data
```

```
## , , C1
##
## B1 B2 B3
## A1 1 4 7
## A2 2 5 8
## A3 3 6 9
##
## , , C2
##
## B1 B2 B3
## A1 10 13 16
## A2 11 14 17
## A3 12 15 18
##
## , , C3
##
## B1 B2 B3
## A1 19 22 25
## A2 20 23 26
## A3 21 24 27
```

The biggest part of data in R are given in a tabular form. Thats why the data frames in R are particularly important and this is an example on how to declare a data frame in R.

```
studentID <- c(1, 2, 3)
age <- c(25, 34, 28)
course <- c("SoftwareIng", "Statistics", "DataScience")
grades <- c("A", "B", "A")
studentdata <- data.frame(studentID, age, course, grades)
studentdata
```

```
## studentID age course grades
## 1 1 25 SoftwareIng A
## 2 2 34 Statistics B
## 3 3 28 DataScience A
```

You can access the columns by using the $ operator in the following way.

`studentdata$course`

```
## [1] SoftwareIng Statistics DataScience
## Levels: DataScience SoftwareIng Statistics
```

Or like a matrix. In this case you point to the second row of the data frame :

`studentdata[2,]`

```
## studentID age course grades
## 2 2 34 Statistics B
```

You can perform different queries on the data of a data frame for example by taking all the rows that have values above a certain threshold. In this case the students that are less that 30 years old.

`studentdata[studentdata$age < 30,]`

```
## studentID age course grades
## 1 1 25 SoftwareIng A
## 3 3 28 DataScience A
```

There is also the possibility to add a column to an existing dataframe by using the names attribute as follows :

`studentdata$names = c("Brian", "Jack", "Bob")`

In R lists are an ordered collection of objects. Unlike the elements of vectors, list elements or components do not need to be of the same type, mode, or length. The components of a list are always numbered and may also have a name attached to them. Lets see an example of a list:

```
data_as_list <- list(studentid=1,
studentname="Dan",
studentmarks=c(7,10,12,12))
```

You can check the number of components of a list by using the length() function:

`length(data_as_list)`

`## [1] 3`

It is possible to concatenate the lists using the c() function. In this example we concatenate data_as_list with data_as_list2.

```
data_as_list2 <- list(workerid = 1, workername = "Bob")
result <- c(data_as_list, data_as_list2)
result
```

```
## $studentid
## [1] 1
##
## $studentname
## [1] "Dan"
##
## $studentmarks
## [1] 7 10 12 12
##
## $workerid
## [1] 1
##
## $workername
## [1] "Bob"
```

These are the most important data structures in R and it is better go deep with this argument if you want to have a solid knowledge in this fundamental area. This web page was generated automatically by using the R markdown and in particular the knitr R package. If you want to learn more on this wonderful package check this link on knitr

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