Being a data analyst who uses R programming to crunch the data, it is mesentery to identify and understand the data structures. Before you analyze the data, you should be able to represent the data first. R offers 6 different types of R data structures. Let’s understand all of those in a detailed way.
6 types of primary R data structure –
- Atomic Vectors
- Matrix
- Arrays
- Factors
- Data Frames
- Lists
Have a look at the table below which shows the type and dimensionality of the R data structures.
1. Atomic Vectors
Atomic vectors are the simplest R data structure. It is one-dimensional. In atomic vectors, all the elements are in the same type.Â
The various data types in atomic vectors are –
- Numeric
- Integer
- Character
- Logical
Examples –
#Numeric
a <- c(1,2,3,-4,5)
class(a)
“numeric”
#Charecter
b <- c('one','two','three','four','five')
class(b)
“character”
#Logical
c <- c(TRUE, FALSE, TRUE,FALSE,FALSE,TRUE)
class(c)
“logical”
To create a vector, you need not write all the elements in it. You can make use of colon ':' to print the range of numbers.
#colon ':'
x <- 1:10
x
y <- -2:5
y
x -> 1 2 3 4 5 6 7 8 9 10
y -> -2 -1 0 1 2 3 4 5
2. Matrix in R
A matrix in R is very similar to the atomic vectors with a dimensional attribute. All the elements in a matrix should be of the same type. It can be a number, character, or logical.
The nrow and ncol parameters are responsible to arrange the elements in a matrix.
By default, the matrix works in column-wise elements assignment. But, passing the argument byrow = TRUE, you can turn it row-wise.
#Matrix - column wise assignment
matrix(1:9, nrow = 3, ncol = 3)
[,1] [,2] [,3]
[1,] 1 4 7
[2,] 2 5 8
[3,] 3 6 9
You can see that the elements are assigned column-wise. Now, you can pass the byrow argument as advised above.
#Matrix - Row wise assignment
matrix(1:9, nrow = 3, ncol = 3, byrow = TRUE)
[,1] [,2] [,3]
[1,] 1 2 3
[2,] 4 5 6
[3,] 7 8 9
Awesome 😛
To access the elements in the matrix, you can use the index of elements. I will show you how.
#Indexing
#Here we are accessing the elements in rows 1,2,3 and columns 2 & 3.
mat[c(1,2,3),c(2,3)]
[,1] [,2]
[1,] 2 3
[2,] 5 6
[3,] 8 9
That’s it. You can play around with multiple combinations.
3. Arrays in R
Arrays in R are similar to the matrix but have more than 2 dimensions. You can call it an N-D array.
#Arrays
arr <- array(1:10, dim = c(5,4,3))
arr
, , 1
[,1] [,2] [,3] [,4]
[1,] 1 6 1 6
[2,] 2 7 2 7
[3,] 3 8 3 8
[4,] 4 9 4 9
[5,] 5 10 5 10
, , 2
[,1] [,2] [,3] [,4]
[1,] 1 6 1 6
[2,] 2 7 2 7
[3,] 3 8 3 8
[4,] 4 9 4 9
[5,] 5 10 5 10
, , 3
[,1] [,2] [,3] [,4]
[1,] 1 6 1 6
[2,] 2 7 2 7
[3,] 3 8 3 8
[4,] 4 9 4 9
[5,] 5 10 5 10
The arrays take vectors as input to create an array. As shown above, you have to mention a vector and rows and columns as well. The last argument shows the number of arrays to be created.
You can check the dimension of the array using the dim function.
#dimension
dim(arr)
5 4 3
4. Factors in R
Factors in R are used to categorize the data on different levels. It takes only finite categorical values as input.
You will understand this better with this example.
#Factors
demo <- c('male','female','male','male','male','female')
factor(demo)
male female male male male female
Levels: female male
You have to use factor() function to describe the factors in input data. Just like arrays and matrices, you can access the elements in a factor as well.
#Access the elements
demo[2]
“female”
5. Data Frames in R
The data frames are the most used R data structure. The data frames are very similar to the matrix but it includes data of different data types.
You can use data.frame() function in R to create a dataframe from the input values. Pass the StringAsFactor = False to avoid converting the categorical data into factors.
#dataframe
name <- c('Jay','Kevin','Reshaine','Rose')
age <- c(23,21,20,22)
weight <- c(56,67,65,72)
df <- data.frame(name,age,weight)
df
Yes, you can access the individual columns as well. Let’s see how.
#Accessing
out <- df['name']
out
name
1 Jay
2 Kevin
3 Reshaine
4 Rose
6. Lists in R programming
Lists are the most complex data structures in R. It includes elements of different data types. A list can be a combination of vector, matric, dataframe, or even multiple lists.Â
You can use list() function in R to create a list.
#list
vec <- c(1,2,3,4,5,6)
mat <- matrix(vec,3,3)
list_t <- list(vec,mat)
list_t
[[1]]
[1] 1 2 3 4 5 6
[[2]]
[,1] [,2] [,3]
[1,] 1 4 1
[2,] 2 5 2
[3,] 3 6 3
That’s cool!!!
R Data structures – Conclusion
As a data analyst or scientist, you will always get a blend of numeric, categorical values in your data. So, it is most important that you represent the data in the correct way to analyze it further. I have discussed all 6 R data structures here. Keep this in mind and it will come in handy at all times. I hope you get to know something from this story.
That’s all for now. Happy R!!!
More read: Data types and structures in R