Basic Data Types in r

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(This article was first published on Data Perspective, and kindly contributed to R-bloggers)
As part of tutorial series on Data Science with R from Data Perspective, this first tutorial introduces the very basics of R programming language about basic data types in R.What we learn:

• Assignment Operator
• Numeric
• Integer
• Complex number
• logical
• Character
• Factor
• Vector
• Data Frame
  

After the end of the chapter, you are provided with R console so that you can practice what you have learnt in this chapter.
R assignment operator

1. x = 'welcome to R programming' # assigning string literal to variable x
   
2. x
   
3. [1] "welcome to R programming"
   
4. typeof(x) #to check the data type of the variable x
   
5. [1] "character"

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NumericNumeric data represents decimal data.

1. x = 1.5 #assigning decimal value1.5 to x
   
2. x
   
3. [1] 1.5

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To check the data type we use class() function:

1. class(x)
   
2. [1] "numeric"

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To check if the variable “x” is of numerical or not, we use

1. is.numeric(x)
   
2. [1] TRUE

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To convert any compatible data into numeric, we use:

1. x = '1' #assigning value 1 to variable x
   
2. class(x)
   
3. [1] "character"
   
4. x = as.numeric(x)
   
5. [1] 1
   
6. class(x)
   
7. [1] "numeric"
   
8. 
   
9. Note: if we try to convert a string literal to numeric data type we get the following result.
   
10. 
    
11. x= 'welcome to R programming'
    
12. as.numeric(x)
    
13. [1] NA
    
14. Warning message:
    
15. NAs introduced by coercion

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IntegerWe use as.integer() function to convert into integers. This converts numeric value to integer values.

1. x = 1.34
   
2. [1] 1.34
   
3. class(x)
   
4. [1] "numeric"
   
5. y = as.integer(x)
   
6. class(y)
   
7. [1] "integer"
   
8. y
   
9. [1] 1

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Note: to check if the value is integer or not we use is.integer() function.
In the below example ‘y’ is numerical or decimal value whereas x is integer.

1. is.integer(y)
   
2. [1] TRUE
   
3. is.integer(x)
   
4. [1] FALSE

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Complex:Complex data types are shown as below, though we use it very less in our day to day data analysis:

1. c = 3.5+4i
   
2. [1] 3.5+4i
   
3. is.complex(c)
   
4. [1] TRUE
   
5. class(c)
   
6. [1] "complex"

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LogicalLogical data type is one of the frequently used data type usually used for comparing two values. Values a logical data type takes is TRUE or FALSE.

1. logical = T
   
2. logical
   
3. [1] TRUE
   
4. l = FALSE
   
5. l
   
6. [1] FALSE

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CharacterString literals or string values are stored as Character objects in R.

1. str = "R Programming"
   
2. str
   
3. [1] "R Programming"
   
4. class(str)
   
5. [1] "character"
   
6. is.character(str)
   
7. [1] TRUE

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We can convert other data types to character data type using as.character() function.

1. x = as.character(1)
   
2. x
   
3. [1] "1"
   
4. class(x)
   
5. [1] "character"

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Note: There are a variety of operations that can be applied on characters such as substrings, finding lengths; etc will be dealt as when appropriate.
So far we learnt about the basic data types in R, let’s get into a bit complex data types.

VectorHow do we hold collection of same data types? We come across this requirement very frequently. We have vector data type to solve this problem.
Consider a numerical vector below:

1. num_vec = c(1,2,3,4,5)
   
2. num_vec
   
3. [1] 1 2 3 4 5
   
4. class(num_vec)
   
5. [1] "numeric"

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We can apply many operations on the vector variables such as length, accessing values or members of the vector variable.
Length of the vector can be found using length() function.

1. length(num_vec)
   
2. [1] 5

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We access each element or member of the vector num_vec using its indexes starting from
In the below example we can access the members at 1st,2nd,3rd positons.

1. num_vec[1]
   
2. [1] 1
   
3. num_vec[2]
   
4. [1] 2
   
5. num_vec[3]
   
6. [1] 3

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Similarly string vectors, logical vectors, integer vectors can be created.

1. char_vec = c("A", "Course","On","Data science","R rprogramming")
   
2. char_vec
   
3. [1] "A"  "Course" "On" "Data science" "R rprogramming"
   
4. length(char_vec)
   
5. [1] 5
   
6. char_vec[1]
   
7. [1] "A"
   
8. char_vec[2]
   
9. [1] "Course"
   
10. char_vec[4]
    
11. [1] "Data science"

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MatrixMatrix data type is used when we want to represent the data as collection of numerical values in mXn, m by n, dimensions. Matrices are used mostly when dealing with mathematical equations, machine learning, text mining algorithms.
Now how do we create a matrix?

1. m = matrix(c(1,2,3,6,7,8),nrow = 2,ncol = 3)
   
2. m
   
3.      [,1] [,2] [,3]
   
4. [1,]    1    3    7
   
5. [2,]    2C    6    8
   
6. class(m)
   
7. [1] "matrix"

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Knowing the dimension of the matrix is:

1. dim(m)
   
2. [1] 2 3

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How do we access elements of matrix m:

1. #accessing individual elements are done using the indexes shown as below. In the below example we are accessing 1st, 2nd, 6th element of matrix m.
   
2. m[1]
   
3. [1] 1
   
4. m[2]
   
5. [1] 2
   
6. m[6]
   
7. [1] 8
   
8. m[2,3] #  here we accessing 2nd row 3rd column element.
   
9. [1] 8
   
10. # accessing all elements of rows of the matrix m shown below.
    
11. m[1,]
    
12. [1] 1 3 7
    
13. m[2,]
    
14. [1] 2 6 8
    
15. #accessing all elements of each column
    
16. m[,1]
    
17. [1] 1 2
    
18. m[,2]
    
19. [1] 3 6
    
20. m[,3]
    
21. [1] 7 8

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What happens when we add different data types to a vector?

1. v = c("a","b",1,2,3,T)
   
2. v
   
3. [1] "a"    "b"    "1"    "2"    "3"    "TRUE"
   
4. class(v)
   
5. [1] "character"
   
6. v[6]
   
7. [1] "TRUE"
   
8. class(v[6])
   
9. [1] "character"

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What happened in the above example is, R coerced all different data types into a single data type of character type to maintain the condition of single data type.

ListWhat if we want to handle different data types in a single object?
List data type helps us in storing elements of different data types in a single object.
We create list objects using list() function.
In the below example I have created a list object “list_exp” with 6 different elements of character, numeric and logical data types.

1. list_exp = list("r programming","data perspective",12345,67890,TRUE,F)
   
2. list_exp
   
3. [[1]]
   
4. [1] "r programming"
   
5. [[2]]
   
6. [1] "data perspective"
   
7. [[3]]
   
8. [1] 12345
   
9. [[4]]
   
10. [1] 67890
    
11. [[5]]
    
12. [1] TRUE
    
13. [[6]]
    
14. [1] FALSE

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Using str() function, we can know the structure of the list object, i.e. the internal structure of the list objects can be known. This is one of the very important functions which we use in our day to day analysis.
In the below example we can see a list of 6 elements of character, numerical and logical data types.

1. str(list_exp)
   
2. List of 6
   
3. $ : chr "r programming"
   
4. $ : chr "data perspective"
   
5. $ : num 12345
   
6. $ : num 67890
   
7. $ : logi TRUE
   
8. $ : logi FALSE
   
9. #accessing the  data type of list_exp
   
10. class(list_exp)
    
11. [1] "list"
    
12. length(list_exp)
    
13. [1] 6
    
14. list_exp[1]
    
15. [[1]]
    
16. [1] "r programming"
    
17. #accessing the list elements using indexing.
    
18. list_exp[[1]]
    
19. [1] "r programming"
    
20. list_exp[[6]]
    
21. [1] FALSE
    
22. list_exp[[7]] # when we try accessing not existing elements we get the below error.
    
23. Error in list_exp[[7]] : subscript out of bounds
    
24. # finding the class of individual list element
    
25. class(list_exp[[6]])
    
26. [1] "logical"
    
27. class(list_exp[[3]])
    
28. [1] "numeric"
    
29. class(list_exp[[1]])
    
30. [1] "character"

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Data Frame:Most of us would be from a bit of SQL background and we would be very much comfortable in handling data in the form of SQL table because of the functionalities which a SQL table offers while working the data.
How would it be if we have such data type object available in R which can be used to store the data and manipulate data in very easy, efficient and convenient way?
R offers a data frame data type object. It is another way that information is stored as data frames. We can treat a data frame similar to a SQL table.
How do we create a data frame?

1. #creating a data frame
   
2. data_frame = data.frame(first=c(1,2,3,4),second=c("a","b","c","d"))
   
3. data_frame
   
4.   first second
   
5.      1      a
   
6.      2      b
   
7.      3      c
   
8.      4      d
   
9. #accessing  the data type of the object
   
10. class(data_frame)
    
11. [1] "data.frame"
    
12. #finding out the row count of data_frame using nrow()
    
13. nrow(data_frame)
    
14. [1] 4
    
15. #finding out the column count of data_frame using ncol()
    
16. ncol(data_frame)
    
17. [1] 2
    
18. #finding out the dimensions of data_frame using dim()
    
19. dim(data_frame)
    
20. [1] 4 2
    
21. #finding the structure of the data frame using str()
    
22. str(data_frame)
    
23. 'data.frame': 4 obs. of  2 variables:
    
24. $ first : num  1 2 3 4
    
25. $ second: Factor w/ 4 levels "a","b","c","d": 1 2 3 4
    
26. #accessing the entire row of data frame using row index number. Observe below that if we use data_frame[1,] without specifying the column number it means that we want to access all the columns of row 1.
    
27. data_frame[1,]
    
28.   first second
    
29. 1     1      a
    
30. #similarly to access only 1st column values without row information use data_frame[,1]
    
31. data_frame[,1]
    
32. [1] 1 2 3 4
    
33. #accessing the row names of the data frame.
    
34. rownames(data_frame)
    
35. [1] "1" "2" "3" "4"
    
36. #accessing the column names of the data frame
    
37. colnames(data_frame)
    
38. [1] "first"  "second"
    
39. #column data can accessed using the column names explicitly instead of column indexes
    
40. data_frame$first
    
41. [1] 1 2 3 4
    
42. data_frame$second
    
43. [1] a b c d
    
44. Levels: a b c d
    
45. #accessing individual values using row and column indexes
    
46. data_frame[1,1] # accessing first row first column
    
47. [1] 1
    
48. data_frame[2,2] # accessing second row second column
    
49. [1] b
    
50. Levels: a b c d
    
51. data_frame[3,2]  # accessing third row second column
    
52. [1] c
    
53. Levels: a b c d
    
54. data_frame[3,1] # accessing third row first column
    
55. [1] 3

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Note: Observe the below data frame:

1. dt_frame = data.frame(first=c(1,2,3,4,5,6,7),second=c("Big data","Python","R","NLP","machine learning","data science","data perspective"))
   
2. dt_frame
   
3.   first           second
   
4.      1         Big data
   
5.      2           Python
   
6.      3                R
   
7.      4              NLP
   
8.      5 machine learning
   
9.      6     data science
   
10.      7 data perspective

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Assume we have a dataset with 1000 rows instead of 6 rows shown in above data frame. If we want to see a sample of data of the data frame, how do we do?
Using head() function.

1. head(dt_frame)
   
2.   first           second
   
3.      1         Big data
   
4.      2           Python
   
5.      3                R
   
6.      4              NLP
   
7.      5 machine learning
   
8.      6     data science

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head() function returns us the first six rows of any data frame so that we can have a look of what the data frame is like.
Also we can use tail() function to see the last six rows of the data frame.

1. tail(dt_frame)
   
2.   first           second
   
3.      2           Python
   
4.      3                R
   
5.      4              NLP
   
6.      5 machine learning
   
7.      6     data science
   
8.      7 data perspective

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We have View() function to see the values of a data frame in a tabular form.
View(dt_frame)



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