(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

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NumericNumeric data represents decimal data.

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

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

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To convert any compatible data into numeric, we use:

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

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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.

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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:

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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.

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

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

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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:

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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.

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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.

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

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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?

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Knowing the dimension of the matrix is:

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How do we access elements of matrix m:

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

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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.

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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.

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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?

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Note: Observe the below data frame:

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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.

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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.

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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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