Introduction to Function

R Program Functions are the programming artifacts that are supported by the R runtime environment to process the programming logic efficiently. R language supported both native function syntax to create a custom function and system define functions that do some predefined task. Some of the examples of a system define function would be print() function is used to print some data to the R console. Similarly, plot() function is used to create graphical representations using the R language. In R program function there is an object which takes zero or more parameter, to process some programming operations and provides the result as the return value. R program function is useful for reusability and intuitive code writing in R language.

A function should be

written to carry out a specified task.
may or may not include arguments
contain a body
may or may not return one or more values.

Functions in R

R has many built-in functions which are used for the specific tasks

Here some important and frequently used functions in Data Science

are listed below

1. mean ()

It is used to find the mean for the object.

Ex: a<-c(0:10, 40) xm<-mean(a) print(xm)

Output:

[1] 7.916667

2. sd ()

It returns the standard deviation of an object.

a<-c(0:10, 40) xm<-sd(a) print(xm)

Output:

[1] 10.58694

3. median()

It returns median.

a<-c(0:10, 40) xm<-meadian(a) print(xm)

Output:

[1] 5.5

4. sum()

It returns sum.

a<-c(0:10, 40) xm<-sum(a) print(xm)

Output:

[1] 95

5. min()

It returns minimum value.

a<-c(0:10, 40) xm<-min(a) print(xm)

Output:

[1] 0

6. max()

It returns maximum value.

a<-c(0:10, 40) xm<-max(a) print(xm)

Output:

[1] 40

7. is.na ()

It returns the empty rows.

The output is either TRUE OR FALSE.

True for empty rows and False for nonempty ones.

which (is.na ())- It returns the index of the empty rows.
help () – used to display the documentation of modules, functions, classes, keywords, etc.

There are many other built-in functions that can be used by importing respective libraries.

Apart from these built-in functions, we can create our own functions as per the need.

Conclusion – R Program Functions

The primary uses of R are and will always be, statistics, visualization, and machine learning, which requires a lot of calculations and visualizations meaning we will require a lot of functions. Few statistical calculations like mean, median, standard deviation, etc. are required in almost all Data Science projects that’s why we have a lot of inbuilt libraries that consist of many functions that are used frequently. If we need new functionality to be implemented, we can create our own functions.