Basics of R

A comprehensive guide for beginners in R
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  • Lectures 22
  • Length 43 mins
  • Skill Level Beginner Level
  • Languages English
  • Includes Lifetime access
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    Available on iOS and Android
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About This Course

Published 1/2016 English

Course Description

R is a programming language and a software environment for statistical computing and graphics. It is a very popular language in areas of statistics and analytics. With this course, you will be able to build a strong foundation in R and can later branch out into other applications of R. The course will cover a range of topics ranging from basic data types in R to statistics and plots in R. You will be able to work alongside the instructor in each lecture. This course is designed for students who are completely new to R programming or those who need quick refresher in the basics of R. This course is broken down into easy to assimilate lectures and there are two quiz sections which test your understanding of the material. By the end of this course, you will be able to write programs in R, use basic statistics to analyze data and also build plots in R.

What are the requirements?

  • Download R and RStudio

What am I going to get from this course?

  • Write programs in R
  • Use basic statistics in R to perform various analyses
  • Gain insights into data by subsetting the data and plotting in R

Who is the target audience?

  • This course is for newbies who are not familiar with R and/or students looking for quick refresher on R. No prior R experience is needed for this course.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.


Section 1: Introduction
Introduction and Installation

In this lecture, we discuss how to create objects in R. As well as we cover listing and deletion of objects in R.

Section 2: Data Types

There are four basic data types in R. In this lecture, we look at 3 main data types in R.


A vector is a sequence of data elements of the same data type. Here we cover the creation of vectors of each data type in R.


A matrix is a collection of data elements of the same data type in a two-dimensional rectangular layout. Here we cover the matrix construction in R.


When a variable can take only a set of values, it is necessary in most circumstances to treat it as a categorical variable with different levels. In this lecture, we see how to create factors and look at summary and levels of a factor.


Another way information is stored in R is using data frame. Here we look at how to create data frames in R.

Section 3: Operations

This lecture covers the various operations like addition, subtraction, multiplication, division and indexing on vectors.


This lecture covers how to do indexing on a matrix and access its elements.

Section 4: Input in R

This lecture covers how one can read a comma separated values file in R into a data frame and access the data.


In this lecture, we will work with the iris data set and look at various commands used to access rows and columns in the dataset.


In this lecture, we will look at how to subset the dataset. Subsetting the dataset can be useful in scenarios where we need to answer questions about the dataset.

Quiz 1
4 questions
Section 5: Plots in R

In this lecture, we look at how to produce scatter plots. Scatter plots provide graphical relationship between two numeric variables.


In this lecture, we will look at how to produce histograms in R. Histograms are a very common plot in R used to plot frequency that the data appears within certain ranges.


Boxplots provide graphical view of median, quartiles, maximum and minimum in the data set. In this lecture, we cover how to produce boxplots.


Bar charts and pie charts are used for summarizing distribution of categorical variable. In this lecture, we will look at how to produce bar charts and pie charts.

Section 6: Basic Statistics in R

We look at the four functions which are used to generate values associated with normal distribution in this lecture.


In this lecture, we look at the various commands used to generate binomial distribution. The commands we look at are dbinom, pbinom, qbinom, and rbinom.


In this lecture, we discuss the lm() command to perform the least square regression. We also look at the abline() and summary() functions.

Section 7: Functions and loops

In this lecture, we look at three ways to loop in R. They are for loop, while loop and repeat loop. We look at an example of each.


In this lecture, we give a basic introduction to functions in R. We work on an example on how to write functions in R.


In this lecture we look at the apply function and work on two examples to use the apply() function.

Quiz 2
4 questions

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Instructor Biography

Nisha Kiran, Instructor

Nisha has been teaching since her grad school years as a Masters student in Computer Science where she worked as a teaching assistant for numerous courses in programming. Currently, she works in the Elearning industry and also helps students with programming problems. Nisha has worked as a software developer for various firms prior to teaching and understands how important it is to have a good grasp over programming fundamentals.

During her grad school, she has gained experience in teaching and how to effectively communicate a concept to someone new to programming. Nisha has worked with numerous students ranging from beginner to advanced and understands the needs of both kinds of audience.

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