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Basics of R

A comprehensive guide for beginners in R
3.6 (8 ratings)
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590 students enrolled
Created by Nisha Kiran
Last updated 1/2016
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  • 43 mins on-demand video
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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.

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.
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What Will I Learn?
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
View Curriculum
  • Download R and RStudio
Curriculum For This Course
Expand All 22 Lectures Collapse All 22 Lectures 42:56
2 Lectures 02:22

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

Preview 01:44
Data Types
5 Lectures 07:41

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

Preview 01:02

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.

Preview 01:29

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.

Preview 01:20

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.

Preview 02:06

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

Preview 01:44
2 Lectures 04:02

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

Operations on vectors

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

Operations on matrices
Input in R
3 Lectures 06:24

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

Reading a csv file

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.

Working with datasets

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.

Subsetting the dataset

Quiz 1
4 questions
Plots in R
4 Lectures 07:32

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

Scatter plots

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.

Bar charts and Pie charts
Basic Statistics in R
3 Lectures 09:08

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

Normal distribution

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.

Binomial distribution

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

Linear Regression
Functions and loops
3 Lectures 05:47

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.

Loops in R

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

Functions in R

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

apply() function

Quiz 2
4 questions
About the Instructor
4.1 Average rating
70 Reviews
2,921 Students
6 Courses

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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