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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.
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Section 1: Introduction  

Lecture 1 
Introduction and Installation
Preview

00:38  
Lecture 2  01:44  
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  
Lecture 3  01:02  
There are four basic data types in R. In this lecture, we look at 3 main data types in R. 

Lecture 4  01:29  
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. 

Lecture 5  01:20  
A matrix is a collection of data elements of the same data type in a twodimensional rectangular layout. Here we cover the matrix construction in R. 

Lecture 6  02:06  
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. 

Lecture 7  01:44  
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  
Lecture 8  02:37  
This lecture covers the various operations like addition, subtraction, multiplication, division and indexing on vectors. 

Lecture 9  01:25  
This lecture covers how to do indexing on a matrix and access its elements. 

Section 4: Input in R  
Lecture 10  01:46  
This lecture covers how one can read a comma separated values file in R into a data frame and access the data. 

Lecture 11  02:11  
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. 

Lecture 12  02:27  
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 
Quiz 1

4 questions  
Section 5: Plots in R  
Lecture 13  01:36  
In this lecture, we look at how to produce scatter plots. Scatter plots provide graphical relationship between two numeric variables. 

Lecture 14  01:47  
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. 

Lecture 15  01:24  
Boxplots provide graphical view of median, quartiles, maximum and minimum in the data set. In this lecture, we cover how to produce boxplots. 

Lecture 16  02:45  
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  
Lecture 17  04:27  
We look at the four functions which are used to generate values associated with normal distribution in this lecture. 

Lecture 18  02:37  
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. 

Lecture 19  02:04  
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  
Lecture 20  02:39  
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. 

Lecture 21  01:15  
In this lecture, we give a basic introduction to functions in R. We work on an example on how to write functions in R. 

Lecture 22  01:53  
In this lecture we look at the apply function and work on two examples to use the apply() function. 

Quiz 2 
Quiz 2

4 questions 
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.