Course Overview
What is R
R is a programming language that is used for statistical computation. It was developed at Bell laboratories and is more similar to the S language. R is considered different from S only with some minor differences, but most of the codes used in R and S exactly the same and can be run without any alteration. R is an open-source software that offers a wide range of statistical techniques. R can also be run on a wide variety of operating systems. One of the greatest advantages of R is that it has a very good designed publication which helps you to use the formula and mathematical symbols with ease.
Why R for data analytics?
R is one of the languages which can be used for data analysis, but it has become so popular than other languages. The reasons are listed here.
- It is an interactive language.
- The excellent mechanism for creating data structures
- Easily producible graphical reports
- Great to handle missing values
- Functions can be used like data in R
- R has a package system that lets users add their own functionality
- It has a very strong community dedicated to data analysis
Advantages of using R
R is being used widely by many organizations because of its following advantages.
- R is a language by itself and not just a statistical software
- R is more flexible and powerful
- R deals with tons of business data
- R provides the best data visualization in a cool graphical format
- R has a strong package system
Statistics for R Programming Course Objectives
At the end of this course, you will be able to
- Learn the fundamentals of R programming
- The basic syntax of R programming
- Know the major R data structures
- Create your own visualizations using R
Prerequisites
- Interest in statistical programming
- Computer with R and R Studio installed in it
- Basic knowledge of statistics and data structure
Target audience
- Students who need to know about R
- Web developers who wanted to learn R for their career purpose
- Anyone interested in learning statistics and data sciences
- Researchers who want to use R for data analysis
Statistics for R Programming Course Description
Section 1: Understanding R
Basics of R
This chapter will help you to learn about the basics of R. You will learn how to use consoles and how to assign variables in R. This also includes the basic data types in R.
Basic R Functions
This chapter lists all the basic functions of R under five categories, along with its uses.
- General
- Mathematical
- Graphical
- Fitting/ regression/optimization
- Statistical
Data Types
Various R data types frequently occur in R calculations. The frequently used data types are explained in detail in this chapter.
- Vectors
- Lists
- Matrices
- Arrays
- Factors
- Data Frames
Recycling Rule
This section explains what a recycling rule is, how it is used to perform arithmetic operations on vectors, and how it is implemented in R.
Special Numerical Values
R has four symbols to represent special numeric values. These symbols are explained in detail in this chapter with a few examples.
Parallel Summary Functions
This chapter deals with an Introduction to parallel computing in R and packages of R which can be used for parallel computation in R.
Logical Conjunctions
A conjunction is a compound statement that combines two statements using its connector “and,” “or.” Here you will learn these conjunctions with examples.
Pasting Strings together
Pasting strings together in R is called concatenation. This chapter will let you learn the arguments, usage, and details of string concatenation.
Type Coercion
This section contains topics like what is coercion, types of coercion, and creating coercion methods. An example is given to make you understand the use of coercion in R easily.
Array & Matrix
In this section, you will learn what array and matrix are, the difference between the two, the syntax for creating arrays and matrices in R, Rows, columns, and dimensions, combining matrices and Array arithmetic
Factor
Factors are variables in R. This chapter contains the usage, arguments, syntax, details, and values of factors in R.
Repository and Packages
In this chapter, you will learn about the different packages available in R, their installation, and how to write your own packages and repository policies.
Importing Data
Importing data into R is very simple. The data can be imported from Excel, SPSS, SAS, Table, Stata, and Systat. This chapter explains the method through which the data is imported into R using codes
Working with Data
In this chapter, we will see how to read, manage and clean up the data frames, and this section contains the following topics.
- Reading and saving data
- Building data frames
- Attaching data
- Detecting duplicates
- Creating and removing variables
- Detecting missing values
- Reshaping and expanding a data frame
Data Aggregation
R has powerful methods for aggregating data. It is easily done in R using BY variables and a defined function. The format for aggregating data is given, along with an example.
Section 2: Data Manipulation & Statistics Basics
Merging
The merge function is used to merge the two data frames in R. This section contains description, arguments, merge function for adding columns, and merge function for adding rows with an illustration.
Data Creation
Here you will learn the common data creation commands used in R, creating a data frame from vectors in R, and the functions used to generate data within R are given with an explanation.
What is Statistics
Statistical programming has become a necessity in today’s world because of the huge amount of data. R is a programming language that is used to solve tedious statistical problems with ease. The solution is R is simple, short, self-contained, and requires only minimum effort and time.
Variables
Creating a new variable is a simple task in R. The assignment operator <- is used to create a new variable. This section explains how to create new variable, how to recode the variables, rename the variables, and how to merge the variables. The topics are explained with codes in R.
Quantiles
This section contains the description, usage, arguments, values, and types of quantiles with a few examples.
Calculating Variance
The variance is the measurement that tells you how the data value is dispersed around the mean. The two types of variance are Sample variance and population variance. This chapter explains both types of variance in detail with examples, solution,s and problems.
Calculating Covariance
The covariance measures how the two variables are related linearly. The types of covariance, its formula, and the codes used in R are explained in this section with examples.
Cumulative Frequency
This is a tutorial on computing the cumulative frequency of quantitative data in statistics using R. It also includes how to plot the cumulative graph in R.
Library (mass)
MASS is a package in R that supports functions and datasets. This section tells you how to install MASS packages in R and how to use it.
Head (faithful)
Faithful is a built-in data frame in R. It consists of a set of observations. The head(faithful) function is explained in brief with examples in this chapter.
Scatter Plot
There are a lot of ways to create a scatter plot in R. The scatter plot combines values of quantitative variables in a data set. The topics covered in this section include
- Simple Scatter plot
- Scatter plot matrices
- High-density scatter plots
- 3D scatter plots
Control Flow
Control flow is the order in which the code has to be executed in R. This can be done by setting a condition. There are a few control structures that come under two main categories – Conditional statements and Repeating operations. The common control structures are explained in this section with an example.
- If, else
- For
- While
- Repeat
- Break
- Next
- Return
Section 3: Statistics, Probability & Distribution
Statistics, Probability & Distribution
Probability distribution explains how the random variable is distributed in R. This section includes
- Function for Probability distribution in R
- Normal distribution, which includes Direct Look-Up, Inverse Look-Up, Density, Random Variables
- A binomial distribution that includes Direct Look-Up points, Direct Look-Up Intervals, and Inverse Look-Up
- Log-normal distribution
Random Variable
The R function for a random variable is rnorm. In this tutorial, you will learn how to use rnorm in R with an example with a full explanation.
Discrete Distribution
This chapter will let you learn what Discrete distribution, The Bernoulli distribution, and the Binomial distribution are. These sections are explained with the respective functions used in R, and the plotting of such distribution is also included. The coin flipping and coin tossing experiment is given in this section for your easy understanding.
Continuous Case
Here you will learn about continuous joint distributions, Joint density, Uniform joint distributions, and computations with Joint distributions with examples.
Exponential Distribution Practice Problem
In this tutorial, we will see what exponential distribution is, its formula, how it is to be computed in R, its probability density function in R, and a graph of the exponential distribution. It is explained with an example of the checkout time of the employees in a supermarket.
Expected Value
This chapter explains the expected value and how to get an expected value for a dataset. This section also includes the following topics.
- Calculation of expected value
- Probability theory and expected value
- Gambling scenario and the formula to calculate the expected value
Deal or no deal
Deal or No Deal game is another example where the expected value can be calculated. This section gives you a Deal or No Deal game scenario and helps you to have a closer look at the game. In the end, it will teach you how to calculate the expected result of this game.
Distribution details
This section gives a brief introduction to the different types of distributions in R, along with the functions used in R.
- Beta distribution
- Binomial Distribution
- Cauchy Distribution
- Chi-squared Distribution
- Exponential Distribution
- F Distribution
- Gamma Distribution
- Geometric Distribution
- Hypergeometric Distribution
- Log normal Distribution
- Multinomial Distribution
- Negative Binomial Distribution
- Normal Distribution
- Poisson Distribution
- Student’s T Distribution
- Uniform Distribution
- Weibull Distribution
Binomial Distribution
Under this topic, we will see what is binomial distribution, its formula, functions of binomial distribution in R and a practice problem along with the solution. This section also covers the formula to find out the expected value of a binomial distribution.
Uniform Random Variables
This section contains the explanation for uniform distribution, the functions used for uniform random variables, its examples.
Probability distributions
This section contains what is a probability distribution, types of a probability distribution, functions of a probability distribution, and examples of such distributions.
FAQs on Statistics for R Programming
- What background knowledge is required?
Some programming knowledge and mathematical knowledge is considered as an added advantage to this course.
- What will I gain from this course?
Upon completing this course, you will get a foundational knowledge of R and use this knowledge to improve your career and to get a good job offer.
Testimonials
Sharon
Statistics for R Programming course is a good introduction to the R programming for statistics. The content is clear and has a smooth transition into the R language. Examples given was great and was easy to understand and follow. The course covers all the important sections of R. It is a perfect tutorial on R which is highly recommended for the beginners.
Lee
I loved this Statistics for R Programming course from the start to the end. This is my first online course in learning the R programming language. This course gave me good insights into R which covers the core areas of working with R. I highly recommend this course before you start to learn R by yourself. The course is well designed and I enjoyed this course.
Where do our learners come from? |
Professionals from around the world have benefited from eduCBA’s Statistics for R Programming courses. Some of the top places that our learners come from include New York, Dubai, San Francisco, Bay Area, New Jersey, Houston, Seattle, Toronto, London, Berlin, UAE, Chicago, UK, Hong Kong, Singapore, Australia, New Zealand, India, Bangalore, New Delhi, Mumbai, Pune, Kolkata, Hyderabad, and Gurgaon among many. |