Mastering R Programming (Apr 2023)

This lesson will walk you through the basics of R language, understanding the features of R programming, Installing and configuring the R environment.

You will be using RStudio to write the R scripts, understanding the components in RStudio and creating project in RStudio.

In this practice, you will download R and RStudio. Install and configure your R Programming Environment

In this practice, you will explore how to install R-package in RStudio.

In this practice, you will create and Run your First R Script in RStudio.

After completing this lesson, you will have knowledge on variables in R, you will work with the different Data types of R, understand the difference between Matrix and Dataframe and work with the different types of R objects.

In this practice, you will create user defined functions and call these functions using modules and packages.

In this practice, you will work with different R Objects such as Vectors, Lists, Matrices, Arrays, Factors and Data Frames. Also, you will create and manipulate the examples of these.

You will work with different types of Operators, Decision making structures and looping statements.

In this practice, you will learn how to use different types of operators in R programming, to meet various business requirement

In this practice, you will work with all the Decision Statements and Switch Statement in R Programming, and explore its usage in the context of business logic implementation.

In this practice, you will learn the Looping statements in R, and understand how the flow execution happens in these loop statements based on the condition expression.

You will learn about Data science and the libraries in this lesson. Further, you will be learning on processing data from CSV, XLS and JSON file. You will also learn what is Data operation, cleansing, aggregation, chart properties and styling.

In this practice, you will create a user defined function and invoke it by passing the necessary parameters.

After completing this lesson, you will learn about the user defined and in-build functions, and will work with different types of Function calling mechanisms in R. Further, you will explore on what is lazy evaluation and the functions in RStudio.

In this practice, you will learn how to connect to Datasources such as CSV, Binary, XML and Excel. You will also learn to Read and Write Data from and to these Datasources.

In this practice, you will learn how to read the data from JSON File.

In this practice, you will learn how to connect to MYSQL Database, run a Query and get the result set into the Data Frames in R programming.

After completing this lesson, you will be able to explain what is data visualization and work with different types of charts using the data from the vector.

In this practice, you will learn to create Pie Charts, Bar Charts, boxplot, Histogram, line graph and scatter plots for a given Vector.

In this lesson you will learn the different types of statistical functions in R such as time series, finding mean median and mode. You will learn to work with the linear regression, binomial distribution and other statistical functions.

In this practice, you will create a Time Series Analysis for the year 2020 considering the Number of Amazon Prime User Subscriptions across two regions and plot a comparative graph for the same.

In this practice, you will find and display the Mean, Median and Mode for a given vector.

In this practice, you will learn to create statistical model and build the relationship between data considering average rainfall of a region versus agricultural output for region on a quarterly basis. Based on this model, the agricultural output can be predicted for given average rainfall value.

In this practice, you will learn to use the Binomial Distribution Function in R Programming and plot the graph of the same.

This lesson will walk you through Machine Learning concepts and the steps of Machine Learning. You will know the different types of learning algorithms and R Packages used for Machine Learning. Understanding the benefits of choosing R programming for Machine Learning.