Introduction to R programming & RStudio for beginners

 

Introduction to R programming & RStudio for beginners

GETTING STARTED

Introduction to R programming & RStudio for beginners This one-hour introduction covers how to get started quickly with the basics of research statistics in R, providing an emphasis on reading data into R, exploratory ...New

What you'll learn

• Comprehensive introduction to R programming & R Studio
• Introduction to R coding
• Introduction to data science
• introduction to data analytics
• how to install R studio
• how to analyse data using R and R studio

Requirements

• Some secondary level mathematics might be helpful, but not compulsory
• you should have a basic understanding of Computer Programming terminologies.
• A basic understanding of any of the programming languages will help you in understanding the R programming concepts and move fast on the learning track.

Description

(Please note: this course is a basic introduction to R and RStudio, meant for beginner level. More advanced courses coming soon.)


R is currently one of the most requested programming languages in the Data Science job market that makes it the hottest trend nowadays.

R is a programming language and free software environment for statistical computing, data manipulation & analysis, graphics representation and reporting supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.

No one is born a data scientist. Every person who works with R today was once a complete beginner. No matter how much you know about the R ecosystem already, you’ll always have more to learn.

Applications of R:

• We use R for Data Science. It gives us a broad variety of libraries related to statistics. It also provides the environment for statistical computing and design.
• R is used by many quantitative analysts as its programming tool. Thus, it helps in data importing and cleaning.
• R is the most prevalent language. So many data analysts and research programmers use it. Hence, it is used as a fundamental tool for finance.
• Tech giants like Google, Facebook, bing, Accenture, Wipro and many more using R nowadays.

Why R Programming Language?

• R programming is used as a leading tool for machine learning, statistics, and data analysis. Objects, functions, and packages can easily be created by R.
• It’s a platform-independent language. This means it can be applied to all operating system.
• It’s an open-source free language. That means anyone can install it in any organization without purchasing a license.
• R programming language is not only a statistic package but also allows us to integrate with other languages (C, C++). Thus, you can easily interact with many data sources and statistical packages.
• The R programming language has a vast community of users and it’s growing day by day.

Statistical Features of R:

• Basic Statistics: The most common basic statistics terms are the mean, mode, and median. These are all known as “Measures of Central Tendency.” So using the R language we can measure central tendency very easily.
• Static graphics: R is rich with facilities for creating and developing interesting static graphics. R contains functionality for many plot types including graphic maps, mosaic plots, biplots, and the list goes on.
• Probability distributions: Probability distributions play a vital role in statistics and by using R we can easily handle various types of probability distribution such as Binomial Distribution, Normal Distribution, Chi-squared Distribution and many more.

R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, etc) and graphical techniques, and is highly extensible. The S language is often the vehicle of choice for research in statistical methodology, and R provides an Open Source route to participation in that activity.

One of R’s strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.

R, like S, is designed around a true computer language, and it allows users to add additional functionality by defining new functions. Much of the system is itself written in the R dialect of S, which makes it easy for users to follow the algorithmic choices made. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. Advanced users can write C code to manipulate R objects directly.

R & RStudio includes

• an effective data handling and storage facility,
• a suite of operators for calculations on arrays, in particular matrices,
• a large, coherent, integrated collection of intermediate tools for data analysis,
• graphical facilities for data analysis and display either on-screen or on hardcopy, and
• a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.