Introduction to R programming & RStudio for beginners – with practical exercises
What You Will 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
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.
(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.
Who this course is for:
- data science students
- data analytics students
- statistics students
- statistical analysis students
- data engineering students
- people interested in data science
- people interested in data analytics with R
- people with python coding skills, interested to learn more about R programming
- Data Science beginners
- This course is designed for software programmers, statisticians and data miners who are looking forward for developing statistical software using R programming.
- If you are trying to understand the R programming language as a beginner, this course will give you enough understanding on almost all the concepts of the language from where you can take yourself to higher levels of expertise.
Why Data Scientists use R?