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Learn R - Unlock the most valuable asset for Data Science

anujgupta profile image Anuj Gupta ・11 min read

R is one of the cutting-edge programming languages at present. Moreover, it is the entry pass to get into the world of data science. It is the most capable programming language for statistical computing and data visualization.

With every passing day, more and more applications of R are being devised in the world. What’s more? The R community is constantly improving the R environment with new features and packages.

If you are a neophyte to R and want to gain expertise in the same, then you have landed at the perfect place. Here, I’ll knock around how you can get started with the R as a novice and become a pro at it. 

Already familiar with the basics? Then try hands on Top Real-time R Projects


Before getting started with R, let’s squeeze out what is R? R is an open-source programming language which was conceived by Robert Gentleman and Ross Ihaka in 1992. The aim behind conceiving R language was to propose a tool that can easily handle statistical as well as mathematical calculations. It was designed for the use of students and, therefore, needed to be easy to learn and cheap. R is being used widely in many places. For example, data science, data visualization, machine learning, etc. 

Everything you need to know about R 


Make your machine ready for R by installing the base R packages from the official R project website and any one of the following tools:

  • RStudio
  • Lintr
  • Caret
  • Tidyverse
  • R notebooks or Jupyter notebooks


After having a short introduction to R, now we will get started with it by grabbing its basic concepts. 


Start your journey to R by learning its Data Types. Here are five essential data types of R:

  • Numeric
  • Character
  • Complex
  • Integers
  • Logical


After procuring knowledge in R data types, let’s move towards the R Data Structures. Here is the list of essential data structures of R:

  • Vectors

    It is one of the most elemental data structure of R. R vectors come in two parts: lists and atomic vectors. Vectors only hold data of a single type.
  • Matrix

    We all are well familiar with matrices. It is the arrangement of numbers in a fixed number of columns and rows. You can think of matrices in R as vectors but with rows and columns instead. Here, matrices are used for showcasing real-time data, conducting geological surveys, etc.
  • Arrays

    An array is a multi-dimensional data structure in R which means data can be stored in more than two dimensions. Data in the R array is stored in the same way it is stored in a matrix. In fact, arrays can be imagined as a collection of matrices layer one on top of another.
  • Lists

    In R, a list is an object. Lists can store different data types. For example, Integer, String, vectors, etc. Along with this, it also stores matrix as well as functions. 
  • Data Frames

    It is used for storing the data tables. In a data frame, every column acts as a vector. Moreover, these vectors are of the same length and cannot have empty cells. 


Control structures control the flow of a program. Add control structures after learning data structures in your to-do list. Here is the list of different control structures:

  • If-else statements
  • Switch
  • While loops
  • Next statement
  • Break statement
  • For loops
  • Repeat loops


Functions in R are created with the keyword function. Here is the list of important parts of a Function in R which you need to cover:

  • Function name
  • Function body
  • Arguments 
  • Return statement



After gaining insights into the basic concepts of R, let’s take it up a notch and move to the advanced concepts of R. When you rack up knowledge in advanced concepts of R, only then you’ll be able to apply it in data science.

  • Principle component analysis

    Principal component analysis is a technique that is used to reduce the number of variables in a dataset. Such a technique is called a multivariate analysis technique. The main aim of this technique is to reduce the number of variables needed to be analyzed without affecting the information conveyed by them.
  • Factor analysis

    It is another technique that is used for reducing the number of variables that needs processing. Multivariate analysis techniques like factor analysis make the calculations easier and less resource-intensive. 
  • Graphical models

    Graphical models are techniques that help in visualizing the data into different visual contexts. 
  • Debugging functions

    R comes with many pre-defined debugging functions. Moreover, libraries of R are also used for debugging. 
  • Hypothesis testing

    Hypothesis testing is a technique that helps in validating assumptions that are drawn out of the data set. 
  • Linear Regression

    This technique is used for catching the linear relationships between two variables. 
  • Logistic Regression

    It is a non-linear analysis technique i.e. it tries to find non-linear relationships between a set of variables. It majorly deals with categorical data. 
  • Decision trees

    It is a machine learning algorithm. This technique is quite popular in data mining. It is majorly used for solving decision-making consequences. 
  • Clustering

    This technique is used to make clusters of similar data. Clustering is done by plotting the data in a graphical space and identifying clusters of observations that are close together and, therefore, may have similarities.
  • Classification

    This technique is used for classifying the data based on some characteristics. This technique helps in grouping observations. The classification has a lot of practical applications in the world of data science and computer science, for eg: e-commerce websites use classification to group customers with similar interests. This makes online advertising easier and also improves cross-product suggestions.

You are doing great so far!
Let’s keep up your momentum and look at a few more advanced topics in R programming. Here, take a look at the rest of the concepts :

  • SVM training
  • Testing models
  • Bayesian Networks
  • Normal distribution 
  • Poisson distribution 
  • Predictive analysis
  • Survival analysis
  • ANOVA algorithm
  • Chi-square test
  • T-test



R comes with an ample amount of packages which is one of its most amazing features. Grab the names of some communal libraries of R:

  • Tidyverse
  • Ggraphs
  • Shiny
  • Ggmap
  • Plotly
  • Stringr
  • Tidyr
  • Caret
  • RandomForest
  • E1071
  • Ggplot
  • Dygraphs
  • Leaflet
  • R markdown
  • Devtools
  • Reshape
  • Digest
  • MLR
  • MASS
  • Sentimentr


A Series of R Programming Tutorials


Data reshaping must be your anterior step whenever you do data analysis. In the data reshaping process, the data is formatted as well as cleaned such that the data can be analyzed easily. R has many libraries and functions as well for data reshaping. 



When it comes to data visualization, R is the first thing that comes to our mind. Data visualization is another compelling aspect of R. It makes quality plots as well as quality graphs with just a single click. Any kind of visualization is possible in R. 



After grabbing knowledge in all aspects of R, upsurge towards the real-time projects in R. Do practice whatever you have learned. Your knowledge will be of no use until and unless you apply it practically. Here is the list of some compelling real-time projects of R:

Topmost R Projects with source code


Get into the world of data science by cracking the interview and live your dream to data science. Pin down the most prevalent technical questions on R. Give a try out to your knowledge and warm up yourself for the interview. Start practicing with the basic level and then proceed further accordingly. 

Collection of Latest R Interview Questions

This is how your R journey looks like. Learning R programming is the best investment you'd ever make. R Programming will surely take you closer to your Data Science dream.

'Coz it's your time to be a Data Science StaR with R

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