R is among the most popular programming languages among data science professionals. In this guide learn about the basic concepts and various functionalities it offers.

1. GET STARTED
WITH FOR
R
DATA SCIENCE
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2. In modern-day businesses, no organization can afford to ignore the
importance of data science. By leveraging the power of data
science, many companies have reached new heights that used to be
impossible once. By properly analyzing the data, these
organizations are able to make a more accurate data-driven
decision that not only helps to improve their business operations
but also enhances their customer experience.
The recent ranking released by the in May
World Economic Forum
2023 ranking the fastest growing jobs has ranked the data science
th
jobs 5 on the list. Also, the data science market is witnessing a
rapid growth rate and it is expected to reach a market value of $501
billion by 2032, as reported by Precedence Research.
With the growing amount of data, where millions of terabytes of
data are generated every day, businesses are looking for skilled data
science professionals who can process this enormous amount of
data for their organization’s best interest. And it is the perfect time
to step into this domain for a successful career ahead.
When it comes to performing data science tasks, there are several
programming languages and tools that come in handy such as
Python, R, Scala, Java, SQL, etc. Here we will guide you about one
of the most popular languages used for Data Science – the R
programming language. So, if you are a beginner, then this
document is for you to learn everything you need to know about R.
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3. What is R?
R is among the very popular programming
languages that several data science professionals
use specifically for data analysis and statistical
computing. This programming language was
created by Ross Ihaka and Robert Gentleman
from the University of Auckland, New Zealand in
the 1990s. And since then, R has gained immense
popularity in the data science community. As this
language offers extensive libraries and robust
statistical capabilities, it has managed to garner a
huge user base since its inception.
How to Install R?
Installing R is very easy. It is available for all
major platforms including Windows, MacOS, and
Linux. Before you can start using this software,
you can download it from the Comprehensive R
Archive Network (CRAN) website (https://cran.r-
project.org/) and install it in your system. Ensure
you have downloaded the latest version to make
use of the latest features. You will get the
installation instructions for your operating
system on this website as well.
Getting started with Rstudio
So, now that you have downloaded and installed
R on your system, it’s time to get started with
RStudio. It is a popular Integrated Development
Environment that makes working with R more
user-friendly. Even though R can be used from
the command line, many data science
professionals prefer using IDEs i.e. RStudion for
their work.
Rstudio can be downloaded from their official
website
https://www.rstudio.com/products/rstudio/dow
nload/. After installing this, you will see a user-
friendly interface where you get access to panels
for writing code, viewing data, and visualizing
results.
Basic concepts of R:
So now let us get started with the basics of R and
its components. The first thing to understand is,
that R is an object-oriented language which
means every operation you perform in R is
around objects. These objects also known as
building blocks of R, are:
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Variables
InR,youcanassignvaluestovariablesusingtheassignmentoperator<-or=.Forexample:
Thisassignsthevalue10tothevariable‘x’.
DataStructures
Data Structure refers to the nouns of Programming in R and data items of different types are organized
intodatastructures.Thesedatastructurescantaketheformof
Ÿ Vectors–theyareone-dimensionalarraysthatcanholdmultiplevaluesofthesamedatatype
Ÿ Data frames - Two-dimensional tables used to store data, with rows and columns, similar to
spreadsheets
Ÿ Lists-Containersthatcanholdelementsofdifferentdatatypes
Functions
Another thing that makes R a popular programming language is its built-in functions that can perform
various functions. For example, you can use the ‘mean()’ function to calculate the mean of a vector of
numbers:

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Packages
PackagesinRrefertothecollectionsoffunctions,data,anddocumentation.ByinstallingandloadingR
packages, you can enhance the R’s functionality in doing specific data science tasks. The
‘install.packages()’functioninstallspackages,whilethe‘library()’functionloadsthem.Forexample:
Thisassignsthevalue10tothevariable‘x’.
DataManipulationinR
Data manipulation is the most important part of data analysis and R offers several packages to perform data
manipulationtasks.‘dplyr’and‘tidyr’aresuchpackagesthatareusedtoperformthebelow-mentionedtasks
easily:
DataImport
You need to import the data first before you can start working on it. R can work on various data formats
such as CSV, Excel, and different databases. The ‘read.csv()’ function is commonly used for reading CSV
files.Forexample:
DataExploration
After the data has been imported, you can now explore these using different functions like ‘head()’,
‘tail()’,and‘summary()’.Thesefunctionswillhelpyouprovideaquickoverviewofyourdataset.
DataFiltering
The ‘filter()’ function in ‘dplyr’ is used to subset data based on specific criteria. For example, if you want
tofilterdatawithagemorethan30,thenusethecommand
4

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DataTransformation
Data transformation refers to the modification of variables or the creation of new ones. To perform this
‘mutate()’functioniscommonlyusedin‘dplyr’.Hereisanexampleofatransformfunction:
DataAggregation
Itinvolvesaggregationofdatai.e.summarizingorgroupingthemtoobtaininsights.Forexample:
DataVisualizationinR
The core of any data science project is Data Visualization which refers to creating beautiful and interactive
visuals of the findings from the data analysis. With this technique, complex insights can be easily conveyed to
stakeholders. With R, packages like ‘ggplot2’ can be used for data visualization. With this package, the
followingtypesofplotscanbecreated:
ScatterPlot
Itisusedtovisualizetherelationshipbetweentwonumericalvalues.

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Histogram
Histogramsareusedtorepresentthedistributionofsinglevariables.
BarChart
Itissuitableforvisualizingcategoricaldata.
These are just only a few of the examples of how you can use ggplot2 for data visualization. You can
explore it more and use your creativity to visualize your data in your own way. Since the R and its
package offer great flexibility, you can customize the visualization for your findings.
StatisticalAnalysisinR
What distinguishes R from other programming languages is its statistical capabilities. And this makes it an
ideal choice among data scientists. With the help of R, you can perform various statistical tests and analyses
toderivemeaningfulinsightsfromyourdata.SomecommonstatisticalfunctionsinRinclude:
Ÿ T-tests–forcomparingmeansoftwogroups
Ÿ ANOVA–foranalysisofvariancetestsforcomparingmultiplegroups
Ÿ LinearRegression–forbuildingrelationshipsbetweenvariables
Ÿ Correlation – it measures the strength and direction of the relationship between two numerical
values

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Conclusion
So, these are some basics of R that can help you get
started with this programming language for data
science. You must remember, that R is a comprehensive
programming language and offers several functions and
packages. It requires a lot of practice and working on
several different types of data and projects to completely
understandallitsfunctionalities.
The more you practice, the more you will concepts will
get cleared. So, download it now, jump into online
communities, dive into video tutorials, get enrolled in
data science certification, and master this incredible tool
fordatascience.

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