{"id":9287,"date":"2022-11-01T13:00:33","date_gmt":"2022-11-01T13:00:33","guid":{"rendered":"https:\/\/www.gologica.com\/elearning\/?p=9287"},"modified":"2025-04-09T07:26:10","modified_gmt":"2025-04-09T07:26:10","slug":"data-science-with-r-getting-started","status":"publish","type":"post","link":"https:\/\/www.gologica.com\/elearning\/data-science-with-r-getting-started\/","title":{"rendered":"Chapter 5: Data Science with R: Getting Started"},"content":{"rendered":"\t\t
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Table Of Content<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter : Overview \u2013 Data Science Tutorial for Beginners<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 1: What is data and the importance of data 2022?<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 2: Introduction to Data Science<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 3: Data Scientist vs Data Analyst vs Data Engineer: Job Role, Skills, and Salary<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 4: Top 15 Data Science Tools Everyone Should Know in 2022<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 5: Data Science with R: Getting Started<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 6: Getting Started With Linear Regression In R<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 7: A Guide to Time Series Forecasting in R You Should Know<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 8: How to Build a Career in Data Science<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tChapter 9: How to Become a Data Scientist in 2022<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t<\/div>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
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Data Science with R: Getting Started<\/span><\/h1>

\"\"<\/a><\/p>

The latest data surge will not abate anytime soon. In fact, according to IDC research, the volume of data created in 2025 will surpass 175 zettabytes. Dealing with this vast volume of data is a challenge for all businesses across all industries. As a result, companies worldwide search for individuals who can interpret data and generate relevant and actionable insights.<\/span><\/p>

Here comes data science. In this guide, you will learn data science with R.<\/span><\/p>

Introduction to R<\/span><\/h2>

R is a free, open-source programming language extensively used in statistical applications and data analysis. R often has a command-line interface. R is accessible on popular operating systems, including Windows, Linux, and macOS.Furthermore, the R programming language is the most recent cutting-edge technology.<\/span><\/p>

It was created in New Zealand by Ross Ihaka and Robert Gentleman and is being developed by the R Development Core Team. The R programming language is a variant of the S programming language. It integrates Scheme-inspired lexical scoping semantics. It was conceived in 1992, with an early version released in 1995 and a stable beta version issued in 2000.<\/span><\/p>

Features of R<\/span><\/h2>

R provides several statistical and graphical tools. It contains an extensive package library that simplifies developing machine learning algorithms. In addition, it is simple to combine with popular tools such as Tableau and Microsoft SQL Server.<\/span><\/p>

R is more than simply a programming language; it also features a global repository system called CRAN (Comprehensive R Archive Network). It is available at https:\/\/cran.r-project.org\/.<\/span><\/p>

It includes all major updates, R sources, R binaries, R packages, and documentation. CRAN hosts about 10,000 R packages.<\/span><\/p>

Statistical Features:<\/b><\/h3>
  1. Basic Statistics:<\/b> The mean, mode, and median are the most often used fundamental statistics words. All of them refer to “Measures of Central Tendency.” So, we can assess central tendency with the R programming language.<\/span><\/li>
  2. Static graphics:<\/b> R provides several tools for producing and developing intriguing static graphics. R supports many plot forms, including graphic maps, mosaic plots, biplots, etc.<\/span><\/li>
  3. Probability distributions:<\/b> Probability distributions are important in statistics, and we can easily handle several types of probability distributions using R, such as the Binomial Distribution, Normal Distribution, Chi-squared Distribution, and many more.<\/span><\/li>
  4. Data analysis:<\/b> It provides comprehensive, consistent, and integrated data analysis capabilities.<\/span><\/li><\/ol>

    Programming Features:<\/b><\/h3>
    1. R Packages: <\/b>One of R’s most notable aspects is its abundance of libraries. CRAN (Comprehensive R Archive Network) is a repository for R that contains over 10,000 packages.<\/span><\/li>
    2. Distributed computing:<\/b> Distributed computing is a technique in which software system components are shared among numerous computers to increase efficiency and performance. In November 2015, two new R packages for distributed programming, DDR and multidplyr, were published.<\/span><\/li><\/ol>

      Applications of R:<\/span><\/h2>
      1. For data science, R is incredibly useful. R provides data scientists with a wide range of statistics-related libraries. It also serves as a platform for statistical computing and design.<\/span><\/li>
      2. Many quantitative analysts utilize R as a programming language. As a result, it aids in data import and cleansing.<\/span><\/li>
      3. R is one of the most common languages besides Python. A large number of data analysts and research programmers use it. As a result, it is employed as a fundamental financial instrument.<\/span><\/li>
      4. R is used by tech behemoths such as Google, Facebook, Bing, Twitter, Accenture, Wipro, and many others.<\/span><\/li><\/ol>

        R and Python are both essential tools in data research. However, it is difficult for newcomers to decide whether R or Python is better or more appropriate.<\/span><\/p>

        Installation of R<\/span><\/h2>

        R is available for free on the CRAN website. You may download an operating system by selecting it and clicking on it. To complete the installation, stick to the default settings.<\/span><\/p>

        You may also install RStudio, an integrated R development environment. It is available in two different formats: RStudio Desktop is a standard desktop program. Simultaneously, the RStudio Server operates on a distant server and provides RStudio access via a web browser.<\/span><\/p>

        Install packages and their dependencies before you begin programming in R. Packages are pre-assembled groups of functions and objects. The CRAN repository hosts each package. You can add any package anytime as not all of them are loaded by default.<\/span><\/p>

        To add a new package to RStudio, navigate to Tools -> Install Packages.<\/span><\/p>

        Then you may search for the package you want to install and choose where to install it.<\/span><\/p>

        There are various data structures accessible in the R programming language:<\/span><\/p>

        1. Vectors: <\/b>The most fundamental R object with atomic values.<\/span><\/li>
        2. Matrices:<\/b> These are R objects with elements organized in a two-dimensional grid. They also have the same sorts of components.<\/span><\/li>
        3. Arrays<\/b>: Arrays are data structures that can hold data in more than two dimensions. Making an array with two, three, or four dimensions will generate four rectangular matrices. Each matrix has two rows and three columns.<\/span><\/li>
        4. Data Frames:<\/b> It is a table containing one variable’s values in each column and one set of values from each column in each row.<\/span><\/li>
        5. Lists: A list comprises components of various sorts (numbers, strings, vectors, etc.) Its elements can either be a matrix or a function. The list() method is used to generate the list.<\/span><\/li><\/ol>

          Importing and Exporting files in R<\/span><\/h2>

          Importing files in R\u00a0<\/b><\/h3>

          Using R, you can import data from a variety of sources:<\/span><\/p>

          1. Table: We can use the read.table function in R to load a table.<\/span><\/li>
          2. CSV: Use the read.csv command to import an a.csv file.<\/span><\/li><\/ol>

            Exporting Files in R<\/b><\/h3>

            In R, you may also export various files to a different place.<\/span><\/p>

            1. Write.table to export a table.<\/span><\/li>
            2. Write.xls to export an Excel file.<\/span><\/li>
            3. To export a CSV file, use the following syntax: Write.csv(file name, “c:\/file name.csv”).<\/span><\/li><\/ol>

              Data Visualization in R<\/span><\/h2>

              R includes robust graphics tools that aid with data visualization. These drawings may be seen on the screen and saved in various formats such as .pdf, .png, .jpg, .wmf, and.ps. In addition, it may be adjusted to meet your specific graphic demands and allows you to copy & paste into Word or PowerPoint documents.<\/span><\/p>

              We can create a bar chart, pie chart, histogram, kernel density plot, line chart, boxplot, heat map, and word cloud.<\/span><\/p>

              Consider boxplots in R.<\/span><\/p>

              Boxplots are frequently referred to as whisker diagrams. They will show the data distribution depending on the following parameters:<\/span><\/p>