000			05387nam a22002057a 4500
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005			20230322095414.0
008			230322b ||||| |||| 00| 0 eng d
020			_a9789390421923
082			_a005.133 _bRAT
100			_aRatnoo, Saroj Dahiya _911450
245			_aEssentials of R for data analytics
260			_bWiley India Pvt. Ltd. _aNew Delhi _c2021
300			_axiv, 317 p.
365			_aINR _b469.00
504			_aTable of content Chapter 1 Introduction to HR Analytics 1.1 Introduction 1.2 About the R Environment 1.3 Starting R and RStudio 1.4 Entering and Executing Commands 1.5 Setting Variables 1.6 Knowing about Objects 1.7 Structure of Objects 1.8 Managing Objects in R’s Workspace 1.9 Creating Sequences 1.10 Operator Precedence 1.11 Setting Working Directory 1.12 Making and Executing Code from Script Files 1.13 Packages in R Chapter 2 Getting Help in R 2.1 Introduction 2.2 Top-Level Help 2.3 Help On Functions 2.4 Searching Documentation Through Keywords 2.5 Getting Help from Web 2.6 Searching for Relevant Packages 2.7 Getting Help from R Mailing Lists Chapter 3 Vectors and Factors in R 3.1 Introduction 3.2 Vectors 3.3 Factors Chapter 4 Matrices in R 4.1 Introduction 4.2 Arrays 4.3 Creating Matrices 4.4 Naming the Dimensions of a Matrix 4.5 Accessing Elements of Matrices 4.6 Arithmetic Operations on Matrices 4.7 Concatenating Matrices 4.8 Replicating Matrices 4.9 Other Useful Operations on Matrices Chapter 5 Lists and Data Frames in R 5.1 Introduction 5.2 Lists in R 5.3 Data Frames in R Chapter 6 Strings and Dates in R 6.1 Introduction 6.2 Handling Strings 6.3 Handling Date and Time Chapter 7 Input Output in R 7.1 Introduction 7.2 Reading Data from Console 7.3 Reading Data from Files 7.4 Displaying Data to Screen 7.5 Saving Objects to Files 7.6 Writing Data to Files Chapter 8 Conditional Statements and Loops in R 8.1 Introduction 8.2 Control Structures for Conditional Execution 8.3 Looping Structures in R Chapter 9 Writing Functions in R 9.1 Introduction 9.2 Functions in R 9.3 Defining a Function 9.4 Anonymous Functions 9.5 Scope of objects 9.6 Return Value of a Function 9.7 Named and Default Arguments 9.8 Passing Arguments to a Function 9.9 The … Arguments 9.10 Modifying a Data Frame Using a Function 9.11 Defining New Binary Operators Chapter 10 An Introduction to Graphics in R 10.1 Introduction 10.2 Pressure Dataset 10.3 Iris Dataset 10.4 My First Plot 10.5 Adding Elements 10.6 Controlling the Type of Scatter Plot 10.7 Controlling the Types of Lines and Points 10.8 Adding Grids 10.9 Customizing Axes 10.10 Scatter Plot with Groups in Data 10.11 Adding Legend 10.12 Adding a Regression Line 10.13 Creating Separate Scatter Plot for Each Factor Level 10.14 Customizing Margins 10.15 Adding Text 10.16 Saving Your Plot 10.17 Working with Multiple Graphics Devices 10.18 Plotting Scatter Plot of all Variables in a Dataset 10.19 Combining Multiple Plots in a Graphics Window 10.20 Graphics Parameters 10.21 A Customized Colourful Plot Chapter 11 Making Graphs and Charts in R 11.1 Introduction 11.2 Frequency Distribution of Categorical Data: Making Bar Charts 11.3 Frequency Distributions of Continuous Data: Making Histograms 11.4 Five-Number Summary: Making Box Plots 11.5 Visualizing Relationships in Continuous Data: Scatter Plot and Line Charts 11.6 Plotting Functions 11.7 Confirming Data Distribution: Making Q–Q Plots 11.8 Other Plots and Charts 11.9 Contour Plots Chapter 12 Graphics using ggplot2 12.1 Introduction 12.2 Scatter Plots 12.3 Geometric Objects in ggplot2: Creating Different Plots 12.4 Overall Appearance of a Plot 12.5 Other Resources and References Chapter 13 Data Transformations in R 13.1 Introduction 13.2 Datasets 13.3 Transformation Functions in “dplyr” 13.4 Data Transformation in Action on iris Dataset 13.5 Answering Questions on flights Dataset Chapter 14 Predictive Analytics: Classification in R 14.1 Introduction 14.2 Classification 14.3 Some Popular Classification Models 14.4 Implementing Classification in R Chapter 15 Predictive Analytics: Regression in R 15.1 Introduction 15.2 Simple Linear Regression Model 15.3 Determination of β0 and β1 15.4 Multiple Linear Regression 15.5 Predictive Modelling Using Regression 15.6 Simple Linear Regression Predictive Modelling in R 15.7 Modelling with Multiple Linear Regression in R 15.8 Regression Modelling with Higher Order Ploynomial Terms 15.9 Regression Modelling with Interaction Term
520			_aWith widespread and exponential growth of data, people with data science background are in great demand. Data analytics, a subdomain of data science, is meant to turn data into insight and actionable knowledge. Data analytics mainly deals with exploring, visualizing, transforming and modelling data for making predictions. Learning R is an essential step towards becoming a data analyst.
650			_aData analytics _9861
700			_aRatnoo, Himmat Singh _912407
942			_2ddc _cBK