R For Data Science

R for Data Science PDF
Author: Hadley Wickham
Publisher: "O'Reilly Media, Inc."
ISBN: 1491910364
Size: 54.81 MB
Format: PDF, Kindle
Category : Computers
Languages : en
Pages : 492
View: 549

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"This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"--

R For Data Science

R for Data Science PDF
Author: Dan Toomey
Publisher: Packt Publishing Ltd
ISBN: 1784392650
Size: 44.82 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 364
View: 3513

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If you are a data analyst who has a firm grip on some advanced data analysis techniques and wants to learn how to leverage the features of R, this is the book for you. You should have some basic knowledge of the R language and should know about some data science topics.

R For Data Science Cookbook

R for Data Science Cookbook PDF
Author: Yu-Wei, Chiu (David Chiu)
Publisher: Packt Publishing Ltd
ISBN: 1784392049
Size: 32.44 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 452
View: 5229

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Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniques About This Book Gain insight into how data scientists collect, process, analyze, and visualize data using some of the most popular R packages Understand how to apply useful data analysis techniques in R for real-world applications An easy-to-follow guide to make the life of data scientist easier with the problems faced while performing data analysis Who This Book Is For This book is for those who are already familiar with the basic operation of R, but want to learn how to efficiently and effectively analyze real-world data problems using practical R packages. What You Will Learn Get to know the functional characteristics of R language Extract, transform, and load data from heterogeneous sources Understand how easily R can confront probability and statistics problems Get simple R instructions to quickly organize and manipulate large datasets Create professional data visualizations and interactive reports Predict user purchase behavior by adopting a classification approach Implement data mining techniques to discover items that are frequently purchased together Group similar text documents by using various clustering methods In Detail This cookbook offers a range of data analysis samples in simple and straightforward R code, providing step-by-step resources and time-saving methods to help you solve data problems efficiently. The first section deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the “dplyr” and “data.table” packages to efficiently process larger data structures. We also focus on “ggplot2” and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the “ggvis” package. Later chapters offer insight into time series analysis on financial data, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this book, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis. Style and approach This easy-to-follow guide is full of hands-on examples of data analysis with R. Each topic is fully explained beginning with the core concept, followed by step-by-step practical examples, and concluding with detailed explanations of each concept used.

Using R For Data Analysis In Social Sciences

Using R for Data Analysis in Social Sciences PDF
Author: Quan Li
Publisher: Oxford University Press
ISBN: 0190656212
Size: 27.16 MB
Format: PDF, ePub, Docs
Category : Business & Economics
Languages : en
Pages : 366
View: 1175

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Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings. Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test, covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and the process of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.

R For Data Science Solutions

R for Data Science Solutions PDF
Author: David Chiu
Publisher:
ISBN:
Size: 41.95 MB
Format: PDF, Docs
Category :
Languages : en
Pages :
View: 1319

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"R is a data analysis software as well as a programming language. Data scientists, statisticians and analysts use R for statistical analysis, data visualization and predictive modeling. R is open source and allows integration with other applications and systems. Compared to other data analysis platforms, R has an extensive set of data products. Problems faced with data are cleared with R's excellent data visualization feature. The first section in this course deals with how to create R functions to avoid the unnecessary duplication of code. You will learn how to prepare, process, and perform sophisticated ETL for heterogeneous data sources with R packages. An example of data manipulation is provided, illustrating how to use the 'dplyr' and 'data.table' packages to efficiently process larger data structures. We also focus on 'ggplot2' and show you how to create advanced figures for data exploration. In addition, you will learn how to build an interactive report using the 'ggvis' package. Later sections offer insight into time series analysis, while there is detailed information on the hot topic of machine learning, including data classification, regression, clustering, association rule mining, and dimension reduction. By the end of this course, you will understand how to resolve issues and will be able to comfortably offer solutions to problems encountered while performing data analysis."--Resource description page.

Beginning Data Science In R

Beginning Data Science in R PDF
Author: Thomas Mailund
Publisher: Apress
ISBN: 1484226712
Size: 77.21 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 352
View: 1300

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Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You’ll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. What You Will Learn Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code Who This Book Is For Those with some data science or analytics background, but not necessarily experience with the R programming language.

R Data Science Quick Reference

R Data Science Quick Reference PDF
Author: Thomas Mailund
Publisher: Apress
ISBN: 1484248945
Size: 57.39 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages : 246
View: 6229

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In this handy, practical book you will cover each concept concisely, with many illustrative examples. You'll be introduced to several R data science packages, with examples of how to use each of them. In this book, you’ll learn about the following APIs and packages that deal specifically with data science applications: readr, dibble, forecasts, lubridate, stringr, tidyr, magnittr, dplyr, purrr, ggplot2, modelr, and more. After using this handy quick reference guide, you'll have the code, APIs, and insights to write data science-based applications in the R programming language. You'll also be able to carry out data analysis. What You Will Learn Import data with readr Work with categories using forcats, time and dates with lubridate, and strings with stringr Format data using tidyr and then transform that data using magrittr and dplyr Write functions with R for data science, data mining, and analytics-based applications Visualize data with ggplot2 and fit data to models using modelr Who This Book Is For Programmers new to R's data science, data mining, and analytics packages. Some prior coding experience with R in general is recommended.

Getting Started With R For Data Science

Getting Started with R for Data Science PDF
Author: Richard Skeggs
Publisher:
ISBN:
Size: 35.21 MB
Format: PDF, ePub, Docs
Category :
Languages : en
Pages :
View: 3311

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"The R language is a powerful open source functional programming language. R is becoming the go-to tool for data scientists and analysts. Its growing popularity is due to its open source nature and extensive development community. This course will take you on a journey to become an efficient data science practitioner as you thoroughly understand the key concepts of R. Starting from the absolute basics, you will quickly be introduced to programming in R. You will see how to load data into R for analysis, and get a good understanding of how to write R scripts. We will delve into data types in R, and you'll gain the ability to read and write data to and from databases as well as files. You will also get to know how to perform basic analysis of the data. By the end of the course, you will know how data science can be applied in practical conditions."--Resource description page.

R Programming For Data Science

R Programming for Data Science PDF
Author: Roger Peng
Publisher: Lulu.com
ISBN: 9781365056826
Size: 56.14 MB
Format: PDF
Category : Computers
Languages : en
Pages : 194
View: 1217

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Data science has taken the world by storm. Every field of study and area of business has been affected as people increasingly realize the value of the incredible quantities of data being generated. But to extract value from those data, one needs to be tra