- Table View
- List View
R Quick Syntax Reference
by Margot TollefsonTheR Quick Syntax Referenceis a handy reference book detailing the intricacies of the R language. Not only is R a free, open-source tool, R is powerful, flexible, and has state of the art statistical techniques available. With the many details which must be correct when using any language, however, the R Quick Syntax Reference makes using R easier. Starting with the basic structure of R, the book takes you on a journey through the terminology used in R and the syntax required to make R work. You will find looking up the correct form for an expression quick and easy. With a copy of the R Quick Syntax Reference in hand, you will find that are able to use the multitude of functions availableto the R user and are even able to write your own functions to explore and analyze data. Takes you through learning R, from download to statistical analysis. Clears the confusion around object types and how to use and convert the types. Tells you how to search for statistical techniques using the R help pages. What you'll learn Download R and R packages for your platform. Work with R within your file structure. Enter data from the keyboard and from external files. Determine and assign modes, classes, and types of objects Do calculations using the computational tools in R. Use R functions and create new functions. Who this book is for TheR Quick Syntax Referenceis for statisticians and other data analysts who are starting to use the R language. It is also for veteran R users who want a quick reference to the language. The book is an excellent choice for the busy data scientist who likes to experiment with new ways of analysis and who needs the flexibility of the data editing available in R. Table of Contents Downloading R and Setting R Up in a File SystemThe R PromptAssignments and OperatorsModes of ObjectsThe Classes and TypesPackaged FunctionsUser Defined FunctionsHow to Use a Function Inputting or Creating Data Outputting Data and Output Manipulating Objects Flow Conditioners Condition Based Functions Some Examples of Conditioning Some Common Functions The Packages base, stats, and graphics Tricks of the Trade"
R Quick Syntax Reference: A Pocket Guide to the Language, APIs and Library
by Margot TollefsonThis handy reference book detailing the intricacies of R updates the popular first edition by adding R version 3.4 and 3.5 features. Starting with the basic structure of R, the book takes you on a journey through the terminology used in R and the syntax required to make R work. You will find looking up the correct form for an expression quick and easy. Some of the new material includes information on RStudio, S4 syntax, working with character strings, and an example using the Twitter API.With a copy of the R Quick Syntax Reference in hand, you will find that you are able to use the multitude of functions available in R and are even able to write your own functions to explore and analyze data.What You Will LearnDiscover the modes and classes of R objects and how to use themUse both packaged and user-created functions in R Import/export data and create new data objects in RCreate descriptive functions and manipulate objects in RTake advantage of flow control and conditional statementsWork with packages such as base, stats, and graphicsWho This Book Is ForThose with programming experience, either new to R, or those with at least some exposure to R but who are new to the latest version.
R Recipes
by Larry A. PaceR Recipes is your handy problem-solution reference for learning and using the popular R programming language for statistics and other numerical analysis. Packed with hundreds of code and visual recipes, this book helps you to quickly learn the fundamentals and explore the frontiers of programming, analyzing and using R. R Recipes provides textual and visual recipes for easy and productive templates for use and re-use in your day-to-day R programming and data analysis practice. Whether you're in finance, cloud computing, big or small data analytics, or other applied computational and data science - R Recipes should be a staple for your code reference library.
R Statistical Application Development by Example Beginner's Guide
by Prabhanjan Narayanachar TattarFull of screenshots and examples, this Beginner's Guide by Example will teach you practically everything you need to know about R statistical application development from scratch. You will begin learning the first concepts of statistics in R which is vital in this fast paced era and it is also a bargain as you do not need to do a preliminary course on the subject.
R Statistics Cookbook: Over 100 recipes for performing complex statistical operations with R 3.5
by Francisco JuretigSolve real-world statistical problems using the most popular R packages and techniques Key Features Learn how to apply statistical methods to your everyday research with handy recipes Foster your analytical skills and interpret research across industries and business verticals Perform t-tests, chi-squared tests, and regression analysis using modern statistical techniques Book Description R is a popular programming language for developing statistical software. This book will be a useful guide to solving common and not-so-common challenges in statistics. With this book, you'll be equipped to confidently perform essential statistical procedures across your organization with the help of cutting-edge statistical tools. You'll start by implementing data modeling, data analysis, and machine learning to solve real-world problems. You'll then understand how to work with nonparametric methods, mixed effects models, and hidden Markov models. This book contains recipes that will guide you in performing univariate and multivariate hypothesis tests, several regression techniques, and using robust techniques to minimize the impact of outliers in data.You'll also learn how to use the caret package for performing machine learning in R. Furthermore, this book will help you understand how to interpret charts and plots to get insights for better decision making. By the end of this book, you will be able to apply your skills to statistical computations using R 3.5. You will also become well-versed with a wide array of statistical techniques in R that are extensively used in the data science industry. What you will learn Become well versed with recipes that will help you interpret plots with R Formulate advanced statistical models in R to understand its concepts Perform Bayesian regression to predict models and input missing data Use time series analysis for modelling and forecasting temporal data Implement a range of regression techniques for efficient data modelling Get to grips with robust statistics and hidden Markov models Explore ANOVA (Analysis of Variance) and perform hypothesis testing Who this book is for If you are a quantitative researcher, statistician, data analyst, or data scientist looking to tackle various challenges in statistics, this book is what you need! Proficiency in R programming and basic knowledge of linear algebra is necessary to follow along the recipes covered in this book.
R Visualizations: Derive Meaning from Data
by David GerbingR Visualizations: Derive Meaning from Data focuses on one of the two major topics of data analytics: data visualization, a.k.a., computer graphics. In the book, major R systems for visualization are discussed, organized by topic and not by system. Anyone doing data analysis will be shown how to use R to generate any of the basic visualizations with the R visualization systems. Further, this book introduces the author’s lessR system, which always can accomplish a visualization with less coding than the use of other systems, sometimes dramatically so, and also provides accompanying statistical analyses. Key Features Presents thorough coverage of the leading R visualization system, ggplot2. Gives specific guidance on using base R graphics to attain visualizations of the same quality as those provided by ggplot2. Shows how to create a wide range of data visualizations: distributions of categorical and continuous variables, many types of scatterplots including with a third variable, time series, and maps. Inclusion of the various approaches to R graphics organized by topic instead of by system. Presents the recent work on interactive visualization in R. David W. Gerbing received his PhD from Michigan State University in 1979 in quantitative analysis, and currently is a professor of quantitative analysis in the School of Business at Portland State University. He has published extensively in the social and behavioral sciences with a focus on quantitative methods. His lessR package has been in development since 2009.
R Web Scraping Quick Start Guide: Techniques and tools to crawl and scrape data from websites
by Olgun AydinWeb Scraping techniques are getting more popular, since data is as valuable as oil in 21st century. Through this book get some key knowledge about using XPath, regEX; web scraping libraries for R like rvest and RSelenium technologies. Key FeaturesTechniques, tools and frameworks for web scraping with RScrape data effortlessly from a variety of websites Learn how to selectively choose the data to scrape, and build your datasetBook DescriptionWeb scraping is a technique to extract data from websites. It simulates the behavior of a website user to turn the website itself into a web service to retrieve or introduce new data. This book gives you all you need to get started with scraping web pages using R programming.You will learn about the rules of RegEx and Xpath, key components for scraping website data. We will show you web scraping techniques, methodologies, and frameworks. With this book's guidance, you will become comfortable with the tools to write and test RegEx and XPath rules. We will focus on examples of dynamic websites for scraping data and how to implement the techniques learned. You will learn how to collect URLs and then create XPath rules for your first web scraping script using rvest library. From the data you collect, you will be able to calculate the statistics and create R plots to visualize them. Finally, you will discover how to use Selenium drivers with R for more sophisticated scraping. You will create AWS instances and use R to connect a PostgreSQL database hosted on AWS. By the end of the book, you will be sufficiently confident to create end-to-end web scraping systems using R.What you will learnWrite and create regEX rulesWrite XPath rules to query your dataLearn how web scraping methods workUse rvest to crawl web pagesStore data retrieved from the webLearn the key uses of Rselenium to scrape dataWho this book is forThis book is for R programmers who want to get started quickly with web scraping, as well as data analysts who want to learn scraping using R. Basic knowledge of R is all you need to get started with this book.
R by Example (Use R!)
by Jim Albert Maria RizzoNow in its second edition, R by Example is an example-based introduction to the statistical computing environment that does not assume any previous familiarity with R or other software packages. R functions are presented in the context of interesting applications with real data. The purpose of this book is to illustrate a range of statistical and probability computations using R for people who are learning, teaching, or using statistics. Specifically, it is written for users who have covered at least the equivalent of (or are currently studying) undergraduate level calculus-based courses in statistics. These users are learning or applying exploratory and inferential methods for analyzing data, and this book is intended to be a useful resource for learning how to implement these procedures in R. The new edition includes expanded coverage of ggplot2 graphics, as well as new chapters on importing data and multivariate data methods.
R for Basic Biostatistics in Medical Research
by Anand Srinivasan Archana Mishra Praveen Kumar-MThe scientific community at the global level is fast becoming aware of the rising use of open-source tools such as R and Python for data analysis. Unfortunately, in spite of the awareness, the conversion of the intrigue to the practical knowledge in utilization of the open-source tools for routine day-to-day data analysis is seriously lacking both among physicians and medical scientists. This book enables physician-scientists to understand the complexity of explaining a programming/ data-analytic language to a healthcare professional and medical scientist. It simplifies and explains how R can be used in medical projects and routine office works. It also talks about the methodologies to convert the knowledge to practice. The book starts with the introduction to the structure of R programming language in the initial chapters, followed with explanations of utilizing R in the basics of data analysis like data importing and exporting, operations on a data frame, parametric and non-parametric tests, regression, sample size calculation, survival analysis, receiver operator characteristic analysis (ROC) and techniques of randomization. Each chapter provides a brief introduction to the involved statistics, for example, dataset, working codes, and a section explaining the codes. In addition to it, a chapter has been dedicated to describing the ways to generate plots using R. This book primarily targets health care professionals and medical/life-science researchers in general.
R for Business Analytics
by A OhriR for Business Analytics looks at some of the most common tasks performed by business analysts and helps the user navigate the wealth of information in R and its 4000 packages. With this information the reader can select the packages that can help process the analytical tasks with minimum effort and maximum usefulness. The use of Graphical User Interfaces (GUI) is emphasized in this book to further cut down and bend the famous learning curve in learning R. This book is aimed to help you kick-start with analytics including chapters on data visualization, code examples on web analytics and social media analytics, clustering, regression models, text mining, data mining models and forecasting. The book tries to expose the reader to a breadth of business analytics topics without burying the user in needless depth. The included references and links allow the reader to pursue business analytics topics. This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy. The book utilizes Albert Einstein's famous remarks on making things as simple as possible, but no simpler. This book will blow the last remaining doubts in your mind about using R in your business environment. Even non-technical users will enjoy the easy-to-use examples. The interviews with creators and corporate users of R make the book very readable. The author firmly believes Isaac Asimov was a better writer in spreading science than any textbook or journal author.
R for Cloud Computing
by A OhriR for Cloud Computing looks at some of the tasks performed by business analysts on the desktop (PC era) and helps the user navigate the wealth of information in R and its 4000 packages as well as transition the same analytics using the cloud. With this information the reader can select both cloud vendors and the sometimes confusing cloud ecosystem as well as the R packages that can help process the analytical tasks with minimum effort, cost and maximum usefulness and customization. The use of Graphical User Interfaces (GUI) and Step by Step screenshot tutorials is emphasized in this book to lessen the famous learning curve in learning R and some of the needless confusion created in cloud computing that hinders its widespread adoption. This will help you kick-start analytics on the cloud including chapters on both cloud computing, R, common tasks performed in analytics including the current focus and scrutiny of Big Data Analytics, setting up and navigating cloud providers. Readers are exposed to a breadth of cloud computing choices and analytics topics without being buried in needless depth. The included references and links allow the reader to pursue business analytics on the cloud easily. It is aimed at practical analytics and is easy to transition from existing analytical set up to the cloud on an open source system based primarily on R. This book is aimed at industry practitioners with basic programming skills and students who want to enter analytics as a profession. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. It will also help researchers and academics but at a practical rather than conceptual level. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy. The cloud computing paradigm is firmly established as the next generation of computing from microprocessors to desktop PCs to cloud.
R for Data Science
by Dan ToomeyIf 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
by Chiu Yu-WeiOver 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.
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
by Garrett Grolemund Hadley WickhamLearn how to use R to turn raw data into insight, knowledge, and understanding. 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 is designed to get you doing data science as quickly as possible.Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way.You’ll learn how to:Wrangle—transform your datasets into a form convenient for analysisProgram—learn powerful R tools for solving data problems with greater clarity and easeExplore—examine your data, generate hypotheses, and quickly test themModel—provide a low-dimensional summary that captures true "signals" in your datasetCommunicate—learn R Markdown for integrating prose, code, and results
R for Data Science: Import, Tidy, Transform, Visualize, and Model Data
by Garrett Grolemund Hadley Wickham Mine Çetinkaya-RundelUse R to turn data into insight, knowledge, and understanding. With this practical book, aspiring data scientists will learn how to do data science with R and RStudio, along with the tidyverse—a collection of R packages designed to work together to make data science fast, fluent, and fun. Even if you have no programming experience, this updated edition will have you doing data science quickly.You'll learn how to import, transform, and visualize your data and communicate the results. And you'll get a complete, big-picture understanding of the data science cycle and the basic tools you need to manage the details. Updated for the latest tidyverse features and best practices, new chapters show you how to get data from spreadsheets, databases, and websites. Exercises help you practice what you've learned along the way.You'll understand how to:Visualize: Create plots for data exploration and communication of resultsTransform: Discover variable types and the tools to work with themImport: Get data into R and in a form convenient for analysisProgram: Learn R tools for solving data problems with greater clarity and easeCommunicate: Integrate prose, code, and results with Quarto
R for Health Data Science
by Ewen Harrison Riinu PiusIn this age of information, the manipulation, analysis, and interpretation of data have become a fundamental part of professional life; nowhere more so than in the delivery of healthcare. From the understanding of disease and the development of new treatments, to the diagnosis and management of individual patients, the use of data and technology is now an integral part of the business of healthcare. Those working in healthcare interact daily with data, often without realising it. The conversion of this avalanche of information to useful knowledge is essential for high-quality patient care. R for Health Data Science includes everything a healthcare professional needs to go from R novice to R guru. By the end of this book, you will be taking a sophisticated approach to health data science with beautiful visualisations, elegant tables, and nuanced analyses. Features Provides an introduction to the fundamentals of R for healthcare professionals Highlights the most popular statistical approaches to health data science Written to be as accessible as possible with minimal mathematics Emphasises the importance of truly understanding the underlying data through the use of plots Includes numerous examples that can be adapted for your own data Helps you create publishable documents and collaborate across teams With this book, you are in safe hands – Prof. Harrison is a clinician and Dr. Pius is a data scientist, bringing 25 years’ combined experience of using R at the coal face. This content has been taught to hundreds of individuals from a variety of backgrounds, from rank beginners to experts moving to R from other platforms.
R for Non-Programmers
by Daniel DauberUnlock the Power of Data Analysis with RWhether you are a researcher, student, or professional new to programming, this book provides a step-by-step guide to mastering R for quantitative and mixed-methods analysis. Designed for those who still need to gain program- ming experience or wish to learn a new one, it demystifies data analysis, helping you tackle challenges from data wrangling to statistical modelling. Packed with practical examples, engaging explanations, and real-world applications, this book equips you with the tools to analyse data confidently, identify trends, and uncover meaningful insights.Transform Your Approach to ResearchThrough clear instructions and hands-on exercises, you will learn to prepare datasets, explore patterns with descriptive statistics, and create impactful visualisations. You will also gain confidence in performing statistical tests such as comparing groups and building predictive models using regression techniques. This book provides strategies and tools to streamline your workflow, whether handling large datasets, managing missing data, or conducting mixed-methods research.Each chapter builds your expertise incrementally, supported by accessible examples and interactive online training. The accompanying training modules, available through the book’s companion package, offer engaging exercises and extended examples to reinforce learning. These features enable you to practise skills and retain knowledge more effectively.This book, which strongly focuses on reproducible research, is an indispensable guide for anyone looking to enhance their analytical toolkit and unlock R’s full potential for data analysis and statistical modelling.
R for Programmers: Advanced Techniques
by Dan ZhangThis book discusses advanced topics such as R core programing, object oriented R programing, parallel computing with R, and spatial data types. The author leads readers to merge mature and effective methdologies in traditional programing to R programing. It shows how to interface R with C, Java, and other popular programing laguages and platforms.
R for Programmers: Mastering the Tools
by Dan ZhangUnlike other books about R, written from the perspective of statistics, this book is written from the perspective of programmers, providing a channel for programmers with expertise in other programming languages to quickly understand R. The contents are divided into four parts: the basics of R, the server of R, databases and big data, and the appendices, which introduce the installation of Java, various databases, and Hadoop. Because this is a reference book, there is no special sequence for reading all the chapters. Anyone new to the subject who wishes to master R comprehensively can simply follow the chapters in sequence.
R for Programmers: Quantitative Investment Applications
by Dan ZhangAfter the fundamental volume and the advanced technique volume, this volume focuses on R applications in the quantitative investment area. Quantitative investment has been hot for some years, and there are more and more startups working on it, combined with many other internet communities and business models. R is widely used in this area, and can be a very powerful tool. The author introduces R applications with cases from his own startup, covering topics like portfolio optimization and risk management.
R for SAS and SPSS Users
by Robert A. MuenchenThis book introduces R using SAS and SPSS terms. It demonstrates which of the add-on packages are most like SAS and SPSS and compares them to R's built-in functions. It compares and contrasts the differing approaches of all three packages.
R for Stata Users
by Robert A. Muenchen Joseph M. HilbeStata is the most flexible and extensible data analysis package available from a commercial vendor. R is a similarly flexible free and open source package for data analysis, with over 3,000 add-on packages available. This book shows you how to extend the power of Stata through the use of R. It introduces R using Stata terminology with which you are already familiar. It steps through more than 30 programs written in both languages, comparing and contrasting the two packages' different approaches. When finished, you will be able to use R in conjunction with Stata, or separately, to import data, manage and transform it, create publication quality graphics, and perform basic statistical analyses. A glossary defines over 50 R terms using Stata jargon and again using more formal R terminology. The table of contents and index allow you to find equivalent R functions by looking up Stata commands and vice versa. The example programs and practice datasets for both R and Stata are available for download.
R for the Rest of Us: A Statistics-Free Introduction
by David KeyesLearn how to use R for everything from workload automation and creating online reports, to interpreting data, map making, and more.Written by the founder of a very popular online training platform for the R programming language!The R programming language is a remarkably powerful tool for data analysis and visualization, but its steep learning curve can be intimidating for some. If you just want to automate repetitive tasks or visualize your data, without the need for complex math, R for the Rest of Us is for you.Inside you&’ll find a crash course in R, a quick tour of the RStudio programming environment, and a collection of real-world applications that you can put to use right away. You&’ll learn how to create informative visualizations, streamline report generation, and develop interactive websites—whether you&’re a seasoned R user or have never written a line of R code.You&’ll also learn how to:• Manipulate, clean, and parse your data with tidyverse packages like dplyr and tidyr to make data science operations more user-friendly• Create stunning and customized plots, graphs, and charts with ggplot2 to effectively communicate your data insights• Import geospatial data and write code to produce visually appealing maps automatically• Generate dynamic reports, presentations, and interactive websites with R Markdown and Quarto that seamlessly integrate code, text, and graphics• Develop custom functions and packages tailored to your specific needs, allowing you to extend R&’s functionality and automate complex tasksUnlock a treasure trove of techniques to transform the way you work. With R for the Rest of Us, you&’ll discover the power of R to get stuff done. No advanced statistics degree required.
R für Dummies (Für Dummies)
by Joris Meys Andrie de VriesWollen Sie auch die umfangreichen Möglichkeiten von R nutzen, um Ihre Daten zu analysieren, sind sich aber nicht sicher, ob Sie mit der Programmiersprache wirklich zurechtkommen? Keine Sorge - dieses Buch zeigt Ihnen, wie es geht - selbst wenn Sie keine Vorkenntnisse in der Programmierung oder Statistik haben. Andrie de Vries und Joris Meys zeigen Ihnen Schritt für Schritt und anhand zahlreicher Beispiele, was Sie alles mit R machen können und vor allem wie Sie es machen können. Von den Grundlagen und den ersten Skripten bis hin zu komplexen statistischen Analysen und der Erstellung aussagekräftiger Grafiken. Auch fortgeschrittenere Nutzer finden in diesem Buch viele Tipps und Tricks, die Ihnen die Datenauswertung erleichtern.
R für Dummies (Für Dummies)
by Joris Meys Andrie de VriesWollen Sie auch die umfangreichen Möglichkeiten von R nutzen, um Ihre Daten zu analysieren, sind sich aber nicht sicher, ob Sie mit der Programmiersprache wirklich zurechtkommen? Keine Sorge - dieses Buch zeigt Ihnen, wie es geht - selbst wenn Sie keine Vorkenntnisse in der Programmierung oder Statistik haben. Andrie de Vries und Joris Meys zeigen Ihnen Schritt für Schritt und anhand zahlreicher Beispiele, was Sie alles mit R machen können und vor allem wie Sie es machen können. Von den Grundlagen und den ersten Skripten bis hin zu komplexen statistischen Analysen und der Erstellung aussagekräftiger Grafiken. Auch fortgeschrittenere Nutzer finden in diesem Buch viele Tipps und Tricks, die Ihnen die Datenauswertung erleichtern.