R is fast becoming the de facto standard for statistical computing and analysis in science, business, engineering, and related fields. This book examines this complex language using simple statistical examples, showing how R operates in a user-friendly context. Both students and workers in fields that require extensive statistical analysis will find this book helpful as they learn to use R for simple summary statistics, hypothesis testing, creating graphs, regression, and much more. It covers formula notation, complex statistics, manipulating data and extracting components, and rudimentary programming.R, the open source statistical language increasingly used to handle statistics and produces publication-quality graphs, is notoriously complex This book makes R easier to understand through the use of simple statistical examples, teaching the necessary elements in the context in which R is actually used Covers getting started with R and using it for simple summary statistics, hypothesis testing, and graphs Shows how to use R for formula notation, complex statistics, manipulating data, extracting components, and regression Provides beginning programming instruction for those who want to write their own scripts
Beginning R offers anyone who needs to perform statistical analysis the information necessary to use R with confidence.
Microsoft Excel is a powerful tool that can transform the way you use data. This book explains in comprehensive and user-friendly detail how to manage, make sense of, explore and share data, giving scientists at all levels the skills they need to maximize the usefulness of their data.
Readers will learn how to use Excel to:
* Build a dataset – how to handle variables and notes, rearrangements and edits to data.
* Check datasets – dealing with typographic errors, data validation and numerical errors.
* Make sense of data – including datasets for regression and correlation; summarizing data with averages and variability; and visualizing data with graphs, pivot charts and sparklines.
* Explore regression data – finding, highlighting and visualizing correlations.
* Explore time-related data – using pivot tables, sparklines and line plots.
* Explore association data – creating and visualizing contingency tables.
* Explore differences – pivot tables and data visualizations including box-whisker plots.
* Share data – methods for exporting and sharing your datasets, summaries and graphs.
Alongside the text, Have a Go exercises, Tips and Notes give readers practical
experience and highlight important points, and helpful self-assessment exercises and summary tables can be found at the end of each chapter. Supplementary material can also be downloaded on the companion website.
Managing Data Using Excel is an essential book for all scientists and students who use data and are seeking to manage data more effectively. It is aimed at scientists at all levels but it is especially useful for university-level research, from undergraduates to postdoctoral researchers.
The study of ecological community data involves many methods of analysis. In this book you will learn many of the mainstays of community analysis including: diversity, similarity and cluster analysis, ordination and multivariate analyses. This book is for undergraduate and postgraduate students and researchers seeking a step-by-step methodology for analysing plant and animal communities using R and Excel.
Microsoft's Excel spreadsheet is virtually ubiquitous and familiar to most computer users. It is a robust program that makes an excellent storage and manipulation system for many kinds of data, including community data. The R program is a powerful and flexible analytical system able to conduct a huge variety of analytical methods, which means that the user only has to learn one program to address many research questions. Its other advantage is that it is open source and therefore completely free. Novel analytical methods are being added constantly to the already comprehensive suite of tools available in R.
The powerful and open-source statistical programming language R is rapidly growing in popularity, but it requires that you type in commands at the keyboard rather than use a mouse, so you have to learn the language of R. But there is a shortcut, and that's where this unique book comes in. A companion book to Visualize This: The FlowingData Guide to Design, Visualization, and Statistics, this practical reference is a library of basic R commands that you can copy and paste into R to perform many types of statistical analyses.
Whether you're in technology, science, medicine, business, or engineering, you can quickly turn to your topic in this handy book and find the commands you need.Comprehensive command reference for the R programming language and a companion book to Visualize This: The FlowingData Guide to Design, Visualization, and Statistics Combines elements of a dictionary, glossary, and thesaurus for the R language Provides easy accessibility to the commands you need, by topic, which you can cut and paste into R as needed Covers getting, saving, examining, and manipulating data; statistical test and math; and all the things you can do with graphs Also includes a collection of utilities that you'll find useful
Simplify the complex statistical R programming language with The Essential R Reference..