This 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 them Use both packaged and user-created functions in R Import/export data and create new data objects in R Create descriptive functions and manipulate objects in R Take advantage of flow control and conditional statements Work with packages such as base, stats, and graphics Who This Book Is For Those with programming experience, either new to R, or those with at least some exposure to R but who are new to the latest version.

The R Quick Syntax Reference is 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 available to 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.

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 LearnImport data with readrWork with categories using forcats, time and dates with lubridate, and strings with stringrFormat data using tidyr and then transform that data using magrittr and dplyrWrite functions with R for data science, data mining, and analytics-based applicationsVisualize 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.

This one-of-a-kind HVAC/R technical reference guide incorporates all the HVAC/R technical terms used in the industry today, and is an indispensable resource for professionals dealing with electricity, controls, refrigeration cycle, heating, psychometrics, boilers, heat pumps, heat transfer, load calculations and more. Covers the entire industry, providing the most comprehensive collection of HVAC/R terms available in one concise location. For those just starting in and seasoned veterans of the HVAC/R industry. The 71 pages of appendices include common industry association abbreviations, business, computer and medical terminology; area of circles; color codes for resistors; CFM tables, decibel ratings & hazardous time exposure of common noises, duct sizing, conversion charts and much, much more.

Learn 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 analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

An essential library of basic commands you can copy and paste into 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. .