1. Click on the "Help" tab on the top left of the RStudio interface >> Click on "R Help" from the menu >> View R resources & manuals appearing in the lower right pane
2. Locate "Help" tab in the lower right pane >> Click on the Home icon links to R resources & manuals, and use the search bar to enter names of functions or packages to review the help documentations.
3. In the Script pane, you can enter help codes, such as:
help.start() opens the main page of the ‘Help’ tab
help(FunctionName) search for help on function
?FunctionName same as help()
R for Data Science by
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: Wrangleâ??transform your datasets into a form convenient for analysis Programâ??learn powerful R tools for solving data problems with greater clarity and ease Exploreâ??examine your data, generate hypotheses, and quickly test them Modelâ??provide a low-dimensional summary that captures true "signals" in your dataset Communicateâ??learn R Markdown for integrating prose, code, and results
R by Example
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, this book 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."
R for Data Science Cookbook by
Over 100 hands-on recipes to effectively solve real-world data problems using the most popular R packages and techniquesAbout 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 analysisWho This Book Is ForThis 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 methodsIn DetailThis 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 approachThis 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 Data Visualization Recipes by
Translate your data into info-graphics using popular packages in RAbout This Book* Use R's popular packages--such as ggplot2, ggvis, ggforce, and more--to create custom, interactive visualization solutions.* Create, design, and build interactive dashboards using Shiny* A highly practical guide to help you get to grips with the basics of data visualization techniques, and how you can implement them using RWho This Book Is ForIf you are looking to create custom data visualization solutions using the R programming language and are stuck somewhere in the process, this book will come to your rescue. Prior exposure to packages such as ggplot2 would be useful but not necessary. However, some R programming knowledge is required.What You Will Learn* Get to know various data visualization libraries available in R to represent data* Generate elegant codes to craft graphics using ggplot2, ggvis and plotly* Add elements, text, animation, and colors to your plot to make sense of data* Deepen your knowledge by adding bar-charts, scatterplots, and time series plots using ggplot2* Build interactive dashboards using Shiny.* Color specific map regions based on the values of a variable in your data frame* Create high-quality journal-publishable scatterplots* Create and design various three-dimensional and multivariate plotsIn DetailR is an open source language for data analysis and graphics that allows users to load various packages for effective and better data interpretation. Its popularity has soared in recent years because of its powerful capabilities when it comes to turning different kinds of data into intuitive visualization solutions.This book is an update to our earlier R data visualization cookbook with 100 percent fresh content and covering all the cutting edge R data visualization tools. This book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization using R. It starts off with the basics of ggplot2, ggvis, and plotly visualization packages, along with an introduction to creating maps and customizing them, before progressively taking you through various ggplot2 extensions, such as ggforce, ggrepel, and gganimate. Using real-world datasets, you will analyze and visualize your data as histograms, bar graphs, and scatterplots, and customize your plots with various themes and coloring options. The book also covers advanced visualization aspects such as creating interactive dashboards using ShinyBy the end of the book, you will be equipped with key techniques to create impressive data visualizations with professional efficiency and precision.Style and approachThis book is packed with practical recipes, designed to provide you with all the guidance needed to get to grips with data visualization with R. You will learn to leverage the power of R and ggplot2 to create highly customizable data visualizations of varying complexities. The readers will then learn how to create, design, and build interactive dashboards using Shiny.