PDF Ebook R Graph Essentials, by David Alexander Lillis
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R Graph Essentials, by David Alexander Lillis
PDF Ebook R Graph Essentials, by David Alexander Lillis
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Use R's powerful graphing capabilities to design and create professional-level graphicsAbout This Book
- Learn how to use Base R to analyze your data and generate statistical graphs
- Create attractive graphics using advanced functions such as qplot and ggplot for research and analysis
- A step-by-step guide, packed with examples using real-world data sets that can prove helpful to R programmers
This book is targeted at R programmers who want to learn the graphing capabilities of R. This book will presume that you have working knowledge of R.
What You Will Learn- Understand and implement the basic graphics syntax and techniques
- Create scatterplots and line plots, and include mathematical expressions in them
- Include more advanced graphics and color palettes in your graph
- Create and customize graphs using the qplot function
- Customize the graphics, backgrounds, sizes, and colors of your graph using ggplot
- Add layers to your graph using ggplot
- Make publication-quality graphs using ggplot
R has undergone an explosion of popularity around the world. The wide range of graphs and options for creating high-impact visualizations makes R a highly effective tool for researchers, data analysts, students, and others.
This book starts with the basics of R and the principles of creating graphics, focusing on scatterplots and line graphs for research and data analysis.
After that you will learn how to include mathematical expressions on your graphs and create bar charts, histograms, boxplots, and so on using advanced functions. The book then dives deeper into shading and coloring your graphs and labeling points on graphs using the qplot function. After that you will learn how to use the ggplot package to control symbol color, size, shape, and many other attributes.
By the end of this book, you will have expert skills to create attractive and informative graphics and design professional-level graphs.
- Sales Rank: #3447049 in Books
- Published on: 2014-09-24
- Released on: 2014-09-22
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .43" w x 7.50" l, .74 pounds
- Binding: Paperback
- 229 pages
About the Author
David Alexander Lillis
David Alexander Lillis is an experienced researcher and statistician, having worked in research evaluation, agricultural and forestry statistics, and education research and statistics. Currently, David delivers lectures in mathematics, statistics, and research methods at the New Zealand Institute of Sport in New Zealand. He is the owner and Director of Sigma Statistics and Research Limited, a New Zealand-based consultancy specializing in training, software development, data analysis, and statistical modelling using R. David holds an Honor's degree and Master's degree in Physics and Mathematics, and a PhD from Curtin University in Perth, Australia.
Most helpful customer reviews
1 of 1 people found the following review helpful.
R graphics is equivalent to many competing packages
By W Boudville
Congratulations on making it this far! Yes, I kid you not. If you are reading my review, then you already know R or know about it. R is now the de facto statistical package favoured by many maths departments throughout the world. What this means is that the researchers in those departments often write and debug the subroutines in R. All for free, as far as you are concerned. So looking at R is a good choice. But given that you are dealing with possibly large data, graphics routines are essential. The book focuses on what comes with R. The base set of graphics.
We see that simple plotting of curves is available. Along with scatter plots and various histograms. The R code to generate those figures is short. Yes, it is programming, but you do not need prior knowledge of a fully fledged computer language like C or java. The R language is quicker to learn.
The graphics examples in the book are nothing different or new from what any statistics text might show. Instead, it shows how R Graph can be equivalent to other packages like Mathematica, Maple, SAS, Splus etc. Those have been around much longer, but typically are not free.
The book is not meant to teach stats graphics per se. So if you are also learning that, consider getting a pure text on it and then also use this Lillis book to aid in actually drawing some graphs.
1 of 1 people found the following review helpful.
I highly recommend this text
By Daniel Strassburger
I highly recommend this text. This book will take you from analyzing to visualizing data in no time. Truly. Lillis does a superb job of breaking down the, all too often unnecessarily complex, syntax so the reader can fully understand how each component of code affects the output. As a PhD student in Social Psychology and earning a minor in Statistics, I can deeply appreciate the practical applications of each of the graphing tutorials Lillis has chosen to include in this text. For those readers who are eager to extend their skill set beyond base R graphics, Lillis introduces the reader to the grammar of ggplot - a highly customizable system of producing graphs. Because of its concise, accurate, practical examples and explanations, this text will proudly sit atop my list of resources as I continue to develop my skills when visualizing statistical analyses. More information about this text (including supplemental files) can be found at the publisher's website here: http://bit.ly/ZvZQDS
1 of 1 people found the following review helpful.
Whole view of the R graph system
By Mariano Rico
At last a nice explanation of the whole R graph system. It is especially interesting qplot, the basic framework to create packages like ggplot2. Also you have a good review of the classical plot() functions. Although plot() provides simpler graphs (compared, for instance, to ggplot2) you can achieve functionality that ggplot2 does not have, for instance, two y-axis graphs or nice fillings for black&white figures. With this book you can learn how to create your own data visualizations (graphs).
A good recommendation despite its short length (180 pages).
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