R for Data Analysis in easy steps

R Programming essentials

Mike McGrath

• Publisher: In Easy Steps Limited
• Year: 2018
• Print ISBN: 9781840787955
• eBook ISBN: 9781840788150
• Topic: Computer Science
• Subtopic: Programming Languages
• Index: Computer Science

The R language is widely used by statisticians for data analysis, and the popularity of R programming has therefore increased substantially in recent years. The emerging Internet of Things (IoT) gathers increasing amounts of data that can be analyzed to gain useful insights into trends. R for Data Analysis in easy stepshas an easy-to-follow style that will appeal to anyone who wants to produce graphic visualizations to gain insights from gathered data. R for Data Analysis in easy stepsbegins by explaining core programming principles of the R programming language, which stores data in "vectors" from which simple graphs can be plotted. Next, the book describes how to create "matrices" to store and manipulate data from which graphs can be plotted to provide better insights. This book then demonstrates how to create "data frames" from imported data sets, and how to employ the "Grammar of Graphics" to produce advanced visualizations that can best illustrate useful insights from your data. R for Data Analysis in easy stepscontains separate chapters on the major features of the R programming language. There are complete example programs that demonstrate how to create Line graphs, Bar charts, Histograms, Scatter graphs, Box plots, and more. The code for each R script is listed, together with screenshots that illustrate the actual output when that script has been executed. The free, downloadable example R code is provided for clearer understanding. By the end of this book you will have gained a sound understanding of R programming, and be able to write your own scripts that can be executed to produce graphic visualizations for data analysis. You need have no previous knowledge of any programming language, so it's ideal for the newcomer to computer programming.

1

Getting started

Welcome to the exciting world of R programming. This chapter describes how to set up an R environment and demonstrates how to create a first R program.

Understanding data

Installing R

Installing RStudio

Exploring RStudio

Setting preferences

Creating an R Script

Summary

Understanding data

The term “data” refers to items of information that describe a (qualitative) status or a (quantitative) measure of magnitude. Various types of data is collected from a huge range of sources and reported for analysis to reveal pattern and trend insights:

This illustration depicts only some of the many data types that can be reported for analysis.

Data is increasingly being collected by devices that are able to report measurements for analysis via the internet (“The Cloud”). For example, devices that have temperature and humidity sensors can report measurements for instant analysis of climate conditions. The recent rapid decline in the cost of device sensors has given rise to the “Internet of Things” (IoT) that can easily and cheaply report vast amounts of data – this is often referred to as “big data”. Big data consists of extremely large data sets that can best be analyzed by computer to reveal pattern and trend insights.

Around 13 billion devices are connected to the internet today. This is predicted to grow to 50 billion by 2020.

Data analysis (a.k.a. “data analytics”) is the practice of converting collected data into information that is useful for decision-making. The collected “raw” data will, however, typically undergo two initial procedures before it can be explored for insights:

•Data processing – the raw data must be organized into a structured format. For example, it may be arranged into rows and columns in a table format for use in a spreadsheet.

•Data cleaning – the organized data must be stripped of incomplete, duplicated, and erroneous items. For, example, by the removal of duplicated rows in a spreadsheet.

“Data Science” is the study of how data can be turned into a valuable resource.

After the data has been processed and cleaned it can be explored to discover its main characteristics. This may require further data cleaning to refine the data to specific areas of interest, or may require additional data to better understand its messages. Descriptive statistics, such as average values, might be calculated to understand the data. Algorithms might be used to identify associations within the data. Data visualization might also be used to produce a graph...

Table of contents

• Cover
• Title
• Copyright
• Contents
• Preface
• 1 Getting started
• 2 Storing values
• 3 Performing operations
• 4 Testing conditions
• 5 Employing functions
• 6 Building matrices
• 7 Constructing data frames
• 8 Producing quick plots
• 9 Storytelling with data
• 10 Plotting perfection
• Back Cover