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R Programming Fundamentals
R Programming Fundamentals

R Programming Fundamentals: Deal with data using various modeling techniques

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Profile Icon Kaelen Medeiros
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€18.99 per month
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (1 Ratings)
Paperback Sep 2018 206 pages 1st Edition
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Arrow left icon
Profile Icon Kaelen Medeiros
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€18.99 per month
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (1 Ratings)
Paperback Sep 2018 206 pages 1st Edition
eBook
€8.99 €22.99
Paperback
€28.99
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Free Trial
Renews at €18.99p/m
eBook
€8.99 €22.99
Paperback
€28.99
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Renews at €18.99p/m

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R Programming Fundamentals

Data Visualization and Graphics

Data visualizations are very important in data science. They are used as a part of Exploratory Data Analysis (EDA), to familiarize yourself with data, to examine the distributions of variables, to identify outliers, and to help guide data cleaning and analysis. They are also used to communicate results to a variety of audiences, from other data scientists to customers.

EDA is the general name for the process of using numerical summaries, plots, and aggregating methods to explore a dataset to familiarize yourself with its contents. It will almost certainly involve you examining the distribution of variables in the dataset, looking at missingness, deciding whether there are any outliers or errors, and generally getting a feel for what is contained in your data.

In this chapter, you'll learn about base plots, ggplot2, and will be briefly introduced...

Creating Base Plots

R can plot data without installing any additional packages. This is commonly referred to as base plotting. It is called base plotting because, like functions that come pre-installed with R in the base package, discussed in Chapter 1, Introduction to R, these plots are built into R. The graphics package comes with a download of R and enables you to plot data without installing any other packages.

To see details on the graphics package, you can search for R graphics package in a search engine of your choice or navigate to the following URL: https://stat.ethz.ch/R-manual/R-devel/library/graphics/html/00Index.html.

Base plots are often not used outside of work done for data cleaning and EDA. Many data scientists use other more aesthetically pleasing plots, such as those generated using ggplot2 or Plotly, for any plots or graphs that a customer may see. It is important...

ggplot2

ggplot2 is an incredibly popular graphics package in R. It can be installed on its own or comes as part of the Tidyverse set of packages.

Developed by Hadley Wickham and Winston Chang, ggplot2 implements the Grammar of Graphics, a pre-existing idea in statistical computing, for R. As we begin making plots with ggplot2, you may recognize the aesthetic of the plots, as ggplots are widely used in publications, data journalism, and blog posts.

When you're using ggplot2, both as you're learning how to use it and even when you're more seasoned, the official RStudio ggplot2 cheat sheet will be a resource you may want to keep close for your reference. It will not only remind you of the basics (and more advanced implementations) of how to use ggplot2, it gives suggestions for which plots to use when you have certain types of variables (for example, if you have one...

Interactive Plots

Learning to build interactive plots is not within the scope of this book, but it is likely that you will see (and admire!) them on your data science with R journey, so a few examples are laid out in this topic.

Plotly

Plotly is an R package designed to allow you to create interactive plots online. It integrates with ggplot2, which we also learned in this chapter, and can be implemented in a number of programming languages as well, including Scala, Python, and Node.js.

We can view a few of these demos on the Plotly website, such as Dashboards under Plotly Fundamentals. We can see a full dashboard of Plotly charts load, including a plot of the diamonds dataset in the upper left, a bar chart next to it, a map...

Summary

Graphing in R will be crucial in your data science work, and we have covered most of the basics here. However, graphing is one of those things where, most of the time, there are always going to be different types of graphs you haven't heard of yet and options you haven't yet selected, so it's important to know where to look for assistance and how to keep learning.

We have only covered the basics to get you off the ground in ggplot2 in this book, so you'll definitely need to use Stack Overflow and the ggplot2 official documentation on the Tidyverse website to experiment with different graphs and aesthetics. You should look into how to use scales, how to have ggplot2 calculate statistics for you, and the many other different types of plots available.

Let's press forward on to the next topic, where we'll begin to look at some data more...

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Key benefits

  • Get to grips with data cleaning methods
  • Explore statistical concepts and programming in R, including best practices
  • Build a data science project with real-world examples

Description

R Programming Fundamentals, focused on R and the R ecosystem, introduces you to the tools for working with data. You’ll start by understanding how to set up R and RStudio, followed by exploring R packages, functions, data structures, control flow, and loops. Once you have grasped the basics, you’ll move on to studying data visualization and graphics. You’ll learn how to build statistical and advanced plots using the powerful ggplot2 library. In addition to this, you’ll discover data management concepts such as factoring, pivoting, aggregating, merging, and dealing with missing values. By the end of this book, you’ll have completed an entire data science project of your own for your portfolio or blog.

Who is this book for?

R Programming Fundamentals is for you if you are an analyst who wants to grow in the field of data science and explore the latest tools.

What you will learn

  • Use basic programming concepts of R such as loading packages, arithmetic functions, data structures, and flow control
  • Import data to R from various formats such as CSV, Excel, and SQL
  • Clean data by handling missing values and standardizing fields
  • Perform univariate and bivariate analysis using ggplot2
  • Create statistical summary and advanced plots such as histograms, scatter plots, box plots, and interaction plots
  • Apply data management techniques, such as factoring, pivoting, aggregating, merging, and dealing with missing values, on example datasets

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Length: 206 pages
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Table of Contents

5 Chapters
Introduction to R Chevron down icon Chevron up icon
Data Visualization and Graphics Chevron down icon Chevron up icon
Data Management Chevron down icon Chevron up icon
Solutions Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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Unfortunately all of the "<" and ">" characters were printed as "<" and ">". This is not good for a language that uses the syntax like: area_triangle <- function(base, height).Instead you get ( example from page 10): area_triangle <- function(base, height).It's that way until the last ten pages of the book.
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