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Articles & Books From R
The easy way to get started coding and analyzing data in the R programming language Statistical Analysis with R Essentials For Dummies is your reference to all the core concepts about R—the widely used, open-source programming language and data analysis tool. This no-nonsense book gets right to the point, eliminating review material, wordy explanations, and fluff.
A deep dive into the programming language of choice for statistics and data With R All-in-One For Dummies, you get five mini-books in one, offering a complete and thorough resource on the R programming language and a road map for making sense of the sea of data we're all swimming in. Maybe you're pursuing a career in data science, maybe you're looking to infuse a little statistics know-how into your existing career, or maybe you're just R-curious.
Cheat Sheet / Updated 01-11-2023
R provides a wide array of functions to help you with your work — from simple statistics to complex analyses.This Cheat Sheet is a handy reference for Base R statistical functions, interactive applications, machine learning, databases, and images.Base R statistical functionsHere’s a selection of statistical functions that come with the standard R installation.
Cheat Sheet / Updated 01-26-2022
R provides a wide array of functions to help you with statistical analysis with R—from simple statistics to complex analyses. Several statistical functions are built into R and R packages. R statistical functions fall into several categories including central tendency and variability, relative standing, t-tests, analysis of variance and regression analysis.
Article / Updated 04-11-2018
If you’ve been working with images, animated images, and combined stationary images in R, it may be time to take the next step. This project walks you through the next step: Combine an image with an animated image.This image shows the end product — the plot of the iris data set with comedy icons Laurel and Hardy positioned in front of the plot legend.
Article / Updated 04-11-2018
Here, you learn about books and websites that help you learn more about R programming. Without further ado. . . Interacting with users If you want to delve deeper into R applications that interact with users, start with this tutorial by shiny guiding force Garrett Grolemund.For a helpful book on the subject, consider Chris Beeley’s web Application Development with R Using Shiny, 2nd Edition (Packt Publishing, 2016).
Article / Updated 04-11-2018
Try out this R project to see how one variable might affect an outcome. It’s conceivable that weather conditions could influence flight delays. How do you incorporate weather information into the assessment of delay?One nycflights13 data frame called weather provides the weather data for every day and hour at each of the three origin airports.
Article / Updated 04-11-2018
One benefit of Rattle is that it allows you to easily experiment with whatever it helps you create with R. Here’s a little project for you to try. You’ll learn more about neural networks if you can see how the network error rate decreases with the number of iterations through the training set.So the objective is to plot the error rate for the banknote.
Article / Updated 04-11-2018
To introduce k-means clustering for R programming, you start by working with the iris data frame. This is the iris data frame that’s in the base R installation. Fifty flowers in each of three iris species (setosa, versicolor, and virginica) make up the data set. The data frame columns are Sepal.Length, Sepal.Width, Petal.
Article / Updated 04-11-2018
Discovering exactly how the neurons process inputs and send messages has sometimes been the basis for winning the Nobel prize. Now, take a look at artificial neural networks to understand how machine learning works in R programming. Overview An ML neural network consists of simulated neurons, often called units, or nodes, that work with data.