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Producing Simple Graphs with R

Producing Simple Graphs with R
The following is an introduction for producing simple graphs with the R Programming Language. Each example builds on the previous one. The areas in bold indicate new lines which were added from the previous example. The graph produced by each example is shown on the right. I hope you'll find this useful. Feel free to provide me any feedback at Email:. Jump to a section:

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Forecasting: principles and practice Welcome to our online textbook on forecasting. This textbook is intended to provide a comprehensive introduction to forecasting methods and to present enough information about each method for readers to be able to use them sensibly. We don’t attempt to give a thorough discussion of the theoretical details behind each method, although the references at the end of each chapter will fill in many of those details. The book is written for three audiences: (1) people finding themselves doing forecasting in business when they may not have had any formal training in the area; (2) undergraduate students studying business; (3) MBA students doing a forecasting elective. We use it ourselves for a second-year subject for students undertaking a Bachelor of Commerce degree at Monash University, Australia.

R tips pages This page introduces the basics of working with data sets having multiple variables, often of several types. The focus here is on data frames, which are the most convenient data objects in R. Matrices and lists are also useful data objects, and these are introduced briefly at the end. Manage data Start with a spreadsheet program An Introduction to R Table of Contents This is an introduction to R (“GNU S”), a language and environment for statistical computing and graphics. R is similar to the award-winning1 S system, which was developed at Bell Laboratories by John Chambers et al.

Lecture: Graphing in R SPLUS/R Library: Notes From Roger's Seminars on R Graphing in R This page contains the lecture notes from Graphing in R which was a part of a series of seminars given by Roger Peng at the Statistical Department at UCLA during the summer of 2002. We thank Roger Peng for his permission to adapt and distribute this page via our web site. 1. Plotting Devices2. Category:Created with R From Wikimedia Commons, the free media repository If you : Subcategories This category has the following 2 subcategories, out of 2 total. Mann-Whitney-Wilcoxon Test Two data samples are independent if they come from distinct populations and the samples do not affect each other. Using the Mann-Whitney-Wilcoxon Test, we can decide whether the population distributions are identical without assuming them to follow the normal distribution. Example In the data frame column mpg of the data set mtcars, there are gas mileage data of various 1974 U.S. automobiles. > mtcars$mpg [1] 21.0 21.0 22.8 21.4 18.7 ...

Statistical Learning About This Course This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). Crossfilter Fast Multidimensional Filtering for Coordinated Views Crossfilter is a JavaScript library for exploring large multivariate datasets in the browser. Crossfilter supports extremely fast (<30ms) interaction with coordinated views, even with datasets containing a million or more records; we built it to power analytics for Square Register, allowing merchants to slice and dice their payment history fluidly. Since most interactions only involve a single dimension, and then only small adjustments are made to the filter values, incremental filtering and reducing is significantly faster than starting from scratch. Crossfilter uses sorted indexes (and a few bit-twiddling hacks) to make this possible, dramatically increasing the perfor­mance of live histograms and top-K lists. For more details on how Crossfilter works, see the API reference.

Software and Programmer Efficiency Research Group A plot can be themed by adding a theme. ggplot2 provides two built-in themes: theme_grey() - the default theme, with a grey background theme_bw() - a theme with a white background To be more precise, ggplot2 provides functions that create a theme. These functions can be used to add a specific theme to a plot: ggplot() + ... + theme_bw() Prototype: More Web-Friendly Visualizations in R « TechPolicy.ca I've spent some more time thinking about how best to put together the package for creating web-friendly, interactive data visualizations in R. I have a pretty substantial JavaScript package that does a lot of basic visualizations now, and it's really exciting to see where this is going. With this in mind, I'm releasing a new version of the R package prototype I keep discussing in this blog. A few examples of the visualizations are shown below, along with the necessary R code to get them to display.

Data Mining With R: TIME SERIES using R FITTING ARIMA MODEL in RAuto Regressive Integrated Moving Average ( ARIMA) model is generalisation of Auto Regressive Moving Average Model (ARMA) and used to predict future points in Time series.But what is time series , Acc to google "A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average." and more in a general way it is a series of values of a quantity obtained at successive times, often with equal intervals between them.ARIMA models are defined for stationary time series , stationary time series is one whose mean, variance, autocorrelation are all constant over time. But for checking that the time series is stationary or not , we have several statistical tests for them namely. 1) For the Box.test, if p-value < 0.05 => stationary 2) For the adf.test, if p-value < 0.05 => stationary

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