IBM Many Eyes Democratizing visualization Advanced visualization from IBM can help you gain insight from the myriad of data that your company generates. You can understand much more about the underlying numbers in your data when you can see them. For your visualization to be effective, you need technology that simplifies the visualization creation process and guidance from visualization specialists who can show you the best format for presenting your data. IBM Many Eyes, a web community that connects visualization experts, practitioners, academics and enthusiasts, offers this technology and expertise, along with ways to share and learn from others. The appeal of the Many Eyes website is that it democratizes visualization.

FAQ: Using a plugin to connect to a database How do I connect to a database by using a Stata plugin? ODBC vs. plugin The easiest way to import data from a database directly into Stata is to use the odbc command. However, there are occasions where the odbc command will not work or is not the best solution for importing data. For example, the odbc command will not work on your operating system (Solaris), there is not an ODBC driver for the database in question, or ODBC is too slow. R Programming Welcome to the R programming Wikibook This book is designed to be a practical guide to the R programming language[1]. R is free software designed for statistical computing.

What is R? During the last decade, the momentum coming from both academia and industry has lifted the R programming language to become the single most important tool for computational statistics, visualization and data science. Worldwide, millions of statisticians and data scientists use R to solve their most challenging problems in fields ranging from computational biology to quantitative marketing. R has become the most popular language for data science and an essential tool for Finance and analytics-driven companies such as Google, Facebook, and LinkedIn.

knitr: Elegant, flexible and fast dynamic report generation with R Overview The knitr package was designed to be a transparent engine for dynamic report generation with R, solve some long-standing problems in Sweave, and combine features in other add-on packages into one package (knitr ≈ Sweave + cacheSweave + pgfSweave + weaver + animation::saveLatex + R2HTML::RweaveHTML + highlight::HighlightWeaveLatex + 0.2 * brew + 0.1 * SweaveListingUtils + more). This package is developed on GitHub; for installation instructions and FAQ’s, see README. Data Repository List From Open Access Directory This list is part of the Open Access Directory. This is a list of repositories and databases for open data.

8.7 ARIMA modelling in R How does auto.arima() work ? The auto.arima() function in R uses a variation of the Hyndman and Khandakar algorithm which combines unit root tests, minimization of the AICc and MLE to obtain an ARIMA model. The algorithm follows these steps. Hyndman-Khandakar algorithm for automatic ARIMA modelling The number of differences $d$ is determined using repeated KPSS tests.The values of $p$ and $q$ are then chosen by minimizing the AICc after differencing the data $d$ times.

R Starter Kit R Starter Kit This page is intended for people who: These materials have been collected from various places on our website and have been ordered so that you can, in step-by-step fashion, develop the skills needed to conduct common analyses in R. Getting familiar with R

Julia Studio Beginner These tutorials will help you to familiarize yourself with the Julia Studio environment and the basics of the language. Hello, World! 1 Star This tutorial will walk you through creating a project in Julia Studio and have your write your first program. A GUI for R - Deducer Manual An R Graphical User Interface (GUI) for Everyone Deducer is designed to be a free easy to use alternative to proprietary data analysis software such as SPSS, JMP, and Minitab. It has a menu system to do common data manipulation and analysis tasks, and an excel-like spreadsheet in which to view and edit data frames. The goal of the project is two fold.

New York Times APIs The Article Search API Search Times articles from 1851 to today, retrieving headlines, abstracts and links to associated multimedia. The Books API Retrieve New York Times book reviews and get data from all best-seller lists. The Campaign Finance API ARIMA Modelling of Time Series Description Fit an ARIMA model to a univariate time series. Usage arima(x, order = c(0L, 0L, 0L), seasonal = list(order = c(0L, 0L, 0L), period = NA), xreg = NULL, include.mean = TRUE, transform.pars = TRUE, fixed = NULL, init = NULL, method = c("CSS-ML", "ML", "CSS"), n.cond, SSinit = c("Gardner1980", "Rossignol2011"), optim.method = "BFGS", optim.control = list(), kappa = 1e6) Time Series Analysis In the following topics, we will first review techniques used to identify patterns in time series data (such as smoothing and curve fitting techniques and autocorrelations), then we will introduce a general class of models that can be used to represent time series data and generate predictions (autoregressive and moving average models). Finally, we will review some simple but commonly used modeling and forecasting techniques based on linear regression. For more information see the topics below.

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