HTTP+JSON Services in Modern Java At Airbnb, we build most of our user facing apps in Ruby on Rails, or more recently Node.js and our own Rendr framework. We also have a number of internal services, and those are mainly written in Java for stability and performance. Coming from a Ruby world, building anything in Java can feel pretty painful and boring. But thankfully there are modern Java libraries that make it easy and even fun. R Programming Welcome to the R programming Wikibook This book is designed to be a practical guide to the R programming language. R is free software designed for statistical computing. There is already great documentation for the standard R packages on the Comprehensive R Archive Network (CRAN) and many resources in specialized books, forums such as Stackoverflow and personal blogs, but all of these resources are scattered and therefore difficult to find and to compare. The aim of this Wikibook is to be the place where anyone can share his or her knowledge and tricks on R.
Getting started with the `boot' package in R for bootstrap inference The package boot has elegant and powerful support for bootstrapping. In order to use it, you have to repackage your estimation function as follows. R has very elegant and abstract notation in array indexes. Suppose there is an integer vector OBS containing the elements 2, 3, 7, i.e. that OBS <- c(2,3,7);.
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 Class notes: There is no point in waiting to take an introductory class on how to use R. Instead, we have notes of our introductory class that you can download and view.
Bootstrapping Nonparametric Bootstrapping The boot package provides extensive facilities for bootstrapping and related resampling methods. You can bootstrap a single statistic (e.g. a median), or a vector (e.g., regression weights). This section will get you started with basic nonparametric bootstrapping. The main bootstrapping function is boot( ) and has the following format: Programming in R The R languageData structuresDebuggingObject Oriented Programming: S3 ClassesObject Oriented Programming: S3 ClassesData storage, Data import, Data exportPackagesOther languages(Graphical) User InterfaceWeb interface: RpadWeb programming: RZopeWeb servicesClusters, parallel programmingMiscellaneousNumerical optimizationMiscellaneousDirty Tricks In this part, after quickly listing the main characteristics of the language, we present the basic data types, how to create them, how to explore them, how to extract pieces of them, how to modify them. We then jump to more advanced subjects (most of which can -- should? -- be omitted by first-time readers): debugging, profiling, namespaces, objects, interface with other programs, with data bases, with other languages. The R language Control structures
Python Files I/O This chapter covers all the basic I/O functions available in Python. For more functions, please refer to standard Python documentation. Printing to the Screen The simplest way to produce output is using the print statement where you can pass zero or more expressions separated by commas. This function converts the expressions you pass into a string and writes the result to standard output as follows − 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. General Introduction In the following topics, we will review techniques that are useful for analyzing time series data, that is, sequences of measurements that follow non-random orders.
Random forests - classification description Contents Introduction Overview Features of random forests Remarks How Random Forests work The oob error estimate Variable importance Gini importance Interactions Proximities Scaling Prototypes Missing values for the training set Missing values for the test set Mislabeled cases Outliers Unsupervised learning Balancing prediction error Detecting novelties A case study - microarray data Classification mode Variable importance Using important variables Variable interactions Scaling the data Prototypes Outliers A case study - dna data Missing values in the training set Missing values in the test set Mislabeled cases Case Studies for unsupervised learning Clustering microarray data Clustering dna data Clustering glass data Clustering spectral data References Introduction This section gives a brief overview of random forests and some comments about the features of the method. Overview
Introduction to R for Data Mining For a quick start: Find a way of orienting yourself in the open source R worldHave a definite application area in mindSet an initial goal of doing something useful and then build on it In this webinar, we focus on data mining as the application area and show how anyone with just a basic knowledge of elementary data mining techniques can become immediately productive in R.
MongoDB – State of the R Naturally there are two reasons for why you need to access MongoDB from R: MongoDB is already used for whatever reason and you want to analyze the data stored therein You decide you want store your data in MongoDB instead of using native R technology like data.table or data.frame In-memory data storage like data.table is very fast especially for numerical data, provided the data actually fits into your RAM – but even then MongoDB comes along with a bag of goodies making it a tempting choice for a number of use cases: Flexible schema-less data structures spatial and textual indexing spatial queries persistence of data easily accessible from other languages and systems In case you would like to learn more about MongoDB then I have good news for you – MongoDB Inc. provides a number of very well made online courses catering to various languages.
Applied Time Series Analysis [Home] [Lectures] [Assignments] [Exams] Introduction Model-based forecasting methods; autoregressive and moving average models; ARIMA, ARMAX, ARCH, and state-space models; estimation, forecasting and model validation; missing data; irregularly spaced time series; parametric and nonparametric bootstrap methods for time series; multiresolution analysis of spatial and time-series signals; and time-varying models and wavelets.