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Using R for Data Management, Statistical Analysis, and Graphics by Nicholas J. Horton, Ken Kleinman

Using R for Data Management, Statistical Analysis, and Graphics Nicholas J. Horton, Ken Kleinman ebook
Publisher: CRC
ISBN: 1439827559, 9781439827550
Format: pdf
Page: 296

2011 · Leave a Comment · In rstats, Statistics. Chapter 2: Starting With R And Data Working with data from files Working with relational databases Chapter 3: Exploring Data Using Summary Statistics to Spot Problems Spotting Problems Using Graphics and Visualization Chapter 4: Managing Data Cleaning Data Sampling for 1) Collecting and parsing unstructured data: How to programmatically pull in unstructured information from a range of sources, parse it, clean it, and shape it into an analysis-ready format. The R language is widely used among statisticians and data miners for developing statistical software and data analysis. R is an open source programming language and software environment for statistical computing and graphics. Exploratory Multivariate Analysis by Example - Using R Jones O. Statistical Reporting, Linking S Output with Report Documents, Literate Programming, Managing Analyses, and Documenting Programs and Data | Reproducible Research | RR Planet | Department Reproducible Reporting Activities. Data Tactics Big Data Insights. Using R for Data Management, Statistical Analysis, and Graphics Husson F. Statistical Tables and Plots using S and Heavy use is made of the Hmisc library's summary.formula function for semi-advanced table making and conversion of selected tables to graphics. This website tries to provide a quick overview of basic data management and statistical capabilities of R for current SAS, SPSS, Stata, and Systat users. Interest in the open source statistical package R has grown over the last few years as researchers discover its powerful graphic capabilities, a suite of packages that extend its functionality, and its data import capabilities. Regular insights from industry experts in Big Data management, Cloud Computing, Social Science, Information Analysis, and IT Solutions. Within ScaleR programmers use R for data wrangling (rxDataStep), data visualization (basic viz functions for big data), and statistical analysis (it comes with a variety of scalable statistical algorithms). Upstart BI vendors Datameer and Platfora provide data wrangling and visualization tools. Startup Alpine Data Labs lets users connect to a variety of data sources, manage their data science workflows, and access a limited set of advanced algorithms. A Primer of Ecology with R Horton N.