425 research outputs found
tourr: An R Package for Exploring Multivariate Data with Projections
No full textThis paper describes an R package which produces tours of multivariate data. The package includes functions for creating different types of tours, including grand, guided, and little tours, which project multivariate data (p-D) down to 1, 2, 3, or, more generally, d (⤠p) dimensions. The projected data can be rendered as densities or histograms, scatterplots, anaglyphs, glyphs, scatterplot matrices, parallel coordinate plots, time series or images, and viewed using an R graphics device, passed to GGobi, or saved to disk. A tour path can be stored for visualisation or replay. With this package it is possible to quickly experiment with different, and new, approaches to tours of data. This paper contains animations that can be viewed using the Adobe Acrobat PDF viewer.
lanl/NEONiso: 0.6.1 CRAN release
No full textUpdates in 0.6.1:
Exports new helper function for getting sites with water isotopes,
water_isotope_sites().
The reference_corrections vignette was blank in the previous release -
it is updated in this release (#81)
Makes select functions used internally consistent with upcoming changes
to tidyselect (h/t Hadley Wickham
Reshaping Data with the reshape Package
No full textThis paper presents the reshape package for R, which provides a common framework for many types of data reshaping and aggregation. It uses a paradigm of 'melting' and 'casting', where the data are 'melted' into a form which distinguishes measured and identifying variables, and then 'cast' into a new shape, whether it be a data frame, list, or high dimensional array. The paper includes an introduction to the conceptual framework, practical advice for melting and casting, and a case study.
ggplot2: elegant graphics for data analysis
No full textThis new edition to the classic book by ggplot2 creator Hadley Wickham highlights compatibility with knitr and RStudio. ggplot2 is a data visualization package for R that helps users create data graphics, including those that are multi-layered, with ease. With ggplot2, it's easy to: • produce handsome, publication-quality plots with automatic legends created from the plot specification • superimpose multiple layers (points, lines, maps, tiles, box plots) from different data sources with automatically adjusted common scales • add customizable smoothers that use powerful modeling capabilities of R, such as loess, linear models, generalized additive models, and robust regression • save any ggplot2 plot (or part thereof) for later modification or reuse • create custom themes that capture in-house or journal style requirements and that can easily be applied to multiple plots • approach a graph from a visual perspective, thinking about how each component of the data is represented on the final plot This book will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page. New to this edition:< • Brings the book up-to-date with ggplot2 1.0, including major updates to the theme system • New scales, stats and geoms added throughout • Additional practice exercises • A revised introduction that focuses on ggplot() instead of qplot() • Updated chapters on data and modeling using tidyr, dplyr and broom
mps9506/rATTAINS: rATTAINS 0.1.4
No full textfixes for compatibility with tidyselect and prep for purrr 1.0.0 (PR #26 @hadley).
breaking change - removed caching functionality and dependency on hoardr (archived)
R for Data Science (2e)
No full textThis E-textbook, written by statisticians Hadley Wickham, Mine Çetinkaya-Rundel, and Garrett Grolemund, provides learners with the information needed to use R to put data science into practice. The material in the textbook has been designed for those with a data science background and focuses on using R to transform data, change its structure, visualize data, and model it. Within each part, students learn how to perform a variety of R functions relating to data science, such as importing data, tidying data, R markdown, transforming data, vectors, and more. The E-book has 29 chapters split into the following parts:Part 1: Whole GamePart 2: Data VisualizationPart 3: Transforming DataPart 4: Importing DataPart 5: ProgrammingPart 6: Communicating Dat
The Split-Apply-Combine Strategy for Data Analysis
No full textMany data analysis problems involve the application of a split-apply-combine strategy, where you break up a big problem into manageable pieces, operate on each piece independently and then put all the pieces back together. This insight gives rise to a new R package that allows you to smoothly apply this strategy, without having to worry about the type of structure in which your data is stored. The paper includes two case studies showing how these insights make it easier to work with batting records for veteran baseball players and a large 3d array of spatio-temporal ozone measurements.
R for Data Science
No full textThis E-textbook, written by statisticians Garrett Grolemund and Hadly Wickham, teaches learners how to use R to transform data, change its structure, visualize it, and model the data. The material has been designed for those with a data science background and focuses on using R to put data science into practice. Within each part, students learn how to perform a variety of functions that assist with using R for data science, such as importing data, tidying data, R markdown, transforming data, vectors, and more. The E-book has thirty chapters split into the following parts:Part 1: Data ExplorationPart 2: Data WranglingPart 3: ProgrammingPart 4: Data ModelingPart 5: Communicating DataThis version of R for Data Science has been superseded by new edition, which is available to view separately
Henry Taylor Wickham and the Virginia Senate, 1888-1907
No full textThe decade of the 1890s was a complex period in the political history of Virginia. Virginia had experienced prosperity because of the development of railroads after the Civil War. Because of this contribution to statewide growth, the railroads had developed monopolistic characteristics which prompted an ambivalent response from most Virginians. It was said that during the 1890s Virginia\u27s railroads controlled the state legislature through the medium of the Democratic Party. During this period before the creation of laws dealing with political conflicts of interests, Henry Taylor Wickham represented the counties of Caroline and Hanover in the Virginia Senate. In 1937, when the Senator was eighty-eight years old, an associate suggested that Wickham should write a memoir of the Senator\u27s career in the era of railroad politics. Wickham refused to devote his declining years to a project which he felt would create unnecessary controversy. Wickham had considered it best to bury the past, and he left no personal account of his role in Democratic state politics. Wickham, a high ranking railroad official, had been in the inner sanctum of the Virginia Democracy. This thesis represents the first attempt to interpret Wickham\u27s role as a statesman during the controversial era of the 1890s. The author has drawn extensively from newspaper accounts in his research
Dates and Times Made Easy with lubridate
No full textThis paper presents the lubridate package for R, which facilitates working with dates and times. Date-times create various technical problems for the data analyst. The paper highlights these problems and offers practical advice on how to solve them using lubridate. The paper also introduces a conceptual framework for arithmetic with date-times in R.
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