From fc49f48c6ba307503bae2378dcc7cc12a45273be Mon Sep 17 00:00:00 2001 From: Christoph Glur Date: Tue, 8 Sep 2015 20:22:20 +0200 Subject: [PATCH 1/3] removed downloads for vignettes, to make build faster --- DESCRIPTION | 5 +- inst/extdata/flare.json | 380 +++++++++++++++++++++++++++++++++ inst/extdata/useR15.yaml | 425 ------------------------------------- vignettes/applications.Rmd | 12 +- vignettes/data.tree.Rmd | 6 +- 5 files changed, 388 insertions(+), 440 deletions(-) create mode 100644 inst/extdata/flare.json delete mode 100644 inst/extdata/useR15.yaml diff --git a/DESCRIPTION b/DESCRIPTION index df36e63..1a030e2 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,8 +1,8 @@ Package: data.tree Type: Package Title: General Purpose Hierarchical Data Structure -Version: 0.2.0-3 -Date: 2015-09-07 +Version: 0.2.0-4 +Date: 2015-09-08 Author: Christoph Glur Maintainer: Christoph Glur VignetteBuilder: knitr @@ -18,7 +18,6 @@ Suggests: DiagrammeR, igraph, treemap, - curl, doParallel, foreach, htmlwidgets diff --git a/inst/extdata/flare.json b/inst/extdata/flare.json new file mode 100644 index 0000000..a05a948 --- /dev/null +++ b/inst/extdata/flare.json @@ -0,0 +1,380 @@ +{ + "name": "flare", + "children": [ + { + "name": "analytics", + "children": [ + { + "name": "cluster", + "children": [ + {"name": "AgglomerativeCluster", "size": 3938}, + {"name": "CommunityStructure", "size": 3812}, + {"name": "HierarchicalCluster", "size": 6714}, + {"name": "MergeEdge", "size": 743} + ] + }, + { + "name": "graph", + "children": [ + {"name": "BetweennessCentrality", "size": 3534}, + {"name": "LinkDistance", "size": 5731}, + {"name": "MaxFlowMinCut", "size": 7840}, + {"name": "ShortestPaths", "size": 5914}, + {"name": "SpanningTree", "size": 3416} + ] + }, + { + "name": "optimization", + "children": [ + {"name": "AspectRatioBanker", "size": 7074} + ] + } + ] + }, + { + "name": "animate", + "children": [ + {"name": "Easing", "size": 17010}, + {"name": "FunctionSequence", "size": 5842}, + { + "name": "interpolate", + "children": [ + {"name": "ArrayInterpolator", "size": 1983}, + {"name": "ColorInterpolator", "size": 2047}, + {"name": "DateInterpolator", "size": 1375}, + {"name": "Interpolator", "size": 8746}, + {"name": "MatrixInterpolator", "size": 2202}, + {"name": "NumberInterpolator", "size": 1382}, + {"name": "ObjectInterpolator", "size": 1629}, + {"name": "PointInterpolator", "size": 1675}, + {"name": "RectangleInterpolator", "size": 2042} + ] + }, + {"name": "ISchedulable", "size": 1041}, + {"name": "Parallel", "size": 5176}, + {"name": "Pause", "size": 449}, + {"name": "Scheduler", "size": 5593}, + {"name": "Sequence", "size": 5534}, + {"name": "Transition", "size": 9201}, + {"name": "Transitioner", "size": 19975}, + {"name": "TransitionEvent", "size": 1116}, + {"name": "Tween", "size": 6006} + ] + }, + { + "name": "data", + "children": [ + { + "name": "converters", + "children": [ + {"name": "Converters", "size": 721}, + {"name": "DelimitedTextConverter", "size": 4294}, + {"name": "GraphMLConverter", "size": 9800}, + {"name": "IDataConverter", "size": 1314}, + {"name": "JSONConverter", "size": 2220} + ] + }, + {"name": "DataField", "size": 1759}, + {"name": "DataSchema", "size": 2165}, + {"name": "DataSet", "size": 586}, + {"name": "DataSource", "size": 3331}, + {"name": "DataTable", "size": 772}, + {"name": "DataUtil", "size": 3322} + ] + }, + { + "name": "display", + "children": [ + {"name": "DirtySprite", "size": 8833}, + {"name": "LineSprite", "size": 1732}, + {"name": "RectSprite", "size": 3623}, + {"name": "TextSprite", "size": 10066} + ] + }, + { + "name": "flex", + "children": [ + {"name": "FlareVis", "size": 4116} + ] + }, + { + "name": "physics", + "children": [ + {"name": "DragForce", "size": 1082}, + {"name": "GravityForce", "size": 1336}, + {"name": "IForce", "size": 319}, + {"name": "NBodyForce", "size": 10498}, + {"name": "Particle", "size": 2822}, + {"name": "Simulation", "size": 9983}, + {"name": "Spring", "size": 2213}, + {"name": "SpringForce", "size": 1681} + ] + }, + { + "name": "query", + "children": [ + {"name": "AggregateExpression", "size": 1616}, + {"name": "And", "size": 1027}, + {"name": "Arithmetic", "size": 3891}, + {"name": "Average", "size": 891}, + {"name": "BinaryExpression", "size": 2893}, + {"name": "Comparison", "size": 5103}, + {"name": "CompositeExpression", "size": 3677}, + {"name": "Count", "size": 781}, + {"name": "DateUtil", "size": 4141}, + {"name": "Distinct", "size": 933}, + {"name": "Expression", "size": 5130}, + {"name": "ExpressionIterator", "size": 3617}, + {"name": "Fn", "size": 3240}, + {"name": "If", "size": 2732}, + {"name": "IsA", "size": 2039}, + {"name": "Literal", "size": 1214}, + {"name": "Match", "size": 3748}, + {"name": "Maximum", "size": 843}, + { + "name": "methods", + "children": [ + {"name": "add", "size": 593}, + {"name": "and", "size": 330}, + {"name": "average", "size": 287}, + {"name": "count", "size": 277}, + {"name": "distinct", "size": 292}, + {"name": "div", "size": 595}, + {"name": "eq", "size": 594}, + {"name": "fn", "size": 460}, + {"name": "gt", "size": 603}, + {"name": "gte", "size": 625}, + {"name": "iff", "size": 748}, + {"name": "isa", "size": 461}, + {"name": "lt", "size": 597}, + {"name": "lte", "size": 619}, + {"name": "max", "size": 283}, + {"name": "min", "size": 283}, + {"name": "mod", "size": 591}, + {"name": "mul", "size": 603}, + {"name": "neq", "size": 599}, + {"name": "not", "size": 386}, + {"name": "or", "size": 323}, + {"name": "orderby", "size": 307}, + {"name": "range", "size": 772}, + {"name": "select", "size": 296}, + {"name": "stddev", "size": 363}, + {"name": "sub", "size": 600}, + {"name": "sum", "size": 280}, + {"name": "update", "size": 307}, + {"name": "variance", "size": 335}, + {"name": "where", "size": 299}, + {"name": "xor", "size": 354}, + {"name": "_", "size": 264} + ] + }, + {"name": "Minimum", "size": 843}, + {"name": "Not", "size": 1554}, + {"name": "Or", "size": 970}, + {"name": "Query", "size": 13896}, + {"name": "Range", "size": 1594}, + {"name": "StringUtil", "size": 4130}, + {"name": "Sum", "size": 791}, + {"name": "Variable", "size": 1124}, + {"name": "Variance", "size": 1876}, + {"name": "Xor", "size": 1101} + ] + }, + { + "name": "scale", + "children": [ + {"name": "IScaleMap", "size": 2105}, + {"name": "LinearScale", "size": 1316}, + {"name": "LogScale", "size": 3151}, + {"name": "OrdinalScale", "size": 3770}, + {"name": "QuantileScale", "size": 2435}, + {"name": "QuantitativeScale", "size": 4839}, + {"name": "RootScale", "size": 1756}, + {"name": "Scale", "size": 4268}, + {"name": "ScaleType", "size": 1821}, + {"name": "TimeScale", "size": 5833} + ] + }, + { + "name": "util", + "children": [ + {"name": "Arrays", "size": 8258}, + {"name": "Colors", "size": 10001}, + {"name": "Dates", "size": 8217}, + {"name": "Displays", "size": 12555}, + {"name": "Filter", "size": 2324}, + {"name": "Geometry", "size": 10993}, + { + "name": "heap", + "children": [ + {"name": "FibonacciHeap", "size": 9354}, + {"name": "HeapNode", "size": 1233} + ] + }, + {"name": "IEvaluable", "size": 335}, + {"name": "IPredicate", "size": 383}, + {"name": "IValueProxy", "size": 874}, + { + "name": "math", + "children": [ + {"name": "DenseMatrix", "size": 3165}, + {"name": "IMatrix", "size": 2815}, + {"name": "SparseMatrix", "size": 3366} + ] + }, + {"name": "Maths", "size": 17705}, + {"name": "Orientation", "size": 1486}, + { + "name": "palette", + "children": [ + {"name": "ColorPalette", "size": 6367}, + {"name": "Palette", "size": 1229}, + {"name": "ShapePalette", "size": 2059}, + {"name": "SizePalette", "size": 2291} + ] + }, + {"name": "Property", "size": 5559}, + {"name": "Shapes", "size": 19118}, + {"name": "Sort", "size": 6887}, + {"name": "Stats", "size": 6557}, + {"name": "Strings", "size": 22026} + ] + }, + { + "name": "vis", + "children": [ + { + "name": "axis", + "children": [ + {"name": "Axes", "size": 1302}, + {"name": "Axis", "size": 24593}, + {"name": "AxisGridLine", "size": 652}, + {"name": "AxisLabel", "size": 636}, + {"name": "CartesianAxes", "size": 6703} + ] + }, + { + "name": "controls", + "children": [ + {"name": "AnchorControl", "size": 2138}, + {"name": "ClickControl", "size": 3824}, + {"name": "Control", "size": 1353}, + {"name": "ControlList", "size": 4665}, + {"name": "DragControl", "size": 2649}, + {"name": "ExpandControl", "size": 2832}, + {"name": "HoverControl", "size": 4896}, + {"name": "IControl", "size": 763}, + {"name": "PanZoomControl", "size": 5222}, + {"name": "SelectionControl", "size": 7862}, + {"name": "TooltipControl", "size": 8435} + ] + }, + { + "name": "data", + "children": [ + {"name": "Data", "size": 20544}, + {"name": "DataList", "size": 19788}, + {"name": "DataSprite", "size": 10349}, + {"name": "EdgeSprite", "size": 3301}, + {"name": "NodeSprite", "size": 19382}, + { + "name": "render", + "children": [ + {"name": "ArrowType", "size": 698}, + {"name": "EdgeRenderer", "size": 5569}, + {"name": "IRenderer", "size": 353}, + {"name": "ShapeRenderer", "size": 2247} + ] + }, + {"name": "ScaleBinding", "size": 11275}, + {"name": "Tree", "size": 7147}, + {"name": "TreeBuilder", "size": 9930} + ] + }, + { + "name": "events", + "children": [ + {"name": "DataEvent", "size": 2313}, + {"name": "SelectionEvent", "size": 1880}, + {"name": "TooltipEvent", "size": 1701}, + {"name": "VisualizationEvent", "size": 1117} + ] + }, + { + "name": "legend", + "children": [ + {"name": "Legend", "size": 20859}, + {"name": "LegendItem", "size": 4614}, + {"name": "LegendRange", "size": 10530} + ] + }, + { + "name": "operator", + "children": [ + { + "name": "distortion", + "children": [ + {"name": "BifocalDistortion", "size": 4461}, + {"name": "Distortion", "size": 6314}, + {"name": "FisheyeDistortion", "size": 3444} + ] + }, + { + "name": "encoder", + "children": [ + {"name": "ColorEncoder", "size": 3179}, + {"name": "Encoder", "size": 4060}, + {"name": "PropertyEncoder", "size": 4138}, + {"name": "ShapeEncoder", "size": 1690}, + {"name": "SizeEncoder", "size": 1830} + ] + }, + { + "name": "filter", + "children": [ + {"name": "FisheyeTreeFilter", "size": 5219}, + {"name": "GraphDistanceFilter", "size": 3165}, + {"name": "VisibilityFilter", "size": 3509} + ] + }, + {"name": "IOperator", "size": 1286}, + { + "name": "label", + "children": [ + {"name": "Labeler", "size": 9956}, + {"name": "RadialLabeler", "size": 3899}, + {"name": "StackedAreaLabeler", "size": 3202} + ] + }, + { + "name": "layout", + "children": [ + {"name": "AxisLayout", "size": 6725}, + {"name": "BundledEdgeRouter", "size": 3727}, + {"name": "CircleLayout", "size": 9317}, + {"name": "CirclePackingLayout", "size": 12003}, + {"name": "DendrogramLayout", "size": 4853}, + {"name": "ForceDirectedLayout", "size": 8411}, + {"name": "IcicleTreeLayout", "size": 4864}, + {"name": "IndentedTreeLayout", "size": 3174}, + {"name": "Layout", "size": 7881}, + {"name": "NodeLinkTreeLayout", "size": 12870}, + {"name": "PieLayout", "size": 2728}, + {"name": "RadialTreeLayout", "size": 12348}, + {"name": "RandomLayout", "size": 870}, + {"name": "StackedAreaLayout", "size": 9121}, + {"name": "TreeMapLayout", "size": 9191} + ] + }, + {"name": "Operator", "size": 2490}, + {"name": "OperatorList", "size": 5248}, + {"name": "OperatorSequence", "size": 4190}, + {"name": "OperatorSwitch", "size": 2581}, + {"name": "SortOperator", "size": 2023} + ] + }, + {"name": "Visualization", "size": 16540} + ] + } + ] +} \ No newline at end of file diff --git a/inst/extdata/useR15.yaml b/inst/extdata/useR15.yaml deleted file mode 100644 index 29df7da..0000000 --- a/inst/extdata/useR15.yaml +++ /dev/null @@ -1,425 +0,0 @@ -name: useR -children: - Session 1: - end: 01.07.2015 12:00 - sessionName: Interfacing - start: 01.07.2015 10:30 - children: - Aalborghallen: - seats: 790 - children: - Federico Marini: - presentation: 'flowcatchR: A user-friendly workflow solution for the analysis - of time-lapse cell flow imaging data' - Jonathan Clayden: - presentation: Image processing and alignment with RNiftyReg and mmand - Carel F. W. Peeters: - presentation: 'rags2ridges: Ridge estimation and graphical modeling for - high-dimensional precision matrices' - Henrik Tobias Madsen: - presentation: 'dgRaph: Discrete factor graphs in R' - Gæstesalen: - seats: 149 - children: - Costas Varsos: - presentation: Optimized R functions for analysis of ecological community - data using the R virtual laboratory (Rvlab) - David L Miller: - presentation: Building ecological models bit-by-bit - Andrew Dolman: - presentation: 'Simulating ecological microcosms with systems of differential - equations: tools for the scientific, technical and communication challenges' - Marcel Austenfeld: - presentation: A Graphical User Interface for R in an Integrated Development - Environment for Ecological Modeling, Scientific Image Analysis and Statistical - Analysis - Musiksalen: - seats: 160 - children: - Gergely Daroczi: - presentation: 'fbRads: Analyzing and managing Facebook ads from R' - Peter Meißner: - presentation: Web scraping with R - A fast track overview. - Antonio Rivero Ostoic: - presentation: 'multiplex: Analysis of Multiple Social Networks with Algebra' - Gabor Csardi: - presentation: What's new in igraph and networks - Det lille Teater: - seats: 224 - children: - Karthik Ram: - presentation: 'rOpenSci: A suite of reproducible research tools in R' - Michael Lawrence: - presentation: Enhancing reproducibility and collaboration via management - of R package cohorts - Joshua R. Polanin & Emily A. Hennessy: - presentation: A Review of Meta-Analysis Packages in R - David Smith: - presentation: Simple reproducibility with the checkpoint package - Radiosalen: - seats: 216 - children: - Kasper D. Hansen: - presentation: Some lessons relevant to including external libraries in - your R package - Karl Millar: - presentation: 'CXXR: Modernizing the R Interpreter' - Matt P. Dziubinski: - presentation: Naturally Sweet Rcpp with Modern C++ and Boost - Dan Putler: - presentation: Linking R to the Spark MLlib Machine Learning Library - Session 2: - end: 01.07.2015 15:00 - sessionName: Computational Performance - start: 01.07.2015 13:30 - children: - Aalborghallen: - seats: 790 - children: - Przemyslaw Biecek: - presentation: 'archivist: Tools for Storing, Restoring and Searching for - R Objects' - Joseph B. Rickert: - presentation: R User Groups - Richard M. Heiberger: - presentation: 'Computational Precision and Floating-Point Arithmetic: - A Teacher''s Guide to Answering FAQ 7.31' - Rasmus Bååth: - presentation: Tiny Data, Approximate Bayesian Computation and the Socks - of Karl Broman - Gæstesalen: - seats: 149 - children: - Johannes Breidenbach: - presentation: Using R for small area estimation in the Norwegian National - Forest Inventory - Ivan Kasanický: - presentation: Using R for natural gas market balancing in the Czech republic - Jakob W. Messner: - presentation: Heteroscedastic censored and truncated regression for weather - forecasting - Helle Sørensen: - presentation: Multinomial functional regression with application to lameness - detection for horses - Musiksalen: - seats: 160 - children: - Anders Ellern Bilgrau: - presentation: Unsupervised Clustering and Meta-Analysis using Gaussian - Mixture Copula Models - Claudia Beleites: - presentation: 'Hierarchical Cluster Analysis of hyperspectral Raman images: - a new point of view leads to 10000fold speedup' - Silvia Liverani: - presentation: Dirichlet process Bayesian clustering with the R package - PReMiuM - Thomas Jagger: - presentation: Examining the Environmental Characteristics of Tornado Outbreaks - in the United States using Spatial Clustering - Det lille Teater: - seats: 224 - children: - Filip Schouwenaars: - presentation: 'Taking testing to another level: testwhat' - Tony Fischetti: - presentation: 'Failing fast and early: assertive/defensive programming - for R data analysis pipelines' - Hadley Wickham: - presentation: Getting your data into R - Christoph Glur: - presentation: A better way to manage hierarchical data - Indrajit Roy, Michael Lawrence: - presentation: A proposal for distributed data-structures in R - Radiosalen: - seats: 216 - children: - E. James Harner: - presentation: Running R+Hadoop using Docker Containers - Matt P. Dziubinski: - presentation: 'Algorithmic Differentiation for Extremum Estimation: An - Introduction Using RcppEigen' - Kirill Müller: - presentation: Improving computational performance with algorithm engineering - Helena Kotthaus: - presentation: 'Performance Analysis for Parallel R Programs: Towards Efficient - Ressource Utilization' - David Scott: - presentation: Refactoring the xtable Package - Session 3: - end: 01.07.2015 17:30 - sessionName: Databases - start: 01.07.2015 16:00 - children: - Aalborghallen: - seats: 790 - children: - Friedrich Schuster: - presentation: Coding for the enterprise server - what does it mean for - you? - Lukas Stadler: - presentation: R as a citizen in a polyglot world - the promise of the - Truffle framework - Tobias Verbeke: - presentation: Architect. An IDE for Data Science and R - Balasubramanian Narasimhan: - presentation: Distributed computing with R - Gæstesalen: - seats: 149 - children: - Peter Baker: - presentation: 'Statistical consulting using R: a DRY approach from the - Australian outback' - Stefan Milton Bache: - presentation: Using R in Production - Giuseppe Bruno: - presentation: Hedging and Risk Management of CDOs portfolio with R - Jim Porzak: - presentation: Data Driven Customer Segmentation with R - Musiksalen: - seats: 160 - children: - Ian Cook: - presentation: Bringing Geospatial Tasks into the Mainstream of Business - Analytics - Jin Li: - presentation: Novel hybrid spatial predictive methods of machine learning - and geostatistics with applications to terrestrial and marine environments - in Australia - Matthias Eckardt: - presentation: Graphical Modelling of Multivariate Spatial Point Patterns - Virgilio Gomez-Rubio: - presentation: Spatial Econometrics Models with R-INLA - Det lille Teater: - seats: 224 - children: - Sebastian Meyer: - presentation: Spatio-Temporal Analysis of Epidemic Phenomena Using the - R Package surveillance - Willem Ligtenberg: - presentation: Rango - Databases made easy - Hannes Mühleisen: - presentation: Ad-Hoc User-Defined Functions for MonetDB with R - Mateusz Zoltak: - presentation: 'R database connectivity: what did we leave behind?' - Radiosalen: - seats: 216 - children: - Jeroen Ooms: - presentation: jsonlite and mongolite - Michael Wurst: - presentation: Using R Efficiently with Large Databases - Session 4: - end: 02.07.2015 11:00 - sessionName: Interactive graphics - start: 02.07.2015 10:30 - children: - Radiosalen: - seats: 216 - children: - A. Jonathan R. Godfrey: - presentation: While my base R gently weeps - Amitai Golub: - presentation: Rapid Deployment of Automatic Scoring Models to Hadoop Production - Systems - Monika Huhn: - presentation: D3 and R Shiny - Making your graphs come to life - Michael Sachs: - presentation: Interactive Graphics with ggplot2 and gridSVG - Joe Cheng: - presentation: Interactive visualization using htmlwidgets and Shiny - Adrian Waddell: - presentation: Interactive Data Visualization using the Loon package - Wayne Oldford: - presentation: New interactive visualization tools for exploring high dimensional - data in R - Aalborghallen: - seats: 790 - children: - Matt Dowle: - presentation: Fast, stable and scalable true radix sorting - Arunkumar Srinivasan: - presentation: Fast, flexible and memory efficient data manipulation using - data.table - Gæstesalen: - seats: 149 - children: - Marvin Steijaert: - presentation: 'Phenotypic deconvolution: the next frontier in pharma' - Lara Lusa: - presentation: 'medplot: A Web Application for Dynamic Summary and Analysis - of Longitudinal Medical Data Based on R and shiny' - Paul Metcalfe: - presentation: Using R and free software to improve the delivery of life - changing medicine to patients - Heidi Seibold: - presentation: Stratified medicine using the partykit package - Musiksalen: - seats: 160 - children: - Han Lin Shang: - presentation: The ilc package - Andrew Bray: - presentation: Approximately Exact Calculations for Linear Mixed Models - Alexandra Kuznetsova: - presentation: 'Shiny application for analyzing consumer preference and - sensory data in a mixed effects model framework: introducing SensMixed - package' - Chenjerai Kathy Mutambanengwe: - presentation: Spatial regression of quantiles based on parametric distributions - Det lille Teater: - seats: 224 - children: - Helen Ogden: - presentation: 'glmmsr: fitting GLMMs with sequential reduction' - Michael Sannella: - presentation: 'Supporting the Rapi C-language API in an R-compatible engine ' - Woo J. Jung: - presentation: 'Enabling R for Big Data with PL/R and PivotalR: Real World - Examples on Hadoop & MPP Databases' - Ron Pearson: - presentation: The DataRobot R Package - Lou Bajuk-Yorgan: - presentation: Applying the R Language in Streaming Applications and Business - Intelligence - Session 5: - end: 02.07.2015 14:30 - sessionName: Visualisation 1 - start: 02.07.2015 13:00 - children: - Aalborghallen: - seats: 790 - children: - Aimee Gott: - presentation: Formalising R Development - ValidR Enterprise - Christoph Best: - presentation: Integrating R with the Go programming language using interprocess - communication - Jennifer Bryan: - presentation: Fun times with R and Google Sheets - Jonathan Digby-North: - presentation: A Comparative Study of Complex Estimation Software - Oliver Keyes: - presentation: 'Software Standards in the R Community: An Analysis' - Gæstesalen: - seats: 149 - children: - Miranda Y Mortlock: - presentation: SWOT analysis on using R for online training - Eric Hare: - presentation: Manipulation of Discrete Random Variables in R with discreteRV - Matthias Gehrke: - presentation: 'Teaching R in heterogeneous settings: Lessons learned' - Chris Wild: - presentation: Interactive applications written in R to accelerate statistical - learning - James Curran: - presentation: Classroom experiments - Musiksalen: - seats: 160 - children: - Thomas Kiefer: - presentation: 'TAM: An R Package for Item Response Modelling' - Genaro Sucarrat: - presentation: 'gets: General-to-Specific (GETS) Modelling' - Thouvenot Vincent: - presentation: 'R Package CASA: Component Automatic Selection in Additive - models' - Christian Ritz: - presentation: Dose-response analysis using R revisited - Kaylea Haynes: - presentation: Changepoints over a Range of Penalties using the changepoint - package - Det lille Teater: - seats: 224 - children: - Neda Daneshgar, Majid Sarmad: - presentation: Word Alignment tools in R - Markus Loecher: - presentation: Rapid detection of spatiotemporal clusters - Arash Fard, Vishrut Gupta: - presentation: Scalable distributed random-forest in R - Marie Chavent: - presentation: 'Multivariate analysis of mixed data: The PCAmixdata R package' - Natalia da Silva: - presentation: PPforest - Radiosalen: - seats: 216 - children: - Katrin Grimm: - presentation: Reordering and selecting continuous variables for scatterplot - matrices - Kirsten Van Hoorde: - presentation: R-package to assess and visualize the calibration of multiclass - risk predictions - Martijn Tennekes: - presentation: 'tmap: creating thematic maps in a flexible way' - Tal Galili: - presentation: The dendextend R package for manipulation of dendograms,visualization - and comparison - Session 6: - end: 02.07.2015 17:30 - sessionName: Visualisation 2 - start: 02.07.2015 16:00 - children: - Aalborghallen: - seats: 790 - children: - Gabor Csardi: - presentation: The METACRAN experiment - Pedro J. Aphalo: - presentation: Using R in photobiology - Sven Jesper Knudsen: - presentation: Industrial Big Data Analytics for Wind Turbines - Andrie de Vries: - presentation: The Network Structure of R Packages - Gæstesalen: - seats: 149 - children: - Gail Potter: - presentation: Web Application Teaching Tools for Statistics Using Shiny - and R - an online: - presentation: Teaching R in - Jonathan Cornelissen: - presentation: class - Colin Rundel: - presentation: Teaching R using the github ecosystem - Musiksalen: - seats: 160 - children: - Mine Cetinkaya-Rundel: - presentation: Using R, RStudio, and Docker for introductory statistics - teaching - Christoph Sax: - presentation: 'seasonal: An X-13 interface for seasonal adjustment' - Sören Möller: - presentation: Estimating the Linfoot correlation in R - Alexander Kowarik: - presentation: Seasonal Adjustment with the R packages x12 and x12GUI - Det lille Teater: - seats: 224 - children: - Il Do Ha: - presentation: 'frailtyHL: R package for variable selection in general - frailty models for various survival data' - Jan Wijffels: - presentation: Massive Online Data Stream Mining using R and MOA - Søren Havelund Welling: - presentation: 'forestFloor: a package to visualize and comprehend the - full curvature of random forests' - Douglas Mason: - presentation: Machine Learning for Internal Product Measurement - Erin LeDell: - presentation: h2oEnsemble for Scalable Ensemble Learning in R - Radiosalen: - seats: 216 - children: - Thomas Levine: - presentation: Plotting data as music videos in R - Eric Bonnet: - presentation: NaviCell Web Service for Network-based Data Visualization - Laure Cougnaud: - presentation: Easy visualizations of high-dimensional genomic data - Paul Murrell: - presentation: The gridGraphics Package - diff --git a/vignettes/applications.Rmd b/vignettes/applications.Rmd index 7015e7e..9b5f42e 100644 --- a/vignettes/applications.Rmd +++ b/vignettes/applications.Rmd @@ -722,21 +722,17 @@ You'll learn how to convert a complex JSON into a data.frame, and how to use thi The data represents the [Flare](http://flare.prefuse.org/) class hierarchy, which is a library for creating visualizations. The JSON is long, deeply nested, and complicated. ```{r} -library(jsonlite) -library(curl) - -con <- curl("http://bl.ocks.org/mbostock/raw/4063269/flare.json") -flareJSON <- paste(readLines(con, warn = FALSE), collapse = "\n") +flarePath <- '../inst/extdata/flare.json' +flareJSON <- readChar(flarePath, file.info(flarePath)$size) cat(substr(flareJSON, 1, 300)) -close(con) ``` So, let's convert it into a data.tree structure: ```{r} - -flareLoL <- fromJSON("http://bl.ocks.org/mbostock/raw/4063269/flare.json", +library(jsonlite) +flareLoL <- fromJSON(file(flarePath), simplifyDataFrame = FALSE ) diff --git a/vignettes/data.tree.Rmd b/vignettes/data.tree.Rmd index 746a539..c034bb1 100644 --- a/vignettes/data.tree.Rmd +++ b/vignettes/data.tree.Rmd @@ -454,11 +454,9 @@ simpleNetwork(acmeNetwork[-3], fontSize = 12) Another example, which at the same time shows conversion from csv: + ```{r} -library(curl) -#load all the presentations from useR15 in Aalborg -url <- curl("https://raw.github.com/gluc/useR15/master/00_data/useR15.csv") -useRdf <- read.csv(url) +useRdf <- read.csv('../inst/extdata/useR15.csv', stringsAsFactors = FALSE) #define the hierarchy (Session/Room/Speaker) useRdf$pathString <- paste("useR", useRdf$session, useRdf$room, useRdf$speaker, sep="|") #convert to Node From 566ac1ea59f9ef693bfadf330ee6e96a82b46513 Mon Sep 17 00:00:00 2001 From: Christoph Glur Date: Tue, 8 Sep 2015 22:10:00 +0200 Subject: [PATCH 2/3] adjusted version --- DESCRIPTION | 3 ++- data.tree.Rproj | 1 + 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 1a030e2..b7aba6b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: data.tree Type: Package Title: General Purpose Hierarchical Data Structure -Version: 0.2.0-4 +Version: 0.2.1 Date: 2015-09-08 Author: Christoph Glur Maintainer: Christoph Glur @@ -9,6 +9,7 @@ VignetteBuilder: knitr Imports: R6, stringr, methods Suggests: + graphics, testthat, knitr, ape, diff --git a/data.tree.Rproj b/data.tree.Rproj index a52ec59..6ccebc5 100644 --- a/data.tree.Rproj +++ b/data.tree.Rproj @@ -13,6 +13,7 @@ RnwWeave: knitr LaTeX: pdfLaTeX BuildType: Package +PackageUseDevtools: Yes PackageInstallArgs: --no-multiarch --with-keep.source PackageBuildArgs: --resave-data PackageBuildBinaryArgs: --resave-data From fa378a063dd3e2e44d83555c29b4e8a985f47202 Mon Sep 17 00:00:00 2001 From: Christoph Glur Date: Tue, 8 Sep 2015 22:30:44 +0200 Subject: [PATCH 3/3] not evaluation parallel code in vignette --- vignettes/applications.Rmd | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/vignettes/applications.Rmd b/vignettes/applications.Rmd index 9b5f42e..5259d0f 100644 --- a/vignettes/applications.Rmd +++ b/vignettes/applications.Rmd @@ -979,6 +979,7 @@ Generate 100 sample trees and get the frequency of the feature in the last gener system.time(x <- sapply(1:100, function(x) FreqLastGen(GenerateChildrenTree()))) ``` + Plot a histogram of the frequency of the defect in the last generation: ```{r} @@ -991,16 +992,25 @@ For larger populations, you might consider parallelisation, of course. See below It is straight forward to parallelise the simulation. If, as in this example, you do not need to pass around a data.tree structure from one process (fork) to another, it is also rather efficient. -```{r} +```{r, eval = FALSE} library(foreach) library(doParallel) -registerDoParallel(makeCluster(2)) +registerDoParallel(makeCluster(3)) #On Linux, there are other alternatives, e.g.: library(doMC); registerDoMC(3) system.time(x <- foreach (i = 1:100, .packages = "data.tree") %dopar% FreqLastGen(GenerateChildrenTree())) stopImplicitCluster() ``` +```{r, echo = FALSE} + +print(c(user = 0.07, system = 0.02, elapsed = 1.40)) +``` + + + + + For the more complicated case where you want to parallelise operations on a single tree, see below. # Tic-Tac-Toe (game complexity)