Presented by: Joseph Rickert, Data Scientist Community Manager, Revolution Analytics, Sep 25 2014.
Whenever data scientists are asked about what software they use R always comes up at the top of the list. In one recent survey, only SQL was rated higher than R. In this webinar we will explore what makes R so popular and useful. Starting with the big picture, we describe how R is organized and how to find your way around the R world. Then we will work through some examples highlighting features of R that make it attractive for data science work including:
Acquiring data
Data manipulation
Exploratory data analysis
Model building
Machine learning
This document discusses demos and tools for linking knowledge discovery (KDD) and linked data. It summarizes several tools that integrate linked data and KDD processes like data preprocessing, mining, and postprocessing. OpenRefine, RapidMiner, R, Matlab, ProLOD++, DL-Learner, Spark, KNIME, and Gephi were highlighted as tools that support tasks like enriching data, running SPARQL queries, loading RDF data, and visualizing linked data. The document concludes by asking about gaps and how to increase adoption, noting linked data could benefit KDD with validation, enrichment, and reasoning over semantic web data.
Integration of data ninja services with oracle spatial and graphData Ninja API
Data Ninja Services provides a set of cloud-based APIs that can extract entities from the document texts as well as their relationships, and produce RDF triples which can be populated into an Oracle Spatial and Graph in a seamless integration. The risk analysis case study based on the Zika virus binds actionable insights from Oracle with the semantic content produced by the Data Ninja services.
- OpenRefine is a free, open source tool for cleaning and transforming messy data.
- It allows users to work with data in a visual, interactive way through facets, clustering, and scripting capabilities like GREL.
- Common tasks include cleaning data, transforming formats, reconciling concepts with external sources, and cross-referencing data.
This document discusses using Perl and Raku for data science. It begins by noting the growth of data science jobs and examines common programming languages used, including Perl, Python, and R. While there were no Raku modules for statistics at the time, basic statistics functions can be written easily in Raku. Examples are provided demonstrating calculating statistics and creating graphs using Perl modules. The future of data science is seen to include areas like data mining, artificial intelligence, and machine learning.
Publishing Linked Statistical Data: Aragón, a case studyOscar Corcho
Presentation at the Semstats2017 workshop (http://semstats.org/2017/) for the paper "Publishing Linked Statistical Data: Aragón, a Case Study", by Oscar Corcho, Idafen Santana-Pérez, Hugo Lafuente, David Portolés, César Cano, Alfredo Peris, José María Subero.
Linked lists represent a countable number of ordered values, and are among the most important abstract data types in computer science. With the advent of RDF as a highly expressive knowledge representation language for the Web, various implementations for RDF lists have been proposed. Yet, there is no benchmark so far dedicated to evaluate the performance of triple stores and SPARQL query engines on dealing with ordered linked data. Moreover, essential tasks for evaluating RDF lists, like generating datasets containing RDF lists of various sizes, or generating the same RDF list using different modelling choices, are cumbersome and unprincipled. In this paper, we propose List.MID, a systematic benchmark for evaluating systems serving RDF lists. List.MID consists of a dataset generator, which creates RDF list data in various models and of different sizes; and a set of SPARQL queries. The RDF list data is coherently generated from a large, community-curated base collection of Web MIDI files, rich in lists of musical events of arbitrary length. We describe the List.MID benchmark, and discuss its impact and adoption, reusability, design, and availability.
New Advances in High Performance Analytics with R: 'Big Data' Decision Trees ...Revolution Analytics
Revolution R Enterprise 6.1 includes two important advances in high performance predictive analytics with R: (1) big data decision trees, and (2) the ability to easily extract and perform predictive analytics on data stored in the Hadoop Distributed File System (HDFS).
Classification and regression trees are among the most frequently used algorithms for data analysis and data mining. The implementation provided in Revolution Analytics’ RevoScaleR package is parallelized, scalable, distributable, and designed with big data in mind.
Decision trees and all of the other high performance prediction analytics functions provided with RevoScaleR (such as linear and logistic regression, generalized linear models, and k-means clustering) can now also be used to analyze data stored in the HDFS file system. After specifying the connection parameters to the HDFS file system, some or all of the data can be directly explored, analyzed or quickly and efficiently extracted into a native file system.
Presented by: Joseph Rickert, Data Scientist Community Manager, Revolution Analytics, Sep 25 2014.
Whenever data scientists are asked about what software they use R always comes up at the top of the list. In one recent survey, only SQL was rated higher than R. In this webinar we will explore what makes R so popular and useful. Starting with the big picture, we describe how R is organized and how to find your way around the R world. Then we will work through some examples highlighting features of R that make it attractive for data science work including:
Acquiring data
Data manipulation
Exploratory data analysis
Model building
Machine learning
This document discusses demos and tools for linking knowledge discovery (KDD) and linked data. It summarizes several tools that integrate linked data and KDD processes like data preprocessing, mining, and postprocessing. OpenRefine, RapidMiner, R, Matlab, ProLOD++, DL-Learner, Spark, KNIME, and Gephi were highlighted as tools that support tasks like enriching data, running SPARQL queries, loading RDF data, and visualizing linked data. The document concludes by asking about gaps and how to increase adoption, noting linked data could benefit KDD with validation, enrichment, and reasoning over semantic web data.
Integration of data ninja services with oracle spatial and graphData Ninja API
Data Ninja Services provides a set of cloud-based APIs that can extract entities from the document texts as well as their relationships, and produce RDF triples which can be populated into an Oracle Spatial and Graph in a seamless integration. The risk analysis case study based on the Zika virus binds actionable insights from Oracle with the semantic content produced by the Data Ninja services.
- OpenRefine is a free, open source tool for cleaning and transforming messy data.
- It allows users to work with data in a visual, interactive way through facets, clustering, and scripting capabilities like GREL.
- Common tasks include cleaning data, transforming formats, reconciling concepts with external sources, and cross-referencing data.
This document discusses using Perl and Raku for data science. It begins by noting the growth of data science jobs and examines common programming languages used, including Perl, Python, and R. While there were no Raku modules for statistics at the time, basic statistics functions can be written easily in Raku. Examples are provided demonstrating calculating statistics and creating graphs using Perl modules. The future of data science is seen to include areas like data mining, artificial intelligence, and machine learning.
Publishing Linked Statistical Data: Aragón, a case studyOscar Corcho
Presentation at the Semstats2017 workshop (http://semstats.org/2017/) for the paper "Publishing Linked Statistical Data: Aragón, a Case Study", by Oscar Corcho, Idafen Santana-Pérez, Hugo Lafuente, David Portolés, César Cano, Alfredo Peris, José María Subero.
Linked lists represent a countable number of ordered values, and are among the most important abstract data types in computer science. With the advent of RDF as a highly expressive knowledge representation language for the Web, various implementations for RDF lists have been proposed. Yet, there is no benchmark so far dedicated to evaluate the performance of triple stores and SPARQL query engines on dealing with ordered linked data. Moreover, essential tasks for evaluating RDF lists, like generating datasets containing RDF lists of various sizes, or generating the same RDF list using different modelling choices, are cumbersome and unprincipled. In this paper, we propose List.MID, a systematic benchmark for evaluating systems serving RDF lists. List.MID consists of a dataset generator, which creates RDF list data in various models and of different sizes; and a set of SPARQL queries. The RDF list data is coherently generated from a large, community-curated base collection of Web MIDI files, rich in lists of musical events of arbitrary length. We describe the List.MID benchmark, and discuss its impact and adoption, reusability, design, and availability.
New Advances in High Performance Analytics with R: 'Big Data' Decision Trees ...Revolution Analytics
Revolution R Enterprise 6.1 includes two important advances in high performance predictive analytics with R: (1) big data decision trees, and (2) the ability to easily extract and perform predictive analytics on data stored in the Hadoop Distributed File System (HDFS).
Classification and regression trees are among the most frequently used algorithms for data analysis and data mining. The implementation provided in Revolution Analytics’ RevoScaleR package is parallelized, scalable, distributable, and designed with big data in mind.
Decision trees and all of the other high performance prediction analytics functions provided with RevoScaleR (such as linear and logistic regression, generalized linear models, and k-means clustering) can now also be used to analyze data stored in the HDFS file system. After specifying the connection parameters to the HDFS file system, some or all of the data can be directly explored, analyzed or quickly and efficiently extracted into a native file system.
Skillshare - Let's talk about R in Data JournalismSchool of Data
This document provides an introduction to R, an open source statistical computing and graphics programming language. It outlines R's capabilities for data manipulation, visualization, and analysis. It then demonstrates how to install R and RStudio. Several popular R packages for working with data are listed for tasks like obtaining, cleaning, analyzing, and visualizing data. Finally, resources for learning R like tutorials, articles, books, and influential members of the R community are recommended.
Analytics and Access to the UK web archiveLewis Crawford
The document summarizes the background, purpose, and methods of the UK Web Archive. It discusses how the archive collects, stores, and provides access to snapshots of UK websites over time to preserve digital cultural heritage. It also describes challenges of scale due to the immense size of web content and techniques like full-text search and data analytics that are used to facilitate discovery of information within the archive.
Many Linked Data datasets model elements in their domains in the form of lists: a countable number of ordered resources.
When publishing these lists in RDF, an important concern is making them easy to consume.
Therefore, a well-known recommendation is to find an existing list modelling solution, and reuse it.
However, a specific domain model can be implemented in different ways and vocabularies may provide alternative solutions.
In this paper, we argue that a wrong decision could have a significant impact in terms of performance and, ultimately, the availability of the data.
We take the case of RDF Lists and make the hypothesis that the efficiency of retrieving sequential linked data depends primarily on how they are modelled (triple-store invariance hypothesis).
To demonstrate this, we survey different solutions for modelling sequences in RDF, and propose a pragmatic approach for assessing their impact on data availability.
Finally, we derive good (and bad) practices on how to publish lists as linked open data.
By doing this, we sketch the foundations of an empirical, task-oriented methodology for benchmarking linked data modelling solutions.
Linked Statistical Data: does it actually pay off?Oscar Corcho
Invited keynote at the ISWC2015 Workshop on Semantics and Statistics (SemStats 2015). http://semstats.github.io/2015/
The release of the W3C RDF Data Cube recommendation was a significant milestone towards improving the maturity of the area of Linked Statistical Data. Many Data Cube-based datasets have been released since then. Tools for the generation and exploitation of such datasets have also appeared. While the benefits for the usage of RDF Data Cube and the generation of Linked Data in this area seem to be clear, there are still many challenges associated to the generation and exploitation of such data. In this talk we will reflect about them, based on our experience on generating and exploiting such type of data, and hopefully provoke some discussion about what the next steps should be.
This document discusses INEGI's use of Twitter as a source of big data. It outlines INEGI's process for collecting over 260 million geo-tagged tweets from Twitter's API and analyzing them using Apache Spark. The tweets are analyzed to extract sentiment indicators and examine mobility patterns. INEGI has also integrated tweets with other data sources and is exploring various applications of the Twitter data like tracking tourism, migration, and subjective wellbeing.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
aRangodb, un package per l'utilizzo di ArangoDB con RGraphRM
Lingua talk: Italiano.
Descrizione:
In questo talk parleremo di come integrare e utilizzare ArangoDB, un database multi-modello con supporto nativo ai grafi, con R. Presenteremo quindi aRangodb, il package che abbiamo sviluppato per interfacciarsi in modo più semplice e intuitivo al database. Nel corso del talk mostreremo come il package possa essere utilizzato in ambito data science usando alcuni case studies concreti.
Speaker:
Gabriele Galatolo - Data Scientist - Kode srl
Maximising (Re)Usability of Library metadata using Linked Data Asuncion Gomez-Perez
This document discusses maximizing the reusability of library metadata using linked data. It motivates the use of linked data by describing the current heterogeneous data landscape with issues around language, format, and lack of interoperability. It then discusses how linked data allows for uniform access through agreed upon vocabularies and standards. Specific issues around language, provenance, license and the linked data process are covered. Uses of linked library metadata are also discussed.
This document provides cheat sheets and resources for various programming languages and tools used for data science. It defines a data scientist as someone who can write code in languages like R, Python, Java, SQL and Hadoop, understands statistics, and can derive insights from data to help businesses make decisions. Links are included for quick reference sheets on topics like Java, Linux, SQL, Hive QL, Python, R, Pig, HDFS, and Git to aid data scientists in their work.
Iterative data discovery and transformation with open refineMartin Magdinier
OpenRefine is a free and open source tool that allows users to clean, transform, and integrate data. It bridges the gap between technical and business users by providing an intuitive interface for data preparation tasks like deduplicating values, handling multi-value cells, changing data formats, and joining datasets. These iterative data discovery and transformation capabilities help users understand their data better and prepare it for analysis.
Semantic Technologies and Triplestores for Business IntelligenceMarin Dimitrov
This document provides an introduction to semantic technologies and triplestores. It discusses the Semantic Web vision of making data on the web more accessible and linked. Key concepts covered include RDF, ontologies, OWL, SPARQL and Linked Data. It also introduces triplestores as RDF databases for storing and querying semantic data and compares their features to traditional databases.
This document discusses the programming language R and reasons for learning and using it. R is a statistical computing language that is open-source, cross-platform, and has powerful tools for data analysis, machine learning, and visualization. It has a large user community and is used by many top companies for tasks like advertising effectiveness analysis and data visualization. While R has a steep learning curve and requires more memory than some other languages, learning R provides access to cutting-edge algorithms and is valuable for mastering data science and working with large datasets. The document concludes that R offers immense benefits and tools to work with data at scale, making it a good choice for both technical fields and business applications.
R is an open source programming language used for statistical analysis and graphics. It allows users to create objects like vectors, matrices, data frames and lists to manipulate and analyze data. RStudio is an integrated development environment for R that provides a user interface, debugging tools and package management. The document introduces key R concepts like data types, packages and resources for learning R. It also provides best practices for file management, naming conventions and version control when programming in R.
This document provides an introduction to R, including:
- R is a software environment for data manipulation, statistical computing, and graphical data analysis. It is widely used in academia, healthcare, finance, and by large companies.
- R has two originators from New Zealand and Canada. It is developed by the R Core Team and has over 13,000 contributed packages.
- Examples of how companies like Google, Facebook, banks, John Deere, the New York Times, and Ford use R for tasks like data analysis, visualization, forecasting, and statistical modeling.
The document provides an introduction to Prof. Dr. Sören Auer and his background in knowledge graphs. It discusses his current role as a professor and director focusing on organizing research data using knowledge graphs. It also briefly outlines some of his past roles and major scientific contributions in the areas of technology platforms, funding acquisition, and strategic projects related to knowledge graphs.
This document summarizes a presentation given by Thomas Hütter on using R for data analysis and visualization. The presentation provided an overview of R's history and ecosystem, introduced basic data types and functions, and demonstrated connecting to a SQL Server database to extract and analyze sales data from a Dynamics Nav system. It showed visualizing the results with ggplot2 and creating interactive apps with the Shiny framework. The presentation emphasized that proper data understanding is important for reliable analysis and highlighted resources for learning more about R.
- Current databases cannot handle the complexity of data and queries required for artificial intelligence systems. As data complexity increases, the ability of databases to process and analyze that data has not kept pace.
- Grakn is a new type of "hyper-relational" database designed specifically for knowledge-oriented systems like artificial intelligence. It uses a novel knowledge representation and can naturally represent complex domains, perform real-time inference, and enable automated distributed analytics algorithms on large datasets.
- Grakn aims to do for AI what relational databases did for business intelligence - provide the necessary infrastructure to build and query complex, knowledge-based systems at scale.
Data Tactics Analytics Brown Bag (November 2013)Rich Heimann
This document summarizes a brown bag presentation on analytics by Data Tactics Corporation. It introduces new analytic tools from the company including work on cyber intelligence and detection, the open source RAccumulo library, and Data Science for Program Managers. Case studies on discontinuities analysis and data science in Afghanistan are also mentioned. The document concludes by discussing the Shiny tool for building interactive web apps in R and providing contact information.
This document provides an introduction to using the Deducer graphical user interface (GUI) for R. It discusses loading and installing the Deducer and DeducerSurvival packages, exploring a sample dataset using the GUI's data viewer and generating frequency tables and graphs. Instructions are given on reading Excel data, converting variables to factors, and using the GUI to summarize categorical and continuous variables and add smoothing to graphs.
This document provides an overview and introduction to data mining using R and Rattle. It discusses data mining concepts and applications. It then introduces R as a programming language for statistical analysis and data mining. Rattle is presented as a graphical user interface tool built on R to make data mining more accessible. The document walks through installing and using Rattle to explore, visualize, model and evaluate data. It also discusses resources for learning more about R, Rattle and data mining.
Skillshare - Let's talk about R in Data JournalismSchool of Data
This document provides an introduction to R, an open source statistical computing and graphics programming language. It outlines R's capabilities for data manipulation, visualization, and analysis. It then demonstrates how to install R and RStudio. Several popular R packages for working with data are listed for tasks like obtaining, cleaning, analyzing, and visualizing data. Finally, resources for learning R like tutorials, articles, books, and influential members of the R community are recommended.
Analytics and Access to the UK web archiveLewis Crawford
The document summarizes the background, purpose, and methods of the UK Web Archive. It discusses how the archive collects, stores, and provides access to snapshots of UK websites over time to preserve digital cultural heritage. It also describes challenges of scale due to the immense size of web content and techniques like full-text search and data analytics that are used to facilitate discovery of information within the archive.
Many Linked Data datasets model elements in their domains in the form of lists: a countable number of ordered resources.
When publishing these lists in RDF, an important concern is making them easy to consume.
Therefore, a well-known recommendation is to find an existing list modelling solution, and reuse it.
However, a specific domain model can be implemented in different ways and vocabularies may provide alternative solutions.
In this paper, we argue that a wrong decision could have a significant impact in terms of performance and, ultimately, the availability of the data.
We take the case of RDF Lists and make the hypothesis that the efficiency of retrieving sequential linked data depends primarily on how they are modelled (triple-store invariance hypothesis).
To demonstrate this, we survey different solutions for modelling sequences in RDF, and propose a pragmatic approach for assessing their impact on data availability.
Finally, we derive good (and bad) practices on how to publish lists as linked open data.
By doing this, we sketch the foundations of an empirical, task-oriented methodology for benchmarking linked data modelling solutions.
Linked Statistical Data: does it actually pay off?Oscar Corcho
Invited keynote at the ISWC2015 Workshop on Semantics and Statistics (SemStats 2015). http://semstats.github.io/2015/
The release of the W3C RDF Data Cube recommendation was a significant milestone towards improving the maturity of the area of Linked Statistical Data. Many Data Cube-based datasets have been released since then. Tools for the generation and exploitation of such datasets have also appeared. While the benefits for the usage of RDF Data Cube and the generation of Linked Data in this area seem to be clear, there are still many challenges associated to the generation and exploitation of such data. In this talk we will reflect about them, based on our experience on generating and exploiting such type of data, and hopefully provoke some discussion about what the next steps should be.
This document discusses INEGI's use of Twitter as a source of big data. It outlines INEGI's process for collecting over 260 million geo-tagged tweets from Twitter's API and analyzing them using Apache Spark. The tweets are analyzed to extract sentiment indicators and examine mobility patterns. INEGI has also integrated tweets with other data sources and is exploring various applications of the Twitter data like tracking tourism, migration, and subjective wellbeing.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
aRangodb, un package per l'utilizzo di ArangoDB con RGraphRM
Lingua talk: Italiano.
Descrizione:
In questo talk parleremo di come integrare e utilizzare ArangoDB, un database multi-modello con supporto nativo ai grafi, con R. Presenteremo quindi aRangodb, il package che abbiamo sviluppato per interfacciarsi in modo più semplice e intuitivo al database. Nel corso del talk mostreremo come il package possa essere utilizzato in ambito data science usando alcuni case studies concreti.
Speaker:
Gabriele Galatolo - Data Scientist - Kode srl
Maximising (Re)Usability of Library metadata using Linked Data Asuncion Gomez-Perez
This document discusses maximizing the reusability of library metadata using linked data. It motivates the use of linked data by describing the current heterogeneous data landscape with issues around language, format, and lack of interoperability. It then discusses how linked data allows for uniform access through agreed upon vocabularies and standards. Specific issues around language, provenance, license and the linked data process are covered. Uses of linked library metadata are also discussed.
This document provides cheat sheets and resources for various programming languages and tools used for data science. It defines a data scientist as someone who can write code in languages like R, Python, Java, SQL and Hadoop, understands statistics, and can derive insights from data to help businesses make decisions. Links are included for quick reference sheets on topics like Java, Linux, SQL, Hive QL, Python, R, Pig, HDFS, and Git to aid data scientists in their work.
Iterative data discovery and transformation with open refineMartin Magdinier
OpenRefine is a free and open source tool that allows users to clean, transform, and integrate data. It bridges the gap between technical and business users by providing an intuitive interface for data preparation tasks like deduplicating values, handling multi-value cells, changing data formats, and joining datasets. These iterative data discovery and transformation capabilities help users understand their data better and prepare it for analysis.
Semantic Technologies and Triplestores for Business IntelligenceMarin Dimitrov
This document provides an introduction to semantic technologies and triplestores. It discusses the Semantic Web vision of making data on the web more accessible and linked. Key concepts covered include RDF, ontologies, OWL, SPARQL and Linked Data. It also introduces triplestores as RDF databases for storing and querying semantic data and compares their features to traditional databases.
This document discusses the programming language R and reasons for learning and using it. R is a statistical computing language that is open-source, cross-platform, and has powerful tools for data analysis, machine learning, and visualization. It has a large user community and is used by many top companies for tasks like advertising effectiveness analysis and data visualization. While R has a steep learning curve and requires more memory than some other languages, learning R provides access to cutting-edge algorithms and is valuable for mastering data science and working with large datasets. The document concludes that R offers immense benefits and tools to work with data at scale, making it a good choice for both technical fields and business applications.
R is an open source programming language used for statistical analysis and graphics. It allows users to create objects like vectors, matrices, data frames and lists to manipulate and analyze data. RStudio is an integrated development environment for R that provides a user interface, debugging tools and package management. The document introduces key R concepts like data types, packages and resources for learning R. It also provides best practices for file management, naming conventions and version control when programming in R.
This document provides an introduction to R, including:
- R is a software environment for data manipulation, statistical computing, and graphical data analysis. It is widely used in academia, healthcare, finance, and by large companies.
- R has two originators from New Zealand and Canada. It is developed by the R Core Team and has over 13,000 contributed packages.
- Examples of how companies like Google, Facebook, banks, John Deere, the New York Times, and Ford use R for tasks like data analysis, visualization, forecasting, and statistical modeling.
The document provides an introduction to Prof. Dr. Sören Auer and his background in knowledge graphs. It discusses his current role as a professor and director focusing on organizing research data using knowledge graphs. It also briefly outlines some of his past roles and major scientific contributions in the areas of technology platforms, funding acquisition, and strategic projects related to knowledge graphs.
This document summarizes a presentation given by Thomas Hütter on using R for data analysis and visualization. The presentation provided an overview of R's history and ecosystem, introduced basic data types and functions, and demonstrated connecting to a SQL Server database to extract and analyze sales data from a Dynamics Nav system. It showed visualizing the results with ggplot2 and creating interactive apps with the Shiny framework. The presentation emphasized that proper data understanding is important for reliable analysis and highlighted resources for learning more about R.
- Current databases cannot handle the complexity of data and queries required for artificial intelligence systems. As data complexity increases, the ability of databases to process and analyze that data has not kept pace.
- Grakn is a new type of "hyper-relational" database designed specifically for knowledge-oriented systems like artificial intelligence. It uses a novel knowledge representation and can naturally represent complex domains, perform real-time inference, and enable automated distributed analytics algorithms on large datasets.
- Grakn aims to do for AI what relational databases did for business intelligence - provide the necessary infrastructure to build and query complex, knowledge-based systems at scale.
Data Tactics Analytics Brown Bag (November 2013)Rich Heimann
This document summarizes a brown bag presentation on analytics by Data Tactics Corporation. It introduces new analytic tools from the company including work on cyber intelligence and detection, the open source RAccumulo library, and Data Science for Program Managers. Case studies on discontinuities analysis and data science in Afghanistan are also mentioned. The document concludes by discussing the Shiny tool for building interactive web apps in R and providing contact information.
This document provides an introduction to using the Deducer graphical user interface (GUI) for R. It discusses loading and installing the Deducer and DeducerSurvival packages, exploring a sample dataset using the GUI's data viewer and generating frequency tables and graphs. Instructions are given on reading Excel data, converting variables to factors, and using the GUI to summarize categorical and continuous variables and add smoothing to graphs.
This document provides an overview and introduction to data mining using R and Rattle. It discusses data mining concepts and applications. It then introduces R as a programming language for statistical analysis and data mining. Rattle is presented as a graphical user interface tool built on R to make data mining more accessible. The document walks through installing and using Rattle to explore, visualize, model and evaluate data. It also discusses resources for learning more about R, Rattle and data mining.
This document provides instructions for installing R and R-Studio, two programs for performing advanced data analytics. It explains that R can be downloaded from its website for Linux, MacOS, and Windows, while R-Studio can also be downloaded from its website for those operating systems. The document then demonstrates how to use R-Studio, which displays the workspace, console, and ability to show graphics and other information across multiple tabs. It includes example R commands to help orient users and demonstrates assigning a value to a variable to show mastery of the basics.
This document compares two integrated development environments (IDEs) for the R programming language: R-Studio and Rcmdr. R-Studio is a more powerful and flexible IDE that provides direct access to R code and facilitates interactions with R through its graphical interface. Rcmdr is simpler and more user-friendly, focusing on statistical analysis through buttons and menus. Both allow viewing data, but neither support data editing. The document provides guidelines for choosing between them and notes additional R IDEs under development.
Introduction to R Short course Fall 2016Spencer Fox
The document provides instructions for an introductory R session, including downloading materials from a GitHub repository and opening an R project file. It outlines logging in, downloading an R project folder containing intro materials, and opening the project file in RStudio.
Brief presentation that lays out the landscape and current roadmap for automatic or guided semi-automatic #machinelearning in H2O. Tags: #automl #datascience #bigdata #analytics #ai
https://www.youtube.com/watch?v=ZZSe3osXK_E
400 million Search Results -Predict Contextual Ad Clicks Sri Ambati
H2O.ai is an open source machine learning platform used for predictive analytics and data science. The document discusses H2O.ai's products and algorithms for machine learning tasks like click prediction. It provides an overview of the company, executives and advisors, and the platform's features like its open source APIs for R and Python, Spark integration, and cutting-edge machine learning algorithms like deep learning and gradient boosted machines. An example use case is presented on using H2O.ai to build a click prediction model from a Kaggle competition dataset.
Robsonalves fotografia Fine Art 2016-2Robson Alves
José Diniz é um fotógrafo brasileiro nascido em Niterói que mora no Rio de Janeiro. Ele publicou vários livros de fotografia e teve seu trabalho exposto em diversos museus e galerias no Brasil, Argentina, Uruguai, Estados Unidos, França e Holanda. Diniz já recebeu vários prêmios e suas fotografias fazem parte de coleções de importantes instituições culturais.
Alice Lindorfer é uma fotógrafa de 22 anos que se formou em Fotografia em 2015 e tem seu próprio estúdio há um ano e meio. Ela sempre se identificou com a fotografia como forma de comunicação através de sentimentos e gosta de criar vínculos com os clientes fotografando as diferentes fases de suas vidas, incluindo newborns desde seu primeiro ensaio gratuito há três anos.
Automating Machine Learning - Is it feasible?Manuel Martín
Facing a machine learning problem for the first time can be overwhelming. Hundreds of methods exist for tackling problems such as classification, regression or clustering. Selecting the appropriate method is challenging, specially if no much prior knowledge is known. In addition, most models require to optimise a number of hyperparameters to perform well. Preparing the data for the learning algorithm is also a labour-intensive process that includes cleaning outliers and imperfections, feature selection, data transformation like PCA and more. A workflow connecting preprocessing methods and predictive models is called a multicomponent predictive system (MCPS). This talk introduces the problem of automating the composition and optimisation of MCPSs and also how they can be adapted in changing environments.
This document discusses R packages, loading packages, and the dplyr package. It notes that packages contain functions, data, and code and are stored in the library. R comes with standard packages and others can be downloaded and installed. Once installed, packages must be loaded into an R session to use. The dplyr package provides tools for manipulating datasets and is organized around verbs like select, filter, arrange, mutate, group_by, and summarise.
This document provides an introduction to using R, an open-source programming language for statistical analysis and graphics. It covers downloading and installing R, basic syntax and data types, importing and manipulating data, common statistical analyses like linear regression, and creating graphics. The document is intended for users new to R and guides them through examples applying various R functions and packages.
This document summarizes a brown-bag seminar on introducing R and data visualization. It covers loading and exploring data; basic data types and data frames; performing operations and asking questions of the data; subsetting, filtering, and handling duplicates and missing values; basic statistical analysis and graphs; and using the ggplot2 package to create bar plots, scatter plots, line graphs, box plots and more to visualize relationships in the data. Key points covered include using geom_bar with stat="bin", adding colors and facets, setting linetype and point shapes, and layering geoms to overlay points and lines on the same plot.
Paquete ggplot - Potencia y facilidad para generar gráficos en RNestor Montaño
El paquete ggplot de R proporciona un poderoso sistema que hace que sea fácil de producir gráficos complejos de varias capas, automatiza varios aspectos tediosos del proceso de graficar manteniendo al mismo tiempo la habilidad de construir paso a paso un gráfico pues se compone de una serie de pequeños bloques de construcción independientes, esto reduce la redundancia dentro del código, y hace que sea fácil de personalizar el gráfico para obtener exactamente lo que se desea.
As the complexity of choosing optimised and task specific steps and ML models is often beyond non-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML.
Although it focuses on end users without expert knowledge, AutoML also offers new tools to machine learning experts, for example to:
1. Perform architecture search over deep representations
2. Analyse the importance of hyperparameters.
This document discusses using the dplyr and magrittr packages in R to analyze data. It instructs loading the packages if not already installed, and using the mutate() and summarise() functions on the mtcars dataset to try the dplyr verbs. It further recommends installing the nycflights13 package and using mutate and summarise on its datasets to extend skills with these data analysis functions.
This document provides an overview of using the dplyr package in R for data manipulation and basic statistics. It recaps loading and inspecting data, then covers key dplyr functions like filter() for subsetting rows, arrange() for reordering rows, select() for choosing columns, distinct() for unique rows, mutate() for transforming variables, and summarise() for creating summaries and grouping variables. The document demonstrates examples of these functions on sample data and encourages exploring more dplyr functions and applying them to real datasets.
GNU R in Clinical Research and Evidence-Based MedicineAdrian Olszewski
Is GNU R (an environment for statistical computing) suitable enough for Biostatisticians involved in Clinical Research? Can it replace or support SAS in this area? Well, I think this presentation may help to remove any doubts. If you are a Biostatistician (and probably a SAS user), you may find it useful.
The presentation is under constant improvement.
You can find it also on CRAN (contributed documentation) and at http://www.r-clinical-research.com
Basic of R Programming Language,
Introduction, How to run R, R Sessions and Functions, Basic Math, Variables, Data Types, Vectors, Conclusion, Advanced Data Structures, Data Frames, Lists, Matrices, Arrays, Classes
Basic of R Programming Language
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
R is a popular programming language for statistical analysis and visualization. It allows users to import, clean, analyze, and visualize data, and is commonly used in fields like data science, machine learning, and research. The document provides an overview of R, including how to download and install it, basic usage like starting an R session and running commands, and examples of using R for tasks like data analysis, statistical computing, and machine learning. Key features of R highlighted are that it is open source, runs on various platforms, and has a large collection of packages for data handling and analysis.
R can perform various data analysis and data science tasks for free through its extensive packages and community support. It is an open-source statistical programming language that is widely used for data manipulation, visualization, and machine learning. Some key features of R include its ability to perform interactive visualization, ensemble learning, text/social media mining, and integration with other languages and technologies like SQL, Python, and Tableau. While powerful, R does have some limitations like a steep learning curve and slower execution compared to other languages.
This document compares Python and R for use in data science. Both languages are popular among data scientists, though Python has broader usage among professional developers overall. Python is a general purpose language while R is specialized for statistical computing. Both have extensive libraries for data manipulation, analysis, and visualization. The best choice depends on factors like familiarity, project requirements, and team preferences as both are capable of most data science tasks.
R as supporting tool for analytics and simulationAlvaro Gil
R is a popular open-source language and environment for statistical analysis and visualization. It allows users to perform a wide range of statistical and predictive modeling techniques on data. Many companies use R as their standard tool for analytics due to its extensive library of packages and ability to handle large datasets. R can interface with other languages and platforms, making it a versatile scripting language for data science tasks.
An introduction to R is a document usefulssuser3c3f88
R is a language and environment for statistical computing and graphics. It provides functions for data manipulation, calculation, and graphical displays. Key features of R include its ability to produce publication-quality plots, perform statistical tests, fit models to data, and develop statistical software. R has an extensive library of additional user-contributed packages that extend its capabilities. The document provides information on downloading and using R, reading data into R, customizing plots, and interactive plotting functions.
R is an open-source programming language and software environment for statistical analysis, graphics, and statistical computing. It was originally developed in the early 1990s at the University of Auckland in New Zealand. The document discusses R's history and development, how to obtain R, its key features and specialties compared to other analytics software like SAS, SPSS, Stata, and Matlab. It summarizes surveys and comparisons of R's popularity in terms of downloads, number of users, email discussions, scholarly impact, and job opportunities. R is highlighted as having a large library of packages for statistical analysis, flexible and elegant data visualization capabilities, and an active open-source community of contributors and users.
R was created in 1993 by Ross Ihaka and Robert Gentleman at the University of Auckland, New Zealand to teach introductory statistics. It is an open source software environment excellent for data analysis and graphics using functions in an interpreter. R is used across many industries and can analyze both structured and unstructured data to explore datasets and build predictive models.
Big Data refers to a large amount of data both structured and unstructured. For managing and analyzing this amount of data we need technologies like Hadoop and language like R.
http://www.techsparks.co.in/thesis-in-big-data-with-r/
Algerian R Users Group (Official Kick Off)Fateh Bekioua
Algerian R Users Group is open to anyone who uses R for data analysis, data visualization, or Data Mining, or anyone who is interested in learning R. All skill levels are welcome. Our goal is to support and share R experiences and knowledge among its users in the Algerian community.
The document discusses a course on business analytics using R. It introduces R as a programming language and data analysis software used widely in business and other domains. Key topics covered in the course include an overview of business analytics, uses of R, basic R commands, data visualization in R, and examples of applying R to solve business problems like market basket analysis, churn prediction, and crime forecasting. Live demonstrations of R will also be provided.
This document summarizes a presentation on Big Data analytics using R. It introduces R as a programming language for statistics, mathematics, and data science. It is open source and has an active user community. The presentation then discusses Revolution R Enterprise, a commercial product that builds upon R to enable high performance analytics on big data across multiple platforms and data sources through parallelization, distributed computing, and integration tools. It aims to allow writing analytics code once that can be deployed anywhere.
The document is a presentation about Revolution R Enterprise, a commercial software product from Revolution Analytics that adds functionality to the open source R programming language. It discusses features like a productivity environment for R, multi-threaded math for improved performance, tools for big data analytics on Hadoop and Netezza, and deployment options. Revolution R aims to make R easier to use for enterprise applications and production environments.
Jupyter Notebook is a popular open-source tool that allows users to create documents containing code, equations, visualizations, and text. It supports Python, R, Scala, and Julia and is commonly used for tasks like data cleaning, transformation, modeling, and visualization. R Studio is also open-source and used for operations on data using the R language, including packages for manipulation and visualization. SAS was one of the first analytics tools and was designed for descriptive and predictive analytics. It has been used for over 40 years for statistical analysis and decision making.
R is among the most popular programming languages among data science professionals. In this guide learn about the basic concepts and various functionalities it offers.
R is an open-source programming language and software environment for statistical analysis, graphics, and statistical computing. It is a powerful tool used by statisticians, data analysts, and data scientists to explore, visualize, and model large datasets. The presenter provides an overview of R, including its origins, popular uses like predictive analytics and machine learning, and resources for learning more such as online courses and tutorials.
The document discusses tools for analyzing unstructured data. It describes unstructured data as data that does not have a predefined format or structure. The document then discusses sources of unstructured data like machine-generated and human-generated sources. It also discusses the differences between data analysis and analytics. Finally, it describes several tools that can be used to analyze unstructured data including RapidMiner, Weka, KNIME, and R Language. It provides characteristics and descriptions of each tool.
The document discusses PowerPivot in Excel and its usefulness for supply chain analytics. PowerPivot allows Excel to have the functionality of a relational database by adding a PowerPivot tab. It can link multiple tables together more easily than vlookup, and can link to different database types. PowerPivot is useful for supply chain analytics because supply chain data often involves transaction files that reference multiple tables. The components of PowerPivot include the PowerPivot tab interface and the data model interface. PowerPivot can also connect to other large datasets.
This document provides an introduction to using Excel VBA and macros for logistics and supply chain management. It outlines topics like why VBA is important given Excel's widespread use, what VBA is, how to start with VBA, recording macros, running macros, functions and subroutines, and using user forms. Examples are provided for coding procedures and functions to automate tasks like filling cells, reading cell values, and preparing reports.
Logistics Systems Design for the Yangtze River Delta Regionarttan2001
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This is a summary of Siegel's Predictive Analytics. The presentation encourages the reader to actively look at the capabilities of Predictive Analytics as a tool to make a forecast for one entity.
This is a talk about Big Data, focusing on its impact on all of us. It also encourages institution to take a close look on providing courses in this area.
This is a presentation on the use Ubuntu Linux for Education. It presents why we care about this topic, and we try to identify the proper positioning of Ubuntu Linux in an environment when the institution is not cost sensitive.
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1. Introducing
R and Rcmdr
Statistical Software
FutureVideo
HealthVideo
November 24, 2013 (Sunday)
12:20 PM
Jabria-2 Auditorium
By: Dr. Kang Mun Arturo Tan
Management Sciences Department
Yanbu University College
2. R is the 18th letter of the alphabet.
R is data analysis software.
R is a programming language.
R is an environment for
statistical analysis.
3. A Bit of History (and Credits)
The R Project
The Department
of Statistics of
The University of Auckland, New Zealand
is well known for being the birthplace of the R Project.
4. Founders of the R Project are, at the time senior lecturers
Robert Gentleman and Ross Ihaka, now Associate Professors.
Starting to work in 1991, the R codes were first released in 1996. The R
Project is a language and environment for statistical computing and
graphics.
5. John Hopkins University
University of Washington
Princeton University
Stanford University
Google
Pfizer
Merck
Bank of America
Intercontinental Hotels
Shell
…
It is widely taught around the world and is being used by
Ivy League Universities, Google,
students, and even by school children.
second-year Statistics
6. “R is the
most powerful
statistical computing language on the planet.”
24. R Commander Default Menu Tree [current as of version 2.0-0]
File - Change working directory
|- Open script file
|- Save script
|- Save script as |- Open R Markdown file
|- Save R Markdown file
|- Save R Markdown file as
|- Save output
|- Save output as
|- Save R workspace
|- Save R workspace as
|- Exit - from Commander
|- from Commander and R
Edit - Cut
|- Copy
|- Paste
|- Delete
|- Find
|- Select all
|- Undo
|- Redo
|- Clear Window
25. Data - New data set
|- Load data set
|- Merge data sets
|- Import data - from text file, clipboard, or URL
| |- from SPSS data set | |- from SAS xport file
| |- from Minitab data set
| |- from STATA data set
| |- from Excel, Access, or dBase data set [32-bit Windows only]
| |- from Excel file [currently 64-bit Windows only]
|- Data in packages - List data sets in packages
| |- Read data set from attached package
|- Active data set - Select active data set
| |- Refresh active data set
| |- Help on active data set (if available)
| |- Variables in active data set
| |- Set case names
| |- Subset active data set
| |- Aggregate variables in active data set
| |- Remove row(s) from active data set
| |- Stack variables in active data set
| |- Remove cases with missing data
| |- Save active data set
| |- Export active data set
|- Manage variables in active data set - Recode variable
|- Compute new variable
|- Add observation numbers to data set
|- Standardize variables
|- Convert numeric variables to factors
|- Bin numeric variable
|- Reorder factor levels
|- Define contrasts for a factor
|- Rename variables
|- Delete variables from data set
30. Why use R?
There's lots of software available for data analysis today: spreadsheets like
Excel, batch-oriented procedure-based systems like SAS; point-and-click
GUI-based systems like SPSS; data mining systems, and so on
31. What makes R different?
R is free.
As an open-source project, you can use R free of charge: no worries about subscription
fees, license managers, or user limits. But just as importantly, R is open: you can inspect
the code and tinker with it as much as you like (provided you respect the terms of the GNU
General Public License version 2 under which it is distributed). Thousands of experts
around the world have done just that, and their contributions benefit the millions of
people who use R today.
R is a language.
In R, you do data analysis by writing functions and scripts, not by pointing and clicking.
That may sound daunting, but it's an easy language to learn, and a very natural and
expressive one for data analysis. But once you learn the language, there are many benefits.
As an interactive language (as opposed to a data-in-data-out black-box procedures), R
promotes experimentation and exploration, which improves data analysis and often leads
to discoveries that wouldn't be made otherwise. A script documents all your work, from
data access to reporting, and can instantly be re-run at any time. (This makes it much
easier to update results when the data change.) Scripts also make it easy to automate a
sequence of tasks that can be integrated into other processes. Many R users who have
used other software report that they can do their data analyses in a fraction of the time.
32. Graphics and data visualization.
One of the design principles of R was that visualization of data through charts and graphs is an essential
part of the data analysis process. As a result, it has excellent tools for creating graphics, from staples like
bar charts and scatterplots to multi-panel Lattice charts to brand new graphics of your own devising. R's
graphical system is heavily influenced by thought leaders in data visualization like Bill Cleveland and
Edward Tufte, and as a result graphics based on R appear regularly in venues like the New York Times,
the Economist, and the FlowingData blog.
A flexible statistical analysis toolkit.
All of the standard data analysis tools are built right into the R language: from accessing
data in various formats, to data manipulation (transforms, merges, aggregations, etc.), to
traditional and modern statistical models (regression, ANOVA, GLM, tree models, etc). All
are included in an object-oriented framework that makes it easy to programatically extract
out and combine just the information you need from the results, rather than having to cutand-paste from a static report.
33. Access to powerful, cutting-edge analytics.
Leading academics and researches from around the world use R to develop the latest
methods in statistics, machine learning, and predictive modeling. There are expansive,
cutting-edge extensions to R in finance, genomics, and dozens of other fields. To date,
more than 2000 packages extending the R language in every domain are available for free
download, with more added every day.
A robust, vibrant community.
With thousands of contributors and more than two million users around the world, if
you've got a question about R chances are, someone's answered it (or can). There's a
wealth of community resources for R available on the Web, for help in just about every
domain.
34. Unlimited possibilities.
With R, you're not restricted to choosing a pre-defined set of routines. You can use code
contributed by others in the open-source community, or extend R with your own functions.
And R is excellent for "mash-ups" with other applications: combine R with a MySQL
database, an Apache web-server, and the Google Maps API and you've got yourself a realtime GIS analysis toolkit. That's just one big idea -- what's yours?
“The great beauty of R is that you can modify it to do all
sorts of things,” said Hal Varian, chief economist at
Google. “And you have a lot of prepackaged stuff that’s
already available, so you’re standing on the shoulders of
giants.”
35. Here are our suggestions for the best on-line resources for information about R.
The R Project homepage. Look here for official news from the R Project,
plus links to documentation, mailing lists, the official R FAQs, and more.
StackOverflow. Got a question about R? Search for questions tagged with "r"
and you'll probably find your question already answered. If not, ask away.
R bloggers. For a steady stream of news, tips and articles related to R follow
this blog aggregator for posts from dozens of R bloggers, including the team
from Revolution Analytics.
36. The Video Rchive. Watch recordings of speakers at R user group meetings and
conferences talk about various aspects of using R.
#rstats on Twitter. To listen in on (or contribute to) an information-rich
conversation about R 140 characters at a time, search for the #rstats hastag.
CRAN Task Views. The list of 2000+ add-on packages for R can be daunting, but
these Task Views list the most important ones in domain-specific areas as diverse as
Finance, Clinical Trials, and Machine Learning.
Crantastic.org.On the other hand, if you're looking for a specific page, you can
search by keyword at this interactive directory of all R packages. You can also log in
and rate and comment on packages.