https://www.sthda.com (C) 2005-2026 PHPBoost en PHPBoost https://www.sthda.com/english/articles/19-r-conference/54-the-r6-class-system/ https://www.sthda.com/english/articles/19-r-conference/54-the-r6-class-system/ R6 is an implementation of a classical object-oriented programming system for R. In classical OOP, objects have mutable state and they contain methods to modify and access internal state.

This stands in contrast with the functional style of object-oriented programming provided by the S3 and S4 class systems, where the objects are (typically) not mutable, and the methods to modify and access their contents are external to the objects themselves.

R6 has some similarities with R's built-in Reference Class system. Although the implementation of R6 is simpler and lighter weight than that of Reference Classes, it offers some additional features such as private members and robust cross-package inheritance.

In this talk I will discuss when it makes sense to use R6 as opposed to functional OOP, demonstrate how to use the package, and explore some of the internal design of R6.



Source: useR 2017]]> Thu, 03 Aug 2017 15:55:00 +0200 https://www.sthda.com/english/articles/19-r-conference/53-text-mining-the-tidy-way/ https://www.sthda.com/english/articles/19-r-conference/53-text-mining-the-tidy-way/ text mining easier and more effective.

We introduce the tidytext package for approaching text analysis from a tidy data perspective.

We can manipulate, summarize, and visualize the characteristics of text using the R tidy tool ecosystem; these tools extend naturally to many text analyses and allow analysts to integrate natural language processing into effective workflows already in wide use.

We explore how to implement approaches such as sentiment analysis of texts and measuring tf-idf to quantify what a document is about.



Source: useR 2017]]> Thu, 03 Aug 2017 15:46:00 +0200 https://www.sthda.com/english/articles/19-r-conference/52-a-tidy-data-model-for-natural-language-processing/ https://www.sthda.com/english/articles/19-r-conference/52-a-tidy-data-model-for-natural-language-processing/ cleanNLP, which provides a set of fast tools for converting a textual corpus into a set of normalized tables.

The underlying natural language processing pipeline utilizes Stanford's CoreNLP library, exposing a number of annotation tasks for text written in English, French, German, and Spanish (Marneffe et al. 2016, De Marneffe et al. (2014)).

Annotators include tokenization, part of speech tagging, named entity recognition, entity linking, sentiment analysis, dependency parsing, coreference resolution, and information extraction (Lee et al. 2011).

The functionality provided by the package applies the tidy data philosophy (Wickham 2014) to the processing of raw textual data.

Together, these contributions simplify the process of doing exploratory data analysis over a corpus of text.

The output works seamlessly with both tidy data tools as well as other programming and graphing systems.

The talk will illustrate the basic usage of the cleanNLP package, explain the rational behind the underlying data model, and show an example from a corpus of the text from every State of the Union address made by a United States President (Peters 2016).



Source: useR 2017]]> Thu, 03 Aug 2017 15:39:00 +0200 https://www.sthda.com/english/articles/19-r-conference/51-text-analysis-and-text-mining-using-r/ https://www.sthda.com/english/articles/19-r-conference/51-text-analysis-and-text-mining-using-r/ text analysis and natural language processing in R, with an emphasis on my R package quanteda but also covering other major tools in the R ecosystem for text analysis (e.g. stringi).

The talk would is tutorial covers how to perform common text analysis and natural language processing tasks using R.

Specifically, I will demonstrate how to format and input source texts, how to structure their metadata, and how to prepare them for analysis.

This includes common tasks such as tokenisation, including constructing ngrams and "skip-grams", removing stopwords, stemming words, and other forms of feature selection.

I will also show to how to tag parts of speech and parse structural dependencies in texts.

For statistical analysis, I will show how R can be used to get summary statistics from text, search for and analyse keywords and phrases, analyse text for lexical diversity and readability, detect collocations, apply dictionaries, and measure term and document associations using distance measures.

Our analysis covers basic text-related data processing in the R base language, but most relies on the quanteda package (https://github.com/kbenoit/quanteda) for the quantitative analysis of textual data.

We also cover how to pass the structured objects from quanteda into other text analytic packages for doing topic modelling, latent semantic analysis, regression models, and other forms of machine learning.



Source: useR 2017
]]> Thu, 03 Aug 2017 15:32:00 +0200 https://www.sthda.com/english/articles/19-r-conference/50-interacting-with-databases-from-shiny/ https://www.sthda.com/english/articles/19-r-conference/50-interacting-with-databases-from-shiny/ database from R can be challenging. This is made worse when you need to interact with a database from a live Shiny application. To demystify this process, I'll do two things.

First, I'll talk about best practices when connecting to a database from Shiny. There are three important packages that help you with this and I'll weave them into this part of the talk.

The DBI package does a great job of standardizing how to establish a connection, execute safe queries using SQL (goodbye SQL injections!) and close the connection.

The dplyr package builds on top of this to make even easier to connect to databases and extract data, since it allows users to query the database using regular dplyr syntax in R (no SQL knowledge necessary).

Yet a third package, pool, exists to help you when using databases in Shiny applications, by taking care of connection management, and often resulting in better performance.

Second, I'll demo these concepts in practice by showing how we can connect to a database from Shiny to create a CRUD application. I will show the application running and point out specific parts of the code (which will be publicly available).



Source: useR 2017]]> Thu, 03 Aug 2017 15:24:00 +0200 https://www.sthda.com/english/articles/19-r-conference/49-scraping-data-with-rvest-and-purrr/ https://www.sthda.com/english/articles/19-r-conference/49-scraping-data-with-rvest-and-purrr/ rvest (the knife) and purrr (the frying pan) to scrape interesting data from a bunch of websites.

rvest is a popular R package that makes it easy to scrape data from html web pages.

purrr is a relatively new package that makes it easy to write code for a single element of a list that can be quickly generalized to the rest of that same list.



Source: useR 2017]]> Thu, 03 Aug 2017 15:09:00 +0200 https://www.sthda.com/english/articles/19-r-conference/48-ggiraph-a-ggplot2-extension-for-interactive-graphics/ https://www.sthda.com/english/articles/19-r-conference/48-ggiraph-a-ggplot2-extension-for-interactive-graphics/ ggiraph package combines ggplot2 and D3.js tools. From a user point of view, it enables the production of interactive graphics from ggplot2 objects by using their extension mechanism.

It provides useful interactive capabilities such as tooltips and zoom/pan.

Last but not least, graphical elements can be selected when a ggiraph object is embedded in a Shiny app: selection will be available as a reactive value.

The interface is simple, flexible and does not requires effort to be integrated in R Markdown documents or Shiny applications.

In this talk I will introduce ggiraph and show examples of using it as a data visualisation tools in RStudio, Shiny applications and R Markdown documents.



Source: useR 2017]]> Thu, 03 Aug 2017 14:57:00 +0200 https://www.sthda.com/english/articles/19-r-conference/47-jamovi-a-spreadsheet-for-r/ https://www.sthda.com/english/articles/19-r-conference/47-jamovi-a-spreadsheet-for-r/ amovi project aims to make R and its ecosystem of analyses accessible to this large body of users.

jamovi provides a familiar, attractive, interactive spreadsheet with the usual spreadsheet features: data-editing, filtering, sorting, and real-time recomputation of results.

Significantly, all analyses in jamovi are powered by R, and are available from CRAN. Additionally, jamovi can be placed in 'syntax mode', where the underlying R code for each analysis is produced, allowing for a seamless transition to an interactive R session.

We believe that jamovi represents a significant opportunity for the authors of R packages. With some small modifications, an R package can be augmented to run inside of jamovi, allowing R packages to be driven by an attractive user-interface (in addition to the normal R environment).

This makes R packages accessible to a much larger audience, and at the same time provides a clear pathway for users to migrate from a spreadsheet to R scripting.

This talk introduces jamovi, introduces its user-interface and feature set, and demonstrates the ease with which R packages can be augmented to additionally support the interactive spreadsheet paradigm.



Source: useR 2017]]> Thu, 03 Aug 2017 12:16:00 +0200 https://www.sthda.com/english/articles/19-r-conference/46-dose-response-analysis-using-r-ii/ https://www.sthda.com/english/articles/19-r-conference/46-dose-response-analysis-using-r-ii/ Dose-response analysis using R II



Source: useR 2017]]> Thu, 03 Aug 2017 11:33:00 +0200 https://www.sthda.com/english/articles/19-r-conference/45-introduction-to-optimal-changepoint-detection-algorithms/ https://www.sthda.com/english/articles/19-r-conference/45-introduction-to-optimal-changepoint-detection-algorithms/ optimal changepoint detection algorithms.



Source: useR 2017]]> Thu, 03 Aug 2017 11:27:00 +0200