Fast JSON package for R.

Build robust and maintainable software with object-oriented design patterns in R. Design patterns abstract and present in neat, well-defined components and interfaces the experience of many software designers and architects over many years of solving similar problems. These are solutions that have withstood the test of time with respect to re-usability, flexibility, and maintainability. R6P provides abstract base classes with examples for a few known design patterns. The patterns were selected by their applicability to analytic projects in R. Using these patterns in R projects have proven effective in dealing with the complexity that data-driven applications possess.

Invite JavaScript into your Data Science workflow.

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In this page you can find various blogs and articles that are related to this topic: Working With Data In Shiny Apps

Welcome to the workshop and hello, bslib!

A fast JSON parser, generator and validator which converts JSON, NDJSON (Newline Delimited JSON) and GeoJSON (Geographic JSON) data to/from R objects. The standard R data types are supported (e.g. logical, numeric, integer) with configurable handling of NULL and NA values. Data frames, atomic vectors and lists are all supported as data containers translated to/from JSON. GeoJSON data is read in as simple features objects. This implementation wraps the yyjson C library which is available from .

mlr3: Machine Learning in R - next generation. Contribute to mlr-org/mlr3 development by creating an account on GitHub.

library(shiny) is better with library(bslib), so let’s make it official and update our shinyapp snippet together.

Working with complex, hierarchically nested JSON data in R can be a bit of a pain. In this post, I illustrate how you can convert JSON data into tidy tibbles with particular emphasis on what I’ve found to be a reasonably good, general approach for converting nested JSON into nested tibbles. I use three illustrative examples of increasing complexity to help highlight some pitfalls and build up the logic underlying the approach before applying it in the context of some real-world rock climbing competition data.

My lab investigates how blood pressure can be treated more effectively. Much of that work involves the painstaking development of new concepts and research methods to move forward the state of the art. For example, our work on urinary extracellular vesicles’ mRNA as an ex vivo assay of the ligand-activated transcription factor activity of mineralocorticoid receptors is challenging, fun, and rewarding. With a lot of work from Andrea Berrido and Pradeep Gunasekaran in my lab, we have been moving the ball forward on several key projects on that front.

You can read the original post in its original format on Rtask website by ThinkR here: Pimping your shiny app with a JavaScript library : an example using sweetalert2 You think that some of the components of {shiny} are not very functional or downright austere? Are you looking to implement some feature in your app but it is not available in the {shiny} toolbox? Take a look at JavaScript! JavaScript is a very popular programming language that is often used to add features to web pages. With HTML and This post is better presented on its original ThinkR website here: Pimping your shiny app with a JavaScript library : an example using sweetalert2

Weekly bite-sized tips on DataViz, Shiny and Stats/Machine Learning.

A guide to R development.

Here is how to create a progress bar in R that is versatile when working with loops, for example, by using package purrr.

Welcome to WikiOD - Wiki Online Documentation WikiOD is a collaborative writing project to build the highest quality online documentation of all programming languages, education-related subjects and concepts.

When developing a package it is essential to track the changes you make to your code. This is especially vital if they are breaking changes which have implications for any code written that depends on your package, i.e. a major version bump. Although you can always look back at your version control history in git, it is also convenient to have documentation which summarises the changes. This is where the NEWS file comes in.

Easily automate the following tasks to describe data frames: Summarise the distributions, and labelled missings of variables graphically and using descriptive statistics. For surveys, compute and summarise reliabilities (internal consistencies, retest, multilevel) for psychological scales. Combine this information with metadata (such as item labels and labelled values) that is derived from R attributes. To do so, the package relies on rmarkdown partials, so you can generate HTML, PDF, and Word documents. Codebooks are also available as tables (CSV, Excel, etc.) and in JSON-LD, so that search engines can find your data and index the metadata. The metadata are also available at your fingertips via RStudio Addins.

Yet another post with a focus on package documentation! This time, we’ll cover vignettes a.k.a “long-form package documentation”, both basics around vignette building and infrastructure, and some tips for more maintainer- and user- friendliness. What is a vignette? Where does it live? In this section we shall go over basics of package vignettes. Vignette 101 In the “R packages” book by Hadley Wickham and Jenny Bryan, the vignettes chapter starts with “A vignette is a long-form guide to your package.

[Disclaimer] The package presented in this blog post is stillexperimental at the time of writing these lines (2021-02-18), so itmight face some API changes in the future.Multi-page in {shiny}, and random thoughts about designing web applicationsSomething that has been bugging me for a while is the inability toreally share a specific part of a {shiny} dashboard, at leastnatively, using a specific path. In other words, I’ve always wanted tobe able to do something like my-uberapp.io/contact to share withsomeone a specific part of the app. And only this part.Another need I was facing, probably born out of building webapplications using NodeJS, is a natural way to manipulate endpoints,http requests and responses, so that I could do something prettycommon in web application: a home page, and next to it a login page thatverifies your identity, and redirects you to another page after settinga cookie. That of course, leads to the necessity of having the abilityto define various endpoints with a specific behavior for each: aunique UI, and a 18 server response. Pure, native, multi-endpointapplications, not one big ball of UI with parts hidden using JavaScript& CSS, and a global server function that might launch computation youdon’t need for your page.A few weeks back, I’ve decided to focus on this question. Multi-page in{shiny} is not new: I’ve found both{shiny.router}, and{blaze} that already implement aform of “multi-page”. Note: both these packages work perfectly, and they definitely do whatthey are designed for. There are the product of tremendous work withsmart implementations and my goal is in no way to undermine thesepackages. They just don’t answer the need I was having, hence my newapproach to this question. {brochure} will answer the needs for morelarge and complex applications built with {shiny}, while both the{shiny.router} & {blaze} should be easier to get started with.As far as I saw it, these two packages didn’t answer what I was lookingfor: “real” multi-page. Both {shiny.router} & {blaze} stillproduce a form of Single Page Application, but plays with the URL tomake you feel like you’re on a multi-page application.For example, {shiny.router} hides the parts of the app via JS & CSS –if you browse the source code, you’ll still see the hidden HTML. Thatalso implies that they are potential conflicts between ids: you have tobe sure that all your pages have unique id on their ouputs/inputs,otherwise the app doesn’t render, meaning that a “page” doesn’t haveits own server function. And of course, if you look at the Networktab of your browser developer tool, you’ll see that there is no newGET request made whenever a new “page” is loaded: this new page isstill the same application, with parts hidden through JavaScript.Hiding UI parts is a practice that has always been bothering me in termof performance – on a large {shinydashboard}, for example, you’ll betransferring the full HTML, CSS and JavaScript for the whole applicationwhen the app launches, even if the user never visits all the tabs.{shiny} is very smart when it comes to transferring CSS and JS files:for example, it will only serve the external resources for thesliderInput() if there is one in the app. But if you’ve got a{shinydashboard} dashboard with 19 pages, and the sliderInput() ison page 19 and will only be seen by 0.1% of the visitors, it will stillbe transferred to every user, regardless of whether or not they need it.This might result in lowering the performance of the app, as it might beraising the time to ‘First Meaningful Paint’, and of course it is awaste of resources, especially if your application is served to peoplewith low bandwidth, and/or browsing your app using cellular data. I knowthis is a question you’ve probably never asked yourself before ;) - butperformance is a pretty common question in the web development world,and something to be aware if you’re serving your app at scale. If thisis something you’re interested in, I suggest reading Why does speedmatter? from the Google dev center,and WebPerformancefrom Mozilla Web Docs. See also 14.2.2 Profiling{shiny}from the Engineering Shiny book.And, another final thing I wanted is a way to “flush” objects when youchange page: in the current implementation of {shinydashboard}, if youcreate something on tab1, it will still be there in tab2, takingsome space in the RAM, even if you don’t need it. It’s rather convenientbecause you don’t have to think about these things: you start a{shiny} session, and all the objects will still be there as long asthe session lives. But that also means that as soon as the sessionstops, there is no way to get the values back as they only live in theRAM, inside the session.On the other hand, with an implementation where every page gets its own{shiny} session, you need to find a way to identify the user with aform of id, save the relevant values (potentially write in a DB), thenfetch these values again using the id stored in the browser(typically, you have a session cookie in the browser that is used as akey to search the DB). A process forcing you to think more carefullyabout the data flow of your app. What that also means is that as theid is a cookie in the browser, whenever the app crashes, it will beable to reload to the exact same place as long as the cookie is still inthe browser (well, not exactly but you understand the idea).So, back to our first topic – what was I looking for? My goal was tofind a way to build {shiny} applications that are nativelymulti-page. In other words, an application that answers to a requeston /page2, not an app that silently redirect to /, nor anapplication that requires playing with /#!/page2 in the url.(Reminder, # is traditionally used as an anchor tag in a web page).And, of course, I wanted each page to have its own shiny session, itsown UI and server functions.I was also looking for a way to manipulate both the GET request fromthe server and the httpResponse that is sent back, for example inorder to set httpOnly cookies in the header of the HTTP Response orchange the HTTP status code, the same way you can manipulate thesewhen building an application with NodeJS for example. This is somethingthat you’d find in the{ambiorix} project by theamazing John Coene (who will get some SEOjuice thanks to this backlink). It’s an amazing approach that definitelyresonated with me as I’ve been a NodeJS & express.js user for sometime now, but that’s still far from the way you’d build things in{shiny}, and will probably require a lot of coding to get anapplication up and running (which is good, because I love coding, butlet’s use our {shiny} knowledge for now :) ).To sum up, I wanted a way to get closer to how you build applications inother languages, but will as little deviation as possible from the{shiny} way of building apps.Here comes {brochure}As said on the top of the article, this package is still a work inprogress, so you might see some changes in the near future :)[Disclaimer, again] The way you will build applications with{brochure} is different from the way you usually build {shiny} apps,as we no longer operate under the single page app paradigm. Please keepthis in mind and everything should be fine.InstallationYou can install the development version of {brochure} with:remotes::install_github("ColinFay/brochure")Minimal {brochure} Applibrary(shiny)library(brochure)## ## Attaching package: 'brochure'## The following object is masked from 'package:utils':## ## pagepage()A brochureApp is a series of pages that are defined by an href(the path/endpoint where the page is available), a UI and a serverfunction. This is conceptually important: each page has its own shinysession, its own UI, and its own server.brochureApp( # First page page( href = "/", ui = fluidPage( h1("This is my first page"), plotOutput("plot") ), server = function(input, output, session){ output$plot In your browser, you can now navigate to /, and to /page2.redirect()Redirections can be used to redirect from one endpoint to the other:brochureApp( page( href = "/", ui = tagList( h1("This is my first page") ) ), redirect( from = "/nothere", to = "/" ), redirect( from = "/colinfay", to = "https://colinfay.me" ))- In your browser, if you got to /nothere, and you’ll be redirectedto / .req_handlers & res_handlersSorry what?This is where things get more interesting.Each page, and the global app, have a req_handlers and res_handlersparameters, that can take a list of functions.An *_handler is a function that takes as parameter(s): For req_handlers, req, which is the request object (see belowfor when these objects are created). For example function(req){print(req$PATH_INFO); return(req)}. For res_handlers , res, the response object, & req , therequest object. For example function(res, req){ print(res$content);return(res)}. req_handlers must return req & res_handlers must returnres.They can be used to register log, or to modify the objects, or any kindof things you can think of. If you are familiar with express.js, youcan think of req_handlers as what express.js calls“middleware”. These functions are run when R is building the HTTPresponse to send to the browser (i.e, no server code has been run yet),following this process: R receives a GET request from the browser, creating a requestobject, called req The req_handlers are run using this req object, potentiallymodifying it R creates an httpResponse, using this req and how you definedthe UI The res_handlers are run on this httpResponse (first app levelres_handlers, then page level res_handlers), , potentiallymodifying it The httpResponse is sent back to the browserNote that if any req_handlers returns an httpResponse object, itwill be returned to the browser immediately, without any furthercomputation. This early httpResponse will not be passed to theres_handlers of the app or the page. This process can for example beused to send custom httpR...

I recently built a small Shiny app where I had to search for how to do a lot of things. Here are 6 things I learned doing that. Maybe a Shiny beginner will find something useful in here.