Development

Development

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Package management: Using repositories in production systems | R-bloggers
Package management: Using repositories in production systems | R-bloggers
Data science is characterized among other things using open source tools. An advantage when working with open source languages such as R or Python is the large package world. This provides tools for numerous use cases and problems through the development within huge communities. The packages are organized in digital ...
·r-bloggers.com·
Package management: Using repositories in production systems | R-bloggers
Developing a modern data workflow for regularly updated data
Developing a modern data workflow for regularly updated data
This Community Page article describes a data management workflow that can be readily implemented by small research teams and which solves the core challenges of managing regularly updating data. It includes a template repository and tutorial to assist others in setting up their own regularly updating data management systems.
·journals.plos.org·
Developing a modern data workflow for regularly updated data
Good practices in R programming
Good practices in R programming
R is a free software environment for statistical computing and graphics, available from The R Project for Statistical Computing. At Indiana University, R is ...
·kb.iu.edu·
Good practices in R programming
Excel Models Best Practices - Guide to Creating Great Excel Models
Excel Models Best Practices - Guide to Creating Great Excel Models
When working on large and complicated financial models in Microsoft Excel, it can be quite challenging to document them clearly for the users’ ease of use and understandability. We will discuss here several Excel models best practices and other useful tips and tricks that users can apply to maintain and audit your
·corporatefinanceinstitute.com·
Excel Models Best Practices - Guide to Creating Great Excel Models
Shiny 1.0.4
Shiny 1.0.4
Shiny 1.0.4 is now available on CRAN. To install it, run: install.packages("shiny") For most Shiny users, the most exciting news is that file inputs now support dragging and dropping: It is now possible to add and remove tabs from a tabPanel, with the new functions insertTab(), appendTab(), prependTab(), and removeTab(). It is also possible to hide and show tabs with hideTab() and showTab(). Shiny also has a new a function, onStop(), which registers a callback function that will execute when the application exits.
·blog.rstudio.com·
Shiny 1.0.4
Exploratory
Exploratory
Data Science is not just for Engineers and Statisticians. Exploratory makes it for Everyone.
·exploratory.io·
Exploratory
How to Use Excel: 12 Techniques for Power Users
How to Use Excel: 12 Techniques for Power Users
Excel is great, but trying to figure it out how to use Excel on your own can get you only so far because it isn’t intuitive. But if you use the techniques and tips in this tutorial, you’ll be able...
·business.tutsplus.com·
How to Use Excel: 12 Techniques for Power Users
‎R Programming Compiler on the App Store
‎R Programming Compiler on the App Store
‎Write R code directly on your iPhone, iPad and iPod Touch! This app is ideal for learning and testing code snippets! R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used am…
·apps.apple.com·
‎R Programming Compiler on the App Store
Building a shiny app with drag and drop data interface
Building a shiny app with drag and drop data interface
Introduction Data visualization is an important aspect of the data science work flow. This app enables the analyst to understand the data in question. In this post, we will build an application whi…
·pradeepadhokshaja.wordpress.com·
Building a shiny app with drag and drop data interface
pins: Pin, Discover and Share Resources | RStudio Blog
pins: Pin, Discover and Share Resources | RStudio Blog
Today we are excited to announce the pins package is available on CRAN! pins allows you to pin, discover and share remote resources, locally or in remote storage. If you find yourself using download.file() or asking others to download files before running your R code, use pin() to achieve fast, simple and reliable reproducible research over remote resources. Pins You can use the pins package to: Pin remote resources locally to work offline and cache results with ease, pin() stores resources in boards which you can then retrieve with pin_get().
·blog.rstudio.com·
pins: Pin, Discover and Share Resources | RStudio Blog
Productionizing Shiny and Plumber with Pins · R Views
Productionizing Shiny and Plumber with Pins · R Views
Producing an API that serves model results or a Shiny app that displays the results of an analysis requires a collection of intermediate datasets and model objects, all of which need to be saved. Depending on the project, they might need to be reused in another project later, shared with a colleague, used to shortcut computationally intensive steps, or safely stored for QA and auditing. Some of these should be saved in a data warehouse, data lake, or database, but write access to an appropriate database isn’t always available.
·rviews.rstudio.com·
Productionizing Shiny and Plumber with Pins · R Views
Using Shiny with Scheduled and Streaming Data · R Views
Using Shiny with Scheduled and Streaming Data · R Views
Note: This article is now several years old. If you have RStudio Connect, there are more modern ways of updating data in a Shiny app. Shiny applications are often backed by fluid, changing data. Data updates can occur at different time scales: from scheduled daily updates to live streaming data and ad-hoc user inputs. This article describes best practices for handling data updates in Shiny, and discusses deployment strategies for automating data updates.
·rviews.rstudio.com·
Using Shiny with Scheduled and Streaming Data · R Views
Production
Production
RStudio's webinars offer helpful perspective and advice to data scientists, data science leaders, DevOps engineers and IT Admins. Presenters come from companies around the globe, as well as the RStudio staff.
·rstudio.com·
Production