The purpose of Shiny is to provide an intuitive and user-friendly interface to R. R is a highly popular statistical environment for doing heavy data analysis and constructing statistical models, and therefore is highly popular among data scientists. However, for a user with a non-coding background, using R to conduct such analysis can become quite intensive. This is where Shiny Web Apps come in. Essentially, Shiny allows for a more intuitive graphical user interface that is still capable of conducting sophisticated data analysis — without the need for extensive coding on the part of the end user.
In this article on using Shiny with R and HTML, the author illustrated how an interactive web application can be created to conduct analysis without the need for direct manipulation of code. In this article, the author will use a slightly different model to illustrate how the Shiny environment can be customized to work with the end user in a more intuitive fashion. Essentially, the goal of this article is to illustrate how a user can:
- Build an application by linking the UI and server side
- How to customize the themes available in the Shiny Themes library
- Implement error messages in order to provide guidance to an end user on how to use a particular program
The program itself that is developed for this tutorial is quite basic: a slider input allows the user to manipulate a variable within the program by means of reactivity, which causes instantaneous changes in the line plot output that is developed by means of reactivity.
This inherent function gives Shiny a significant advantage over using R code as a stand-alone. Traditionally, in order to analyze the change in a particular variable, the code must be manipulated directly (or the data from which the code is reading), and this can ultimately become very inefficient. However, Shiny greatly speeds up this process by allowing the user to manipulate the variables in a highly intuitive manner, and changes are reflected instantly.
However, the whole purpose of Shiny is to make an R Script as interactive as possible. In this regard, the user will want to be able to add features to the program that go well beyond reactivity. Two such aspects of this that the author will discuss in this tutorial are:
shinythemesin order to customize the appearance of our Shiny appearance
- Constructing a
validate()function in order to display an alert once variables are manipulated in a certain manner
See the tutorial here: SitePoint
It’s been a long wait, but it’s worth it. A new version of Metabase is ready for you (and end users)!
You can download the new version of Metabase at http://www.metabase.com/start/
To upgrade, see the instructions for your platform at http://www.metabase.com/docs/latest/operations-guide/start.html#upgrading-metabase
Let’s see what Metabase says about their new version:
Data access permissions
A way to control access to sensitive data has been one of the most requested features since we launched. With 0.20, we’ve taken the first major step in giving you the ability to lock down an instance. We now allow you to create user groups, and control their access to databases, tables and raw SQL queries. This lets you control access to sensitive data while still allowing your end users to answer their own questions within the datasets they’re allowed access to.
Getting started guides
In most places we’ve worked, there’s typically an email that gets forwarded around, or a Google doc that describes how to use the analytics systems available. Some more sophisticated setups use an internal wiki or other website that has an inventory of what’s available. We believe that the best way to keep these current is to have them be built into the application. Now you can create a cheatsheet to help new users know which dashboards, metrics and reports are the most important as well as provide caveats for use, advice on who to contact for help, and more.
Charting improvements – Part 2
Following up on our previous releases’ improvements to charting, we’ve added new chart types (progress, scatter and bubble charts), improved your control over axes, and allowed you to customize the display of dashboard cards made up of multiple questions.
Lots of you have been clamoring for a way to use Metabase with Oracle databases. Now you can! Due to Oracle’s license for the underlying JDBC driver, you’ll need to do a few extra steps — check out www.metabase.com/docs/latest/administration-guide/databases/oracle.md for details.
Druid performance and timezone fixes
We’ve made some improvements to how Metabase works with Druid that fix a number of timezone bugs and improve charting performance.
Metabase also fixed many reported issues and bugs from their GitHub page.
“With the advent of data science and the increased need to analyze and interpret vast amounts of data, the R language has become ever more popular. However, there’s increasingly a need for a smooth interaction between statistical computing platforms and the web, given both 1) the need for a more interactive user interface in analyzing data, and 2) the increased role of the cloud in running such applications.
Statisticians and web developers have thus seemed an unlikely mix till now, but make no mistake that the interactions between these two groups will continue to increase as the need for web-based platforms becomes ever more popular in the world of data science. In this regard, the interaction of the R and Shiny platforms is quickly becoming a cornerstone of interaction between the world of data and the web.
In this tutorial, we’ll look primarily at the commands used to build an application in Shiny — both on the UI (user interface) side and the server side. While familiarity with the R programming language is invariably helpful in creating a Shiny app, expert knowledge is not necessary, and this example will cover the building of a simple statistical graph in Shiny, along with some basic commands illustrating how to customize the web page through HTML.” – Michael Grogan
Continue to tutorial: SitePoint