Problem

Shiny apps are, by default, a bit limited when looking at it from this perspective.  It can handle only GET requests by default, unless you are a technical expert in this field, see this stack overflow post. Furthermore shiny apps can have just one entry-point “/”. So you can’t have another entry-point “/page2”. Thus, the e-commerce shop is not possible out of the box in R shiny.

Solution

There are multiple solutions to support multiple pages. The one that I am using since a while is the package brochure developed by Colin Fay. It is still in development, so you might encounter some issues but I haven’t found any major bugs yet. A brochure app consists of a series of pages that are defined by an endpoint/path, a UI and a server function. Thus each page has its own shiny session, its own UI, and its own server! This is important to keep in mind.  A separate session for each page has some advantages but also some disadvantages (e.g. how to pass user data between pages?). A very simple brochureApp looks like this:

library(shiny)
library(brochure)

brochureApp(
  # First page
  page(
    href = "/",
    ui = fluidPage(
      h1("My first page"), 
      plotOutput("plot")
    ),
    server = function(input, output, session){
      output$plot <- renderPlot({
        plot(iris)
      })
    }
  ), 
  # Second page, no server-side function
  page(
    href = "/page2", 
    ui =  fluidPage(
      h1("My second page")
    )
  )
)

Donation app

Coming back to the E-commerce shop example, I have developed an app where one can sponsor me for my open source work on R packages. The app has an integration with Stripe to make a donation and a thank you and error page. When calling Stripe you have to give the two endpoints for these pages and by using brochure, I am able to setup these endpoints. See the app on shinyapps.io and of course I would appreciate it, if you use the app!:-)

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See the live version. Let me know if you like it or you are interested in some other charts. Enjoy the Winter Olympics!

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In order to serve patients as best as they can, researchers need as much (quality) data that can serve their purpose.Surveillance, Tracking and Reporting through Informed Data Collection and Engagement (STRIDE) is an interactive data and visualization dashboard. It includes clinical trials enrollment, bio-specimen inventory, tumor registry analytic cases, and catchment area information.

They have approached me to improve the user interface of their dashboard and that’s what I have been doing! Next to this, I have been helping the person that created the initial dashboard with the following concepts.

• DRY (Don’t Repeat Yourself); this is basically re-using of code that you already wrote so you don’t have to write this code again and you will end up with less code to maintain.
• Reproducible results. When I tried to run the initial dashboard, it didn’t work since some packages were not imported and it was not clear which version of those packages I should use. I have added renv as dependency management, this will add a file to your project containing all packages and their versions. It’s easy to setup and it’s worth it!
• Interactive charts. The initial charts were made with the package ggplot2. This is a nice package for data visualization and it offers many charts and display options. But, it’s not interactive and that’s what most people want and expect in a dashboard.

See below for some screenshots of this app.

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Exploratory Analysis

Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics. This is often done by using data visualization methods. The main purpose of EDA is to help look at data before making any assumptions. For me it’s one of the nicest parts of the data science! Since you don’t know yet what’s in the data and there will always be surprises. It’s like you are on holiday and exploring the area that you seeing for the first time:-)

For example, is a certain variable in the data normally distributed or not? Is there any missing data or duplicated values? In my experience, yes in most cases, there is missing and duplicated data. We need to fix these issues before we can do the real analysis. This phase is called data cleaning, you might have heard about this before.

Representativeness Analyses

In general, a representative sample is a group or set chosen from a larger statistical population that adequately replicates the larger group according to whatever characteristic or quality is under study. In case of CITO, we like to know if the sample data set has more or less the same characteristics regarding scores.  For example, are the average and standard deviation of the sample data set close to the ones of the total data set. I have plotted the distributions of the 2 data sets in a single chart, in order to compare them. In the subtitle one can find the average, standard deviation and the median.

Below you can find some of the charts I made for both EDA and the representativeness Analyses. The code is available in a public repository on github. It can be run using a docker container, R and renv for library management.

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My energy provider is giving me my monthly usage and production. Based on this I have created the visual below. It states for each month the consumption (red) vs the production (blue). The electricity meter is installed at March 1 so there is no data before that date. The solar panels have been installed in May. So far this year the numbers are looking very good, since the solar panels produce the most in spring and summer. In the coming months the bars will be more red! See below for my current balance or have a look at my energy for the current status.

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Patients that participate in the study are put in either a placebo group or the Valsartan group. Next, over the course of time, their hospital status is checked. Do they need mechanical ventilation, are they on the IC or have they lost the battle against the virus. For every patient this data is updated daily. Castor EDC is supporting this trial by giving the Radboud University the possibility to store their data in the Castor EDC web application. They asked me to develop a dashboard to show statistics about the 2 study groups. That’s my small contribution to this project:)

The dashboard is getting the data by using the Castor EDC API.  The frontend is created with the flexdashboard package in R. It’s showing a table with the statistics per group and for each group a plot in time of the number of patients that are either on the IC, need mechanical ventilation or have died. Since the trial has recently started, I can’t share results yet unfortunately. Hope to bring some good news about this later on!

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It has already killed more people than SARS did. To answer the question about the growth of the corona-virus I made a dashboard. It displays the amount of people that:

• are confirmed to have the virus (Confirmed)
• have recovered from it (Recovered)
• have died because of it (Deaths)

The first tab displays the number of people for those categories over time while the second tab displays the number of people per location. In the left sidebar, the total amount of Confirmed, Recovered and Deaths is shown. However, you can also filter by Country or Region.

Looking at the Confirmed cases, there is a huge spike around February 12th. This is caused by the fact that also patients that have symptoms but are not tested positive (yet) are added to the reported cases.

Around, 22 February, there’s a little spike because of new cases found in Italy and South Korea. Let’s hope the numbers are going down to zero soon!

View the live dashboard

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Right sidebar

Many shiny apps are using the shinydashboard package which offers a left sidebar and next to it the main body of the application. When adding lots of inputs the sidebar can get full, so in some cases it’s convenient to have a right sidebar as well. A right sidebar is one of the nice features of the shinydashboardPlus package.

Reset button

The periscope package comes by default with a reset button. This button resets your application to the state at startup. Since it’s not useful for all apps, you can disable it now using the resetbutton parameter in the function create_new_application().

Periscope sample applications

Sample shiny application demonstrating the functionality of the periscope framework. This is very useful if you are starting with the periscope package and want to explore the functionality. The screenshot below is from a sample application with a right sidebar. See sample apps to view the apps live on the periscope server.

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Periscope

Periscope is a new R package which provides a predefined but flexible template for new Shiny applications with a default dashboard layout. Furthermore it provides user alerts, a nice busy indicator, application reset and logging features.

One of the most important features of the Shiny applications created with this framework is the separation by file of functionality that exists in one of the three Shiny scopes. These scopes are: global, server-global, and server-local. The framework forces application developers to consciously consider scoping in Shiny applications by making scoping distinctions very clear without interfering with normal application development. Scoping consideration is important for performance and scaling.  This is critical when working with large datasets and/or across many users. In addition to providing a template application, the framework also contains a number of convenient modules. 

• (multi)file download button module
• downloadable table module.

CanvasXpress

The plots in the application are created using CanvasXpress, which is a package for (interactive) data visualization especially for reproducible research.  It supports a large number of visualizations to display scientific and non-scientific data which includes: Area, AreaLine, Bar, BarLine, Boxplot, Bubble, Candlestick, Chord, Circular, Contour, Correlation, Density, Donnut, DotLine, Dotplot, Genome, Heatmap, Histogram, Kaplan-Meier, Layout, Line, Map, Network, NonLinear-Fit, Oncoprint, ParallelCoordinates, Pie, Radar, Remote-Graphs, Sankey, Scatter2D, Scatter3D, ScatterBubble2D, Stacked, StackedLine, StackedPercent, StackedPercentLine, Sunburst, TagCloud, Tree, Treemap, Venn, Video, Violin. This blogpost gives a good overview on how to get started with CanvasXpress.

View the Interactive Gene Explorer or the source code on github.

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