Interactive IRR Shiny App

Michael S Lee
October 25, 2015

Use Cases

The Cost Recovery App is intended to address the following:

  1. Complex Excel workbooks designed for cost recovery analysis, which often included complex formulas and whose interactivity was often limited to data entry in specified cells.
  2. The need for simplicity and mobility, the type of 'back of the napkin' analysis that could be done on any web browser, and allows users to gain a bird's-eye view of the problem.
  3. Excessively complex reports at meetings. The Cost Recovery App is easy for audiences to digest and understand, and will not divert attention away from the presenter.

Functionality: Revenue Estimate

Each bar in the main panel of the app are revenue scenarios. Their heights differ according to input entered by the user on the far right column.
Slider Bar and Text Input
The interface above allows users to input a price and an estimate of the number of units they expect to sell at that price. To allow analysis of multiple scenarios, the slider and text input fields are replicated six times.

Functionality: Cost Estimate

The two leftmost columns beneath the chart allows users to enter data on sunk costs (and offsets to such costs) and projected costs, respectively.
Cost Estimates
As the interface shows, sunk and projected costs can be further divided into the costs of producing particular products or services, thereby allowing a more accurate analysis of costs and revenues.

Real-Time Visualization

Essentially, the revenue estimates executed with the data input by the user in the rightmost column generates a bar for each pricing/units-sold scenario under consideration. Suppose the simple arithmetic underlying the rightmost column generated revenue projections of $400, $600, and $800.

Suppose further that the user inputs for the cost columns results, as the logic of the app dictates, in a single figure, say, $750. Then the user has immediate access to the visual information on the next page. (R code not hidden for readers' reference)

Visual Information

revenues <- c(400,600,800)
barplot(revenues, col = c("yellow","orange","red"), main = "Projected Revenues")
abline(h = 750, col = "black", lwd = 3)

plot of chunk unnamed-chunk-1

The user might conclude that only under the most optimistic scenario of the three revenue projections would the company recoup its costs. This is valuable information, easily generated, understood, and communicated.