Shiny 1.5

Loading...

From the course by Johns Hopkins University

Developing Data Products

1588 ratings

Johns Hopkins University

Developing Data Products

1588 ratings

Course 9 of 10 in the Specialization Data Science

A data product is the production output from a statistical analysis. Data products automate complex analysis tasks or use technology to expand the utility of a data informed model, algorithm or inference. This course covers the basics of creating data products using Shiny, R packages, and interactive graphics. The course will focus on the statistical fundamentals of creating a data product that can be used to tell a story about data to a mass audience.

From the lesson

Shiny, GoogleVis, and Plotly

Now we can turn to the first substantive lessons. In this module, you'll learn how to develop basic applications and interactive graphics in shiny, compose interactive HTML graphics with GoogleVis, and prepare data visualizations with Plotly.

Meet the Instructors

Brian Caffo, PhD
Professor, Biostatistics
Bloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, Biostatistics
Bloomberg School of Public Health

So now we're starting to get to the point where we actually can use Shiny

in a way that would be helpful for us.

And one of the main ways in which Shiny is useful,

is in creating interactive graphics.

R, by default, doesn't have a lot of capacity for

creating interactive graphics.

Shiny really expands that capacity, and gives you a nice way to disseminate your

results, because they're embedded in a Web page that you could put out very broadly.

So in this case, we're going to create an interactive plot.

We're going to actually have a little bit more code in the server.R functions.

And, I think at this point, once you go through this example,

you should hopefully be able to start using Shiny in a much more realistic

way that would help you with your day to day data science needs.

Okay, so let's get right to it.

Okay, so I've copied the code from the R markdown document.

And let's just run it first and

see the app before we start digging into its internals.

So what it basically does, is it plots two sets of random uniforms.

So you input the number of uniforms, and

see this is a text box that also has an increment and

decrement operator here in the buttons on the right hand side, okay?

And then you have these sliders that can change the range

of the uniforms that you generate both on the X axis and on the Y axis.

Then you have these buttons that say Show/Hide the X Axis Label,

Show/Hide the Y Axis Label, Show/Hide the Title.

So we have these three check boxes here, these two sliders, okay?

And our numeric input right here, okay?

And notice how the code is executed interactively, right?

Every time I change a value it's rerun, the whole set of code is rerun.

So that's something you're going to have to get used to with Shiny, is how to

program with its server calculations and its reactive coding, which operates in

my mind, a little bit differently than standard highly linear R programming.

Okay, so now let's go through the code and see how we accomplish this.

So I think it's usually best to start out going through the UI.r function,

the Shiny UI function, okay?

So title pane, we know about that, okay?

The numeric input, okay, it's labeling that input numeric.

And then here's the title, and then it gives you the starting value, and

then it gives you the minimum and the maximum, so

you don't input values above or below that range.

And then the step sizer for those little buttons,

when you push those little buttons, how much it steps, okay?

Then we have the two sliders, we've gone through the sliders before, okay?

All right, and notice these sliders there.

It had the double slider, it had the two values, okay?

So you need the two values here and the two values here, so

that you can have the slider at the two points.

And here's the Y slider, basically the same thing.

And then here's our three check boxes.

Okay, so just back to the sliders,

notice the first one was sliderX the second one was sliderY.

And then these have very obvious labels, "show_xlab", "show_ylab",

"show_title", okay?

Then in the main panel, it's just going to show the graph, and

it's going to do plotOutput of plot1.

So it's somewhere in our server function we're going to have to create something

called plot1 that is going to get sent back to our user interface, okay?

Now in our server, shinyServer, it looks like it always does.

It has function(input, output), or curly braces, cause it's a function, and

then output$plot1.

Okay, this is good.

This means that the output from our shinyServer is

going to have something labeled plot1, so

that when we display it with the user interface, there will be a plot1 there.

And okay, because it's a plot, the statement is renderPlot, okay?

And then all of our reactive expressions,

reactive means kind of interacting with server calculations, okay?

They're going to have this notation where they have the curly braces next to

the parentheses from the renderPlot statements.

So you gotta get used to writing these curly braces in reactive statements, okay?

So just get used to that code.

Okay, so here we're just setting a random number seed.

Okay, now here we're just doing some regular R coding.

The number of points we're just re-labeling the input$numeric.

And that's just for convenience so we don't have to actually

refer to the input function to make our code look a little bit readable.

And honestly, for pedagogical purposes here, where I'm just showing you how you

grab the numeric value from that numeric input, okay?

So the min for X, the max for X are the elements of the slider.

And these come out as input as a list, and the slider X value is a vector.

And so the first element of the vector is that minimum value,

the second element of the vector is the larger value.

Same thing for our two Y points, and so we're going to do those.

We're going to assign those all to variables,

because it would be a lot to type if we wanted to reuse those every time, okay?

And then our data, the list of random uniform numbers,

is just going to be runif.

That just generates a bunch of random uniform numbers.

And this takes in the number of points, and

the range between the min of the X and the max of the X.

And then our dataY, and that'll just return a vector of uniforms.

DataY, number of points, okay, minY, maxY, okay?

The label, right, is, this is just an ifelse statement.

The input$show_xlab is the check boxes, is either going to be true or false as to

whether or not it's checked, and that's going to be in this label show_xlab,

because remember that's what we labeled that check box, okay?

And then it's saying if that's true, in other words if showing the label,

then give the label "X Axis", if not, give it nothing.

Same thing with ylab.

So, these are two just R variables that are going to be

the text that we're going to display in the plot, okay?

And the same thing with the title, so the title is going to be if this is true,

we're going to have the title labeled "Title",

otherwise it's just going to be labeled nothing, okay?

And then we give our plot.

We're going to plot our X data with our Y data, our xlab is going to be xlab,

where that is defined as either "X Axis" or nothing.

Ylab is going to be ylab, where that's either going to be defined as "Y Axis" or

nothing.

And the main, same thing with the main, is either going to be "Title" or nothing.

And then our x limit is going to be always set, fixed at those values.

That's why the plot is always in the same space.

Y limit is always this, and then we'll return it, okay?

So that's it.

Let's run it again now that we know what it's doing, and look at it again.

So remember what it's doing as we change a value, okay?

It's taking this value, right?

This is input for the server.

This is going to be input, slider X, value one, okay?

This is going to be, yeah, that's the minimum.

I think I said right earlier, but it should have been left.

This is going to be slider X, value two, okay?

This is going to be slider Y, value one,

this is going to be slider Y, value two.

This is going to be true or false for show X lab, this is going to be true or

false for show Y lab, okay?

And so whats going on, is as we put in these values,

the Shiny server is always checking for new things.

And every time it's got a new set of values, it's re-running everything and

re-displaying it, going back and forth between the server and the UI, okay?

So later on we'll show you how to put in a done button in case your calculations

going on in the back-end are much weightier, and

you don't want to run them immediately upon receiving new values.

Right now this is such fast code, that doesn't matter.

But later on we'll show you how to put a done button.

That will help in these instances where when you click a button,

it runs a gigantic Monte Carlo simulation or something like that, okay?

But this a pretty nice, handy little interactive graphic.

I mean, it's not doing anything very terribly meaningful, but I think you could

extend this to your setting, where it's actually doing something quite meaningful.

Okay, so now your homework is go through this example,

create a plot, maybe use some different kinds of inputs.

Don't just use sliders and numeric inputs, try some other kinds of inputs.

And create an interactive graphic, a simple interactive graphic in Shiny

Explore our Catalog

Join for free and get personalized recommendations, updates and offers.

Coursera provides universal access to the world’s best education, partnering with top universities and organizations to offer courses online.

© 2019 Coursera Inc. All rights reserved.

Download on the App Store

Get it on Google Play