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Plotly 1.6

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Développement de produits de données

Université Johns-Hopkins

4.5 (1,735 notes) | 43K étudiants inscrits

Cours 9 de 10 dans Science des données Spécialisation

Visualiser le programme de cours

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.

Compétences que vous apprendrez

Interactivity, Plotly, Web Application, R Programming

Avis

4.5 (1,735 notes)

5 stars
1,145 ratings
4 stars
430 ratings
3 stars
126 ratings
2 stars
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1 star
6 ratings

RO

Jul 28, 2017

Course content was helpful. Some confusion in assignment questions not aligning with what was covered in lectures where it would have helped to clarify that was intentional.

IR

Mar 10, 2017

very helpful and teaching. learning practical tools for producting data products. examples in the course are not very complex, but give a very good intro for several tools.

À partir de la leçon

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.

Enseigné par

Brian Caffo, PhD
Professor, Biostatistics
Jeff Leek, PhD
Associate Professor, Biostatistics
Roger D. Peng, PhD
Associate Professor, Biostatistics

All right, so let's create a map with some nice hover-over effects.

So the first thing I'm going to do is these two statements right here.

There's a little bit of code in this example.

So I'm not going to go over every single little detail.

But let me just create this dataset, and then show it to you.

Let me just get the first couple rows.

Okay, so notice there's 50 of them, so you can see at the end there's 50 of them,

so for each state we have its abbreviation.

Okay, so take Alabama for example,

we have it's population in millions, and then we have some hover-over text.

So this hover-over text is going to be the state abbreviation,

Alabama, then a line break, then population 3,615,000.

This is population in 1975.

Okay.

The borders, this is just going to be an option.

This is kind of unnecessary, but we're just going to make those red.

Okay.

And then map options, again, it's going to be a list.

The scope, we want the USA to be the scope.

And just play around with these options.

I think, you'll have to just, if you want to dramatically change this map, do it for

a different area or country, you'll have to flub your way through it a little bit,

but I think a lot of it is quite obvious.

Like showlakes = TRUE, just means you're going to show the lakes.

Lake color white, so in this case I think we changed the lake color

to white just because the color coding we're using is blue for

population, so we didn't want the lakes to

be blended in with the states with the same color of blue.

Okay, so then plot_ly, again, is the command,

the state_pop is just the data frame, okay.

z is the population, text is equals hover,

that's just means we want some hover-over text.

The locations are given by the column name State,

type is choropleth, location mode is USA-states.

Color is we're going to color-code by the population.

The colors, this is just, the palette it’s using in this case is

just the blues which is just a continuous shade of blue.

And then this gives how it's going to draw the markers,

in this case on the borders.

And this is then passed, the output of plot_ly is then passed to layout,

which also then gives it not only a title, but then it brings in these map options.

Okay?

So, let's go ahead and run that.

And for me, it wouldn't, I guess, maybe, this is just too complicated, but

it wouldn't display in the viewer for me.

So I'm going to click Save as Web Page, and just

overwrite the kind of template web page that I've been using for this example.

And there it is in my browser window.

So, now what I hover over a state, it's got the state's population.

So we could go to Alabama, for example.

You can see that's the exact text we wanted right there, Alabama.

Okay?

All right, so try and create a map.

First thing I would say is take this data frame that we're looking at,

it's got other columns in it, not just population, and try and

create some other maps and output them to web pages.

And I think as you go through it, you'll get a better feel for the options.

So as a start, just recreate this map, and

then try to recreate one with some of the different variables.

Try to have the color mean a different thing.

And I think you'll find this is a pretty handy way to create interactive maps

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