In my previous articleI explained how the Pandas library can be used for plotting basic and time series plots. While PandasMatplotliband Seaborn libraries are excellent data plotting libraries, they can only plot static graphs. Static plots are like simple non-interactive images. In most of the cases, static plots are enough to convey the information.

However, in some cases you may like to add user interactivity to your plots. In this article, we will see how the Python's Plotly library can be used to plot interactive plots. We will plot geographical data using plotly and will explain how a user can interact with such plots. To install the Plotly library using the "pip" utility, you need to execute the following command:. In addition to Plotly, we will also use Cufflinkswhich works as a connector between the Pandas library and Plotly, and helps us plot interactive graphs directly using a Pandas dataframe.

Plotly is basically an online library that hosts your data visualizations, however, it also provides an offline data package that can be used to draw interactive plots offline.

Before we can run Plotly in Jupyter notebook, which I am using to run my scripts, I need to import both the Plotly and Cufflinks libraries along with Numpy and Pandas as usual. Next, we need to import the offline versions of the Plotly modules that we will be using in this article. The following script does that:. Before we can execute our scripts, we need to connect the JavaScript to our notebook. Since Plotly plots are interactive, they make use of JavaScript behind the scenes.

The scripts that we are going to run will be executed in the Jupyter notebook. To connect Jupyter notebook with JavaScript, we need to execute the following script:. Finally, we need to import the Cufflink library and make sure that we will be using it offline. To do so, run the following script:. Now we have everything that we need to draw interactive Plotly graphs inside our Jupyter notebooks.

In this section, we will be using the Plotly library to draw basic interactive plots. In the next section, we will see how Plotly can be used to plot geographical data. The dataset that we are going to use for this section is the "Tips" dataset that is downloaded by default with the Seaborn library.

The dataset contains information about the amount spent by a group of people at lunch and dinner. The dataset contains gender, price, tips, age, size, day, time and whether the people who had lunch or dinner were smokers or not. Before using Plotly to draw interactive plots, let's remind ourselves how we used Pandas for plotting static graphs.

Let's call the plot method on our dataframe to see how Pandas plot static plots. Look at the following script:. You can see that to plot a graph, we simply called the plot method on our dataframe.

Interactive plotting with Bokeh

Now let's see how we can draw interactive plots using Plotly. In the section where we imported the libraries, we import the plot function from the plotly.

To plot interactive plots using Pandas dataframe, we simply need to call the iplot method instead of the plot method. Take a look at the following example:. If you hover over the plot you should see values changing. You can zoom in and zoom out of the plot using the options available at the top right corner of the plot. You can also add and remove columns from the plot. Finally, you can also save the graph as a static image. In the rest of the section, we will plot some of the most commonly used interactive plots using Plotly.

To plot the interactive bar plot using Plotly, you can use the iplot function.We host a growing number of open source, interactive textbooks, written by award winning authors. Used by thousands of schools. See the List Take a Tour. Here is a Video that will help you get started. Or, take a tour of the unique interactive features of our books! Watch Video Instructors Guide. For details see This Blog Post.

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Runestone in social media:. Welcome to Runestone Academy We host a growing number of open source, interactive textbooks, written by award winning authors.I love how the modern Web and free software are like Lego blocks you can combine in myriad ways to create new and interesting things.

Click the play button to start interacting. This works great on both desktop and mobile, but mobile users viewing via the Twitter native app will see your visualization open in a browser window instead. The plotly R package makes this step ridiculously simple. Save your ggplot to an object pass it into a call to the ggplotly function. Print the output of that function to see your interactive plot in the RStudio viewer or inline in an RMarkdown document.

This saves and links the JavaScript dependencies in the folder specified by libdir. This step is simply uploading your newly exported standalone visualization to be hosted on your Web server. If your provider includes a control panel with a file manager, you can use that. Make sure to choose an upload location that is public. Create a new folder for this visualization, iris in this case, and upload the index. If using FileZilla, it should look similar to this:.

Keep this link handy for the next step. To enable the Twitter player card, you need to add specific meta tags to the post page.

I was not able to find a WordPress plugin that handled player card metadata, so I just used the Meta Tag Manager plugin to add them manually. Once enabled, this plugin adds a simple interface to the bottom of your post editor in WordPress admin. More information about the tags is also available in the official Twitter documentation. Now when you publish your post and link to it in a tweet, your audience will be able to explore the data right from the comfort of their timelines.

You can double check that everything is working before tweeting using the Twitter Card Validator. Paste your post link in there and you should see your fully interactive tweet. If not, check the troubleshooting section below.

interactive plot java

If your link generates a normal image preview card on Twitter instead of the interactive player card, you probably have a WordPress plugin that is adding additional Twitter meta tags to your post that conflict with the player settings. The good news is the GitHub version is updated and does work! You can install the updated pre-release version using devtools :. You can manually position legend titles by removing the automatic ones and adding them as annotations.

Tweak the x and y values until the placement is correct. I used fixed width and height on the iframe for simplicity in the tutorial, but you can improve the display on varying device sizes with some simple CSS. Twitter has guidelines for the use of the player cardand a full interactive data app would go well beyond them. Submit your contact info below to get started.

There is a plotly. Twitter Card validator is not very informative. But twitter:player is on page and content looks valid to me. Everything looks right in your tags and player, but the validator error indicates a problem the the twitter:player url.Interactive visualization allows deeper exploration of data than static plots. Javascript libraries such as d3 have made possible wonderful new ways to show data.

Luckily the R community has been active in developing R interfaces to some popular javascript libraries to enable R users to create interactive visualizations without knowing any javascript. In this post I have reviewed some of the most common interactive visualization packages in R with simple example plots along with some comments and experiences.

Here are the packages included:. You can either jump straight to the example visualization or read my comments first. The R markdown source code for this blog post with embedded visualizations can be found in Github.

I have probably missed some important features and documentation, and also clear mistakes are possible. It is also important to note that I am a heavy ggplot2 user, and hence my comments may also be biased! Also other libraries for creating interactive visualizations from R do exist, such as clickmeRIGHTggobiiplotsgg2vrVegacranvas and r2d3. Some of these are not under active development anymore. I might include some of those into the comparison here in the future. The d3Network package is also worth checking if you need cool interactive network visualizations.

All four packages use javascript for the interactive visualizations, and are cabable of producing most of the standard plot types. All other packages than googleVis are clearly in an early development phase, which is visible in a limited features and documentation. As an experienced ggplot2 user it was often hard to adapt to the much narrower range of features included in ggvis.

For example faceting is a very important feature that hopefully gets implemented soon. Documentation-wise ggvis and googleVis seem to be the most advanced. This makes it somewhat hard to use, at least when one is used to using tidy data frames.

See the examples below for more details. Plotly is an interesting alternative to the other packages in that it simply takes as input a ggplot2 object and transforms it into an interactive chart that can then be embedded into websites.

Using the service requires authentication, which is a clear limitation. By default all plots are made publicly visible to anyone, but there apparently is a way to produce private plots as well, with a limit in their number in the free account. Interactive visualizations are typically meant to be shared for a larger audience.

Common ways to share interactive visualizations from R are as standalone html files, embedded in R markdown documents, and embedded in Shiny applications. All the studied packages can produce standalone htmls, though possibly with some loss of interactivity. R markdown documents are a very nice way of sharing reproducible analyses, using the knitr and rmarkdown packages.

Outputs from all the studied visualization packages can be embedded in. Rmd documents, though I had some problems see the Issues section below. All packages are also compatible with Shiny documents and applications, and have good tutorials for this. I encountered several problems when preparing this blog post. Specifically, I had issues in embedding the plots into R markdown documents.

This is made more complicated with the various available ways of turning. Here I have listed the most important issues, with solutions when found.

Some things are still unsolved, hope someone can help me with those! In general, being able to produce valid interactive html charts from R markdown without knowing any javascript is great! All of the packages great sensible outputs, but there are also a lot of differences. I love ggplot2, and hence I also like ggvis, as it pays attention to graphical details following the grammar of graphics principles.

However, the package is still missing a lot of important features, such as faceting. In many cases rCharts can do what ggvis can not yetand so it is a good alternative.R in Action 2nd ed significantly expands upon this material.

There are a several ways to interact with R graphics in real time. Three methods are described below. GGobi is an open source visualization program for exploring high-dimensional data. It supports linked interactive scatterplots, barcharts, parallel coordinate plots and tours, with both brushing and identification.

A good tutorial is included with the GGobi manual. You can download the software here. Once GGobi is installed, you can use the ggobi function in the package rggobi to run GGobi from within R. This gives you interactive graphics access to all of your R data! The iplots package provide interactive mosaic plots, bar plots, box plots, parallel plots, scatter plots and histograms that can be linked and color brushed.

For more information, see the iplots website. Install iplots install. On windows platforms, hold down the cntrl key and move the mouse over each graph to get identifying information from points, bars, etc. R offers two functions for identifying points and coordinate locations in plots. With identifyclicking the mouse over points in a graph will display the row number or optionally the rowname for the point. This continues until you select stop.

With locator you can add points or lines to the plot using the mouse. The function returns a list of the x,y coordinates. Again, this continues until you select stop. See scatterplots for a description of rotating 3D scatterplots in R. Explore building interactive plots with ggvis from RStudio in this course. Kabacoff, Ph. Interactive Graphics There are a several ways to interact with R graphics in real time.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

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Learn more. Java Library to generate interactive Graph Ask Question. Asked 7 years, 11 months ago. Active 5 months ago. Viewed 6k times. Marcel Marcel 7 7 silver badges 23 23 bronze badges. Active Oldest Votes. Ben Holland 1, 4 4 gold badges 28 28 silver badges 46 46 bronze badges. Jay Askren Jay Askren 9, 12 12 gold badges 43 43 silver badges 72 72 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password.

Post as a guest Name. Email Required, but never shown. The Overflow Blog. Featured on Meta. Feedback on Q2 Community Roadmap.Creating real-time charts is as simple as calling updateXYSeries for one or more series objects through the XYChart instance and triggering a redraw of the JPanel containing the chart.

Examples demonstrate using the SwingWrapper with repaintChart method as shown here, as well as XChartPanel with revalidate and repaintwhich you would want to use if you already had your own Java Swing application which integrates an XChartPanel.

The following sample code used to generate the following real-time chart can be found here. In the above example, the chart data generation runs on the EventDispatchThreadwhich is definitely not ideal for a responsive GUI.

Additionally, the EventDispatchThread is forced to sleep for ms between each update to the real-time chart. While far from the correct way to build a Java Swing application, it serves the purpose of demonstrating how the XChart components function.

In this example, lets use an additional thread to do the data generation, rather than the EventDispatchThread. This good-practice technique allows for your GUI to remain responsive to user interactions while the charting goes on in the background—except for when it comes time to actually repaint the chart.

This example simulates a common scenario in making real-time charting applications: There is some background thread reading data from a sensor, and you want a live chart to update as new data becomes available.

Interactive visualizations with R - a minireview

So in this example we simulate a data rate of 5 ms per data point and a chart update rate of 24 frames per second, dynamically calculated. The chart animation looks smooth, and no extra CPU cycles are being wasted updating the chart for no reason.

Email required. QuickChart; import org. SwingWrapper; import org. Great job. Couple of questions: — Is there a way to overlay multiple charts?.

interactive plot java

I want to be able to show moving averages along with OHLC charts. Is this possible? Name required. Are you interested in nanotechnology based AI processors?

Drawing graphs in Java

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interactive plot java

SwingWrapper. XYChart. LinkedList; import java. List; import javax. SwingWorker; import org. LinkedList. List. SwingWorker. MySwingWorker mySwingWorker .


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