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Category: Visualization

Table Design in R with mmtable2

Matt Dancho walks through a package to make tables look great in R:

I love ggplot2 for plotting. The grammar of graphics allows us to add elements to plots. Tables seem to be forgotten in terms of an intuitive grammar with tidy data philosophy – Until now. mmtable2 aims to be the ggplot2 for tables, leveraging the awesome GT table package.

The mmtable2 package aims to make it easy to create tables by:

1. Using a ggplot2-style syntax for using a grammar of table operations.

2. Extends the amazing GT table package.

Read on for the process and a demonstration.

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Plotting XGBoost Trees with R

Andrew Treadway shows off a method to visualize the results of training an XGBoost model:

In this post, we’re going to cover how to plot XGBoost trees in R. XGBoost is a very popular machine learning algorithm, which is frequently used in Kaggle competitions and has many practical use cases.

Let’s start by loading the packages we’ll need. Note that plotting XGBoost trees requires the DiagrammeR package to be installed, so even if you have xgboost installed already, you’ll need to make sure you have DiagrammeR also.

Click through for the process. H/T R-Bloggers.

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Grafana Changing License

Alex Woodie has some bad news for us:

Grafana is switching licensing of its core products from Apache License 2.0 to the more restrictive Affero General Public License (GPL) v3. The company made the change in an attempt to balance the value of open source with Grafana’s monetization strategy, CEO Raj Dutt announced yesterday.

Grafana has been considering a license change for some time, Dutt wrote in a blog post on April 20. This week, the company finally felt the time was right to move.

“Oof” was my first response. I know that a pretty large percentage of companies won’t touch AGPL. I don’t know if we’ll see these companies adopt the commercial version of Grafana, see the companies switch over to something else, or see developers fork Grafana and come up with some other product. AGPL is not quite as scary for companies when a product is at the end of the chain, as visualization and dashboarding products tend to be, but for many companies, that doesn’t matter.

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Displaying Metrics on Graphite Dashboards

Nick Campion takes us through working with Graphite:

Graphite is a free and open-source software. It is used as a time-series database monitoring tool, where you can collect, store and display time-series data in real-time. As you can monitor certain metrics of this data using Graphite, it has a very useful and simple dashboard used to visualize these metrics.

This article will show you how to display a metric on your Graphite dashboard.

Click through for more information.

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Creating a Rose Chart in R

Neil Saunders takes a look at a classic chart:

I first heard Florence Nightingale and her Geeks Declare War on Death, an episode of the Cautionary Tales podcast, premiered as a special episode of 99% Invisible. It discusses Nightingale’s work as a statistician and in particular, her visualisation of mortality causes in the Crimean War using the famous “rose chart”, or polar area diagram.

I’m sure you’re thinking: how can I explore that using R? 

Read on to find out.

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knitr Options and Hooks

The folks at Jumping Rivers conclude a series:

As with many aspects of programming, when you are working by yourself you can be (slightly) more lax with styles and set-up. However, as you start working in a team, different styles can quickly become a hindrance and lead to errors.

Using {knitr} is no different. When you work on documents with different team members, it’s helpful to have a consistent set of settings. If the default for eval changes, this can easily waste time as you try to track down an error. At Jumping Rivers, we use {knitr} a lot. From our training courses, to providing feedback to clients, to constructing monthly reports on clients infrastructure. The great thing about {knitr} is it’s really easy to customise. The bad thing is that without some care, it’s really easy for every member of the team to have different default options. This proliferation of different default options, means that when we pick up someone else document, mistakes may creep in.

Read on for different options they use to keep things consistent.

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Including and Resizing External Images in knitr

The folks at Jumping Rivers continue a series on knitr and rmarkdown:

In this third post, we’ll look at including eternal images, such as figures and logos in HTML documents. This is relevant for all R markdown files, including fancy things like {bookdown}, {distill} and {pkgdown}. The main difference with the images discussed in this post, is that the image isn’t generated by R. Instead, we’re thinking of something like a photograph. When including an image in your web-page, the two key points are

– What size is your image?
– What’s the size of your HTML/CSS container on your web-page?

Read the whole thing.

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Choosing an Image File Type

The folks at Jumping Rivers continue a series on image optimization:

As the JPEG compression algorithm significantly reduces file size, JPEG files are ubiquitous across the web. If you take a photo on your camera, it’s almost certainly using a JPEG storage format. Historically the file extension was .jpg as Microsoft Windows only handled three character file extensions (also .htm vs .html). But today both extensions are used (personally I prefer .jpeg, but I’m not very consistent if I’m totally honest).

If you did a little Googling on which file format to use for images, then the answer you would come across is that JPEG’s are the default choice. But remember, figures are different from standard images!

Click through for a review of three viable image formats.

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