Tag: visualization

Feed: R-bloggers. Author: Rstats on Jakub Nowosad's website. In the last few weeks, I was asked a similar question several times – how to calculate landscape metrics for local landscapes?In other words, how to divide the categorical input map into a number of smaller areas, and next calculate selected landscape metrics for each of the areas.Those areas have many names, such as tiles, squares, or motifels.The main goal of this post is to show how to calculate landscape metrics using the landscapemetrics R package. To reproduce the results on your own computer, install and attach the following packages: library(landscapemetrics) # ... Read More

Feed: Cambridge Intelligence. Author: Corey Lanum. We’re often asked about visualizing very large graphs. It’s a common belief that if a few nodes can generate some insight, a few thousand nodes will generate lots of insight. That’s not automatically the case. Many graphs are too densely connected to be usefully visualized in one go.In this blog, we’ll explore some strategies for visualizing large graphs in a meaningful way.Visualizing huge graph datasetsHere’s a graph with 20,000 nodes and 20,000 links.A randomly-generated graph of 20,000 nodes and 20,000 linksAlthough the powerful graph layout does a good job of highlighting the overall graph ... Read More

Feed: R-bloggers. Author: Business Science. [This article was first published on business-science.io, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Product price estimation and prediction is one of the skills I teach frequently – It’s a great way to analyze competitor product information, your own company’s product data, and develop key insights into which product features influence product prices. Learn how to model product car prices and calculate depreciation curves using the brand ... Read More

Feed: R-bloggers. Author: eoda GmbH. R is one of the leading programming languages for data analysis and data visualization. Unlock the potential of data science in our popular R trainings and learn how to turn data into added value with the open source language. With more than 1,500 satisfied participants, eoda’s R trainings are among the leading courses in the German-speaking world. In April and October 2020, the time has come: We will bring our popular trainings „Introduction to R“ and „Machine Learning with R“ to Berlin and Hamburg. Course contents: Introduction to R With practical tips and exercises, this ... Read More

Feed: Cambridge Intelligence. Author: Andrew Disney. Colors can make or break your data visualization.A carefully selected color palette helps you to harness the pre-attentive processing powers of the human brain, and makes insight clearer and easier to find. A badly chosen color palette obscures the information your users need to understand, and makes your data visualization less effective and harder to use.This blog post explores some data visualization examples, basic color theory, and explains how it can help you design visualizations that look good, and make data more compelling.Let’s start with some examples to show you the importance of choosing ... Read More

Feed: R-bloggers. Author: R Views. [This article was first published on R Views, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. One hundred fifty-two packages made it to CRAN in December. Here are my “Top 40” picks in ten categories: Data, Genomics, Machine Learning, Mathematics, Medicine, Science, Statistics, Time Series, Utilities, and Visualization. Data climate v0.3.0: Provides access to meteorological and hydrological data from OGIMET, University of Wyoming – atmospheric vertical profiling data, ... Read More

Feed: R-bloggers. Author: John. Statisticians often come across outliers when working with datasets and it is important to deal with them because of how significantly they can distort a statistical model. Your dataset may havevalues that are distinguishably different from most other values, these arereferred to as outliers. Usually, an outlier is an anomaly that occurs due tomeasurement errors but in other cases, it can occur because the experimentbeing observed experiences momentary but drastic turbulence. In either case, itis important to deal with outliers because they can adversely impact theaccuracy of your results, especially in regression models. In this tutorial, ... Read More

Feed: Cambridge Intelligence. Author: Dan Williams. In this final post in our three-parter exploring the typical challenges you face when visualizing your graph data for the first time, we look at the ‘starburst’ – a pattern where one heavily connected node dominates the chart.Usual suspect #3: it’s hard to see past a starburstWhere do starbursts come from?In previous posts, we saw how the underlying data source plays a significant role in determining whether you see a chart dominated by snowstorms or hairballs. But in both those cases, the main focus was on visualizing a dataset in its entirety.When working with ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Fahad Zaidi. Having spent the better part of the last decade helping organisations in the U.S. and the U.K. use data to drive profit, efficiency, and performance improvements, I thought it would be helpful to jot down best practices for organizations that are looking to get the most value out of their enterprise-level financial and operational data sets. Every organisation entering the roaring 20s knows that they must use their data as a strategic differentiator to gain a competitive advantage and this article lists five things all companies must do to ... Read More

Feed: SAS Blogs. Author: Leo Sadovy. Where in your business process can analytics and AI play a contributing role in enhancing your decision making capability?  At the information interpretation stage.  As a framework for understanding where analytic and AI opportunities may arise, the simple diagram below illustrates the relationships between data, information and knowledge, and how you get from one end to the other.  The key is how you come to understand the arrows (the verbs)  that link the three primary concepts (the nouns). Information is organized data.  The same data can be organized differently to yield different information sets ... Read More