Statistical tools for data analysis and visualization

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Learn to use data visualization tools provided by the VIM package to gain quick insights into the missing data patterns.

Statistical tools for data analysis and visualization

Credit: Vasily Zubarev (vas3k.com)

Learn how these 12 dimensionality reduction techniques can help you extract valuable patterns and insights from high-dimensional datasets.

In the first part of this series, we walked through creating a basic histogram in Bokeh, a powerful Python visualization library. The final result, which shows the distribution of arrival delays of…

The ggraph package provides a ggplot-like grammar for plotting graphs and as such you can produce very neat network visualizations. But as with ggplot, it takes a while to get used to the grammar. There are already a few amazing Rstudio Addins that assist you with ggplot (for example ggplotAssist and ggThemeAssist), but there has not been any equivalent tools for ggraph. Till now. This post introduces snahelper, an Rstudio Addin which provides a tiny GUI for visualizing and analysing networks. Installation and Usage You can install the developer version with: #install.packages(devtools) devtools::install_github("schochastics/snahelper") In order to work properly, the Package also needs the smglr Package, which adds a new layout algorithm. devtools::install_github("schochastics/smglr") In order to use the Addin, simply highlight a network in your script and select snahelper from the Addin dropdown menu. Interface The GUI has the following components: Create and tweak a layout for your network Check and add note attributes Create the node styles Review edge attributes Create the edge styles Review Result Get the Code Let’s go through them step-by-step. Layout The layout tab allows you to choose from all implemented algorithms in igraph and the stress based layout from smglr, which is the default and recommended choice. See this blog post for an explanation. In the tweak section you can move individual nodes around. The implementation is still very crude, so don’t expect miracles from that. The layout is saved as node attributes x and y. Node Attribute Manager The Node Attribute Manager shows all existing node attributes in a sortable table. In addition, you can calculate some new ones (centrality and clustering).NOTE: the centrality indices only work for undirected and unweighted networks so far. Nodes This is where you can style your nodes. You can either do it manually, by choosing a color/size for all nodes together, or based on an attribute. Edge Attribute Manager So far, the Edge Attribute Manager only shows existing edge attributes. Future releases may include the possibility to calculate new ones. Edges You can style your edges here. snahelper automatically detects if your network is directed and adds arrows if the network is directed. The other options are similar to the nodes tab. The curvature option allows you to bend edges. I highly suggest, that you only bend your edges if there are two edges running between the same set of nodes to make them visible. If you do not have reciprocal edges, then just leave it at 0. Result The result tab shows the network in its full size. If you are satisfied with the results, hit the Done button and the R code to produce the plot is automatically inserted in your script. Additional Features So far I only incorporated what I consider the bare minimum to produce a good visualization of a network. Mostly because I did not want to overload the GUI. If you are missing something essential, open an issue and I will try to add the feature.

When we offered CS231n (Deep Learning class) at Stanford, we intentionally designed the programming assignments to include explicit calculations involved in backpropagation on the lowest level. The…

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More and more companies have become reliant on data science and analytics. When journalists pay as much heed to data scientists as they do political pundits

Data Science Using R&Tableau[2018]:Introduction to Data Science and Text Mining-#Data_Science #R #ExcelR_Solutions #Tableau #Introduction #2018Data Science T...

You might have already seen or used the pipe operator when you're working with packages such as dplyr, magrittr,… But do you know where pipes and the famous %>% operator come from, what they exactly are, or how, when and why you should use them? Can you also come up with some alternatives? This tutorial … Continue reading Pipes in R Tutorial For Beginners

An Introduction to R

Is there a way of creating scatterplots with marginal histograms just like in the sample below in ggplot2? In Matlab it is the scatterhist() function and there exist equivalents for R as well. Howe...

I have 82 respondents in 2 groups (43 in Group A and 39 in Group B) that completed a survey of 65 Likert questions each ranging from 1 – 5 (strongly agree - strongly disagree). I therefore have a

R can produce some beautiful graphics, and there are some excellent packages, such as lattice and ggplot2 to represent data in original ways. But sometimes, all you want to do is explore the realtionship between pairs of variables with the minimum of fuss. In this post we’ll use the data which we imported in the […]

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