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Category: R

UpSet Plots for Set Analysis

Published 2019-05-02 by Kevin Feasel

Laura Ellis digs into the UpSetR package:

UpSet plots have a very cool parameter called queries. Queries can be used to define a subset of the data that you would like to highlight in your graph. The queries property takes in a list of query lists which means that you can pass multiple queries into the same graph. Each query list allows you to set a number of properties about how the query should function.

In this example we are viewing the Cycle and Walk set intersection (query and params). We want the query to be highlighted in a nice pink (color). We want to display the query as a highlighted overlap (active) and we will give it a name that we add to the chart legend (query.name)

I’ve not seen an UpSet plot before but it dumps a lot of information into a relatively small space. I’ll have to spend some time learning more about these plots.

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AzureGraph: Microsoft Graph in R

Published 2019-05-01 by Kevin Feasel

Hong Ooi takes us through AzureGraph:

Microsoft Graph is a comprehensive framework for accessing data in various online Microsoft services, including Azure Active Directory (AAD), Office 365, OneDrive, Teams, and more. AzureGraph is an R package that provides a simple R6-based interface to the Graph REST API, and is the companion package to AzureRMR and AzureAuth.

Currently, AzureGraph aims to provide an R interface only to the AAD part, with a view to supporting R interoperability with Azure: registered apps and service principals, users and groups. Like AzureRMR, it could potentially be extended to support other services.

Just to clarify, this is like Facebook Graph API for Azure components, not a graph database that you can store your own data in.

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Data Layout in R with cdata

Published 2019-04-29 by Kevin Feasel

John Mount takes us through a few sample problems and how to reshape data with cdata:

This may seem like a lot of steps, but it is only because we are taking the problems very slowly. The important point is that we want to minimize additional problem solving when applying the cdata methodology. Usually when you need to transform data you are in the middle of some other more important task, so you want to delegate the details of how the layout transform is implemented. With cdata the user is not asked to perform additional puzzle solving to guess a sequence of operators that may implement the desired data layout transform. The cdata solution pattern is always the same, which can help in mastering it.

With cdata, record layout transforms are simple R objects with detailed print() methods- so they are convenient to alter, save, and re-use later. The record layout transform also documents the expected columns and constants of the incoming data.

Check it out.

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Exploratory Analysis of Earthquake Data

Published 2019-04-29 by Kevin Feasel

Giorgio Garziano walks us through an earthquake data set:

Boxplots for each quantitative variables are shown. We take advantage of the quantitative variable names (quantitative_vars) determined before to apply a ggplot2 package based boxplot function. The Y axis labeling and title are determined by the variable to be plot. Further, legend is not displayed and we adopt the coordinate flip option for improved readability.

Check it out to get an idea of how to do exploratory data analysis.

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Naive Bays in R

Published 2019-04-23 by Kevin Feasel

Zulaikha Lateef takes us through the Naive Bayes algorithm and implementations in R:

Naive Bayes is a Supervised Machine Learning algorithm based on the Bayes Theorem that is used to solve classification problems by following a probabilistic approach. It is based on the idea that the predictor variables in a Machine Learning model are independent of each other. Meaning that the outcome of a model depends on a set of independent variables that have nothing to do with each other. 

Naive Bayes is one of the simplest algorithms available and yet it works pretty well most of the time. It’s almost never the best solution but it’s typically good enough to give you an idea of whether you can get a job done.

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Modifying HTML Rendering in Shiny

Published 2019-04-22 by Kevin Feasel

Senthil Thyagarajan gives us an example of uisng the htmltools package to change the way tables render in Shiny:

In order to build the html table I have used a function table_frame which can be used as a container in DT::renderdatatable. This function basically uses htmltools. For more references on the basics of html tables please refer here

In addition to changing the colors, Senthil also shows how to add a couple of buttons which call Javascript functions. H/T R-bloggers

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Forensic Accounting: Cohort Analysis

Published 2019-04-19 by Kevin Feasel

I continue my series on forensic accounting techniques with cohort analysis:

In the last post, we focused on high-level aggregates to gain a basic understanding of our data. We saw some suspicious results but couldn’t say much more than “This looks weird” due to our level of aggregation. In this post, I want to dig into data at a lower level of detail. My working conception is the cohort, a broad-based comparison of data sliced by some business-relevant or analysis-relevant component.

Those familiar with Kimball-style data warehousing already understand where I’m going with this. In the basic analysis, we essentially look at fact data with a little bit of disaggregation, such as looking at data by year. In this analysis, we introduce dimensions (sort of) and slice our data by dimensions.

Click through for some fraud-finding fun.

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Tidying Video Game Data

Published 2019-04-17 by Kevin Feasel

Arvid Kingl has a fun article analyzing data from an open-source video game and applying tidy data principles to it:

You will learn what key principles a tidy data set adheres to, why it is useful to follow them consequently, and how to clean the data you are given. Tidying is also a great way to get to know a new data set.

Finally, in this tutorial you will learn how to write a function that makes your analysis look much cleaner and allows you to execute repetitive elements in your analysis in a very reproducible way. The function will allow you to load the latest version of the data dynamically into a flexible data scheme, which means that large parts of the code will not have to change when new data is added.

Check it out. Bonus point: tidy data is Boyce-Codd Normal Form which is (potentially) subsequently widened back out to include dimensional information.

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Basic Forensic Accounting Techniques

Published 2019-04-17 by Kevin Feasel

I continue my series on forensic accounting techniques:

Growth analysis focuses on changes in ratios over time. For example, you may plot annual revenue, cost, and net margin by year. Doing this gives you an idea of how the company is doing: if costs are flat but revenue increases, you can assume economies of scale or economies of scope are in play and that’s a great thing. If revenue is going up but costs are increasing faster, that’s not good for the company’s long-term outlook.

For our data set, I’m going to use the following SQL query to retrieve bus counts on the first day of each year. To make the problem easier, I add and remove buses on that day, so we don’t need to look at every day or perform complicated analyses.

I get into quite a bit in this post, including a quick tour of multicollinearity, which is only my second-favorite of the three linear regression amigos (heteroskedasticity being my favorite and autocorrelation the hanger-on).

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