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Customizing Click Events: How to Capture and Store Data from JavaScript Objects in R Variables

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Daina Andries
Wednesday, 17 October 2018 / Published in Analytics, Code, Data, Data Science, Data Visualizations, JavaScript, Machine Learning, R, R-Shiny, Tutorials
Interaction Design for Data Exploration Visualizations capable of launching detail views can add value to a data analyst’s user experience. Programming in this kind of interaction automates the creation of complementary charts and increases ease of exploration by linking varied views of the data in a logical way. This tutorial offers a quick example of
AnalyticsCodeData ScienceJavaScriptRR ShinyTutorials

Analytics at Scale: h2o, Apache Spark and R on AWS EMR

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Mark Stephenson
Thursday, 21 June 2018 / Published in Analytics, Data Science, Machine Learning, Predictive Analytics, R, Tutorials
At Red Oak Strategic, we utilize a number of machine learning, AI and predictive analytics libraries, but one of our favorites is h2o.  Not only is it open-source, powerful and scalable, but there is a great community of fellow h2o users that have helped over the years, not to mention the staff leadership at the
Apache SparkCodeData Scienceh2oPredictive AnalyticsRSparkling WaterTutorials

Time Series Forecasting with Machine Learning: An example from Kaggle

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Matt Brown
Monday, 07 May 2018 / Published in Analytics, Code, Data, Data Science, Forecasting, Machine Learning, Predictive Analytics, R, Tutorials
Introduction This post will demonstrate how to use machine learning to forecast time series data. The data set is from a recent Kaggle competition to predict retail sales.       You will learn how to: Build a machine learning model to forecast time series data (data cleansing, feature engineering and modeling) Perform feature engineering to build
CodeData ScienceForecastingPredictive AnalyticsRTime SeriesTutorials

Data Processing with RegEx in Python 3

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Daina Andries
Wednesday, 31 January 2018 / Published in Analytics, Code, Data, Data Science, Databases, Python, Tutorials
Applying Regular Expressions This is a tutorial on processing data with regular expressions using Python. It is also a reflection on the advantages and trade-offs that come into play when you use regular expressions. Once you have identified and defined a set of patterns, you can strategically search and extract data from raw text according
CodeData ProcessingData SciencePythonPython 3RegExTutorial

Selecting Multiple Locations on a Leaflet Map in R Shiny

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Daina Andries
Thursday, 07 December 2017 / Published in Analytics, Code, Data, Data Science, Data Visualizations, R, R-Shiny, Tutorials
Our team recently designed a dashboard using R Shiny Leaflet allowing users to select many locations at one go on an interactive map. We created the map using the package [crayon-5c6c1c13b84a1171726989-i/], which enables users to draw shapes on R Shiny Leaflet maps. When combined with the package [crayon-5c6c1c13b84a9214797641-i/] and a function called [crayon-5c6c1c13b84ab563584335-i/], the [crayon-5c6c1c13b84ae459730287-i/]  drawing tool can
AnalyticsCodeData ScienceMapsRR ShinyTutorials

How To: Apply Family of Functions in R

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Patrick Stewart
Monday, 06 November 2017 / Published in Code, Data, Data Science, R, Tutorials
The apply function in R is used as a fast and simple alternative to loops. It allows users to apply a function to a vector or data frame by row, by column or to the entire data frame. Below are a few basic uses of this powerful function as well as one of it’s sister
CodeData ScienceRTutorials

Recent Posts

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    Interaction Design for Data Exploration Visuali...
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  • Time Series Forecasting with Machine Learning: An example from Kaggle

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  • Exploratory Data Analysis of the CDC’s ‘500 Cities Project’ – Part 2

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