Red Oak Strategic

  • Home
  • About Us
  • Services
  • Blog
Contact Us

Tracking Coronavirus: Building Parameterized Reports to Analyze Changing Data Sources

  • 0
Tyler Sanders
Wednesday, 01 April 2020 / Published in Data Visualizations, JavaScript, R, R-Shiny, Tutorials
The pace of our modern world, and the impressive volume of data we collect on a daily basis, can be dizzying. Take for example, the hour-by-hour updates and colorful dashboards made by news outlets as they track the spread of novel coronavirus (Covid-19). Organizations need quick and consistent solutions for exploring, analyzing, and acting on
CodeData Visualizationggplot2RRShinyTutorials

Draw Rotatable 3D Charts in R Shiny with Highcharts and JQuery

  • 0
Daina Andries
Wednesday, 13 March 2019 / Published in Data Visualizations, JavaScript, R, R-Shiny, Tutorials
While it might be tempting to liven up a report or presentation with a few 3D graphs, two-dimensional representation is generally better when numbers are the primary information you want to communicate. Nevertheless, on occasions when numeric values aren’t the primary focus, and you’re more interested in showing the shape of the data, adding a
CodeData VisualizationHighchartsJavaScriptJQueryRRShinyTutorials

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

  • 0
Matt Brown
Wednesday, 28 March 2018 / Published in Analytics, Code, Data, Data Science, Data Visualizations, R, R-Shiny, Uncategorized
Introduction In Part 1, we built an application to geographically explore the 500 Cities Project dataset from the CDC. In this post, we will demonstrate other exploratory data analysis (EDA) techniques for exploring a new dataset. The analysis will be done with R packages data.table, ggplot2 and highcharter. In this post, you will learn how
CodeDataData ScienceData VisualizationExploratory Data ScienceRR Shiny

Exploratory Data Analysis of the CDC’s ‘500 Cities Project’ – Part 1

  • 0
Matt Brown
Friday, 05 January 2018 / Published in Analytics, Code, Data, Data Science, Data Visualizations, R, R-Shiny
Exploratory data analysis (EDA) is generally the first step in any data science project with the goal being to summarize the main features of the dataset. It helps the analyst gain a better understanding of the available data and often can unearth powerful insights. Data visualization is the most common technique in EDA. During this
CodeData ScienceData VisualizationRR Shiny

Recent Posts

  • Tracking Coronavirus: Building Parameterized Reports to Analyze Changing Data Sources

    The pace of our modern world, and the impressiv...
  • Draw Rotatable 3D Charts in R Shiny with Highcharts and JQuery

    While it might be tempting to liven up a report...
  • Customizing Click Events: How to Capture and Store Data from JavaScript Objects in R Variables

    Interaction Design for Data Exploration Visuali...
  • Analytics at Scale: h2o, Apache Spark and R on AWS EMR

    At Red Oak Strategic, we utilize a number of ma...
  • Time Series Forecasting with Machine Learning: An example from Kaggle

    Introduction This post will demonstrate how to ...

Categories

  • Analytics
  • Blockchain
  • Code
  • Data
  • Data Science
  • Data Visualizations
  • Databases
  • Excel
  • Financial Analytics
  • Forecasting
  • JavaScript
  • Machine Learning
  • Political Analytics
  • Predictive Analytics
  • Python
  • R
  • R-Shiny
  • Tutorials
  • Uncategorized
TOP