Wednesday, 31 January 2018 / Published in RegEx, Data, Code, Data Processing, Analytics, Python 3, Data Science, Databases, Python, Tutorial
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...
Friday, 5 January 2018 / Published in Data, R, Code, Analytics, Data Science, Data Visualization, 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 post, I...
Frequently, we encounter projects that require the combined use of Python, Microsoft Excel and some external databases that can only be accessed via Excel, or use cases that require the end product to be output to that format. Excel is still used as a key program for the vast majority of businesses and we are often challenged to create...
Thursday, 7 December 2017 / Published in R, Code, Maps, Analytics, Data Science, Data Visualization, Tutorials, R Shiny
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 leaflet.extras, which enables users to draw shapes on R Shiny Leaflet maps. When combined with the package sp and a function called findLocations, the leaflet.extras drawing...
Monday, 6 November 2017 / Published in Data, R, Code, Data Science, 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 functions lapply. There are other functions in the apply family (sapply,...
Tuesday, 24 January 2017 / Published in Hillary Clinton, Data, Politics, Donald Trump, Political Analytics, Data Science, 2016 Election
There were a lot of predictions from pundits in the lead up to the 2016 Election, many of which predicted a Hillary Clinton victory. We now know that Donald Trump earned the victory with key wins in a handful of surprise swing states. With this still settling in for some, we took a hard look at the data behind his victory and what drove his...
Thursday, 17 November 2016 / Published in Data, Politics, Analytics, Political Analytics, Data Science, 2016 Election, Polling
This year, the Red Oak Strategic team decided to undertake a new challenge in the world of polling and analytics : conduct a bi-weekly, public, national survey that we would execute and release using the Google Surveys platform. Beginning in August and continuing through the final week of the 2016 election, our partnership with GCS led to the...
Friday, 26 August 2016 / Published in Data, Politics, Analytics, Political Analytics, Data Science, Predictive Analytics, 2016 Election, Polling
Political polling faces a crisis of confidence. Major news outlets repeatedly ask “What’s the matter with polling?” after major misses like the Bernie Sanders’s primary upset in Michigan, where he beat Hillary Clinton 50–48 despite the fact that she was leading by up to 20 points in reputable polls. There is, however, hope for a polling...
Wednesday, 10 August 2016 / Published in Politics, Data Science, 2016 Election
Today, we debuted our national tracking poll, conducted using Google Consumer Surveys. We are one of the first researchers in the political space to embrace the technology and after working close with their team on some Republican primary work, wanted to build upon those successes throughout 2016.
Wednesday, 11 May 2016 / Published in Politics, Uber, Data Science
Last Saturday, in what has now been widely publicized and discussed, Uber and Lyft lost an effort, Proposition 1, that would have rolled back a number of regulations on their services. As a result, in one of America’s most forward-thinking tech centers, the services stopped operating almost immediately.
6 Strategies for Migrating Applications to CloudAt Red Oak Strategic, we understand that...
Business Intelligence Across a Private Equity PortfolioBackground Recognizing an opportunity to expand...
Data Visualization: Empowering Decision MakersTime and again, across Red Oak Strategic’s...
Tracking Coronavirus: Building Parameterized Reports to Analyze Changing Data SourcesThe pace of our modern world, and the impressive...
Draw Rotatable 3D Charts in R Shiny with Highcharts and JQueryWhile it might be tempting to liven up a report...
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