My Most Popular Posts of 2017

-

Although I've made many posts on my Data Analytics Workouts site, some generated more interest than others - nothing here was virally popular - and below are a handful of the most popular, sorted in descending order of views:
The following collection, with some overlap with the list of my most popular posts, are social science related, looking at correlations between personality, culture, politics and human welfare. For cultural dimensions, Geert Hofstede's country-level data was used, while big five personality assessments were used for state-level political prediction. Patent numbers came from WIPO, and social welfare numbers came from the OECD.

Comments

Post a Comment

Popular posts from this blog

Charting Correlation Matrices in R

-
Image
I noticed this very simple, very powerful article by James Marquez, Seven Easy Graphs to Visualize Correlation Matrices in R , in the Google+ community, R Programming for Data Analysis , so thought to give it a try, since I started some of my current analyses a decade ago by generating correlation matrices in Excel, which I've sometimes redone and improved in R. Some of these packages are only designed for display, or as extensions to ggplot2: corrplot: Visualization of a Correlation Matrix GGally: Extension to 'ggplot2' ggcorrplot: Visualization of a Correlation Matrix using 'ggplot2' These two are focused on more complex analysis: PerformanceAnalytics: Econometric tools for performance and risk analysis psych: Procedures for Psychological, Psychometric, and Personality Research As for data, I used Hofstede's culture dimensions, limited to developed countries. Using a broader and larger set of of countries would significantly reduce the correlation
Read more

Attractive Confusion Matrices in R Plotted with fourfoldplot

-
Image
As part of writing analyses using neural networks I thought of displaying a confusion matrix, but went looking for something more. What I found was either ugly and simple, or attractive but complicated. The following code demonstrates using fourfoldplot, with original insight gained from fourfoldplot: A prettier confusion matrix in base R , and a great documentation page, R Documentation's page for fourfoldplot . The code is below, as is the related graph. Source data can be found here . ############################################### # Load and clean data ################################################ Politics.df <- read.csv("BigFiveScoresByState.csv", na.strings = c("", "NA")) Politics.df <- na.omit(Politics.df) ################################################ # Neural Net with nnet and caret ################################################ # load packages library(nnet) library(caret) # set
Read more

Calculating Value at Risk (VaR) with Python or R

-
Image
The following modules linked below are based on a Pluralsight course, Understanding and Applying Financial Risk Modeling Techniques , and while the code itself is nearly verbatim, this is mostly for my own development, working through the peculiarities of Value at Risk (VaR) in both R and Python, and adding commentary as needed. The general outline of this process is as follows: Load and clean Data Calculate returns Calculate historical variance Calculate systemic, idiosyncratic, and total variance Develop a range of stress variants, e.g. scenario-based possibilities Calculate VaR as the worst case loss in a period for a particular probability The modules: In R: Financial Risk - Calculating Value At Risk (VaR) with R In Python: Financial Risk - Calculating Value At Risk (VaR) with Python
Read more