Description

In this episode, I grabbed the tools to conduct mobility data analysis and interactive plots in Python, detected outliers in spherical data, considered the next programing language to learn, and readied the statistical syrup for waffle plots using package ‘baffle’.

References:

• scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data
• spNNGP R Package for Nearest Neighbor Gaussian Process Models
• Feller-Pareto and Related Distributions: Numerical Implementation and Actuarial Applications
• A new outlier detection method for spherical data
• Confidence bands in survival analysis
• How to stagger labels on an axis in PROC SGPLOT
• Experimental Design: Definition and Types
• The Difference Between R-squared and Adjusted R-squared
• Top 5 Python Libraries for Data Science (2023) Edition
• Base-R and Tidyverse Code, Side-by-Side
• R Design Patterns, Base-R vs. Tidyverse With a view toward the teaching of R beginners
• Hello Shiny Python
• Which programming language should I learn?
• R Markdown Tips: Code, Images, Comments, Tables, and more
• Is Data Science a Dying Profession?

R-packages:

• evalR Evaluation of Unverified Code
• goldfish Statistical Network Models for Dynamic Network Data
• baffle Make Waffle Plots with Base Graphics
• ggDoE Modern Graphs for Design of Experiments with 'ggplot2'
• jjAnno An Annotation Package for 'ggplot2' Output
• multinomialLogitMix Clustering Multinomial Count Data under the Presence of Covariates
• IntLIM Integration of Omics Data Using Linear Modeling
• scrutiny Error Detection in Science
• shinyHugePlot Efficient Plotting of Large-Sized Data
• ctmva Continuous-Time Multivariate Analysis
• ggcoverage Visualize Genome Coverage with Various Annotations