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

Ver todos los episodios