Regression and Other Stories - R and visualization

R programming and visualization with R

• For R programming basics see Appendix A of Regression and Other Stories.
• For learning more about R programming we recommend
  • Garrett Grolemund, Hands-On Programming with R
• For learning more about basic and advanced plotting using R we recommend
  • Kieran Healy, Data Visualization - A practical introduction
  • Antony Unwin, Graphical Data Analysis with R
• Further ideas for visualization
  • ggdist: Visualizations of distributions and uncertainty
  • tidybayes: Bayesian analysis + tidy data + geoms
  • Claus O. Wilke, Fundamentals of Data Visualization

Source: R_visualization.Rmd.

The Python version keeps data handling explicit and uses lapylace for Stan-backed generalized linear models, so the statistical model can be read from a formula rather than from handwritten Stan.

from pathlib import Path
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import lapylace as lp

sys.path.append(str(Path.cwd().parent / 'python'))
from data import ros_path

Notes

• Source computation blocks represented: 0.
• Data paths are expressed through the shared ros_path() helper.
• Formula-based Bayesian regressions are routed through lapylace.stan_glm().
• Plotting and simulation work uses NumPy, pandas, matplotlib idioms.