Importing astronomy book data from HTML and JSON into R and comparing the resulting data frames.

My plan to manually create HTML and JSON files for three astronomy books, import them into R, and compare the resulting data frames.

Tidying and analyzing monthly U.S. inflation-rate data using R, tidyr, and dplyr.

Tidying and analyzing New York disposable personal income data from 2012 through 2024 using R, tidyr, and dplyr.

Tidying and analyzing World Bank life-expectancy data, including a comparison between Bangladesh and the United States.

Solutions to Exercises 8.1, 8.5, 8.6, 8.7, 8.8, and 8.9 from Forecasting: Principles and Practice (3rd edition), including R code, forecasts, visualizations, and model comparisons.

My planned approach for tidying and analyzing three wide-format datasets involving life expectancy, income distribution, and U.S. inflation.

Exercises 3.1 and 3.2 from Kuhn and Johnson’s Applied Predictive Modeling. Analysis of the Glass and Soybean datasets, including predictor distributions, relationships, outliers, suggested transformations, near-zero variance, and missing data.

My planned approach for calculating each chess player’s expected score and identifying the tournament’s greatest over performers and under performers.

My planned approach for recreating, tidying, transforming, and analyzing airline arrival data in R.

R code for extracting chess tournament player information and calculating each player’s average opponent pre-rating.

My planned approach for cleaning chess tournament results and calculating each player’s average opponent pre-rating in R.

Exercises 3.1–3.5 and 3.7–3.9

My planned approach for calculating year-to-date and six-day moving averages for multiple stock price series in R.

My planned approach for using movie-rating data to create Global Baseline Estimate recommendations in R.

An analysis of classification performance at probability thresholds of 0.2, 0.5, and 0.8.

My planned approach for loading and transforming the Titanic dataset in R