This book provides a contemporary treatment of quantitative economics, with a focus on data science. It introduces the reader to R and RStudio, and utilizes Hadley Wickham’s tidyverse package for various parts of the data analysis workflow. After a gentle introduction to R code, the reader’s skills are gradually honed through “your turn” exercises.

At the heart of data science is data, and the book equips the reader with techniques for importing and wrangling data, including network data. Early on, readers will begin using the popular ggplot2 package to visualize data, even making basic maps. The book covers the use of R in understanding functions, simulating difference equations, and carrying out matrix operations.

The text delves into Monte Carlo simulation for understanding probability and statistical inference, and introduces the bootstrap method. Causal inference is illuminated using simulations, data graphs, and R code applied to real economic examples, covering experiments, matching, regression discontinuity, difference-in-difference, and instrumental variables.

Additionally, the interplay of growth-related data and models is presented before introducing time series data analysis with graphical methods, simulation, and practical examples. The book concludes by introducing two computationally intensive methods: generalized additive models and random forests (an important and versatile machine learning method), all explained intuitively with applications.

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