Resources

Here is a list of resources and references that can be helpful for this class but also to go beyond the material taught in this class.

If you find a nice resource not listed here and that could be interesting for your classmates (or me), please add it to this shared document.

Textbooks

• Gelman, Hill and Vehtari, Regression and Other Stories: presents regression from a fresh stats perspective, centered on intuition, simulations and design.
• Huntington-Klein, The Effect: An Introduction to Research Design and Causality: an excellent causal inference textbook centered on intuitions and not maths.
• Cunningham, Causal Inference, The Mixtape: a nice overview of causal inference approaches, with R, Python and Stata code and examples
• Angrist, Joshua D. and Jorn-Stefen Prischke, Mostly Harmless Econometrics: An Empiricist’s Companion: the causal inference bible for economists

R

R Textbooks

Here is a list of useful resources.

• R for Data Science: definitely the best resource to learn R and the tidyverse by yourself.
• R cheatsheets: great summaries of the functions in key tidyverse packages.
• The tidyverse style guide: a very short book to help you write more legible R code.
• Tidy design principles a more advanced book to help you write better R code.

Useful R Packages

• tidyverse: a collection of R packages sharing an underlying design philosophy, grammar, and data structures.
• ggplot: THE package to build plots in R

Data viz

Data visualization is key to any statistical analysis, whether it is used to explore the data, build a model, check hypotheses or present and communicate results. Here are a few resources that can help you build nice and relevant data viz in R.

• R for Data Science provides a nice introduction to ggplot, THE package to build plots in R. To go further you can read this book.
• Modern Data Visualization with R and Data Visualization - A practical introduction both discuss key data viz concepts and teaches you how to apply them in R and ggplot.
• Fundamentals of Data Visualization does not provide R code directly but provides a great introduction to data viz and R code can be found on the book’s GitHub.

Finding data

Data are obviously central to statistical analyses. Identifying relevant datasets is often not an easy task. Here is a non-exhaustive list of places where to look for useful datasets.

• Google Dataset Search: a search engine for datasets hosted in thousands of repositories across the Web. Info here.
• Data Europa: European public sector datasets.
• Data.gouv: open platform for French public data.
• Data Is Plural: a weekly newsletter providing useful and often original datasets. There is a spreadsheet archiving all the data sets.
• A currated list of French open data sources.
• Quantitative Social Science Data: a curated list of economics datasets.
• Awesome Public Datasets: a list of topic-centric public data sources.
• PolData: a list of political science datasets.
• ICPSR stores, curates, and provides access to scientific data so others can reuse the data.
• Harvard dataverse: A repository of datasets used in published research articles.