Erik Gahner Larsen

Potpourri: Statistics #59

– Quantitative Politics with R (version Nov 29)
– Why scientists need to be better at data visualization
– Coding habits for data scientists
– Reducing frictions in writing with R Markdown for html and pdf
– Reconstructing Images Using PCA
– A ggplot2 Tutorial for Beautiful Plotting in R
– (Re)introducing skimr v2 – A year in the life of an open source R project
– Streetmaps
– Survey Weights in R
– Introducing BFpack
– Another Book on Data Science
– Another mixed effects model visualization
– Data Science for Lawyers

Potpourri: Statistics #57

– Keep It Together: Using the tidyverse for machine learning
– Learn to purrr
– Mastering Shiny
– A Comprehensive List of Handy R Packages
– The challenges of using machine learning to identify gender in images
– How is polling done around the world?
– How to Get Better at Embracing Unknowns
– Drawing maps in R
– Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics
– Visualizing Locke and Mill: a tidytext analysis
– Tutorial: Cleaning UK Office for National Statistics data in R
– Transitioning into the tidyverse: part 1, part 2
– Your Friendly Guide to Colors in Data Visualisation
– Optimising your R code – a guided example
– Learning data visualization
– Reference Collection to push back against “Common Statistical Myths”
– mutate_all(), select_if(), summarise_at()… what’s the deal with scoped verbs?!
– Tools for Exploring and Comparing Data Frames
– Tom’s Cookbook for Better Viz
– Themes to Improve Your ggplot Figures
– Lesser Known R Features
– What Statistics Can and Can’t Tell Us About Ourselves
– A Graphical Introduction to tidyr’s pivot_*()
– n() cool #dplyr things
– Bayesian Linear Mixed Models: Random Intercepts, Slopes, and Missing Data
– Prepping data for #rstats #tidyverse and a priori planning
– NYT-style urban heat island maps

Potpourri: Statistics #56

– Hands-on Machine Learning with R
– The Truth About Linear Regression
– Data Viz Book Reviews
– Make Multi-point “dumbbell” Plots in ggplot2
– Storyline
– City Intelligence Data Design Guidelines
– shinyApp(), runApp(), shinyAppDir(), and a fourth option
– Reordering and facetting for ggplot2
– R Docker tutorial
– S4: a short guide for the perplexed
– Introducing trendyy
– R you ready to make charts?
– Ten more random useful things in R you may not know about
– Testing statistical software
– Tidy Data Science Workshop
– Using tidyverse tools with Pew Research Center survey data in R
– A Gentle Introduction to tidymodels
– Changing Glyph in legend in ggplot2
– Practical Data Science: an introduction to the PeerJ collection

Potpourri: Statistics #54

– A data.table and dplyr tour
– Mistakes, we’ve drawn a few
– Twenty rules for good graphics
– gganimate: The grammar of animation
– Visualising Intersecting Sets Of Twitter Followers
– Docker and Packrat
– Explore your Researcher Degrees of Freedom
– Teaching material: Data analytics and visualization
– 10 things R can do that might surprise you
– Scraping Data from the Web with rvest
– Common statistical tests are linear models (or: how to teach stats)
– 8 Useful R Packages for Data Science You Aren’t Using (But Should!)
– Easy multi-panel plots in R using facet_wrap() and facet_grid() from ggplot2
– Winners of the 1st Shiny Contest
– Rachael’s R Tutorials
– Web Scraping for Broad City Charts
– Implementing the super learner with tidymodels
– Three things to know beyond base R

Potpourri: Statistics #53

– Why so much hate against propensity score matching?
– R for Political Data
– Data Science Foundations: Know your data. Really, really, know it
– P-values 101: An attempt at an intuitive but mathematically correct explanation
– Merging Comparative Manifesto Project and ParlGov cabinet composition data at the party-level
– Choropleth maps with R – the Belgian edition
– Web Data Scraping
– Interpretable Machine Learning: A Guide for Making Black Box Models Explainable
– Chidi’s budget and utility: doing algebra and calculus with R and yacas
– FR2E: Intro to R Using Afrobarometer Data
– The New Heuristics
– You Need to Start Branding Your Graphs. Here’s How, with ggplot!

R Screencast Tutorials (from Quantitative Social Science Data with R: An Introduction):
– Introduction to R and R Studio
– Finding Data
– Data Management
– Variables & Manipulation
– Univariate & Descriptive Statistics
– Visualising Data
– Hypothesis Testing
– Bivariate Analysis
– Linear Regression & Model Building
– OLS Assumptions & Diagnostic Testing
– Putting it all Together

Potpourri: Statistics #52

– Here’s why 2019 is a great year to start with R: A story of 10 year old R code then and now
– How the BBC Visual and Data Journalism team works with graphics in R
– Special Topics in Data Science: Responsible Data Science
– Causal Data Science
– From Psychologist to Data Scientist
– Causal Graphs Seminar
– R Coding Style Guide
– Explaining the 2016 Democratic Primary with Machine Learning
– A guide to making your data analysis more reproducible
– Exploring the multiplication table with R
– hcandersenr: An R Package for H.C. Andersens fairy tales
– Solving the model representation problem with broom
– Basic Stata Syntax Workshop
– Bayesian Logistic Regression using brms, Part 1
– Half a dozen frequentist and Bayesian ways to measure the difference in means in two groups
– Understanding propensity score weighting
– Causal Inference Book
– 15 new ideas and new tools for R gathered from the RStudio Conference 2019
– Keeping up to date with R news
– tidylog

Potpourri: Statistics #51

– 2018 in Graphics: Bloomberg, FiveThirtyEight, Reuters, Nathan Yau
– Survey Raking: An Illustration
– textrecipes 0.0.1
– Topics in Econometrics: Advances in Causality and Foundations of Machine Learning
– Learning Statistics with R
– EDUC 263: Introduction to Data Management Using R
– Practical R for Mass Communication and Journalism: How Do I? …
– Text classification with tidy data principles
– Easily generate information-rich, publication-quality tables from R
– gganimate: Getting Started
– Text as Data
– A biased tour of the uncertainty visualization zoo

Potpourri: Statistics #50

– Generating data to explore the myriad causal effects that can be estimated in observational data analysis
– A Practical Guide to Mixed Models in R
– Ask the Question, Visualize the Answer
– Statistical Rethinking with brms, ggplot2, and the tidyverse
– Twitter, political ideology & the 115th US Senate
– You Can’t Test Instrument Validity
– Introduction to Econometrics with R
– Hands-On Programming with R
– Tidytext Tutorials
– What’s the best way to learn the programming language R? (Preferably, for free)

Potpourri: Statistics #49

– QuantText: A text-as-data page for political science research
– pollofpolls: R package for calculating poll of polls
– Best of the visualisation web… June 2018
– Custom themes in ggplot2
– DeclareDesign: Declaring and Diagnosing Research Designs
– A personal essay on Bayes factors
– Readings on Visualizing Uncertainty
– p-hacker: Train your p-hacking skills!
– Data Wrangling Part 1: Basic to Advanced Ways to Select Columns
– Data Wrangling Part 2: Transforming your columns into the right shape
– Data Wrangling Part 3: Basic and more advanced ways to filter rows
– Data Wrangling Part 4: Summarizing and slicing your data
– Label line ends in time series with ggplot2
– How good is “good”?
– The hacker’s guide to uncertainty estimates