Data science

Quantitative literature review with R: Exploring Psychonomic Society Journals, Part II

In this tutorial, I’ll show how to use R to quantitatively explore, analyze, and visualize a research literature, using Psychonomic Society publications. This post directly continues from part I of Quantitative literature review with R. Please read that first for context. Part I focused on data cleaning and simple figures, but here we will look at relational data by visualizing some network structures in the data.

Quantitative literature review with R: Exploring Psychonomic Society Journals, Part I

In this tutorial, I’ll show how to use R to quantitatively explore, analyze, and visualize a research literature, using Psychonomic Society’s publications

GitHub-style waffle plots in R

In this post, I’ll show how to create GitHub style “waffle” plot in R with the ggplot2 plotting package. Simulate activity data First, I’ll create a data frame for the simulated data, initializing the data types: library(dplyr) d <- data_frame( date = as.Date(1:813, origin = "2014-01-01"), year = format(date, "%Y"), week = as.integer(format(date, "%W")) + 1, # Week starts at 1 day = factor(weekdays(date, T), levels = rev(c("Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"))), hours = 0) And then simulate hours worked for each date.