patchwork is an R package with a powerful syntax for combining different ggplots into a single figure.
Exploring SIPS Tweets with R.
In this blog post, I use metadata from 70k Psychology journal articles, published in 25 journals from Scopus, to visualize 'consciousness hubs', or academic institutions that publish (more than other institutions) research on consciousness.
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](https://mvuorre.github.io/post/2017/quantitative-literature-review-with-r-part-i/). 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.
In this tutorial, I'll show how to use [R](https://www.r-project.org/) to quantitatively explore, analyze, and visualize a research literature, using [Psychonomic Society's](http://www.psychonomic.org/) publications
An R function for drawing forest plots from meta-analytic models estimated with the brms R package.
Today, we'll take a look at creating a specific type of visualization for data from a within-subjects experiment. You'll often see within-subject data visualized as bar graphs (condition means, and maybe mean difference if you're lucky.) But alternatives exist, and today we'll take a look at within-subjects scatterplots.
Panel plots are a common name for figures showing every person’s (or whatever your sampling unit is) data in their own little panel. This plot is sometimes also known as “small multiples”, although that more commonly refers to plots that illustrate interactions. Here, I’ll illustrate how to add information to a panel plot by arranging the panels according to some meaningful value.
Here’s an example of a panel plot, using the sleepstudy data set from the lme4 package.
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.
Visualizations are great for learning from data, and communicating the results of a statistical investigation. In this post, I illustrate how to create small multiples from data using R and ggplot2.
Small multiples display the same basic plot for many different groups simultaneously. For example, a data set might consist of a X ~ Y correlation measured simultaneously in many countries; small multiples display each country’s correlation in its own panel.