Ratings provided on visual analog scales (VAS), or slider scales, are unlikely to be normally distributed. Nevertheless, researchers typically use the normal distribution to analyze analog scale ratings, such as when they perform ANOVAs, t-tests, and correlations. A potentially better model of analog ratings, which are typically skewed and have lower and upper limits, is the so called zero-one-inflated beta model. In this post, I explain this model, illustrate its use with simulated and data, and compare its performance to t-tests in comparing two groups slider ratings.

patchwork is an R package with a powerful syntax for combining different ggplots into a single figure.

Exploring SIPS Tweets with R.

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.

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