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
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.
2017 will be the year when social scientists finally decided to diversify their applied statistics toolbox, and stop relying 100% on null hypothesis significance testing (NHST). A very appealing alternative to NHST is Bayesian statistics, which in itself contains many approaches to statistical inference. In this post, I provide an introductory and practical tutorial to Bayesian parameter estimation in the context of comparing two independent groups' data.
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.
Introduction Hello everybody! Recently, there’s been a lot of talk about meta-analysis, and here I would just like to quickly show that Bayesian multilevel modeling nicely takes care of your meta-analysis needs, and that it is easy to do in R with the rstan and brms packages. As you’ll see, meta-analysis is a special case of Bayesian multilevel modeling when you are unable or unwilling to put a prior distribution on the meta-analytic effect size estimate.
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.
In this post, I address the following problem: How to obtain regression lines and their associated confidence intervals at the average and individual-specific levels, in a two-level multilevel linear regression.
Background Visualization is perhaps the most effective way of communicating the results of a statistical model. For regression models, two figures are commonly used: The coefficient plot shows the coefficients of a model graphically, and can be used to replace or augment a model summary table.