Boxplots are a good way to get some insight in your data, and while R provides a fine ‘boxplot’ function, it doesn’t label the outliers in the graph. However, with a little code you can add labels yourself:The numbers plotted next to ...

R allows you to create different plot types, ranging from the basic graph types like density plots, dot plots, bar charts, line charts, pie charts, boxplots and scatter plots, to the more statistically complex types of graphs such as probability plots, mosaic plots and correlograms. In addition, R is pretty known for its data visualization The post

This afternoon, we’ve seen in the training on data science that it was possible to use AIC criteria for model selection. > library(splines) > AIC(glm(dist ~ speed, data=train_cars, family=poisson(link="log"))) 438.6314 > AIC(glm(dist ~ speed, data=train_cars, family=poisson(link="identity"))) 436.3997 > AIC(glm(dist ~ bs(speed), data=train_cars, family=poisson(link="log"))) 425.6434 > AIC(glm(dist ~ bs(speed), data=train_cars, family=poisson(link="identity"))) 428.7195 And I’ve been asked...

I don’t understand why any researcher would choose not to use panel/multilevel methods on panel/hierarchical data. Let’s take the following linear regression as an example: , where is a random effect for the i-th group. A pooled OLS regression model for the above is unbiased and consistent. However, it will be inefficient, unless for all

We were asked a question on how to (in R) aggregate quarterly data from what I believe was a daily time series. This is a pretty common task and there are many ways to do this in R, but we’ll focus on one method using the zoo and dplyr packages. Let’t get those imports out of the way: library(dplyr) library(zoo) library(ggplot2) Now, we need...

It’s not on CRAN yet, but there’s a devtools-installable R package for getting data from the OMDB API. It covers all of the public API endpoints: find_by_id: Retrieve OMDB info by IMDB ID search find_by_title: Retrieve OMDB info by title search get_actors: Get actors from an omdb object as a vector get_countries: Get countries from

There has been a trend in the last few years to put interesting-looking but non-informative figures in papers; the pie chart is the worst recurrent offender. I have no idea how they keep getting included, as they’re famously misleading and awful. I want my work to look as much like the cockpit of a mecha or … Continue reading...

Dotplots are useful for the graphical visualization of small to medium-sized datasets. These simple plots provide an overview of how the data is distributed, whilst also showing the individual observations. It is however possible to make the simple dotplots more informative by overlaying them with data summaries and/or smooth distributions. This post is about creating