System

This post shows how to perform local polynomial regression using the loess function and how to add this curved line in base R and ggplot2 plots. We will also see an alternative method using ggplot2 which consists in directly perform and plot the regression using the geom_smooth function.

Two years ago, the entire NBA season went into lockout because of mostly financial reasons. However, one central point was also about keeping a competitive balance within the NBA, so that large and…

Communicate science and statistics with beautiful graphics

Create curved text paths in ggplot2. Contribute to AllanCameron/geomtextpath development by creating an account on GitHub.

R package for flexible correlation matrices based on ggplot2 - r-link/corrmorant

An extensive tutorial containing a general introduction to ggplot2 as well as many examples how to modify a ggplot, step by step. It covers several topics such as different chart types, themes, design choices, plot combinations, and modification of axes, labels, and legends, custom fonts, interactive charts and many more.

Abstract Plots including multiple regression lines are added to a matrix of plots generated with the GGally package in R.1 Background Built upon ggplot2, GGally provides templates for combining plots into a matrix through the ggpairs function. Such...

Understanding basic concepts around plotting in R using the ggplot2 package. Based on material presented in ‘Building Data Visualization Tools’ course by JHU at Coursera #R #plotting #ggplot2

Multiplot function in R can create Multiple graphs on one page using ggplot2 package. Multiplot function can take any number of plot…

This post shows how to create bar plots with error bars in R (ggplot2)

Learn creating data visualizations using ggplot2 in R with our comprehensive guide, designed for beginners and experienced users alike.

Introduction In the previous post I explored the use of linear model in the forms most commonly used in agricultural research. Clearly, when we are talking about linear models we are implicitly assuming that all relations between the dependent variable y and the predictors x are linear. In fact, in a linear model we could specify different shapes for the relation between y and x, for example by including polynomials (read for example...

R Language Tutorials for Advanced Statistics