How To Add Regression Equation In R Ggplot2

In my early days as an annotator, adding regression line equations and R² to my plots in Microsoft Excel was a adept way to make an impression on the management. Because maths. In R, information technology is a piffling harder to attain. There are two main means to attain it: manually, and using the ggpubr library. In this web log post, I explain how to do it in both means.

First, let's get some dummy data from the mtcars information prepare, load necessary packages and remove scientific notation. Our starting time plot — without the equation — looks like this.

          library(ggplot2)  options(scipen=999) # no scientific notation  data(mtcars) df <- mtcars  ggplot(df,aes(x = wt, y = hp)) +    geom_point() +    geom_smooth(method = "lm", se=FALSE)        

The following solution was proposed 10 years ago in a Google Group and simply involved some base functions. I updated the solution a petty fleck and this is the resulting code. By passing the x and y variable to the eq part, the regression object gets stored in a variable. The coefficients and the R² are concatenated in a long cord.

          eq <- office(10,y) {   m <- lm(y ~ x)   equally.graphic symbol(     every bit.expression(       substitute(italic(y) == a + b %.% italic(10)*","~~italic(r)^ii~"="~r2,                 list(a = format(coef(g)[i], digits = 4),                 b = format(coef(m)[2], digits = 4),                 r2 = format(summary(m)$r.squared, digits = 3)))     )   ) }  ggplot(df,aes(x = wt, y = hp)) +    geom_point() +    geom_smooth(method = "lm", se=Faux) +   geom_text(x = 2, y = 300, label = eq(df$wt,df$hp), parse = TRUE)        

I haven't inverse the theme 1 little bit to keep the solution every bit uncomplicated as possible.

True, that's a lot of code for something that seems obvious for an Excel user. Using the ggpubr bundle, you lot can plot the regression and a broad range of measures. The eq.label and the rr.label are use respectively to access the regression line equation and the R².

          library(ggpubr) ggplot(df,aes(x = wt, y = hp)) +    geom_point() +    geom_smooth(method = "lm", se=Imitation) +   stat_regline_equation(label.y = 400, aes(label = ..eq.characterization..)) +   stat_regline_equation(label.y = 350, aes(label = ..rr.label..))        

Hither are the other measures you lot can access:

• ..eq.characterization..: equation for the fitted polynomial every bit a character cord to exist parsed
• ..rr.label..: R2 of the fitted model as a graphic symbol cord to be parsed
• ..adj.rr.label..: Adjusted R2 of the fitted model as a character string to be parsed
• ..AIC.labe..l: AIC for the fitted model.
• ..BIC.characterization..: BIC for the fitted model.

By the way, you can easily use the measures from ggpubr in facets using facet_wrap() or facet_grid(). For every subset of your information, there is a different regression line equation and accompanying measures.

          ggplot(df,aes(x = wt, y = hp)) +    geom_point() +    geom_smooth(method = "lm", se=FALSE) +   stat_regline_equation(label.y = 400, aes(label = ..eq.label..)) +   stat_regline_equation(label.y = 350, aes(label = ..rr.label..)) +   facet_wrap(~vs)        

By the way, if y'all're having problem agreement some of the code and concepts, I can highly recommend "An Introduction to Statistical Learning: with Applications in R", which is the must-have data science bible. If you simply need an introduction into R, and less into the Data Scientific discipline part, I can admittedly recommend this volume by Richard Cotton fiber. Hope it helps!

Congratulations, you tin now add the regression line equation and several measures to your ggplot2 visualizations.

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Technologies get updated, syntax changes and honestly… I brand mistakes too. If something is incorrect, incomplete or doesn't work, let me know in the comments beneath and aid thousands of visitors.

How To Add Regression Equation In R Ggplot2,

Source: https://www.roelpeters.be/how-to-add-a-regression-equation-and-r-squared-in-ggplot2/

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