# ML Regression in ggplot2

How to make ML Regression Plots in ggplot2 with Plotly.

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Sometimes it's nice to quickly visualise the data that went into a simple linear regression, especially when you are performing lots of tests at once. Here is a quick solution with ggplot2.

library(plotly)
library(ggplot2)

data(iris)

p <- ggplot(iris, aes(x = Petal.Width, y = Sepal.Length)) +
geom_point() +
stat_smooth(method = "lm", col = "red")

ggplotly(p)


You can create a quick function to pull the data out of a linear regression, and return important values (R-squares, slope, intercept and P value) at the top of a nice ggplot graph with the regression line.

library(plotly)
library(ggplot2)

data(iris)

ggplotRegression <- function (fit) {
ggplot(fit$model, aes_string(x = names(fit$model), y = names(fit$model))) + geom_point() + stat_smooth(method = "lm", col = "red") + labs(title = paste("Adj R2 = ",signif(summary(fit)$adj.r.squared, 5),
"Intercept =",signif(fit$coef[],5 ), " Slope =",signif(fit$coef[], 5),
" P =",signif(summary(fit)$coef[2,4], 5))) } fit1 <- lm(Sepal.Length ~ Petal.Width, data = iris) p <- ggplotRegression(fit1) ggplotly(p)  ### What About Dash? Dash for R is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library. Learn about how to install Dash for R at https://dashr.plot.ly/installation. Everywhere in this page that you see fig, you can display the same figure in a Dash for R application by passing it to the figure argument of the Graph component from the built-in dashCoreComponents package like this: library(plotly) fig <- plot_ly() # fig <- fig %>% add_trace( ... ) # fig <- fig %>% layout( ... ) library(dash) library(dashCoreComponents) library(dashHtmlComponents) app <- Dash$new()
app$layout( htmlDiv( list( dccGraph(figure=fig) ) ) ) app$run_server(debug=TRUE, dev_tools_hot_reload=FALSE) 