ML Regression in ggplot2

How to make ML Regression Plots in ggplot2 with Plotly.

New to Plotly?

Plotly is a free and open-source graphing library for R. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

Linear regerssion plot

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)

Disaplay additional statistics

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)[2], y = names(fit$model)[1])) + 
    geom_point() +
    stat_smooth(method = "lm", col = "red") +
    labs(title = paste("Adj R2 = ",signif(summary(fit)$adj.r.squared, 5),
                       "Intercept =",signif(fit$coef[[1]],5 ),
                       " Slope =",signif(fit$coef[[2]], 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)