2021-08-04-ml-regression.Rmd

---
description: How to make ML Regression Plots in ggplot2 with Plotly.
name: ML Regression
permalink: ggplot2/ml-regression/
thumnail_github: ml-regression.png
layout: base
language: ggplot2
display_as: ai_ml
page_type: u-guide
order: 1
output:
  html_document:
    keep_md: true
---

```{r, echo = FALSE, message=FALSE}
knitr::opts_chunk$set(message = FALSE, warning=FALSE)
```

## 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.

```{r}
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)
```


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## 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.


```{r}
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)
```

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