Often you may want to display a fitted regression line equation on top of a scatterplot in ggplot2.

One of the easiest ways to do so is by using the **stat_regline_equation****()** function from the **ggpubr **package in R, which can be used to perform this exact task.

The following example shows how to use the **stat_regline_equation****()** function in practice.

**Note**: Before using the **stat_regline_equation****()** function, you may need to first install the **ggpubr **package. You can use the following syntax to do so:

**install.packages('ggpubr')**

Once the **ggpbur **package has been installed, you can proceed to use the **stat_regline_equation****()** function.

**Example: How to Use the stat_regline_equation() Function in R**

Suppose that we create the following data frame in R that contains one predictor variable (x) and one response variable (y):

**#create data frame
df <- data.frame(y=c(6, 7, 7, 9, 12, 13, 13, 15, 16, 19, 22, 23, 23, 25, 26),
x=c(1, 2, 2, 3, 4, 4, 5, 6, 6, 8, 9, 9, 11, 12, 12))
#view data frame
df
y x
1 6 1
2 7 2
3 7 2
4 9 3
5 12 4
6 13 4
7 13 5
8 15 6
9 16 6
10 19 8
11 22 9
12 23 9
13 23 11
14 25 12
15 26 12**

Suppose that we would like to fit a simple linear regression model to this dataset, using the **x** variable as the predictor variable and the **y** variable as the response variable.

We can use the following syntax with the **lm()** function to do so:

**#fit linear regression model to data frame
model <- lm(y~x, data=df)
#view model summary
summary(model)
Call:
lm(formula = y ~ x, data = df)
Residuals:
Min 1Q Median 3Q Max
-1.4444 -0.8013 -0.2426 0.5978 2.2363
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.20041 0.56730 7.404 5.16e-06 ***
x 1.84036 0.07857 23.423 5.13e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.091 on 13 degrees of freedom
Multiple R-squared: 0.9769, Adjusted R-squared: 0.9751
F-statistic: 548.7 on 1 and 13 DF, p-value: 5.13e-12**

The output shows the fitted regression model.

From the coefficients in the output we can construct the fitted regression equation:

**y = 4.20041 + 1.84036(x)**

Now suppose that we would like to display this fitted regression equation on a scatterplot in ggplot2 so that we can visualize the relationship between x and y along with a neat summary of the regression equation.

We can use the **stat_regline_equation()** function with the following syntax to do so:

**library(ggplot2)
library(ggpubr)
#create plot to visualize fitted linear regression model
ggplot(df, aes(x, y)) +
geom_point() +
stat_regline_equation()**

This produces the following plot:

Notice that the fitted regression equation is shown in the top left corner of the scatterplot.

Note that we could also use the **geom_smooth()** argument to display the actual fitted regression line:

**library(ggplot2)
library(ggpubr)
#create plot to visualize fitted linear regression model
ggplot(df, aes(x, y)) +
geom_point() +
geom_smooth(method='lm') +
stat_regline_equation()**

This produces the following plot:

Notice that the linear regression line along with standard error bars are now shown in the plot, along with the fitted regression line equation in the top left corner.

**Note**: We used the argument **method=’lm’** within the **geom_smooth()** function to specify that we wanted to fit a linear model to the dataset. This ensures that the line shown in the plot matches the equation shown by the **stat_regline_equation()** function.

**Additional Resources**

The following tutorials explain how to perform other common tasks in R:

How to Perform Spline Regression in R

How to Perform Power Regression in R

How to Perform OLS Regression in R

How to Perform Piecewise Regression in R