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You can use the R visualization library ggplot2 to plot a fitted linear regression model using the following basic syntax:

ggplot(data,aes(x, y)) + geom_point() + geom_smooth(method='lm')

The following example shows how to use this syntax in practice.

**Example: Plot a Linear Regression Line in ggplot2**

Suppose we fit a simple linear regression model to the following dataset:

#create dataset data #fit linear regression model to dataset and view model summary model |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 following code shows how to visualize the fitted linear regression model:

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

By default, ggplot2 adds standard error lines to the chart. You can disable these by using the argumentÂ **se=FALSE** as follows:

library(ggplot2) #create regression plot with no standard error lines ggplot(data,aes(x, y)) + geom_point() + geom_smooth(method='lm', se=FALSE)

Lastly, we can customize some aspects of the chart to make it more visually appealing:

library(ggplot2) #create regression plot with customized style ggplot(data,aes(x, y)) + geom_point() + geom_smooth(method='lm', se=FALSE, color='turquoise4') + theme_minimal() + labs(x='X Values', y='Y Values', title='Linear Regression Plot') + theme(plot.title = element_text(hjust=0.5, size=20, face='bold'))

*Refer to this post for a complete guide to the best ggplot2 themes.*

**Additional Resources**

An Introduction to Multiple Linear Regression in R

How to Plot a Confidence Interval in R