You will learn how to plot **smooth line using ggplot2**.

Contents:

#### Related Book

GGPlot2 Essentials for Great Data Visualization in R## Prerequisites

- Load the ggplot2 package and set the default theme to
`theme_minimal()`

:

```
library(ggplot2)
theme_set(
theme_bw() +
theme(legend.position = "top")
)
```

- Demo dataset:

`head(cars)`

```
## speed dist
## 1 4 2
## 2 4 10
## 3 7 4
## 4 7 22
## 5 8 16
## 6 9 10
```

## Key R function: geom_smooth()

- Key R function:
`geom_smooth()`

for adding smoothed conditional means / regression line. - Key arguments:
`color`

,`size`

and`linetype`

: Change the line color, size and type.`fill`

: Change the fill color of the confidence region.

A simplified format of the function `geom_smooth():

`geom_smooth(method="auto", se=TRUE, fullrange=FALSE, level=0.95)`

**method**: smoothing method to be used. Possible values are lm, glm, gam, loess, rlm.**method = “loess”**: This is the default value for small number of observations. It computes a smooth local regression. You can read more about**loess**using the R code**?loess**.**method =“lm”**: It fits a**linear model**. Note that, it’s also possible to indicate the formula as**formula = y ~ poly(x, 3)**to specify a degree 3 polynomial.

**se**: logical value. If TRUE, confidence interval is displayed around smooth.**fullrange**: logical value. If TRUE, the fit spans the full range of the plot**level**: level of confidence interval to use. Default value is 0.95

## Regression line

To add a regression line on a scatter plot, the function `geom_smooth()`

is used in combination with the argument `method = lm`

. `lm`

stands for linear model.

```
p <- ggplot(cars, aes(speed, dist)) +
geom_point()
# Add regression line
p + geom_smooth(method = lm)
# loess method: local regression fitting
p + geom_smooth(method = "loess")
```

## Loess method for local regression fitting

```
# loess method: local regression fitting
p + geom_smooth(method = "loess")
```

## Polynomial interpolation

```
# Remove the confidence bande: se = FALSE
p + geom_smooth(method = "lm", formula = y ~ poly(x, 3), se = FALSE)
```

## Spline interpolation

```
spline.d <- as.data.frame(spline(cars$speed, cars$dist))
p + geom_line(data = spline.d, aes(x = x, y = y))
```

## Recommended for you

This section contains best data science and self-development resources to help you on your path.

### Coursera - Online Courses and Specialization

#### Data science

- Course: Machine Learning: Master the Fundamentals by Stanford
- Specialization: Data Science by Johns Hopkins University
- Specialization: Python for Everybody by University of Michigan
- Courses: Build Skills for a Top Job in any Industry by Coursera
- Specialization: Master Machine Learning Fundamentals by University of Washington
- Specialization: Statistics with R by Duke University
- Specialization: Software Development in R by Johns Hopkins University
- Specialization: Genomic Data Science by Johns Hopkins University

#### Popular Courses Launched in 2020

- Google IT Automation with Python by Google
- AI for Medicine by deeplearning.ai
- Epidemiology in Public Health Practice by Johns Hopkins University
- AWS Fundamentals by Amazon Web Services

#### Trending Courses

- The Science of Well-Being by Yale University
- Google IT Support Professional by Google
- Python for Everybody by University of Michigan
- IBM Data Science Professional Certificate by IBM
- Business Foundations by University of Pennsylvania
- Introduction to Psychology by Yale University
- Excel Skills for Business by Macquarie University
- Psychological First Aid by Johns Hopkins University
- Graphic Design by Cal Arts

### Amazon FBA

#### Amazing Selling Machine

### Books - Data Science

#### Our Books

- Practical Guide to Cluster Analysis in R by A. Kassambara (Datanovia)
- Practical Guide To Principal Component Methods in R by A. Kassambara (Datanovia)
- Machine Learning Essentials: Practical Guide in R by A. Kassambara (Datanovia)
- R Graphics Essentials for Great Data Visualization by A. Kassambara (Datanovia)
- GGPlot2 Essentials for Great Data Visualization in R by A. Kassambara (Datanovia)
- Network Analysis and Visualization in R by A. Kassambara (Datanovia)
- Practical Statistics in R for Comparing Groups: Numerical Variables by A. Kassambara (Datanovia)
- Inter-Rater Reliability Essentials: Practical Guide in R by A. Kassambara (Datanovia)

#### Others

- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham & Garrett Grolemund
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Géron
- Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce & Andrew Bruce
- Hands-On Programming with R: Write Your Own Functions And Simulations by Garrett Grolemund & Hadley Wickham
- An Introduction to Statistical Learning: with Applications in R by Gareth James et al.
- Deep Learning with R by François Chollet & J.J. Allaire
- Deep Learning with Python by François Chollet

Version: Français

## No Comments