# Scatter Plots - R Base Graphs

Previously, we described the essentials of R programming and provided quick start guides for importing data into **R**.

**scatter plot**. A

**scatter plot**can be created using the function

**plot**(x, y). The function

**lm**() will be used to fit linear models between y and x. A

**regression line**will be added on the plot using the function

**abline**(), which takes the output of

**lm**() as an argument. You can also add a smoothing line using the function

**loess**().

# Pleleminary tasks

**Launch RStudio**as described here: Running RStudio and setting up your working directory**Prepare your data**as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files**Import your data**into**R**as described here: Fast reading of data from txt|csv files into R: readr package.

Here, we’ll use the R built-in mtcars data set.

# R base scatter plot: plot()

```
x <- mtcars$wt
y <- mtcars$mpg
# Plot with main and axis titles
# Change point shape (pch = 19) and remove frame.
plot(x, y, main = "Main title",
xlab = "X axis title", ylab = "Y axis title",
pch = 19, frame = FALSE)
# Add regression line
plot(x, y, main = "Main title",
xlab = "X axis title", ylab = "Y axis title",
pch = 19, frame = FALSE)
abline(lm(y ~ x, data = mtcars), col = "blue")
```

```
# Add loess fit
plot(x, y, main = "Main title",
xlab = "X axis title", ylab = "Y axis title",
pch = 19, frame = FALSE)
lines(lowess(x, y), col = "blue")
```

# Enhanced scatter plots: car::scatterplot()

The function **scatterplot**() [in **car** package] makes enhanced scatter plots, with box plots in the margins, a non-parametric regression smooth, smoothed conditional spread, outlier identification, and a regression line, …

- Install
**car**package:

`install.packages("car")`

- Use
**scatterplot**() function:

```
library("car")
scatterplot(wt ~ mpg, data = mtcars)
```

The plot contains:

- the points
- the regression line (in green)
- the smoothed conditional spread (in red dashed line)
- the non-parametric regression smooth (solid line, red)

```
# Suppress the smoother and frame
scatterplot(wt ~ mpg, data = mtcars,
smoother = FALSE, grid = FALSE, frame = FALSE)
```

```
# Scatter plot by groups ("cyl")
scatterplot(wt ~ mpg | cyl, data = mtcars,
smoother = FALSE, grid = FALSE, frame = FALSE)
```

It’s also possible to add labels using the following arguments:

**labels**: a vector of point labels**id.n**,**id.cex**,**id.col**: Arguments for labeling points specifying the number, the size and the color of points to be labelled.

```
# Add labels
scatterplot(wt ~ mpg, data = mtcars,
smoother = FALSE, grid = FALSE, frame = FALSE,
labels = rownames(mtcars), id.n = nrow(mtcars),
id.cex = 0.7, id.col = "steelblue",
ellipse = TRUE)
```

```
## Mazda RX4 Mazda RX4 Wag Datsun 710 Hornet 4 Drive Hornet Sportabout Valiant
## 1 2 3 4 5 6
## Duster 360 Merc 240D Merc 230 Merc 280 Merc 280C Merc 450SE
## 7 8 9 10 11 12
## Merc 450SL Merc 450SLC Cadillac Fleetwood Lincoln Continental Chrysler Imperial Fiat 128
## 13 14 15 16 17 18
## Honda Civic Toyota Corolla Toyota Corona Dodge Challenger AMC Javelin Camaro Z28
## 19 20 21 22 23 24
## Pontiac Firebird Fiat X1-9 Porsche 914-2 Lotus Europa Ford Pantera L Ferrari Dino
## 25 26 27 28 29 30
## Maserati Bora Volvo 142E
## 31 32
```

Other arguments can be used such as:

**log**to produce log axes. Allowed values are log = “x”, log = “y” or log = “xy”**boxplots**: Allowed values are:- “x”: a box plot for x is drawn below the plot
- “y”: a box plot for y is drawn to the left of the plot
- “xy”: both box plots are drawn
- “” or FALSE to suppress both box plots.

**ellipse**: if TRUE data-concentration ellipses are plotted.

# 3D scatter plots

To plot a **3D scatterplot** the function **scatterplot3D** [in **scatterplot3D** package can be used].

The following R code plots a 3D scatter plot using *iris* data set.

`head(iris)`

```
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
```

```
# Prepare the data set
x <- iris$Sepal.Length
y <- iris$Sepal.Width
z <- iris$Petal.Length
grps <- as.factor(iris$Species)
# Plot
library(scatterplot3d)
scatterplot3d(x, y, z, pch = 16)
```

```
# Change color by groups
# add grids and remove the box around the plot
# Change axis labels: xlab, ylab and zlab
colors <- c("#999999", "#E69F00", "#56B4E9")
scatterplot3d(x, y, z, pch = 16, color = colors[grps],
grid = TRUE, box = FALSE, xlab = "Sepal length",
ylab = "Sepal width", zlab = "Petal length")
```

- Read more about static and interactive 3D scatter plot:

# Summary

**Create a scatter plot**:

- Using R base function:

`with(mtcars, plot(wt, mpg, frame = FALSE))`

- Using
**car**package:

```
car::scatterplot(wt ~ mpg, data = mtcars,
smoother = FALSE, grid = FALSE)
```

- 3D scatter plot:

```
library(scatterplot3d)
with(iris,
scatterplot3d(x = Sepal.Length, y = Sepal.Width,
z = Petal.Length, pch = 16,
grid = TRUE, box = FALSE)
)
```

# See also

# Infos

This analysis has been performed using **R statistical software** (ver. 3.2.4).

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