Prepare the data

mtcars data sets are used in the examples below.

# Convert cyl column from a numeric to a factor variable
mtcars$cyl <- as.factor(mtcars$cyl)
head(mtcars)

##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

Basic scatter plots

Simple scatter plots are created using the R code below. The color, the size and the shape of points can be changed using the function geom_point() as follow :

geom_point(size, color, shape)

library(ggplot2)
# Basic scatter plot
ggplot(mtcars, aes(x=wt, y=mpg)) + geom_point()
# Change the point size, and shape
ggplot(mtcars, aes(x=wt, y=mpg)) +
  geom_point(size=2, shape=23)

Note that, the size of the points can be controlled by the values of a continuous variable as in the example below.

# Change the point size
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point(aes(size=qsec))

Read more on point shapes : ggplot2 point shapes

Label points in the scatter plot

The function geom_text() can be used :

ggplot(mtcars, aes(x=wt, y=mpg)) +
  geom_point() + 
  geom_text(label=rownames(mtcars))

Read more on text annotations : ggplot2 - add texts to a plot

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

# Add the regression line
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point()+
  geom_smooth(method=lm)
# Remove the confidence interval
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point()+
  geom_smooth(method=lm, se=FALSE)
# Loess method
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point()+
  geom_smooth()

# Change the point colors and shapes
# Change the line type and color
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point(shape=18, color="blue")+
  geom_smooth(method=lm, se=FALSE, linetype="dashed",
             color="darkred")
# Change the confidence interval fill color
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point(shape=18, color="blue")+
  geom_smooth(method=lm,  linetype="dashed",
             color="darkred", fill="blue")

Scatter plots with multiple groups

This section describes how to change point colors and shapes automatically and manually.

# Change point shapes by the levels of cyl
ggplot(mtcars, aes(x=wt, y=mpg, shape=cyl)) +
  geom_point()
# Change point shapes and colors
ggplot(mtcars, aes(x=wt, y=mpg, shape=cyl, color=cyl)) +
  geom_point()
# Change point shapes, colors and sizes
ggplot(mtcars, aes(x=wt, y=mpg, shape=cyl, color=cyl, size=cyl)) +
  geom_point()

# Add regression lines
ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl)) +
  geom_point() + 
  geom_smooth(method=lm)
# Remove confidence intervals
# Extend the regression lines
ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl)) +
  geom_point() + 
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)

ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl)) +
  geom_point() + 
  geom_smooth(method=lm, aes(fill=cyl))

# Change point shapes and colors manually
ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl)) +
  geom_point() + 
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
  scale_shape_manual(values=c(3, 16, 17))+ 
  scale_color_manual(values=c('#999999','#E69F00', '#56B4E9'))+
  theme(legend.position="top")
  
# Change the point sizes manually
ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl))+
  geom_point(aes(size=cyl)) + 
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
  scale_shape_manual(values=c(3, 16, 17))+ 
  scale_color_manual(values=c('#999999','#E69F00', '#56B4E9'))+
  scale_size_manual(values=c(2,3,4))+
  theme(legend.position="top")

p <- ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl)) +
  geom_point() + 
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
  theme_classic()
# Use brewer color palettes
p+scale_color_brewer(palette="Dark2")
# Use grey scale
p + scale_color_grey()

Add marginal rugs to a scatter plot

The function geom_rug() can be used :

geom_rug(sides ="bl")

sides : a string that controls which sides of the plot the rugs appear on. Allowed value is a string containing any of “trbl”, for top, right, bottom, and left.

# Add marginal rugs
ggplot(mtcars, aes(x=wt, y=mpg)) +
  geom_point() + geom_rug()
# Change colors
ggplot(mtcars, aes(x=wt, y=mpg, color=cyl)) +
  geom_point() + geom_rug()
# Add marginal rugs using faithful data
ggplot(faithful, aes(x=eruptions, y=waiting)) +
  geom_point() + geom_rug()

Scatter plots with the 2d density estimation

The functions geom_density_2d() or stat_density_2d() can be used :

# Scatter plot with the 2d density estimation
sp <- ggplot(faithful, aes(x=eruptions, y=waiting)) +
  geom_point()
sp + geom_density_2d()
# Gradient color
sp + stat_density_2d(aes(fill = ..level..), geom="polygon")
# Change the gradient color
sp + stat_density_2d(aes(fill = ..level..), geom="polygon")+
  scale_fill_gradient(low="blue", high="red")

Read more on ggplot2 colors here : ggplot2 colors

Scatter plots with ellipses

The function stat_ellipse() can be used as follow:

# One ellipse arround all points
ggplot(faithful, aes(waiting, eruptions))+
  geom_point()+
  stat_ellipse()
# Ellipse by groups
p <- ggplot(faithful, aes(waiting, eruptions, color = eruptions > 3))+
  geom_point()
p + stat_ellipse()
# Change the type of ellipses: possible values are "t", "norm", "euclid"
p + stat_ellipse(type = "norm")

Scatter plots with rectangular bins

The number of observations is counted in each bins and displayed using any of the functions below :

• geom_bin2d() for adding a heatmap of 2d bin counts
• stat_bin_2d() for counting the number of observation in rectangular bins
• stat_summary_2d() to apply function for 2D rectangular bins

The simplified formats of these functions are :

plot + geom_bin2d(...)
plot+stat_bin_2d(geom=NULL, bins=30)
plot + stat_summary_2d(geom = NULL, bins = 30, fun = mean)

• geom : geometrical object to display the data
• bins : Number of bins in both vertical and horizontal directions. The default value is 30
• fun : function for summary

The data sets diamonds from ggplot2 package is used :

head(diamonds)

##   carat       cut color clarity depth table price    x    y    z
## 1  0.23     Ideal     E     SI2  61.5    55   326 3.95 3.98 2.43
## 2  0.21   Premium     E     SI1  59.8    61   326 3.89 3.84 2.31
## 3  0.23      Good     E     VS1  56.9    65   327 4.05 4.07 2.31
## 4  0.29   Premium     I     VS2  62.4    58   334 4.20 4.23 2.63
## 5  0.31      Good     J     SI2  63.3    58   335 4.34 4.35 2.75
## 6  0.24 Very Good     J    VVS2  62.8    57   336 3.94 3.96 2.48

# Plot
p <- ggplot(diamonds, aes(carat, price))
p + geom_bin2d()

Change the number of bins :

# Change the number of bins
p + geom_bin2d(bins=10)

Or specify the width of bins :

# Or specify the width of bins
p + geom_bin2d(binwidth=c(1, 1000))

Scatter plot with marginal density distribution plot

Step 1/3. Create some data :

set.seed(1234)
x <- c(rnorm(500, mean = -1), rnorm(500, mean = 1.5))
y <- c(rnorm(500, mean = 1), rnorm(500, mean = 1.7))
group <- as.factor(rep(c(1,2), each=500))
df <- data.frame(x, y, group)
head(df)

##             x          y group
## 1 -2.20706575 -0.2053334     1
## 2 -0.72257076  1.3014667     1
## 3  0.08444118 -0.5391452     1
## 4 -3.34569770  1.6353707     1
## 5 -0.57087531  1.7029518     1
## 6 -0.49394411 -0.9058829     1

Step 2/3. Create the plots :

# scatter plot of x and y variables
# color by groups
scatterPlot <- ggplot(df,aes(x, y, color=group)) + 
  geom_point() + 
  scale_color_manual(values = c('#999999','#E69F00')) + 
  theme(legend.position=c(0,1), legend.justification=c(0,1))
scatterPlot
# Marginal density plot of x (top panel)
xdensity <- ggplot(df, aes(x, fill=group)) + 
  geom_density(alpha=.5) + 
  scale_fill_manual(values = c('#999999','#E69F00')) + 
  theme(legend.position = "none")
xdensity
# Marginal density plot of y (right panel)
ydensity <- ggplot(df, aes(y, fill=group)) + 
  geom_density(alpha=.5) + 
  scale_fill_manual(values = c('#999999','#E69F00')) + 
  theme(legend.position = "none")
ydensity

Create a blank placeholder plot :

blankPlot <- ggplot()+geom_blank(aes(1,1))+
  theme(plot.background = element_blank(), 
   panel.grid.major = element_blank(),
   panel.grid.minor = element_blank(), 
   panel.border = element_blank(),
   panel.background = element_blank(),
   axis.title.x = element_blank(),
   axis.title.y = element_blank(),
   axis.text.x = element_blank(), 
   axis.text.y = element_blank(),
   axis.ticks = element_blank()
     )

Step 3/3. Put the plots together:

To put multiple plots on the same page, the package gridExtra can be used. Install the package as follow :

install.packages("gridExtra")

Arrange ggplot2 with adapted height and width for each row and column :

library("gridExtra")
grid.arrange(xdensity, blankPlot, scatterPlot, ydensity, 
        ncol=2, nrow=2, widths=c(4, 1.4), heights=c(1.4, 4))

Read more on how to arrange multiple ggplots in one page : ggplot2 - Easy way to mix multiple graphs on the same page

Customized scatter plots

# Basic scatter plot
ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point()+
  geom_smooth(method=lm, color="black")+
  labs(title="Miles per gallon \n according to the weight",
       x="Weight (lb/1000)", y = "Miles/(US) gallon")+
  theme_classic()  
# Change color/shape by groups
# Remove confidence bands
p <- ggplot(mtcars, aes(x=wt, y=mpg, color=cyl, shape=cyl)) + 
  geom_point()+
  geom_smooth(method=lm, se=FALSE, fullrange=TRUE)+
  labs(title="Miles per gallon \n according to the weight",
       x="Weight (lb/1000)", y = "Miles/(US) gallon")
p + theme_classic()  

Change colors manually :

# Continuous colors
p + scale_color_brewer(palette="Paired") + theme_classic()
# Discrete colors
p + scale_color_brewer(palette="Dark2") + theme_minimal()
# Gradient colors
p + scale_color_brewer(palette="Accent") + theme_minimal()

Read more on ggplot2 colors here : ggplot2 colors

Infos

This analysis has been performed using R software (ver. 3.2.4) and ggplot2 (ver. 2.1.0)