# Introduction

A scatterplot is a useful and straightforward way to visualize the relationship between two variables,eventually revealing a correlation. It is often used to make initial diagnoses before any other statistical analyses are conducted.This tutorial will not only teach you how to make scatterplots, but also explore the ways to help you design your own styling scatterplots.

## Keep in Mind

• REMEMBER always clean your dataset before you try to make scatterplots since in the real world, the dataset is always messier than the iris dataset used below.
• Scatterplots may not work well if the variables that you are interested in are discrete, or if there are a large number of data points.
• Be more careful if you have Date (which is time-series data) as your x-variable, Date can be very tricky in many ways.

## Also Consider

• If one of your variables is discrete, then instead of scatterplots, you may want to check how to make bar graphs here.

Specifically in R:

• Formatting graph legends is important for styling scatterplots. So check here if you want to work with graph legends.
• If you are working with time series visualization with ggplot2 package, see here for more help.
• Check here for more data visualization with ggplot2 package.

# Implementations

## R

For this R demonstration, we will introduce how to use ggplot2 package to create nice scatterplots.

• Before we create the scatterplots, we need to make sure that we install and library all the packages we need. Using the function p_load() in the pacman package is able to allow us to install and library all the packages we need at once time.
if (!require("pacman")) install.packages("pacman")


### Step 1: Basic Scatterplot

Let’s start with the basic scatterplot. Say we want to check the relationship between Sepal width and Sepal length of the iris species. There are a few steps to construct the scatterplot:

• Step1: specify the dataset that we want to visualize
• Step2: tell which variable to show on x and y axis
• Step3: add a geom_point() in order to show the points

If you have questions about how to use ggplot and aes, check Here for more help.

ggplot(data = iris, aes(
## Put Sepal.Length on the x-axis, Sepal.Width on the y-axis
x=Sepal.Length, y=Sepal.Width))+
## Make it a scatterplot with geom_point()
geom_point() ### Step 2: Map a variable to marker feature

One of the most powerful and magic abilities of the ggplot2 package is to map a variable to marker features.

Notice that attributes set outside of aes() apply to all points (like size=4 here), while attributes set inside of aes() set the attribute separately for the values of the variable.

#### Transparency

We can distinguish the Species by alpha (transparency).

ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width,
## Where transparency comes in
alpha=Species)) +
geom_point(size =4, color="seagreen") #### Shape

shape is also a common way to help us to see relationship between two variables within different groups. Additionally, you can always change the shape of the points. Check here for more ideas.

ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width,
## Where shape comes in
shape=Species)) +
geom_point(size = 4,color="orange") #### Size

size is a great option that we can take a look at as well. However, note that size will work better with continuous variables.

ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width,
## Where size comes in
size=Species)) +
geom_point(shape = 18, color = "#FC4E07") #### Color

Last but not least, let’s color these points depends on the variable Species in the iris dataset.

## First, we need to make sure that 'Species' is a factor variable
## class(iris$Species) ## Since 'Species' is already a factor variable, we do not need to do conversion ## However, in case 'Species' is not a factor variable, we can solve this question using as.factor() function, like below ## iris$Species <- as.factor(iris\$Species)

## Then, we are ready to plot
ggplot(data = iris, aes(x=Sepal.Length, y=Sepal.Width,
## distinguish the species by color
color=Species))+
geom_point() • ##### Note
• If you do not like the default colors in the ggplot2, there are a couple of ways to change that.The RColorBrewerpackage will definitely help. If you want to know more about RColorBrewer package,see here. Additionally,the viridis package is also very helpful to change the default colors. For more information of the viridis package, check here.
• If you do not like all the options that the RColorBrewer and viridis packages provide, see here to work with color in the ggplot2 package.
ggplot(data = iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species))+
geom_point()+
## Where RColorBrewer package comes in
scale_colour_brewer(palette = "Set1") ## There are more options available for palette

ggplot(data = iris, aes(x=Sepal.Length, y=Sepal.Width, color=Species))+
geom_point()+
## Where viridis package comes in
scale_color_viridis(discrete=TRUE,option = "D")  ## There are more options to choose


This first graph is using RColorBrewer package,and the second graph is using viridis package.  #### Put all the options together

Of course, we can always mix color,transparency,shape and size together to get prettier plot. Simply set more than one of them in aes()!

### Step 3: Find the comfortable themes

The next step that we can do is to figure out what the most fittable themes to match all the hard work we have done above.

#### Themes from **ggplot2 package**

In fact, ggplot2 package has many cool themes available alreay such as theme_classic(), theme_minimal() and theme_bw(). Another famous theme is the dark theme: theme_dark(). Let’s check out some of them.

ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width,
col=Species,
shape=Species)) +
geom_point(size=3) +
scale_color_viridis(discrete=TRUE,option = "D") +
theme_minimal(base_size = 12) #### Themes from the ggthemes package

ggthemes package is also worth to check out for working any plots (maps,time-series data, and any other plots) that you are working on. theme_gdocs(), theme_tufte(), and theme_calc() all work very well. See here to get more cool themes.

ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width,
col=Species,
shape=Species)) +
geom_point(size=3) +
scale_color_viridis(discrete=TRUE,option = "D") +
## Using the theme_tufte()
theme_tufte() If you do not like themes that ggplot2 and ggthemes packages have, don’t worry. You can always create your own style for your themes. Check here to desgin your own unique style.

### Step 4: Play with labels

It is time to label all the useful information to make the plot be clear to your audiences.

#### Basic Labelling

Both labs() and ggtitle() are great tools to deal with labelling information. In the following code, we provide the example how to use labs() to label the all the things that we need. Take a look here if you want to learn how to use ggtitle().

ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width,
col=Species,
shape=Species)) +
geom_point(size=3) +
scale_color_viridis(discrete=TRUE,option = "D") +
theme_minimal(base_size = 12)+
## Where the labelling comes in
labs(
## Tell people what x and y variables are
x="Sepal Length",
y="Sepal Width",
## Title of the plot
title = "Sepal length vs. Sepal width",
subtitle = " plot within different Iris Species"
) #### Postion and Appearance

After the basic labelling, we want to make them nicer by playing around the postion and appearance (text size, color and faces).

ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width,
col=Species,
shape=Species)) +
geom_point(size=3) +
scale_color_viridis(discrete=TRUE,option = "D") +
labs(
x="Sepal Length",
y="Sepal Width",
title = "Sepal length vs. Sepal width",
subtitle = "plot within different Iris Species"
)+
theme_minimal(base_size = 12) +
## Change the title and subtitle position to the center
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))+
## Change the appearance of the title and subtitle
theme (plot.title = element_text(color = "black", size = 14, face = "bold"),
plot.subtitle = element_text(color = "grey40",size = 10, face = 'italic')
) ### Step 5: Show some patterns

After done with step 4, you should end with a very neat and unquie plot. Let’s end up with this tutorial by checking whether there are some specific patterns in our dataset.

#### Linear Trend

According to the plot, it seems like there exists a linear relationship between sepal length and sepal width. Thus, let’s add a linear trend to our scattplot to help readers see the pattern more directly using geom_smooth(). Note that the method argument in geom_smooth() allows to apply different smoothing method like glm, loess and more. See the doc for more.

ggplot(iris, aes(x=Sepal.Length, y=Sepal.Width,
col=Species,
shape=Species)) +
geom_point(size=3) +
scale_color_viridis(discrete=TRUE,option = "D") +
labs(
x="Sepal Length",
y="Sepal Width",
title = "Sepal length vs. Sepal width",
subtitle = "plot within different Iris Species"
)+
theme_minimal(base_size = 12) +
theme(plot.title = element_text(hjust = 0.5),
plot.subtitle = element_text(hjust = 0.5))+
theme (plot.title = element_text(color = "black", size = 14, face = "bold"),
plot.subtitle = element_text(color = "grey40",size = 10, face = 'italic')) +
## Where linear trend + confidence interval come in
geom_smooth(method = 'lm',se=TRUE) 