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Data Visualization with ggplot2

Zulquar Nain

AMU

2025-03-18

GGPLOT2

INTRODUCTION

INTRODUCTION

ggplot2:- a package for data visualization

INTRODUCTION

ggplot2:- a package for data visualization

Simple

INTRODUCTION

ggplot2:- a package for data visualization

Simple

Complicated

INTRODUCTION

ggplot2:- a package for data visualization

Simple

Complicated

ggplot2 is the most popular data visualisation R package

INTRODUCTION

INTRODUCTION

  • Core function:- ggplot()

INTRODUCTION

  • Core function:- ggplot()

    • gg refers grammar of graphics

INTRODUCTION

  • Core function:- ggplot()

    • gg refers grammar of graphics

  • ggplot2 is highly modular

INTRODUCTION

  • Core function:- ggplot()

    • gg refers grammar of graphics

  • ggplot2 is highly modular

    • ONE function for ONE work

INTRODUCTION

  • Core function:- ggplot()

    • gg refers grammar of graphics

  • ggplot2 is highly modular

    • ONE function for ONE work
    • almost every little operation that you want to perform has a separate function

INTRODUCTION

  • Core function:- ggplot()

    • gg refers grammar of graphics

  • ggplot2 is highly modular

    • ONE function for ONE work

    • almost every little operation that you want to perform has a separate function

    • like little Lego building blocks that you can snap together

INTRODUCTION

INTRODUCTION

  • gglpot2 follows layered approach

INTRODUCTION

  • gglpot2 follows layered approach

  • Layers and design elements are added on top of one another

INTRODUCTION

  • gglpot2 follows layered approach

  • Layers and design elements are added on top of one another

  • Each command for a Layer is added to the previous ones with a plus symbol (+)

INTRODUCTION

  • gglpot2 follows layered approach

  • Layers and design elements are added on top of one another

  • Each command for a Layer is added to the previous ones with a plus symbol (+)

  • At the end, we have a multi-layer plot object that can be saved, modified, printed, exported, etc.

?Help

?Help

Data visualization cheat sheet from ~R Studio

?Help

Data visualization cheat sheet from ~R Studio

A Layered grammar of graphics by Hadley Wickham

?Help

Data visualization cheat sheet from ~R Studio

A Layered grammar of graphics by Hadley Wickham

The BOOK

?Help

Data visualization cheat sheet from ~R Studio

A Layered grammar of graphics by Hadley Wickham

The BOOK

Another source

?Help

Data visualization cheat sheet from ~R Studio

A Layered grammar of graphics by Hadley Wickham

The BOOK

Another source

The Package

Basics

Basics

THE GGPLOT2 OPERATES ON DATAFRAMES

Basics

THE GGPLOT2 OPERATES ON DATAFRAMES

THE BASICS OF GGPLOT SYNTAX

Basics

THE GGPLOT2 OPERATES ON DATAFRAMES

THE BASICS OF GGPLOT SYNTAX

THE SYNTAX

THE SYNTAX

The ggplot() function

THE SYNTAX

The ggplot() function

The ggplot() function is the core function of ggplot2

THE SYNTAX

The ggplot() function

The ggplot() function is the core function of ggplot2

Everything else in the ggplot2 system is built “on top of” this function.

THE SYNTAX

The ggplot() function

The ggplot() function is the core function of ggplot2

Everything else in the ggplot2 system is built “on top of” this function.

ggplot()

--

Creates a plot area/canvas upon which layers are added

Ends with +

THE SYNTAX

The ggplot() function

The ggplot() function is the core function of ggplot2

Everything else in the ggplot2 system is built “on top of” this function.

ggplot()

--

Creates a plot area/canvas upon which layers are added

Ends with +

THE SYNTAX

THE SYNTAX

The DATA parameter

THE SYNTAX

The DATA parameter

Inside of the the ggplot() function, the first parameter is the data parameter

THE SYNTAX

The DATA parameter

Inside of the the ggplot() function, the first parameter is the data parameter

The data parameter essentially specifies the data that we want to visualize

THE SYNTAX

The DATA parameter

Inside of the the ggplot() function, the first parameter is the data parameter

The data parameter essentially specifies the data that we want to visualize

Alert: ggplot2 works with data.frame ONLY

THE SYNTAX

THE SYNTAX

The DATA parameter

ggplot(data=iris)

THE SYNTAX

The DATA parameter

ggplot(data=iris)

output

THE SYNTAX

THE SYNTAX

The DATA parameter

iris %>% ggplot()

THE SYNTAX

The DATA parameter

iris %>% ggplot()

output

GEOMS: GEOMETRIC OBJECTS

GEOMS: GEOMETRIC OBJECTS

Geoms are the geometric objects of a data visualization. They are the things that get drawn in a data visualization

GEOMS: GEOMETRIC OBJECTS

Geoms are the geometric objects of a data visualization. They are the things that get drawn in a data visualization

On a blank canvas, we add geometrics(shapes) according to a systemic rule representing our data

GEOMS: GEOMETRIC OBJECTS

Geoms are the geometric objects of a data visualization. They are the things that get drawn in a data visualization

On a blank canvas, we add geometrics(shapes) according to a systemic rule representing our data

Let's see some example

GEOMS: GEOMETRIC OBJECTS

Geoms are the geometric objects of a data visualization. They are the things that get drawn in a data visualization

On a blank canvas, we add geometrics(shapes) according to a systemic rule representing our data

Let's see some example

GEOMS: GEOMETRIC OBJECTS

GEOMS: GEOMETRIC OBJECTS

We draw GEOMS with geom_ function

GEOMS: GEOMETRIC OBJECTS

We draw GEOMS with geom_ function

There are many types of GEOMS, all starts withgeoms_

GEOMS: GEOMETRIC OBJECTS

We draw GEOMS with geom_ function

There are many types of GEOMS, all starts withgeoms_

In one plot, we can plot more than one geoms by using +

GEOMS: GEOMETRIC OBJECTS

We draw GEOMS with geom_ function

There are many types of GEOMS, all starts withgeoms_

In one plot, we can plot more than one geoms by using +

GEOMS HAVE ATTRIBUTES LIKE COLOR AND SIZE

GEOMS: GEOMETRIC OBJECTS

We draw GEOMS with geom_ function

There are many types of GEOMS, all starts withgeoms_

In one plot, we can plot more than one geoms by using +

GEOMS HAVE ATTRIBUTES LIKE COLOR AND SIZE

geoms we draw has attributes like its position in the coordinate system, color, size, shape, etc.

GEOMS: GEOMETRIC OBJECTS

We draw GEOMS with geom_ function

There are many types of GEOMS, all starts withgeoms_

In one plot, we can plot more than one geoms by using +

GEOMS HAVE ATTRIBUTES LIKE COLOR AND SIZE

geoms we draw has attributes like its position in the coordinate system, color, size, shape, etc.

For example, point geoms have attributes like color, size, x-position, and y-position

GEOMS: GEOMETRIC OBJECTS

We draw GEOMS with geom_ function

There are many types of GEOMS, all starts withgeoms_

In one plot, we can plot more than one geoms by using +

GEOMS HAVE ATTRIBUTES LIKE COLOR AND SIZE

geoms we draw has attributes like its position in the coordinate system, color, size, shape, etc.

For example, point geoms have attributes like color, size, x-position, and y-position

These are known as aesthetic attributes. Aesthetic attributes are essentially the visual details about the color, size, and position of geometric objects

THE AES FUNCTION

THE AES FUNCTION

Now, we have DATA PARAMETER & GEOMS:VISUAL PARAMETERS

THE AES FUNCTION

Now, we have DATA PARAMETER & GEOMS:VISUAL PARAMETERS

For the data visualization process to work properly, there needs to be a connection between the data (the dataframe) and the visual objects that we draw (the geoms)

THE AES FUNCTION

Now, we have DATA PARAMETER & GEOMS:VISUAL PARAMETERS

For the data visualization process to work properly, there needs to be a connection between the data (the dataframe) and the visual objects that we draw (the geoms)

But, how do we connect them?

THE AES FUNCTION

Now, we have DATA PARAMETER & GEOMS:VISUAL PARAMETERS

For the data visualization process to work properly, there needs to be a connection between the data (the dataframe) and the visual objects that we draw (the geoms)

But, how do we connect them?

THE AES FUNCTION

MAPPINGS FROM DATA TO GEOMS

MAPPINGS FROM DATA TO GEOMS

we need a mapping from the underlying data to visual objects that get drawn (the geoms)

MAPPINGS FROM DATA TO GEOMS

we need a mapping from the underlying data to visual objects that get drawn (the geoms)

We do this with the aes() function

MAPPINGS FROM DATA TO GEOMS

we need a mapping from the underlying data to visual objects that get drawn (the geoms)

We do this with the aes() function

the aes() function, basically connects variables in our dataframe to the aesthetic attributes of our geoms

MAPPINGS FROM DATA TO GEOMS

we need a mapping from the underlying data to visual objects that get drawn (the geoms)

We do this with the aes() function

the aes() function, basically connects variables in our dataframe to the aesthetic attributes of our geoms

The dataframe is specified by the data parameter and the geom is specified by the geom that we choose (e.g., geom_line, geom_bar, etc).

MAPPINGS FROM DATA TO GEOMS

we need a mapping from the underlying data to visual objects that get drawn (the geoms)

We do this with the aes() function

the aes() function, basically connects variables in our dataframe to the aesthetic attributes of our geoms

The dataframe is specified by the data parameter and the geom is specified by the geom that we choose (e.g., geom_line, geom_bar, etc).

The aes() function is what enables us to connect these two things.

THE AES FUNCTION: AN EXAMPLE

THE AES FUNCTION: AN EXAMPLE

ggplot(data = iris , 
       mapping = aes(x = Sepal.Length , y = Sepal.Width))

THE AES FUNCTION: AN EXAMPLE

ggplot(data = iris , 
       mapping = aes(x = Sepal.Length , y = Sepal.Width))

THE AES FUNCTION: AN EXAMPLE

THE AES FUNCTION: AN EXAMPLE

ggplot(data = iris ,mapping = aes(x = Sepal.Length , y = Sepal.Width))+
  geom_point()

THE AES FUNCTION: AN EXAMPLE

ggplot(data = iris ,mapping = aes(x = Sepal.Length , y = Sepal.Width))+
  geom_point()

THE AES FUNCTION: A STRUCTURAL EXAMPLE

THE AES FUNCTION: A STRUCTURAL EXAMPLE

Let’s say that we want to plot line geoms, that is essentially,want to create a line chart

Line geoms have aesthetic attributes like their position on the x axis, position on the y axis, and color

By using the aes() function, we can connect the variables in the dataframe to those aesthetic attributes, which will cause the line to vary on the basis of the underlying data.

THE AES FUNCTION: A STRUCTURAL EXAMPLE

Let’s say that we want to plot line geoms, that is essentially,want to create a line chart

Line geoms have aesthetic attributes like their position on the x axis, position on the y axis, and color

By using the aes() function, we can connect the variables in the dataframe to those aesthetic attributes, which will cause the line to vary on the basis of the underlying data.

ggplot(data = dummy_data, aes(x = var1, y = var2) + geom_line()

THE AES FUNCTION: A STRUCTURAL EXAMPLE

Let’s say that we want to plot line geoms, that is essentially,want to create a line chart

Line geoms have aesthetic attributes like their position on the x axis, position on the y axis, and color

By using the aes() function, we can connect the variables in the dataframe to those aesthetic attributes, which will cause the line to vary on the basis of the underlying data.

ggplot(data = dummy_data, aes(x = var1, y = var2) + geom_line()

THE AES FUNCTION: AN EXAMPLE-color

THE AES FUNCTION: AN EXAMPLE-color

ggplot(data = iris ,mapping = aes(x = Sepal.Length , y = Sepal.Width))+
  geom_point(color="red")

THE AES FUNCTION: AN EXAMPLE-color

ggplot(data = iris ,mapping = aes(x = Sepal.Length , y = Sepal.Width))+
  geom_point(color="red")

THE AES FUNCTION: AN EXAMPLE-shape

THE AES FUNCTION: AN EXAMPLE-shape

ggplot(data = iris ,mapping = aes(x = Sepal.Length , y = Sepal.Width))+
  geom_point(color="red", shape=5)

THE AES FUNCTION: AN EXAMPLE-shape

ggplot(data = iris ,mapping = aes(x = Sepal.Length , y = Sepal.Width))+
  geom_point(color="red", shape=5)

OTHER PLOT AESTHETICS

- shape = Display a point with geom_point() as a dot, star, triangle, or square…

OTHER PLOT AESTHETICS

- shape = Display a point with geom_point() as a dot, star, triangle, or square…

- fill = The interior color (e.g. of a bar or boxplot)

OTHER PLOT AESTHETICS

- shape = Display a point with geom_point() as a dot, star, triangle, or square…

- fill = The interior color (e.g. of a bar or boxplot)

- color = The exterior line of a bar, boxplot, etc., or the point color if using geom_point()

OTHER PLOT AESTHETICS

- shape = Display a point with geom_point() as a dot, star, triangle, or square…

- fill = The interior color (e.g. of a bar or boxplot)

- color = The exterior line of a bar, boxplot, etc., or the point color if using geom_point()

- size = Size (e.g. line thickness, point size)

OTHER PLOT AESTHETICS

- shape = Display a point with geom_point() as a dot, star, triangle, or square…

- fill = The interior color (e.g. of a bar or boxplot)

- color = The exterior line of a bar, boxplot, etc., or the point color if using geom_point()

- size = Size (e.g. line thickness, point size)

- alpha = Transparency (1 = opaque, 0 = invisible)

OTHER PLOT AESTHETICS

- shape = Display a point with geom_point() as a dot, star, triangle, or square…

- fill = The interior color (e.g. of a bar or boxplot)

- color = The exterior line of a bar, boxplot, etc., or the point color if using geom_point()

- size = Size (e.g. line thickness, point size)

- alpha = Transparency (1 = opaque, 0 = invisible)

- binwidth = Width of histogram bins

OTHER PLOT AESTHETICS

- shape = Display a point with geom_point() as a dot, star, triangle, or square…

- fill = The interior color (e.g. of a bar or boxplot)

- color = The exterior line of a bar, boxplot, etc., or the point color if using geom_point()

- size = Size (e.g. line thickness, point size)

- alpha = Transparency (1 = opaque, 0 = invisible)

- binwidth = Width of histogram bins

- width = Width of “bar plot” columns

OTHER PLOT AESTHETICS

- shape = Display a point with geom_point() as a dot, star, triangle, or square…

- fill = The interior color (e.g. of a bar or boxplot)

- color = The exterior line of a bar, boxplot, etc., or the point color if using geom_point()

- size = Size (e.g. line thickness, point size)

- alpha = Transparency (1 = opaque, 0 = invisible)

- binwidth = Width of histogram bins

- width = Width of “bar plot” columns

- linetype = Line type (e.g. solid, dashed, dotted)

LABELS

Names of axes, plot titles etc

Done within the labs() function

added to the plot with + just as the geoms

Within labs()we can provide character strings to these arguements:

  • x = and y = The x-axis and y-axis title (labels)
  • title = The main plot title
  • subtitle = The subtitle of the plot, in smaller text below the title
  • caption = The caption of the plot, in bottom-right by default

LABELS: AN EXAMPLE

LABELS: AN EXAMPLE

ggplot(data = iris ,
       mapping = aes(x = Sepal.Length,
                   y = Sepal.Width))+
  geom_point(color="red", shape=5)+
  labs(
    title = "Sepal width Vs length", 
    subtitle = "Flower of IRIS species",
    x= "Sepal Length",
    y= "Sepal Wdith",
    caption = "A caption here")

LABELS: AN EXAMPLE

ggplot(data = iris ,
       mapping = aes(x = Sepal.Length,
                   y = Sepal.Width))+
  geom_point(color="red", shape=5)+
  labs(
    title = "Sepal width Vs length", 
    subtitle = "Flower of IRIS species",
    x= "Sepal Length",
    y= "Sepal Wdith",
    caption = "A caption here")

THEMES

ggplot2 is quite flexible and a lot of control over the plot we have.

THEMES

ggplot2 is quite flexible and a lot of control over the plot we have.

THEMES

ggplot2 is quite flexible and a lot of control over the plot we have.

For example, the plot background color, presence/absence of gridlines, and the font/size/color/alignment of text (titles, subtitles, captions, axis text…)

THEMES

ggplot2 is quite flexible and a lot of control over the plot we have.

For example, the plot background color, presence/absence of gridlines, and the font/size/color/alignment of text (titles, subtitles, captions, axis text…)

These adjustments can be done in one of two ways:

  • Add a complete theme theme_() function to make sweeping adjustments - these include theme_classic(), theme_minimal(), theme_dark(), theme_light() theme_grey(), theme_bw() among others

THEMES

ggplot2 is quite flexible and a lot of control over the plot we have.

For example, the plot background color, presence/absence of gridlines, and the font/size/color/alignment of text (titles, subtitles, captions, axis text…)

These adjustments can be done in one of two ways:

  • Add a complete theme theme_() function to make sweeping adjustments - these include theme_classic(), theme_minimal(), theme_dark(), theme_light() theme_grey(), theme_bw() among others
  • Adjust each tiny aspect of the plot individually within theme()

THEMES: Some Example

catter <- ggplot(data=iris, 
        aes(x = Sepal.Length, 
          y = Sepal.Width)) 
scatter + geom_point(aes(color=Species,
                      shape=Species)) +
 labs(  title = "Sepal width Vs length",
    subtitle = "Flower of IRIS species",
    x= "Sepal Length",  y= "Sepal Wdith",
    caption = "A caption here") +
  theme_bw()

THEMES: Some Example

catter <- ggplot(data=iris, 
        aes(x = Sepal.Length, 
          y = Sepal.Width)) 
scatter + geom_point(aes(color=Species,
                      shape=Species)) +
 labs(  title = "Sepal width Vs length",
    subtitle = "Flower of IRIS species",
    x= "Sepal Length",  y= "Sepal Wdith",
    caption = "A caption here") +
  theme_bw()

THEMES: Some Example

ggplot(data=iris, 
      aes(x = Sepal.Length, 
          y = Sepal.Width)) + 
geom_point(aes(color=Species,
                shape=Species)) +
 labs(title = "theme_bw()",) +
  theme_bw()
ggplot(data=iris, 
    aes(x = Sepal.Length, 
      y = Sepal.Width)) + 
geom_point(aes(color=Species,
                  shape=Species)) +
 labs(title = "theme_classic()",) +
  theme_classic()

THEMES: Some Example

ggplot(data=iris, 
      aes(x = Sepal.Length, 
          y = Sepal.Width)) + 
geom_point(aes(color=Species,
                shape=Species)) +
 labs(title = "theme_bw()",) +
  theme_bw()
ggplot(data=iris, 
    aes(x = Sepal.Length, 
      y = Sepal.Width)) + 
geom_point(aes(color=Species,
                  shape=Species)) +
 labs(title = "theme_classic()",) +
  theme_classic()
ggplot(data=iris, 
        aes(x = Sepal.Length, 
          y = Sepal.Width)) +
geom_point(aes(color=Species,
                      shape=Species)) + 
 labs(title = "theme_minimal()",) +
  theme_minimal()
ggplot(data=iris, 
        aes(x = Sepal.Length, 
          y = Sepal.Width)) +
  geom_point(aes(color=Species,
              shape=Species)) + 
 labs(title = "theme_grey()",) +
  theme_grey()

THEMES: Some Example

THEMES: Some Example

THEMES: ELEMENTS

theme() can be also modified

The theme() function has a large number of arguments, each of which edits a very specific aspect of the plot.

The basic syntax is this:

  • Within theme() write the argument name for the plot element you want to edit, like plot.title =
  • Provide an element_() function to the argument
  • Most often, use element_text(), but others include element_rect() for canvas background colors, or element_blank() to remove plot elements
  • Within the element_() function, write argument assignments to make the fine adjustments you desire

THEMES: ELEMENTS

THEMES: ELEMENTS

plot + 
 theme_classic()+                                # pre-defined theme adjustments
  theme(
  legend.position = "bottom",                    # move legend to bottom
    plot.title = element_text(size = 30),          # size of title to 30
    plot.caption = element_text(hjust = 0),        # left-align caption
    plot.subtitle = element_text(face = "italic"), # italicize subtitle
    axis.text.x = element_text(color = "red", size = 15, angle = 90),
  # adjusts only x-axis text
    axis.text.y = element_text(size = 15),         # adjusts only y-axis text
    axis.title = element_text(size = 20)           # adjusts both axes titles
    )

THEMES: ELEMENTS

THEMES: ELEMENTS

THANKS

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