Yebelay Berehan - Data Visualization Yebelay Berehan - Data Visualization Yebelay Berehan - Data Visualization Yebelay Berehan Welcome to my website Welcome to my website

What is data visualization?

Data visualization is the presentation of data in a pictorial or graphical format, and
A data visualization tool is the software that generates this presentation.
Effective data visualization provides users with intuitive means to
- interactively explore and analyze data,
- enabling them to effectively identify interesting patterns,
- infer correlations and causalities, and
- supports sense-making activities.
Good visual presentations tend to enhance the message of the visualization.

What is data visualization?

What are the key principles, methods, and concepts required to visualize data for publications, reports, or presentations?
The effectiveness of data visualization depends on several factors
What would you like to communicate?
Who is your audience? Researchers? Journalists? General public? Grant reviewers?
What is the best way to represent your data and your message?
- Is it through a box plot?
- Should you use blue or red?
- What scale should you use?
- Should you add or should you remove information?

Impoerant packages to create figures

A few packages to create figures in R are

ggplot2grammer of graphics
cowplot for composing ggplots
ggforce visual data investigations
ggrepel for nice text labeling
ggridges for ridge plots
ggsci for nice color palettes
ggtext for advanced text rendering
ggthemes for additional themes
grid for creating graphical objects
gridExtra additional functions grid

patchwork for multi-panel plots
prismatic for manipulating colors
rcartocolor for great color palettes
scico perceptional uniform palettes
showtext for custom fonts
charter interactive visualizations
echarts4rinteractive visualizations
ggiraph interactive visualizations
highcharterinteractive visualizations
plotly interactive visualizations

install packages to create figures

Code

# install CRAN packages
install.packages(
  c("ggplot2", "tibble", "tidyr", "forcats", "purrr", "prismatic", "corrr", 
    "cowplot", "ggforce", "ggrepel", "ggridges", "ggsci", "ggtext", "ggthemes", 
    "grid", "gridExtra", "patchwork", "rcartocolor", "scico", "showtext",
    "shiny", "plotly", "highcharter", "echarts4r"))

The basic components of plot using ggplot2 Package

ggplot2 is a system for declaratively creating graphics, based on the Grammar of Graphics.
You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

Why ggplot2?

A grammar of graphics is a grammar used to describe and create a wide range of statistical graphics.
The promise of a grammar for graphics.
Easy to manage, save, etc.
Graphs are composed of layers.
Easy to add stuff to existing graphs.
ggplot2 graphics take less work to make beautiful and eye-catching graphics.
Enables the creation of reproducible visualization patterns.
Publication quality & beyond

ggplot2 mechanics: the basics

A ggplot is built up from a few basic elements:

Data: The raw data that you want to plot.
Geometries geom_: The geometric shapes that will represent the data.
Aesthetics aes(): Aesthetics of the geometric and statistical objects, such as position, color, size, shape, and transparency
Scales scale_: Maps between the data and the aesthetic dimensions, such as data range to plot width or factor values to colors.
Statistical transformations stat_: Statistical summaries of the data, such as quantiles, fitted curves, and sums.
Coordinate system coord_: The transformation used for mapping data coordinates into the plane of the data rectangle.
Facets facet_: The arrangement of the data into a grid of plots.
Visual themes theme(): The overall visual defaults of a plot, such as background, grids, axes, default typeface, sizes and colors.

Components of the layered grammar

Layer
- Data
- Mapping
- Statistical transformation (stat)
- Geometric object (geom)
- Position adjustment (position)
Scale
Coordinate system (coord)
Faceting (facet)

Data

Data defines the source of the information to be visualized.
Must be a data.frame
Gets pulled into the ggplot() object

Aesthetics (`aes()`) (a.k.a. mapping)

x, y: variables
colour: colours the lines of geometries
fill: fill geometries or fill color
group: groups based on the data
shape: shape of point, an integer value 0 to 24, or NA
linetype: type of line, a integer value 0 to 6 or a string
size: sizes of elements, a non-negative numeric value
alpha: changes the transparency,a numeric value 0 to 1

Data

Code

# data and aesthetics
ggplot(data, mapping = aes(x, y, ...))

shape values

line type value

Geometries (geom_*()) function

The general syntax is:

ggplot(data = data, mapping = aes(mapings))+ geom_function()

Geom Components

Geom	Description	Input
geom_histogram	Histograms	Continous x
`geom_bar`	Bar plot with frequncies	Discrete x
`geom_point`	Points/scattorplots	Discrete/continuous x and y
`geom_boxplot`	Box plot	Disc. x and cont. y
`geom_smooth`	function line based on data
`geom_line`	Line plots	Discrete/continuous x and y
`geom_abline`	Reference	line intercept and slope value
`geom_hline`	geom_vline	Reference lines xintercept or yintercept

geom_*() functions

Positions

geom_bar(position = "<position >")
When we have aesthetics mapped, how are they positioned?
bar: dodge, fill, stacked (default)
point: jitter

Facets

facet_grid vs facet_wrap

facet_grid() facets the plot with a variable in a single direction (horizontal or vertical)
facet_wrap() simply places the facets next to each other and wraps them accoridng to the provided number of columns and/or rows.

The following table describes how facet formulas work in facet_grid() and facet_wrap():

Type	Formula	Description
Grid	facet_grid(. ~ x)	Facet horizontally across x values
`Grid`	facet_grid(y ~ .)	Facet vertically across y values
`Grid`	facet_grid(y ~ x)	Facet 2-dimensionally
`Wrap`	facet_wrap(~ x)	Facet across x values
`Wrap`	facet_wrap(~ x + y)	Facet across x and y values

Facets

Statistics (stat_*()) computed on the data.
- stat_*()-like functions perform computations such as means, counts, linear models, and other statistical summaries of data.
Coordinates (coord_*()) establish representation rules to print the data
- coord_cartesian() for the Cartesian plane;
- coord_polar() for circular plots;
- coord_map() for different map projections.

Themes

Code

plot + theme_gray(base_size = 11, base_family = "")

Theme is what controls the overall appearance of the ggplot visualiation.
ggplot2 offers several predefined themes that can be quickly applied to the ggplot object. see the details in section Themes

Practice with ggplot2

Create a simple plot object: plot.object <- ggplot()
Add geometric layers: plot.object <- plot.object + geom_*()
Add appearance layers: plot.object <- plot.object + coord_*() + theme()
Repeat steps 2 and 3 until satisfied, then print: plot.object or print(plot.object)

Practice with ggplot2

dataset to practice: palmerpenguins

We will use the palmerpenguins data set:

This data set contains size measurements for three penguin species observed on three islands in the Palmer Archipelago, Antarctica.

This dataset is often used to replace the iris dataset, which has some problems for teaching data science, including its ties to eugenics.

Let us take a look at the variables in the penguins data set:

Code

library(palmerpenguins)
data(penguins)
#str(penguins)

Practice with ggplot2

species, island, and sex are factor variables,
bill measurements depicted in the image are numeric variables,
two integer variables (flipper length and body mass).
Prepare data for ggplot2
ggplot2 requires you to prepare the data as an object of class data.frame or tibble (common in the tidyverse).

Code

library(tibble)
class(penguins) # all set!

[1] "tbl_df"     "tbl"        "data.frame"

Code

peng <- as_tibble(penguins) # acceptable
class(peng)

[1] "tbl_df"     "tbl"        "data.frame"

Practice with ggplot2

More complex plots in ggplot2 require the long data frame format.

Scientific questions about penguins

Scientific questions
Is there a relationship between the length & the depth of bills?
Does the size of the bill & flipper vary together ?
How are these measures distributed among the 3 penguin species ?

How can we graphically address these questions with ggplot2?

ggplot() layers

Code

library(ggplot2)
ggplot(data = penguins)

Code

ggplot(data = penguins, aes(x = bill_length_mm, y = bill_depth_mm))

Code

ggplot(data = penguins,
       aes(x = bill_length_mm, y = bill_depth_mm)) +
  geom_point()

Code

ggplot(data = penguins,
       aes(x = bill_length_mm, y = bill_depth_mm)) +
  geom_point() +  facet_wrap(~species) +
  coord_trans(x = "log10", y = "log10")

Let us explore how some of this data is structured by species:

Code

ggplot(data = penguins,               # Data
       aes(x = bill_length_mm,        # Your X-value
           y = bill_depth_mm,         # Your Y-value
           col = species)) +          # Aesthetics
  geom_point(size = 5, alpha = 0.8) + # Point
  geom_smooth(method = "lm")         # Linear regression

Customize Our Plot

Customizing plots involves adjusting various elements to enhance their readability, presentation, and informativeness.

Here are some key aspects you can customize:

Axes, Titles and Legends

Title and axes components: changing size, colour and face

Change Axis Titles

Axes, Titles and Legends

Customizing Axis Labels with labs()
labs(): This function is used to modify plot labels, including x-axis, y-axis, and plot title.

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")

Axes, Titles and Legends

xlab() and ylab(): These functions specifically set the x-axis and y-axis labels, respectively.

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) + 
  xlab("Bill length (mm)")+ ylab("Bill depth (mm)")

Axes, Titles and Legends

expression(): This function allows you to include mathematical expressions, special characters, and symbols in axis labels, such as Greek letters, superscripts, and subscripts.

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = expression(paste("X Axis Label with ", mu^2, " and ", sigma)))

Axes, Titles and Legends

Increasing Space Between Axis and Axis Titles

element_text(): While primarily used in theme() for overall theme customization, element_text() can be used to specify text properties such as size, color, and font face for axis labels.

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
      theme(axis.title = element_text(size = 15, face = "italic"))

Axes, Titles and Legends

To change vertical alignment using vjust which controls the vertical alignment, typically ranging between 0 and 1, but can extend beyond this range.

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  theme(axis.title.x = element_text(vjust = 0, size = 15),
        axis.title.y = element_text(vjust = 2, size = 15))

Axes, Titles and Legends

To change the distance, you can specify the margin() function’s with parameters t and r which refer to top and right, respectively.

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  theme(axis.title.x = element_text(margin = margin(t = 10), size = 15),
        axis.title.y = element_text(margin = margin(r = 10), size = 15))

Axes, Titles and Legends

To adjust the space on the y-axis, change the right margin, not the bottom margin.
The face argument can be set to bold, italic, or bold.italic:

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  theme(axis.title = element_text(color = "sienna", size = 15, face = "bold"),
        axis.title.y = element_text(face = "bold.italic"))

Axes, Titles and Legends

axis.text can modify the appearance of axis text (numbers) and its sub-elements axis.text.x and axis.text.y:

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  theme(axis.text = element_text(color = "dodgerblue", size = 12),
        axis.text.x = element_text(face = "italic"))

Axes, Titles and Legends

angle, hjust and vjust can rotate any text element. hjust and vjust used to adjust the position horizontally (0 = left, 1 = right) and vertically (0 = top, 1 = bottom):

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
theme(axis.text.x = element_text(angle = 50, vjust = 1, hjust = 1, size = 12))

Axes, Titles and Legends

element_blank() used to remove axis text and ticks,

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  theme(axis.ticks.y = element_blank(), axis.text.y = element_blank())

Axes, Titles and Legends

The element_blank() function is used to remove an element entirely but to remove axis titles by setting them to NULL or empty quotes " " in the labs() function:

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
 labs(x = NULL, y = "")

💡 Using NULL removes the element, while empty quotes ” ” keep the space for the axis title but print nothing.

Axes, Titles and Legends

ylim() and xlim() functions are used to limiting axis range

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+ ylim(c(0, 20))

Alternatively, use scale_y_continuous(limits = c(0, 20)) or coord_cartesian(ylim = c(0, 20)). The former removes data points outside the range, while the latter adjusts the visible area without removing data points.

Axes, Titles and Legends

scale_x_continuous() and scale_y_continuous(): While primarily for scaling continuous axes, these functions can also adjust axis labels using name.

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      scale_x_continuous(name = "New X Axis Label") +
    scale_y_continuous(name = "New Y Axis Label")

Adding Title

To customize titles in ggplot2, you can use a combination of ggtitle(), labs(), and theme() functions. Below is a list of the main functions and their key arguments for title customization:

Main Functions and Arguments

ggtitle(): used to label the text for the main title.
- Example: ggtitle("Main Title")
labs():
- title: The text for the main title.
- subtitle: The text for the subtitle.
- caption: The text for the caption.
- tag: The text for a tag.
- Example: labs(title = "Main Title", subtitle = "Subtitle", caption = "Caption", tag = "Fig. 1")

Adding Title

theme(): Customize the appearance of the text elements.
- plot.title: Customize the main title text, subtitle, caption and tag text. Example:
- theme(plot.title = element_text(face = "bold", size = 14, hjust = 0.5))
- theme(plot.subtitle = element_text(size = 12, hjust = 0.5))
- theme(plot.caption = element_text(size = 10, hjust = 0))
- theme(plot.tag = element_text(size = 8, hjust = 1))
- element_text(face, size, family, hjust, vjust, margin, lineheight): Control the font face, size, family, alignment, margin, and line height.

Adding Title

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)", 
           title = "Relationship between bill length and depth", 
           subtitle = "for different penguin species", caption = "scatter plot",  
           tag = "Fig. 1")

Bold Title and Margin

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)",  
           title = "Relationship between bill length and depth")+ 
  theme(plot.title = element_text(face = "bold", 
                                  margin = margin(10, 0, 10, 0), size = 14))

Using Non-Traditional Fonts

Code

library(showtext) 
font_add_google("Playfair Display", "Playfair") 
font_add_google("Bangers", "Bangers") 
showtext_auto()
ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)",  
           title = "Relationship between bill length and depth") + 
  theme(plot.title = element_text(family = "Bangers", hjust = 0.5, size = 25),
        plot.subtitle = element_text(family = "Playfair", hjust = 0.5, size = 15))

Changing Line Height in Multi-Line Text

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  ggtitle("Relationship between bill length and depth acrrossdifferent \n 
          species using scatter plot") +   
  theme(plot.title = element_text(lineheight = 0.8, size = 16))

Legends

One nice thing about ggplot2 is that it adds a legend by default when mapping a variable to an aesthetic. You can see that by default the legend title is what we specified in the color argument:

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")

Legends

The main functions and methods to customize legends in ggplot2:

To Turn Off the Legend: we can use the following code
- theme(legend.position = "none")
- guides(color = "none")
- scale_color_discrete(guide = "none")

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
 theme(legend.position = "none")

Legends

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+  
  guides(color = "none")

To Remove Legend Titles

theme(legend.title = element_blank())
scale_color_discrete(name = NULL)
labs(color = NULL

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
theme(legend.title = element_blank())

To Remove Legend Titles

💁 You can achieve the same by setting the legend name to NULL, either via scale_color_discrete(name = NULL) or labs(color = NULL). Expand to see examples.

Change Legend Position
- theme(legend.position = "top")
- theme(legend.position = c(x, y), legend.background = element_rect(fill = "transparent")) to add legend inside the plot

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
theme(legend.position = "top")

To Remove Legend Titles

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
theme(legend.position = c(.15, .15),  
      legend.background = element_rect(fill = "transparent"))

To Remove Legend Titles

Change Legend Direction
- guides(color = guide_legend(direction = "horizontal"))
Change Style of the Legend Title
- theme(legend.title = element_text(family, color, size, face))

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
theme(legend.title = element_text(family = "Playfair", 
                                  color = "chocolate", size =14, face ="bold"))

Change Legend Title

labs(color = "new title")
scale_color_discrete(name = "new title")
guides(color = guide_legend("new title"))

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)", 
           color = "species\nindicated\nby colors:") +   
  theme(legend.title = element_text(family = "Playfair", 
                                    color = "blue", size = 14, face = "bold"))

Change Order of Legend Keys

-   `factor(penguins$species, levels = c("Chinstrap", "Gentoo", "Adelie"))`

Code

library(dplyr)
penguins1 <- penguins %>%
  mutate(species = factor(species, levels = c("Chinstrap", "Gentoo","Adelie")))
ggplot(data = penguins1) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")

Change Legend Labels

scale_color_discrete(name = "species:", labels = c("Adelie type", "Chinstrap type", "Gentoo typ"))

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
scale_color_discrete(name = "species:", labels = c("Adelie type","Chinstrap type", "Gentoo typ"))+
  theme(legend.title = element_text(family = "Playfair", color = "chocolate", size = 14, face = 2))

Change Legend Labels

Change Background Boxes in the Legend
- theme(legend.key = element_rect(fill = "color"))
Change Size of Legend Symbols
- guides(color = guide_legend(override.aes = list(size = size)))

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
theme(legend.key = element_rect(fill = NA), 
      legend.title = element_text(color = "chocolate", size = 14, face = 2)) +
  scale_color_discrete("species:") + 
  guides(color = guide_legend(override.aes = list(size = 6)))

Change Legend Labels

Use Other Legend Styles
- guides(color = guide_legend())
- guides(color = guide_bins()

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
guides(color = guide_legend())

Change Legend Labels

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  guides(color = guide_bins())

theme

theme()

Elements of a theme by (Isabella Benabaye).

theme

Default theme: The default theme is theme_gray().

theme

The predefined theme takes two arguments for the base font size (base_size) and font family (base_family).
base_size input is a number, and base_family is a string (e.g. “serif”, “sans”, “mono”).
In addition, ggthemes pacakge offers additional predefined themes.
We will start with 8 predefined themes provided by ggplot2:

plot+theme_gray()
plot+theme_bw()
plot+theme_linedraw()
plot+theme_light()

plot + theme_dark()
plot + theme_minimal()
plot + theme_classic()
plot + theme_void()

theme

theme() has many arguments to control and modify individual components of a plot theme, including:
all line, rectangular, text and title elements
aspect ratio of the panel
axis title, text, ticks, and lines
legend background, margin, text, title, position, and more
panel aspect ratio, border, and grid lines

Backgrounds & Grid Lines

The main functions to customize the background of the plot in the provided code and explanation involve modifying elements of the theme function in ggplot2. Here are the key functions and elements used:

Changing the Panel Background Color

The panel background refers to the area where the data is plotted.

panel.background: Adjusts the background color and outline of the panel area.
theme(panel.background = element_rect(fill = "#64D2AA", color = "#64D2AA", linewidth = 2))

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  theme(panel.background = element_rect(fill = "#64D2AA", color = "#64D2AA", 
                                        linewidth = 2))

Changing the Panel Border Color

The panel border is an overlay on top of the panel.background which outlines the panel.

panel.border: Sets the border properties of the panel.

theme(panel.border = element_rect(fill = "#64D2AA99", color = "#64D2AA", linewidth = 2))

Code

ggplot(data = penguins) +
    geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) +
          labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
      theme(panel.border = element_rect(fill = "#64D2AA99", color = "#64D2AA", 
                                        linewidth = 2))

Changing Grid Lines

Grid lines help in referencing the data points against the axes.

panel.grid: Changes properties for all grid lines.
panel.grid.major: Changes properties for major grid lines.
panel.grid.minor: Changes properties for minor grid lines.
panel.grid.major.x and panel.grid.major.y: Change properties for major grid lines on the x and y axes separately.
panel.grid.minor.x and panel.grid.minor.y: Change properties for minor grid lines on the x and y axes separately.

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  theme(panel.grid.major = element_line(color = "gray10",linewidth = .5),       
        panel.grid.minor = element_line(color = "gray70", linewidth = .25))

Changing Grid Lines

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  theme(panel.grid.major = element_line(linewidth = .5, linetype = "dashed"),
        panel.grid.minor = element_line(linewidth = .25, linetype = "dotted"), 
        panel.grid.major.x = element_line(color = "red1"),       
        panel.grid.major.y = element_line(color = "blue1"), 
        panel.grid.minor.x = element_line(color = "red4"), 
        panel.grid.minor.y = element_line(color = "blue4"))

Removing Grid Lines

Grid lines can be selectively removed.

element_blank(): Used to remove specific theme elements.
- theme(panel.grid.minor = element_blank())
- theme(panel.grid = element_blank())

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
  labs(x = "Bill length (mm)", y = "Bill depth (mm)")+ theme(panel.grid = element_blank())

Changing the Spacing of Gridlines

You can specify the spacing of grid lines using scale_*_continuous functions.

scale_y_continuous(): Defines the breaks for the y-axis. scale_y_continuous(breaks = seq(0, 100, 10), minor_breaks = seq(0, 100, 2.5))

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  scale_y_continuous(breaks = seq(0, 30, 5), minor_breaks = seq(0, 60, 2.5))

Changing the Plot Background Color

The plot background refers to the entire area of the plot, including the panel and surrounding space.

plot.background: Adjusts the background color and outline of the entire plot area.

theme(plot.background = element_rect(fill = "gray60", color = "gray30", linewidth = 2))

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = body_mass_g)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  theme(plot.background = element_rect(fill = "gray60", 
                                       color = "gray30", 
                                       linewidth = 2))

Customizing multi-panel plots

When creating multi-panel plots in ggplot2, there are several functions and themes available to customize their appearance. Here’s a breakdown of the main functions and customization options based on the provided code:

Creating Facets with facet_grid and facet_wrap

facet_wrap(variable ~ .):
Creates a ribbon of panels based on a single variable.

Code

ggplot(data = penguins) +
  geom_point(mapping = aes(x = bill_length_mm, y = bill_depth_mm,
                           colour = species)) +
  facet_grid(~ species, scales = "free")

facet_grid(rows ~ columns):

Creates a grid of panels based on two variables.

Code

ggplot(data = penguins) +
  geom_point(mapping = aes(x = bill_length_mm, y = bill_depth_mm,
                           colour = species)) +
  facet_grid(year ~ species, scales = "free")

Customizing Layout of Facets
ncol and nrow:
- Control the number of columns and rows in facet_wrap.

facet_wrap

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
          labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
      theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  facet_wrap( ~ species+sex, ncol = 3)

scales:
Allows axes to have free scales with scales = "free" or control specific axis with scales = "free_x" or scales = "free_y".

scales

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  facet_wrap( ~ species, ncol = 3, scales = "free")

Styling Facet Labels
Modifying strip text and background:
Use theme to customize the appearance of facet labels.

Code

ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  facet_wrap(~ species, ncol = 3, scales = "free_x") + 
  theme(strip.text = element_text(face = "bold", color = "white", hjust = 0, size = 20), 
        strip.background = element_rect(fill = "chartreuse4", linetype = "dotted"))

Highlight specific labels using element_textbox_highlight:

Code

library(ggtext)
library(purrr)  # for %||%
ggplot(data = penguins) +
  geom_point(aes( x = bill_length_mm, y = bill_depth_mm, color = species)) +
      labs(x = "Bill length (mm)", y = "Bill depth (mm)")+
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) + 
  facet_wrap(~ species, ncol = 3, scales = "free_x") +  
  theme(strip.background = element_blank(), 
        strip.text = element_textbox_highlight(family = "Playfair", size = 12, 
                                               face = "bold", fill = "white", 
                                               box.color = "chartreuse4", 
                                               color = "chartreuse4", 
                                               halign = .5, linetype = 1, 
                                               r = unit(5, "pt"), 
                                               width = unit(1, "npc"), 
                                               padding = margin(5, 0, 3, 0), 
                                               margin = margin(0, 1, 3, 1), 
                                               hi.labels = c("1997", "1998","1999", "2000"), 
                                               hi.fill = "chartreuse4", 
                                               hi.box.col = "black", 
                                               hi.col = "white" ))

Combining Different Plots

patchwork package:
- Combine multiple plots with simple syntax.
- p1 + p2 p1 / p2 (g + p2) / p1
cowplot package:
Another package for combining multiple plots.

Code

library(cowplot) 
# plot_grid(plot_grid(g, p1), p2, ncol = 1)

Combining Different Plots

{gridExtra} package:
- Provides functions to arrange multiple plots.

Code

library(gridExtra) 
# grid.arrange(g, p1, p2, layout_matrix = rbind(c(1, 2), c(3, 3)))`

Custom layout with patchwork:
- Define complex layouts using a design matrix.

Code

# layout <- "AABBBB#   AACCDDE   ##CCDD#   ##CC### " 
# p2 + p1 + p1 + g + p2 + plot_layout(design = layout)

Colors

Several functions and techniques are highlighted for customizing colors in ggplot2 plots.

color and fill Arguments: Define the outline color (color) and the filling color (fill) of plot elements.
- geom_point(color = "steelblue", size = 2)
- geom_point(shape = 21, size = 2, stroke = 1, color = "#3cc08f", fill = "#c08f3c")

Code

# default
p <- ggplot(penguins, aes( x = bill_length_mm, y = bill_depth_mm, colour= species)) +
  geom_point() + labs(x = "Bill length (mm)", y = "Bill depth (mm)")

Colors

Code

p +  
  geom_point(shape= 21, size= 2, stroke= 1, color= "#3cc08f", fill = "#c08f3c")

scale_color_* and scale_fill_* Functions: Modify colors when they are mapped to variables. - These functions differ based on whether the variable is categorical (qualitative) or continuous (quantitative).

Qualitative Variables:

**`scale_color_manual` and `scale_fill_manual`**: Manually specify colors for categorical variables. `scale_color_manual(values = c("dodgerblue4", "darkolivegreen4", "darkorchid3", "goldenrod1"))`

Code

p + scale_color_manual(values = c("dodgerblue4", "darkorchid3", "goldenrod1"))

scale_color_brewer and scale_fill_brewer: Use predefined color palettes from ColorBrewer.

scale_color_brewer(palette = “Set1”)

Code

p+  scale_color_brewer(palette = "Set1")

Quantitative Variables:

scale_color_gradient and scale_fill_gradient: Apply a sequential gradient color scheme for continuous variables.
scale_color_gradient(low = "darkkhaki", high = "darkgreen")

Code

p2 <- ggplot(penguins, aes( x = bill_length_mm, y = bill_depth_mm, 
                            colour = body_mass_g)) + geom_point()+ 
  labs(x = "Bill length (mm)", y = "Bill depth (mm)") 
    p2 + scale_color_gradient(low = "darkkhaki", high = "darkgreen")

Quantitative Variables:

scale_color_viridis_c and scale_fill_viridis_c: Use the Viridis color palettes, which are perceptually uniform and suitable for colorblind viewers.

scale_color_viridis_c(option = "inferno")

Code

p2 + scale_color_viridis_c(option = "inferno")

Additional Color Palettes from Extension Packages:

The {ggthemes} package for example lets R users access the Tableau colors. Tableau is a famous visualiztion software with a well-known color palette.

scale_color_tableau and scale_fill_tableau (from ggthemes): Use Tableau color palettes.

scale_color_tableau()

Code

library(ggthemes)
p + scale_color_tableau()

The {ggsci} package provides scientific journal and sci-fi themed color palettes with colors that look like being published in Science or Nature.

scale_color_tableau()

scale_color_aaas, scale_color_npg (from ggsci): Use scientific journal and sci-fi themed color palettes.
scale_color_aaas()
scale_color_npg()

Code

library(ggsci)
p + scale_color_aaas()

Code

p+ scale_color_npg()

scale_color_carto_c and scale_fill_carto_c (from rcartocolor): Use CARTO color palettes.
scale_color_carto_c(palette = "BurgYl")

scale_color_tableau()

Code

library(rcartocolor)
p2 + scale_color_carto_c(palette = "BurgYl")

scale_color_scico and scale_fill_scico (from scico): Use perceptually uniform color palettes from the Scico package.
The {scico} package provides access to the color palettes developed by Fabio Crameri.
scale_color_scico(palette = "berlin")

Code

library(scico)
p2 + scale_color_scico(palette = "berlin")

scale_color_tableau()

Code

# Manual
p + scale_colour_manual(values = c("grey55", "orange",  "skyblue")) +
  labs(title = "Manual")

Use a predefined colour palette

Code

#install.packages("RColorBrewer")
require(RColorBrewer)
display.brewer.all()

Code

library(RColorBrewer)
p+scale_colour_brewer(palette = "Dark2") +
  labs(title = "Palette for groups")

Use a predefined colour palette

Code

# Palette for continuous values
p2 + scale_colour_viridis_c()+
  labs(title= "Palette for continuous values")

Use colourblind-friendly palettes

Have you ever considered how your figure might appear under various forms of colourblindness? We can use the package colorBlindness to consider this.

Code

library(colorBlindness)
cvdPlot(p)

Use colourblind-friendly palettes

Code

# Palette for groups
p + 
  scale_colour_viridis_d() +
  labs(title ="Viridis palette for groups")

Code

# Palette for continuous values
p2 + 
  scale_colour_viridis_c() +
  labs(title = "Viridis palette 
       for continuous values")

Lines

geom_hline(): Adds horizontal lines to a plot at specified y-axis values.

yintercept: A numeric vector indicating where to draw the horizontal lines. geom_hline(yintercept = c(12, 23))

Code

p + geom_hline(yintercept = c(12, 23))

geom_vline(): Adds vertical lines to a plot at specified x-axis values.
- xintercept: A numeric vector or aesthetic mapping for x-axis intercepts.
- color, linewidth, linetype: Aesthetics for customizing the appearance of the line.

Lines

geom_vline(aes(xintercept = 45), linewidth = 1.5, color = "firebrick", linetype = "dashed")

Code

p + geom_vline(aes(xintercept = 45), linewidth = 1.5, color = "firebrick", 
               linetype = "dashed")

geom_abline(): Adds lines with a specified slope and intercept to a plot.
- intercept: The intercept of the line.
- slope: The slope of the line.
- color, linewidth: Aesthetics for customizing the appearance of the line. geom_abline(intercept = coefficients(reg)[1], slope = coefficients(reg)[2], color = "darkorange2", linewidth = 1.5)

Lines

Code

reg <- lm(body_mass_g ~ bill_depth_mm, data = penguins) 
p + geom_abline(intercept = coefficients(reg)[1], slope = coefficients(reg)[2], 
                color = "darkorange2", linewidth = 1.5)

geom_linerange(): Adds line segments that do not span the entire plot range. Can be used for highlighting specific ranges.
- x, y: Aesthetics for the starting and ending points of the line.
- xmin, xmax, ymin, ymax: Coordinates for the start and end of the line segments.
- color, linewidth: Aesthetics for customizing the appearance of the line.

Lines

Code

p+geom_linerange(aes(x = 45, ymin = 15, ymax = 22), color = "steelblue", linewidth = 1)+
  geom_linerange(aes(y = 16, xmin = 30, xmax = 45), color = "red", linewidth = 1)

annotate(geom = "segment"): Adds line segments with specified start and end points. Useful for creating lines with arbitrary slopes.
- x, xend, y, yend: Coordinates for the start and end of the line segments.
- color, linewidth: Aesthetics for customizing the appearance of the line.

Lines

Code

p + annotate(geom = "segment", x = 10, xend = 75, y = 20, yend = 5, 
             color = "purple", linewidth = 2)

geom_encircle() in the ggalt package is used to automatically enclose points in a polygon, creating an encircling effect around specified groups of points in a ggplot2 plot. This can be useful for highlighting clusters or groups of points within your data visualization.

Lines

Code

library(ggalt)
p + geom_encircle(data=subset(penguins, species =="Adelie"),
                  colour="blue", spread=0.002) + 
  geom_encircle(data=subset(penguins, species =="Chinstrap"), 
                colour="purple", spread=0.002) +
  geom_encircle(data=subset(penguins, species =="Gentoo"), 
                colour="red", spread=0.002) + ylim(10, 23)

Text

There are a range of functions and techniques to customize text and labels in ggplot2 plots. Let us seea a detailed explanation of each function mentioned, along with examples and their purposes:

geom_label(): Adds labels to points on the plot with a rectangle around the text.

Code

p + geom_label(aes(label = species), hjust = .5, vjust = -.5) + 
  theme(legend.position = "none")

Text

hjust and vjust control the horizontal and vertical justification of the labels.
geom_text(): Similar to geom_label(), but without the rectangle around the text.

Code

p + geom_text(aes(label = sex), hjust = .5, vjust = -.5)+ 
  theme(legend.position = "none")

Text

ggrepel Package: Provides functions to repel overlapping text labels.
- geom_text_repel(): Repels text labels to avoid overlap.
- geom_label_repel(): Repels text labels with a rectangle around them.

Code

library(ggrepel)
p + geom_label_repel(aes(label = species), fontface = "bold")+ 
  theme(legend.position = "none")

Text

geom_label_repel() avoids overlapping by adjusting the position of labels.
annotate(): Adds annotations to a plot.

Code

p + annotate(geom = "text", x = 45, y = 25, fontface = "bold", 
             label = "This is a useful annotation")

Text

annotate() is used to add single text or label annotations at specified coordinates.
annotation_custom(): Adds custom annotations using grid graphical objects.

Code

library(grid)
my_grob <- grobTree(textGrob("This is species type!", x = .1, y = .9, 
                             hjust = 0, gp = gpar(col = "black", fontsize = 15, 
                                                  fontface = "bold"))) 
p + annotation_custom(my_grob) + facet_wrap(~species, scales = "free_x") + scale_y_continuous(limits = c(NA, 20)) + theme(legend.position = "none")

Text

ggtext Package: Enhances text rendering with support for markdown and HTML.
Functions:
- geom_richtext(): Renders text as markdown or HTML.
- geom_textbox(): Provides dynamic wrapping for longer text annotations.

Code

library(ggtext) 
lab_md <- "This plot shows **Bill lngth** in *°mm* versus **Bill depth** in *mm* across Species type" 
p + geom_richtext(aes(x = 45, y = 22.5, label = lab_md, stat = "unique"))

Text

If we need to add long text to annotate our plot

Code

lab_long <- "**Association**<br><i style='font-size:8pt;color:black;'>This graph is a scatter plot showing the association between bill length and bill weidth for each specias type. So we can see that there is a crear association.</i>"

p + geom_textbox(aes(x = 45, y = 20, label = lab_long),
                 width = unit(25, "lines"), stat = "unique")

Coordinates

To customize plots in ggplot2, you can use a variety of functions that modify the coordinates, axes, scales, and themes of your plot. Here is a detailed explanation of the functions mentioned in your provided text, as well as some additional ones commonly used in ggplot2 for customization:

coord_flip(): Flips the x and y coordinates, making horizontal plots vertical and vice versa. This is particularly useful for bar charts and boxplots.

Code

p + coord_flip()

Coordinates

coord_fixed(ratio = 1): Fixes the aspect ratio of the plot, ensuring a specific ratio of units on the x and y axes.

Code

p + scale_x_continuous(breaks = seq(0, 60, by = 5)) + 
  coord_fixed(ratio = 1)

coord_fixed(ratio = 1/3): Sets a different aspect ratio, ensuring a 1:3 ratio of units on the x and y axes.

Coordinates

Code

p + scale_x_continuous(breaks = seq(0, 60, by = 15)) +   
  coord_fixed(ratio = 2/3) +   
  theme(plot.background = element_rect(fill = "grey80"))

Coordinates

coord_polar(): Converts the plot to polar coordinates, often used for circular bar charts and pie charts.

Code

penguins %>%   dplyr::group_by(species) %>%   dplyr::summarize(bd = median(flipper_length_mm)) %>%   ggplot(aes(x = species, y = bd)) +   
  geom_col(aes(fill = species), color = NA) +   
  labs(x = "", y = "Median Ozone Level") +   coord_polar() +   
  guides(fill = "none")

Coordinates

coord_polar(theta = "y"): Used for creating pie charts by specifying the theta parameter as “y”.

Code

library(dplyr)
chic_sum <- penguins %>%  mutate(n_all = n()) %>% group_by(species) %>% dplyr::summarize(Total = n() / unique(n_all))  
ggplot(chic_sum, aes(x = "", y = Total)) +
  geom_col(aes(fill = species), width = 1, color = NA) + 
  coord_polar(theta = "y") +   
  scale_fill_brewer(palette = "Set1", name = "Species:")

Axis Functions

scale_x_continuous() / scale_y_continuous(): Customize the breaks, labels, and limits of continuous scales.

Code

p +  scale_x_continuous(breaks = seq(0, 60, by = 10))

Axis Functions

scale_x_reverse() / scale_y_reverse(): Reverses the direction of the x or y axis, making higher values appear on the left or bottom.

Code

p + scale_y_reverse()

Axis Functions

scale_y_log10(): Transforms the y-axis to a logarithmic scale (base 10), useful for data with a wide range.

Code

p+scale_y_sqrt()

Smoothings

To make smoothing our plot, we can simply use stat_smooth().

This adds a LOESS (locally weighted scatter plot smoothing, method = "loess") if you have fewer than 1000 points or a GAM (generalized additive model, method = "gam") otherwise.

Code

p + geom_point(color = "gray40", alpha = .5) + stat_smooth()

Adding a Linear Fit

Though the default is a LOESS or GAM smoothing, it is also easy to add a standard linear fit:

Code

p + geom_point(color = "gray40", alpha = .5) + 
  stat_smooth(method = "lm", se = FALSE, color = "firebrick", linewidth = 1.3)+ 
  labs(x = "Temperature (°F)", y = "Dewpoint")

Specifying the Formula for Smoothing

ggplot2 allows you to specify the model you want it to use. Maybe you want to use a polynomial regression?

Code

p + geom_point(color = "gray40", alpha = .3) + 
  geom_smooth(method = "lm",
              formula = y ~ x + I(x^2) + I(x^3) + I(x^4) + I(x^5),
              color = "black", fill = "firebrick") +
  labs(x = "Ozone Level", y = "Temperature (°F)")

Interactive Plots

Interactive plots in R are a great way to enhance the user experience by providing dynamic and visually appealing graphics. Some libraries that can be used in combination with ggplot2 or on their own to create interactive visualizations:

There are different interactive Plot Libraries. The following are among the few

Plot.ly} is a tool for creating online, interactive graphics and web apps. The plotly package in R allows you to easily convert your ggplot2 plots into interactive plots.

Code

library(plotly)
ggplotly(p)

Interactive Plots

This function ggplotly(p) converts the ggplot2 object p into an interactive plot.

ggiraph() is an R package that allows you to create dynamic ggplot2 graphs, adding tooltips, animations, and JavaScript actions.

Code

library(ggiraph)
p3 <- p+   geom_line(color = "grey") + 
  geom_point_interactive(aes(color = species, tooltip = species, data_id = species)) +
  scale_color_brewer(palette = "Dark2", guide = "none")
girafe(ggobj = p3)

Interactive Plots

The function girafe(ggobj = p3) creates an interactive plot with tooltips.

highcharter() is a software library for interactive charting. The highcharter package brings this functionality to R.

Code

library(highcharter) 
hchart(penguins, "scatter", hcaes(x = bill_length_mm, y = bill_depth_mm, 
                                  group = species))

Interactive Plots

The hchart function generates a scatter plot using the Highcharts library.
echarts4r is a free, powerful charting and visualization library. The echarts4r package provides an interface to use this library in R.

Code

library(echarts4r) 
penguins %>%   e_charts(bill_length_mm) %>% 
  e_scatter(body_mass_g, symbol_size = 7) %>%   
  e_visual_map(body_mass_g) %>%   e_y_axis(name = "Bill length") %>%   
  e_legend(FALSE)

Interactive Plots

The e_charts function initializes the chart, and subsequent functions customize it.
The charter package allows you to use this framework in R.

Code

#remotes::install_github("JohnCoene/charter")
library(charter) 
chart(data = penguins, caes(bill_length_mm, body_mass_g)) %>%   
  c_scatter(caes(color = species, group = species)) %>% 
  c_colors(RColorBrewer::brewer.pal(4, name = "Dark2"))

The chart function initializes the chart, and subsequent functions customize it.

Create different plots using geom_*()

Code

p1 <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) +
  geom_point()
p2 <- ggplot(penguins, aes(x = bill_length_mm, y = bill_depth_mm, colour = species)) +
  geom_density2d()
p3 <- ggplot(penguins, aes(x = species, fill = island)) +
  geom_bar()
p4 <- ggplot(penguins, aes(x = species, y = bill_depth_mm, fill = species)) +
  geom_boxplot()

library(patchwork)
p1 + p2 + p3 + p4

Create different plots using geom_*()

This is a blank plot, before we add any geom_* to represent variables in the dataset.

Code

ggplot(penguins)

Bar plot using geom_bar()

Bar chart of number of penguins by species. I would like to know how many species we have in this dataset.

Code

ggplot(penguins, aes(x = species, fill = species)) +
  geom_bar() + labs(title = "Number of Penguins by Species",
       x = "Species",  y = "Count", fill = "Species") + theme_minimal()

Bar plot using geom_bar()

Number of Penguin species on each Island

Code

ggplot(data = penguins)+ geom_bar(mapping=aes(x=island, fill=species))+
  labs(title="Population of Penguin species on each Island", y="count of species")+
theme(text=element_text(size=14))

Bar plot using geom_bar()

chart of body mass by species & sex.

Code

ggplot(penguins, aes(x = species, y = body_mass_g, fill = sex)) +
  geom_bar(stat = "identity", position = "dodge") +
  labs(title = "Body Mass by Species and Sex",
       x = "Species", y = "Body Mass (g)", fill = "Sex") +
  theme_minimal()

Labels

You can add labels with geom_label or geom_text. geom_text is just text and geom_label is text inside a rounded white box (this, of course, can be changed).

Histograms: geom_histogram()

A histogram is an accurate graphical representation of the distribution of numeric data. There is only one aesthetic required: the x variable.

Code

ggplot(penguins,
       aes(x = bill_length_mm)) + geom_histogram() +
  ggtitle("Histogram of penguin bill length ")

Boxplot: geom_boxplot()

Boxplot of body mass distribution of penguins by species

Code

ggplot(penguins, aes(x = species, y = body_mass_g, fill = species)) +
  geom_boxplot() +
  labs(title = "Body Mass Distribution of Penguins by Species",
       x = "Species",   y = "Body Mass (g)", fill = "Species") +
  theme_minimal()

Boxplot: geom_boxplot()

Code

ggplot(data = penguins,
       aes(x = species, y = bill_length_mm, fill = species)) +
  geom_boxplot() + labs(title = "Boxplot")

Boxplot: geom_boxplot()

Boxplot with annotations: geom_boxplot() and geom_signif()

Code

library(ggsignif)
ggplot(data = penguins, aes(x = species, y= bill_length_mm, fill = species)) +
  geom_boxplot() +
  # specify the comparison we are interested in
  geom_signif(comparisons = list(c("Adelie", "Gentoo")), map_signif_level=TRUE)

Violin plot: geom_violin()

Violin plot allows to visualize the distribution of a numeric variable for one or several groups. It is really close to a boxplot, but allows a deeper understanding of the distribution.

Violin plot: geom_violin()

Code

violin <- ggplot(data = penguins, aes(x = species, y = bill_length_mm)) +
  geom_violin(trim = FALSE, fill = "grey70", alpha = .5) +
  labs(title = "Violin plot")
violin

Violin plot: geom_violin() + _boxplot() + _jitter()

Code

violin + geom_jitter(shape = 16, position = position_jitter(0.2),
              alpha = .3) + geom_boxplot(width = .05)

Pie chart

Summarise y values: `stat_summary()`

Code

ggplot(mtcars, aes(cyl, mpg)) + geom_point() +
  stat_summary(fun.y = "median", geom = "point",
               colour = "red",size = 6) + labs(title = "Medians")

Pie chart

Code

ggplot(mtcars, aes(cyl, mpg)) + geom_point() +
  stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1.6) + 
  labs(title = "Means and CIs")

Line Charts

You can use geom_line() for line charts to display values over time. geom_line() requires an additional group= aesthetic. If there should be only 1 line because there is only 1 time variable, then use group=1. If you want to split the lines based on another variable, use group=variable_name.

A line graph displaying a single line for year

Code

data(AirPassengers)
airpassengers <- data.frame(AirPassengers, year = trunc(time(AirPassengers)), 
month = month.abb[cycle(AirPassengers)])

airpassengers %>% group_by(year) %>% summarize(sum =sum(AirPassengers, na.rm=T)) %>%
  ggplot()+ geom_line(aes(x=year, y=sum, group=1))

Line Charts

A line graph displaying 1 line per month

Code

ggplot(airpassengers)+ geom_line(aes(x=year, y=AirPassengers, group=month))

We can add labels to the ends of the line using geom_label() (see Labels) but the lines are very close together, so we will use ggrepel() instead. This gives the labels space and connects them with their lines.

Line Charts

Code

library(ggrepel)

ggplot(airpassengers)+ geom_line(aes(x=year, y=AirPassengers, group=month))+
  geom_label_repel(data=airpassengers %>% filter(year == max(year)),
                  aes(x=year, y=AirPassengers, label=month))

Refer more one

The Ultimate Guide to Get Started With ggplot2: Albert Rapp

Visualization