Data Visualization

Yebelay Berehan

Center for Evaluation and Development (C4ED)



2024-08-12

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

# 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:

1. Data: The raw data that you want to plot.
2. Geometries geom_: The geometric shapes that will represent the data.
3. Aesthetics aes(): Aesthetics of the geometric and statistical objects, such as position, color, size, shape, and transparency
4. Scales scale_: Maps between the data and the aesthetic dimensions, such as data range to plot width or factor values to colors.
5. Statistical transformations stat_: Statistical summaries of the data, such as quantiles, fitted curves, and sums.
6. Coordinate system coord_: The transformation used for mapping data coordinates into the plane of the data rectangle.
7. Facets facet_: The arrangement of the data into a grid of plots.
8. 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)

“Source: BloggoType”

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

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

• shape values

“shape: shape value”

• line type value

“shape: shape 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

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

1. Create a simple plot object: plot.object <- ggplot()
2. Add geometric layers: plot.object <- plot.object + geom_*()
3. Add appearance layers: plot.object <- plot.object + coord_*() + theme()
4. 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:

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).

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

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

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

• data
• aesthetics
• geometries
• facets and coordinates
• All in a single code

library(ggplot2)
ggplot(data = penguins)

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

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

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:

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.

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.

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.

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.

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.

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.

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:

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:

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):

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,

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:

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

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.

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

1. ggtitle(): used to label the text for the main title.
   • Example: ggtitle("Main Title")
2. 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

3. 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

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

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

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

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:

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")

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

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

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

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

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))

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"))

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"))`

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"))

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)))

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()

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

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))

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))

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.

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

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())

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))

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))

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.

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.

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

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

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.

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:

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.

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

Combining Different Plots

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

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.

# 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")

# 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

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"))`

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”)

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")

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")

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()

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()

library(ggsci)
p + scale_color_aaas()

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()

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")

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

scale_color_tableau()

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

• Use a predefined colour palette

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

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

• Use a predefined colour palette

# 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.

library(colorBlindness)
cvdPlot(p)

Use colourblind-friendly palettes

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

# 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))

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")

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

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

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

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

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.

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.

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.

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.

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.

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.

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

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.

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.

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

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.

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”.

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.

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.

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.

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.

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:

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?

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.

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.

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.

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.

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.

#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_*()

Create different plots using geom_*()

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.

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.

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

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.

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.

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

Boxplot: geom_boxplot()

Boxplot: geom_boxplot()

• Boxplot of body mass distribution of penguins by species

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()

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()

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()

Violin plot: geom_violin()

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()

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

Pie chart

Summarise y values: stat_summary()

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

Pie chart

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

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

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

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