Visualization with R’s tidyverse

ggplot2

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Compiled on R 4.5.2

ImportantWhat-Why-Who

This site aims to introduce researchers to visualization in R with the ggplot2 package of the tidyverse ecosystem.

Our target audience is primarily the research community at VUB / UZ Brussel, those who have some basic experience in R and want to know more.

We invite you to help improve this document by sending us feedback: wilfried.cools@vub.be

TipKey Message

• Data visualization is inherent to data analysis
  • to understand data properties and statistical results
  • to convince others, and yourself
  • no -fit’s all data visualization-
  • flexible use of data visualization
    • supports more informative and complete visualizations
    • elicits better data exploration and modeling
• Workflow (Hadley Wickham):

• Data visualization is easier and more intuitive with tidy data

R’s tidyverse package: ggplot2

• Current default for visualization (ggplot2) with extensions like ggvis
• Build on the idea of Grammer of Graphics (Leland Wilkinson)
• Largely consistent
• Well appreciated defaults
• Easy and intuitive to build (if you get it)
• Without loosing much flexibility
• Explicitly links to tidy data
• note: requires extensions for 3D plotting and interactive graphics
• note: does not allow for multiple Y-axes (combination of axes)
• Cheat sheets at Posit
• Alternative to base R, grid, trellis/lattice Graphics

Getting ahead of ourselves with ggplot2

toy dataset

• The infamous iris data are used
  • observe it’s structure with str( ) and first 6 observations head( ) function.
  • note: available data with data( )

• Have a tidyverse look at the data with glimpse( )

glimpse(iris)

Rows: 150
Columns: 5
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.…
$ Sepal.Width  <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.…
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.…
$ Petal.Width  <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.…
$ Species      <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s…

• Have a tidyverse look at the data with slice_head( )

iris %>% slice_head(n=6)

  Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1          5.1         3.5          1.4         0.2  setosa
2          4.9         3.0          1.4         0.2  setosa
3          4.7         3.2          1.3         0.2  setosa
4          4.6         3.1          1.5         0.2  setosa
5          5.0         3.6          1.4         0.2  setosa
6          5.4         3.9          1.7         0.4  setosa

examplary data visualization

• Make a scatterplot, boxplot, and histogram

p1 <- ggplot(data=iris,
    aes(y=Petal.Width,
        x=Petal.Length,
        col=Species)) + 
    geom_point()

• With the iris data
  • link dimensions y and x to Petal columns
    
  • link color to column Species
    
  • create a scatterplot

p1

p2 <- ggplot(data=iris,
    aes(y=Sepal.Length,
        x=Species,
        col=Species)
        ) + 
    geom_boxplot()

• x-axis is now Species, no y-axis
  
• Boxplots show a distribution of values for each Species

p2

p3 <- ggplot(data=iris,
    aes(x=Sepal.Width)) + 
    geom_histogram()

• x-axis is now Sepal.Width
  
• A histogram shows the full distribution
• A warning highlights default use of bin
  • Check bins=10

p3

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

• ggsave( ) saves the last made plot

ggsave('plotname.png',width=12,height=6)

Layered building blocks

• ggplot philosophy: -gg- Grammar of Graphics (Leland Wilkinson)
  • build visualization like making a sentence
    • specify building blocks independently
    • combine blocks to create graphical display
  • example of layered building blocks:
    • ggplot( data=iris,
aes(y=Petal.Width,x=Petal.Length,col=Species) ) + geom_point() + geom_smooth()
• General structure includes functions and arguments
  • functions
    • ggplot( ) initialize the ggplot object
    • geom_*( ) visualize geometric objects
    • stat_*( ) visualize statistical objects
      • largely equivalent to geom
    • facet_*( ) conditional visualization
    • theme( ), guides( ), scale_*( ), coord_*( )
  • arguments in ggplot( ), geom_*( ) and stat_*( )
    • data specify data (=input)
    • aes( ) specify aesthetic mapping
      • bridging gap input and output
    • ...
• Grammar of Graphics sparked further developments
  • other packages with -gg- philosophy: ggforce, ggalt, ggpubr, ggraph, tidygraph, GGally, ggcorrplot, ggridges, ....

Dimensionality

• Think of variables to show as dimensions

  • combine those variables that show your story

• aes( ) links a variable to a dimension

  • x: categorical or continuous x-axis
  • y: categorical or continuous y-axis
  • color:
    • categorical: set of colors
    • continuous: shades of colors within range
  • shape: categorical (limited number)
  • linewidth: categorical or continuous (dedicated to lines)
  • linetype: categorical (dedicated to lines)
  • …

• Combinations of variables can be linked to one dimension

• Facets: categorical (panels)

  • a panel for each (combination of) value(s)

• Rethink your visualization if this is not sufficient

  • use dimensions aimed at bringing focus

Visualization essentials

• part 1: how to make a visualization
• part 2: how to further refine

step by step example

• The ggplot object is constructed
  • data is linked to the mtcars data
  • x and y aesthetic are linked to it’s variables mpg and disp
  • does not visualize !
• The internal representation does exist
  • ready to extend for visualization
  • aesthetics x and y are given their default values
    • mpg and disp from mtcars
  • includes a legend and scale for both x and y axis
• Any geometric function (object) that at least uses an x and y axis can be added as a layer

ggplot(
    data=mtcars, 
    aes(y=mpg,x=disp)
)

• Add a layer with the + sign
  • a layer often is geometric function
    
  • a geometric function visualizes a gglot object
• geom_point( ): create a scatterplot
  • geometric function without arguments

ggplot(data=mtcars, 
    aes(y=mpg,x=disp)
) + 
geom_point()

• geom_point() requires data, an x and a y-axis
  • if not specified, inherit from ggplot( )
  • if specified, ignore ggplot( )
• example
  • data is not specified inside, but inherited from ggplot( )
    • data is linked to mtcars
  • x is not specified inside, but inherited from ggplot( )
    • x linked to variable disp
  • y is specified inside within aes( )
    • y linked to variable mpg
• get help using ?, ?geom_point

ggplot(data=mtcars, 
    aes(x=disp)
) + 
geom_point(aes(y=mpg))

• Excercise: for this example:
  • specify all the dimensions in the geom
  • add the data when creating the scatterplot
  • empty constructor !

ggplot() + geom_point(data=mtcars,aes(x=disp,y=mpg))

• Non-essential aesthetics like color can be included
  • specify color dependent on data within the aes( )
    • color extracted from variable gear
  • note: color is a third dimension
• Note: the numerical (continuous) variable gear is assigned a continuous scale of colors
  • a legend for continuous variables is by default included

ggplot(data=mtcars, 
    aes(y=mpg,x=disp,
        color=gear)
) + 
geom_point()

• The type of variable determines how it is visualized
• A categorical version of the same variable is visualized differently
  • the numerical variable is turned into a categorical one (factor())
  • a set of colors is used
  • a categorical legend is included

ggplot(data=mtcars, 
    aes(y=mpg,x=disp,
    color=factor(gear))
) + geom_point()

• Instead of color, use shape
  • not every dimension is equally clear
  • shapes require categorical data

ggplot(data=mtcars, 
    aes(y=mpg,x=disp,
        shape=factor(gear))
) + 
geom_point()

• Default behavior can be overwritten
  • the categorical color variable (geometric function) is used
  • the numerical color variable (constructor) is not considered anymore

ggplot(data=mtcars, 
    aes(y=mpg,x=disp,color=gear)) + 
    geom_point(
        aes(color=factor(gear))
    )

• Excercise: Turn the color into shape, but keep the continuously colored scale as well

ggplot(data=mtcars, 
    aes(y=mpg,x=disp,color=gear)) + 
    geom_point(
        aes(shape=factor(gear))
    )

• Dimensions can also be linked to constants (not variables in the data)
• Assign a dimension outside the aes( ) function
  • aes( ) only serves to link dimensions to variables in the data
  • color is assigned a value, independent of the data
  • color from the ggplot( ) is overwritten

ggplot(data=mtcars, 
    aes(y=mpg,x=disp,color=gear)
) + 
geom_point(color='#FF6600')

• Multiple aesthetics (dimensions) can be specified, in and outside the aes( )
• With color dependent on gear
  • color dependent on a variable is defined inside of aes( )
  • alpha (transparency) is set to .3 (30%), outside of aes( )
    • alpha as a percentage (avoid this, it gives unwanted behavior)
    • alpha related to variables with values between 0 and 1

ggplot(data=mtcars, 
    aes(y=mpg,x=disp)
) + 
geom_point(
    aes(color=factor(gear))
    ,alpha=.3)

• Note: with alpha (transparency) inside of aes( ) at .3 (30%)
  • aes( ) should not be used to link to constants

ggplot(data=mtcars, 
    aes(y=mpg,x=disp)
) + 
geom_point(
    aes(color=factor(gear),alpha=.3)
    )

• Use alpha inside of aes( ) to link it to a variable
  • alpha related to variables with values between 0 and 1

ggplot(data=mtcars, 
    aes(y=mpg,x=disp)
) + 
geom_point(
    aes(color=factor(gear),
        alpha=qsec/max(qsec)
    )
)

• Multiple geometric functions can be included, layered
  • geom_point( ) creates the dots, a scatterplot
  • geom_line( ) connects them over the x-axis
• Note: using the assignment <- code can be build stepwise

myplot <- ggplot(data=mtcars, 
    aes(y=mpg,x=disp)) 
myplot <- myplot + geom_line()
myplot

ggplot(data=mtcars, 
    aes(y=mpg,x=disp)) + 
    geom_line()

ggplot(data=mtcars, aes(y=mpg,x=disp)) + 
    geom_point() + geom_line()

• Use geom_path( ) to connect subsequent observations
  
• Note: re-ordering has an effect (arrange( ))

ggplot(data=mtcars, 
    aes(y=mpg,x=disp)
) + 
geom_point() + geom_path()

ggplot(data=mtcars %>% arrange(drat), 
    aes(y=mpg,x=disp)
) + 
geom_point() + geom_path()

• Excercise: Add a line over the x-axis, on top of the path
• Make the line orange (#FF6600) to highlight it compared to the path

ggplot(data=mtcars %>% arrange(drat), 
    aes(y=mpg,x=disp)
) + 
geom_point() + geom_path() + 
geom_line(color="#FF6600")

• The type of variables in aes( ) determines how it is visualized
• Note, assigning a categorical variable to color groups data (lines/path)

ggplot(data=mtcars, 
    aes(y=mpg,x=disp,
    color=factor(gear))
) + geom_point() + geom_path()

• Note, changing the variable linked to color changes the grouping

ggplot(data=mtcars, 
    aes(y=mpg,x=disp,
    color=factor(cyl))
) + geom_point() + geom_path()

• Specifications in ggplot( ) offer default behavior
• Specifications inside the geom_*( ) overwrite defaults
  • note that the geom_point( ) uses the locally specified color aesthetic
  • note that the geom_line( ) uses the default black (size made smaller than default)
• Beware: types (discrete/continuous) should agree when overwriting ggplot(aes( ))

ggplot(data=mtcars, 
    aes(y=mpg,x=disp)
) + 
geom_point(
    aes(color=factor(gear))
) + 
geom_line(linewidth=.3)

• Geometric functions that add statistics can be included as well
  • geom_smooth( ) offers averaging and standard errors
    • local averages are default (loess lines)
    • global conditional averages (method=‘lm’)

myplot <- ggplot(data=mtcars, 
    aes(y=mpg,x=disp,
    color=factor(gear)))
myplot + geom_point() + geom_smooth()

myplot + geom_point() + 
    geom_smooth(method='lm')

• Grouping through aesthetics works like before
  • the group argument overwrites grouping by other aesthetics like color
  • group=1 groups all observations together
• Note: get help ?geom_smooth for more details

ggplot(data=mtcars, 
    aes(y=mpg,x=disp,
        color=factor(gear),
        group=1)
) + geom_point() + 
geom_smooth(method='lm',color="#FF6600")

`geom_smooth()` using formula = 'y ~ x'

• Excercise: use the above, add a blue path over all groups combined (#003399)
• but change the bullets into diamonds (shape 18)
• make sure these diamonds have a color dependent on the cyl categories

ggplot(data=mtcars %>% arrange(drat), 
    aes(y=mpg,x=disp)
) + 
geom_point(aes(color=factor(cyl)),shape=18,size=4) + 
geom_path(color="#003399") + 
geom_line(color="#FF6600")

recap ggplot( )

• Function to create a ggplot object, ready for visualization.
  • constructor, always required
  • prepares appropriate internal representation
  • can include default data
  • can include default aesthetics

recap aes( )

• Function to link variables (data) to an aesthetic (dimension)
  • used within ggplot( ) or geom_*( )
  • requires variables (part of data)
  • defines dimensions dependent on variable values
    • x and y axes: positions on the axes
    • color: color
    • …
  • possible arguments depent on geom_*( ) if defined
    • shape: symbols in geom_point( )
    • size: size of bullets in geom_point( )
    • linewidth: width of lines in geom_line( )
    • … check the respective help-files
  • group argument used inside aes( ) to identify groups of observations
    • allows grouping without assigning an aesthetic
    • use the value 1 to combine all observations into one group
  • automatically assigns a default legend
    • relates to aesthetic
    • depends on variable type (nominal, ordinal, continuous)
  • note: dimensions defined outside of aes( ) are independent of the data

recap geom_*( )

• Function to turn an internal ggplot representation into a visualization
  • different geoms to create different visualizations
  • different geoms use
    • required aesthetics
      • scatterplots require x and y axis
      • histogram requires x axis
    • optional aesthetics (eg., color)
  • geom-specific arguments overwrite those inherited from ggplot( )
    • can include aes( )
    • can include data argument
    • useful to add or change aesthetics

Visualization extras

• In addition to the basics, there is much more
  • stat layers
  • scales layers
  • facet layer
  • theme layer
  • coord layer

geom and stat layers

• Geometric functions and statistical transformation
  • each add layers
  • geom_* and stat_* are largely equivalent
    • geom_* focus on visualization
    • stat_* focus on transformation of data

• A geom_*( ) has a default stat argument
  • geom_smooth(stat="smooth")
• A stat_*( ) has a default geom argument
  • stat_smooth(geom="smooth")
• Same result
  • + geom_point(stat='summary',fun.y='mean',...)
  • + stat_summary(geom='point',fun.y='mean',...)
• In most cases stick to geom_*( ), occasionally not possible: eg., stat_ecdf( )
  • specific stat_*() function highly dedicated
  • transformations also possible on the data before visualization

ggplot(data=mtcars, 
    aes(y=mpg,x=factor(carb),
    color=factor(gear))
) + 
geom_point() + 
stat_summary(geom='point',fun='mean',shape=13,size=16)

ggplot(mtcars, 
    aes(mpg)
) + stat_ecdf(
    aes(color=factor(cyl)),
    geom = "step"
)

• A layer is a function with arguments
  • data (see before)
  • mapping (aesthetic), defined by the aes( ) (see before)
  • geom (eg., point or smooth)
  • stat (eg., identity or smooth)
  • position (eg., identity)
• Position adjustment with position argument
  • identity, typically the default
  • jitter convenient for points and lines (random perturbation)
  • stack, fill and dodge convenient for bars (on top or next to)

myplot <- ggplot(data=mtcars,
    aes(x=disp,
    fill=factor(gear))
) 
myplot + geom_histogram(binwidth=200, 
    position = position_dodge(width=50),
    alpha=.8)

myplot + geom_histogram(binwidth=200, 
    position = position_stack(),
    alpha=.8,
    col='black'
) + theme(legend.position='none')

ggplot(data=mtcars,
    aes(y=mpg,x=factor(cyl),color=carb)
) + geom_jitter(width=.1)

• Await predefined transformations
  • exist for some geom_*( ) and stat_*( )
  • example: a histogram uses a after_stat(density) as an alternative to counts
  • before this was done: ..density..

myplot + geom_histogram(
    aes(y=after_stat(density)),
    binwidth=200, 
    position = position_stack(),
    alpha=.8,col='black'
) + theme(legend.position='none')

scale_*_*( )

• Each dimension (aesthetic) has a scale
  • serves as a legend
  • helps with interpretation
• Refer to scale with aesthetic and type
  • structure: scale_aesthetic_type
  • examples:
    • scale_x_continuous to assign continuous scale to x-axis aesthetic (default)
    • scale_x_sqrt to square root transform the x-axis aesthetic (short-cut)
    • scale_color_brewer to assign brewer colors to the discrete color aesthetic
    • scale_color_distiller to assign brewer colors to the continuous color aesthetic
    • scale_fill_gradient to assign colors to the fill aesthetic
    • scale_fill_manual to manually assign colors to the fill aesthetic
  • arguments control titles, breaks, labels, limits, … see the help file
  • note: guide argument requires either a name or guides() function for additional control
• Scale types have impact on the visualization and legend (continuous vs. categorical)

ggplot(data=mtcars,
    aes(x=disp,fill=factor(gear))
) + geom_histogram(
    aes(y=after_stat(density)),
    binwidth=200, 
    position = position_stack(),
    alpha=.8,
    col='black'
) + theme(legend.position='none') + 
scale_fill_brewer()

ggplot(data=mtcars,
    aes(y=mpg,x=factor(cyl),
    color=drat)
) + geom_point() + 
scale_color_distiller(palette='Oranges')

• Excercise: show mpg for different categories of drat, with a shape for the cyl (shapes work with categories)
• Use the manual way to change the shapes with the values argument equal to c(“4” = 21, “6” = 22, “8” = 23)

ggplot(mtcars,
    aes(x = drat, y = mpg, shape = factor(cyl))) +
    geom_point(size = 3, stroke = 1.2) +
    scale_shape_manual(values = c("4" = 21, "6" = 22, "8" = 23))

facet_*( )

• Visualizations can be split for subgroups, facilitating conditional comparisons
  • facets can be useful to keep plots simple
  • by default, the axes are kept constant for comparison, changeable
  • facet_grid( ) uses a grid, facet_wrap( ) keeps filling space
• Requires a row and/or column specification
  • separate rows and columns by ~, use . if none
  • use + to combine multiple row and/or column specification

ggplot(data=mtcars,
    aes(y=mpg,x=drat,color=carb)
) + 
geom_jitter() + 
facet_grid(vs~gear)

ggplot(data=mtcars,
    aes(y=mpg,x=drat,color=carb)
) + 
geom_jitter() + 
facet_grid(.~vs+cyl)

• Excercise: instead of using color to differentiate carb, use the facet rows to show the levels of carb, to compare the two levels of gear

ggplot(data=mtcars,
    aes(y=mpg,x=drat)
) + 
geom_jitter() + 
facet_grid(carb~gear)

theme( )

• Themes offer control, unrelated to data
  • themes are layers to (re-)specify theme elements
  • elements are numerous (?theme):
    • line, rect, text, title
    • axis: axis.title, axis.text.x.top, axis.ticks.x.bottom, …
    • legend: legend.spacing.y, legend.key.width, legend.justification, …
    • panel: panel.background, panel.grid.major, panel.ontop, …
    • plot: plot.background, plot.caption, plot.tag, plot.margin, …
    • strip: strip.background, strip.text, strip.switch.pad.wrap, …
  • elements are controlled with a theme function, eg., element_text( )
    • element_text( ), element_line( ), element_rect( ), margin( ), …
  • default themes exist, eg., theme_minimal( ), new can be created
  • labs( ) is a simpler way to specify titles and labels

(myplot <- ggplot(mtcars, 
    aes(wt, mpg)
) + 
geom_point() + 
labs(
    title = "Fuel economy declines as weight increases",
    y='miles per gallon')
)

• The size of the plot title is changed with the element_text( ), the background with the element_rect( )

myplot + theme(plot.title = element_text(size=rel(1.5)), 
    plot.background=element_rect(fill="#FF6600"))

• The inside of the plot is the panel, modifiable too.
  • multiple elements can be specified in one theme
  • multiple themes can be specified by layers

(myplot <- myplot + 
    geom_point(color='#FF6600',size=3) + 
        theme(panel.background = 
            element_rect(fill = "#003399", 
        colour = "#FF6600")
) + 
theme(
    panel.border = element_rect(
        linetype = "dashed", 
        fill = NA
    ), 
    panel.grid.major = element_line(
        colour = "#FF6600")
    )
)

myplot + theme(
  panel.grid.major.y = element_line(colour = "black"),
  panel.grid.minor.y = element_blank()
)

• On the outside are the axes and titles, modifiable too
  • the text, ticks and titles are adjusted for color and size
  • function element_text( ), element_line( ) and unit( ) are used

(myplot <- myplot + theme(
    axis.line = element_line(
        size = 3, 
        colour = "#FF6600")
    ) +
    theme(axis.text = element_text(
        colour = "#003399", 
        size=12)
    ) + 
    theme(axis.ticks.y = 
        element_line(linewidth = 5)
    ) +
    theme(axis.title.y = 
        element_text(size = rel(1.5))
    )
)

Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.

myplot + theme(
  axis.ticks.length.y = unit(.25, "cm"),
  axis.ticks.length.x = unit(-.25, "cm"),
  axis.text.x = element_text(
    margin = margin(t = .3, unit = "cm")
    )
)

• Scales are represented by legends, modifiable too
  • labs can be used to change multiple legend titles
  • legends can be positioned and formatted

myplot <- ggplot(mtcars, 
    aes(wt, mpg)) +
    geom_point(
        aes(colour = factor(cyl), 
            shape = factor(vs))
    ) +
    labs(
        x = "Weight (1000 lbs)",
        y = "Fuel economy (mpg)",
        colour = "Cylinders",
        shape = "Transmission"
    )

myplot + theme(legend.position='none')

myplot + theme(
    legend.justification = "right",
    legend.position = "bottom"
    )

myplot + theme(
  legend.position = "inside",
  legend.position.inside = c(.95, .95),
  legend.justification = c("right", "top"),
  legend.box.just = "right",
  legend.margin = margin(6, 6, 6, 6)
)

• Keys inside legends are modifiable too
  • labs can be used to change multiple legend titles
  • legends can be positioned and formatted, for key, text and title

myplot + theme(
    legend.key = element_rect(
        fill = "#bbbbbb", 
        colour = "#003399")
    ) +
    theme(legend.text = element_text(
        size = 14, 
        colour = "#003399")
    ) +
    theme(legend.title = element_text(
        face = "bold")
    )

• Themes also work on facets, at which strips are defined

myplot <- ggplot(mtcars, 
        aes(wt, mpg)
    ) + 
    geom_point() + 
    facet_wrap(~ cyl)

myplot + theme(strip.background = 
    element_rect(colour = "black", 
    fill = "white")
)

myplot + theme(strip.text.x = 
    element_text(colour = "white", 
    face = "bold")
)

myplot + theme(panel.spacing = 
    unit(1, "lines")
)

• Excercise: create a scatterplot of mpg by hp, but use the theme minimal.

ggplot(data=mtcars,
    aes(y=mpg,x=hp)
) + 
geom_point() + theme_minimal()

coord_*( )

• Typically the default cartesian coordinate system is used, coord_cartesian( )
• Limits of the axes are best specified within the coord_*( ) function
  • works like a zoom
  • alternatively, use xlim( ) and ylim( )
    • beware that values outside the boundary are treated as missing

myplot <- ggplot(data=mtcars,
    aes(y=mpg,x=gear,
    color=factor(vs),
    group=vs)
)
myplot + geom_smooth(method='lm')

`geom_smooth()` using formula = 'y ~ x'

myplot + geom_smooth(method='lm') + 
    coord_cartesian(xlim=c(2.5,4.5))

`geom_smooth()` using formula = 'y ~ x'

myplot + geom_smooth(method='lm') + 
    xlim(2.5,4.5)

`geom_smooth()` using formula = 'y ~ x'

Warning: Removed 5 rows containing non-finite outside the scale range
(`stat_smooth()`).

• Within the cartesian family alternatives exist
  • coord_flip( ) switches x and y-axis
  • coord_fixed( ) sets the ratio for x and y values
    • eg., .1 means 1 unit on x is 10 on y
• Alternatives exist, eg., coord_polar( ), coord_trans( ), and various map related functions

myplot + geom_smooth(method='lm') + 
    coord_flip()

`geom_smooth()` using formula = 'y ~ x'

myplot + geom_smooth(method='lm') + 
    coord_polar()

`geom_smooth()` using formula = 'y ~ x'

summary

• ggplot creates a ggplot object, required for visualization
• aes( ) combines aesthetics and link data to scales, and group them
• geom_*( ) are geom functions that visualize a ggplot object
• stat_*( ) are stat functions that also visualize a ggplot object
• scale_*_*( ) helps fine-tuning visualized dimensions
• facet_grid( ) or facet_wrap( ) split dimensions over panels (faceting)
• coords( ) re-specify the coordinate system, and helps zooming in
• theme( ) re-specifies data independent characteristics

Examples to go into detail

• Through various examples, the cheat sheet is focused upon.
  • Posit Cheatsheets

one-variable

• Various visualizations address one particular variable
  • mostly continuous but possibly also discrete
  • continuous variables are typically ‘binned’
• Note the frequency polygon, and the histogram.

ggplot(data=mtcars,aes(mpg)) + 
geom_freqpoly(binwidth=2.5)

ggplot(data=mtcars,aes(mpg)) + 
geom_histogram(binwidth=2.5)

• Using the geom_area( ), binning must be explicit as stat argument.

ggplot(data=mtcars,aes(mpg)) + 
    geom_area(stat='bin',binwidth=2.5)

• Continuous variables can also be shown continuously, without binning

• Different types of densities are shown

ggplot(data=mtcars,aes(mpg)) + geom_density() + 
    geom_density(kernel='triangular',color="#003399") + geom_density(kernel='optcosine',color="#FF6600")

• The geom_qq( ) requires a sample argument (instead of x)
  • positions are determined by their value

ggplot(data=mtcars,
    aes(sample=mpg)) + 
    geom_qq()

• The bar-plot with geom_bar( ) is similar to the histogram but for categorical data
  • shows the actual values instead of a count per bin

ggplot(data=mtcars,
    aes(factor(cyl))) + 
    geom_bar(fill='#FF6600',color='#003399')

exercises on one variable visualizations

• Make use of the mpg dataset again
  
• Make a histogram for the continuously scaled displ variable

ggplot(data=mpg,aes(x=displ)) + 
    geom_histogram()

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

• Adjust the binwidth to .5

ggplot(data=mpg,aes(x=displ)) + 
    geom_histogram(binwidth=.5)

• Add a frequency polynomial on top (freqpoly)

ggplot(data=mpg,aes(x=displ)) + 
    geom_histogram(binwidth=.5) + 
    geom_freqpoly()

`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

• Notice what happens if the same binwidth is used for the frequency polynomial.

ggplot(data=mpg,aes(x=displ)) + 
    geom_histogram(binwidth=.5) + 
    geom_freqpoly(binwidth=.5)

• Make a barplot for the discretely scaled class variable

ggplot(data=mpg,aes(x=class)) + 
    geom_bar()

• Group the data by filling in colors dependent on the drv variable

ggplot(data=mpg,aes(x=class)) + 
    geom_bar(aes(fill=drv))

• Turn the bars next to one-another
  
• Reduce their width to .5 to increase space between bars

ggplot(data=mpg,aes(x=class)) + 
    geom_bar(aes(fill=drv),
    position='dodge',width=.5)

two-variables

• Various visualizations address the relation between two variables, whether discrete and/or continuous.
• Especially for categorical data data could obscure other data.
  • deal with this using the position argument or with the geom_jitter( )
  • avoid combining both geom_point( ) and geom_jitter( ) as it would draw points each time

ggplot(data=mtcars,aes(cyl,gear)) + 
geom_point()

ggplot(data=mtcars,aes(cyl,gear)) + 
geom_jitter(width=.2,height=.2)

ggplot(data=mtcars,aes(cyl,gear)) + 
geom_point(color='#FF6600') + 
geom_jitter(width=.2,height=.2)

• The smooth function has been shown above

• The confidence band with the middle 50% (quantiles .25 and .75) is used

• A rug at the axes captures the one dimensional distribution.

• Instead of bullet indicators, the row names (or any other set of labels) can be used

  • use the label argument (within the aes( ) when related to data)
  • some jitter reduces overlap

ggplot(data=mtcars, 
    aes(y=mpg,x=disp,color=factor(gear))) + 
    geom_point() + 
    geom_smooth(method='lm',se=FALSE) + 
    geom_rug() + 
    geom_quantile(quantiles=c(.25,.75),linetype=2)

`geom_smooth()` using formula = 'y ~ x'
Smoothing formula not specified. Using: y ~ x
Smoothing formula not specified. Using: y ~ x
Smoothing formula not specified. Using: y ~ x

ggplot(mtcars, aes(wt, mpg)) + 
geom_text(
    aes(label=(rownames(mtcars))),
    size=2,
    position=position_jitter(width = .2,height=3, seed=256)
)

• Bars can be obtained with geom_col( ), or with geom_bar(stat='identity')

  • use of ‘identity’ causes the height to depend on the numbers in the data
  • Note: these numbers are summed if not unique, be careful.

• A count( ) extracts the frequency of the specified grouping

• Note that on the original data this is obtained with the default stat=“count”

ggplot(mtcars,aes(fill=factor(vs),x=gear)) + 
    geom_bar(position="stack")

(freq_by_group <- mtcars %>% group_by(gear,vs) %>% count())

# A tibble: 6 × 3
# Groups:   gear, vs [6]
   gear    vs     n
  <dbl> <dbl> <int>
1     3     0    12
2     3     1     3
3     4     0     2
4     4     1    10
5     5     0     4
6     5     1     1

ggplot(freq_by_group, 
    aes(fill=factor(vs), y=n, x=gear)) + 
geom_bar(position="stack", stat="identity")

ggplot(freq_by_group, 
    aes(fill=factor(vs), y=n, x=gear)) + 
geom_col(position="dodge")

ggplot(mtcars, 
    aes(fill=factor(vs), y=mpg, x=gear)) + 
geom_col(position="dodge") + 
labs(y='sums all mpg values')

• A boxplot nicely summarizes continuous data, possibly for different groups

ggplot(data=mtcars,
    aes(y=mpg,x=factor(cyl))) + 
geom_boxplot()

ggplot(data=mtcars,
    aes(y=mpg,x=factor(cyl))) + 
    geom_boxplot(width=.25,alpha=.2,
        aes(fill=factor(cyl))
    ) + 
    geom_jitter(width=.05)

exercises on two variable visualizations

• Make a scatterplot for the continuously scaled hwy on cty, and color by cyl

ggplot(data=mpg,aes(y=hwy,x=cty,color=cyl)) + 
    geom_point() 

• Jitter the data, so it shows if data points obscure one-another
  
• Make sure that cyl is categorical

ggplot(data=mpg,
    aes(y=hwy,x=cty,color=factor(cyl))) + 
    geom_jitter(width=.5,height=.05)

• Add a conditional average with geom_smooth( ), use the lm method
• Add a color for each class
• Add a shape for each cyl

ggplot(data=mpg,
    aes(y=hwy,x=cty,color=class)) + 
geom_jitter(width=.5,height=.05,
    aes(shape=factor(cyl))) + 
geom_smooth(method='lm')

`geom_smooth()` using formula = 'y ~ x'

• Add a shape dependent on class, notice the restriction on the number of shapes

ggplot(data=mpg,aes(y=hwy,x=cty)) + 
    geom_jitter(
        aes(shape=factor(class))
    ) + 
    geom_smooth(method='lm',aes(color=factor(cyl)))

`geom_smooth()` using formula = 'y ~ x'

Warning: The shape palette can deal with a maximum of 6 discrete values because more
than 6 becomes difficult to discriminate
ℹ you have requested 7 values. Consider specifying shapes manually if you need
  that many of them.

Warning: Removed 62 rows containing missing values or values outside the scale range
(`geom_point()`).

• Switch the color and shapes around (color can have more than 6 if it is really necessary)

ggplot(data=mpg,aes(y=hwy,x=cty)) + 
    geom_jitter(
        aes(shape=factor(cyl))
    ) + 
    geom_smooth(method='lm',aes(color=class))

`geom_smooth()` using formula = 'y ~ x'

• Make sure the symbols do not differ by color (all black), only shape, but keep the regression lines

ggplot(data=mpg,aes(y=hwy,x=cty)) + 
    geom_jitter(aes(shape=factor(cyl))) + 
    geom_smooth(method='lm',aes(color=class))

`geom_smooth()` using formula = 'y ~ x'

• Show boxplots for the hwy for each cyl

ggplot(data=mpg,
    aes(y=hwy,x=factor(cyl))
) + 
geom_boxplot()

• Add the actual data points and make sure they do not obscure each other

ggplot(data=mpg,
    aes(y=hwy,x=factor(cyl))
) + 
geom_boxplot() + 
geom_jitter()

• Give a color to the observations dependent on class

ggplot(data=mpg,
    aes(y=hwy,x=factor(cyl))
) + 
geom_boxplot() + 
geom_jitter(aes(color=class))

• Make a bar chart that sums over all hwy values in each class group

ggplot(data=mpg,
    aes(y=hwy,x=factor(class))
) + 
geom_col()

• Use a coloring of the bars to signal the relative contribution of all cyl categories

ggplot(data=mpg,
    aes(y=hwy,x=factor(class),
    fill=factor(cyl))
) + 
geom_col()

• Flip the coordinates x and y axis

ggplot(data=mpg,
    aes(y=hwy,x=factor(class),
    fill=factor(cyl))
) + 
geom_col() + 
coord_flip()

intervals

• Specialized functions facilitate visualization of errors / confidence intervals
• Standard errors or other intervals can be visualized along with fitted values

(tmp <- tribble(
~set,~fit,~se,
1,3,.2,
2,2,.3,
3,2,.4))

# A tibble: 3 × 3
    set   fit    se
  <dbl> <dbl> <dbl>
1     1     3   0.2
2     2     2   0.3
3     3     2   0.4

(myplot <- ggplot(data=tmp,
    aes(y=fit,x=set)))

myplot + geom_errorbar(
    aes(ymax=fit+2*se,ymin=fit-2*se)
)

myplot + geom_pointrange(
    aes(ymax=fit+2*se,ymin=fit-2*se)
)

third variable implied

• Frequencies and densities can be obtained for two variables
• With geom_bin2d( ) the frequency of combinations is obtained
  • in this case for the factors cyl and gear
• Continuous variables can be used with binning
• Contours can be obtained with geom_density2d( )

ggplot(data=mtcars,
    aes(y=factor(cyl),x=factor(gear))
) + 
geom_bin2d()

`stat_bin2d()` using `bins = 30`. Pick better value `binwidth`.

ggplot(data=mtcars,
    aes(y=mpg,x=disp)
) + geom_density2d(
    aes(colour = factor(gear))
)

three variables

• A z dimension is possible, while typically other aesthetics are used
• A heatmap is the most obvious use
  • to show for example correlations between many variables with colors instead of values
  • a small example is used instead
    • notice the argument for tile is a fill
    • z is the argument for the contour

tmp <- expand_grid(set1=1:10,set2=1:10); 
set.seed(123); 
tmp$score <- runif(100,0,1)
head(tmp)

# A tibble: 6 × 3
   set1  set2  score
  <int> <int>  <dbl>
1     1     1 0.288 
2     1     2 0.788 
3     1     3 0.409 
4     1     4 0.883 
5     1     5 0.940 
6     1     6 0.0456

ggplot(data=tmp,
    aes(y=set2,x=set1)
) + 
geom_tile(aes(fill=score))

ggplot(data=tmp,
    aes(y=set2,x=set1)
) + 
geom_contour(aes(z=score))

primitives

• Primitives are the basic building blocks
  • several primitives exist: point( ), path( ), polygon( ), segment( ), ribbon( ), rect( ), text( ), blank( )
  • only geom_point( ) is used very often
  • geom_ribbon( ) can be interesting for showing intervals
  • typically useful for fine-tuning only

• The polygon( ) is used, with coordinates created in a separate datafile.

• The same is done with tiles( )

• It is possible to create the datafile within the function.

• First a blank plot is drawn, then the rest is added.

blank_plot <- ggplot(mtcars,
    aes(y=mpg,x=cyl)) + 
    geom_blank()

polygon_coordinate_file <- tribble(
~x,~y,
6,17,
6,10,
5,10,
8,21)

tile_coordinate_file <- tribble(
~x,~y,~w,
5,20,2,
6,30,5,
7,15,2)

(updated_plot <- blank_plot + 
    geom_polygon(data=polygon_coordinate_file,
    aes(x=x,y=y),alpha=.3,color="#FF6600"))