+ - 0:00:00
Notes for current slide
Notes for next slide

ICO Workshop R & RStudio

Part 4

Powerful visualisations with ggplot2

Sven De Maeyer & Tine van Daal

2nd - 4th July, 2024

1 / 71

Overview

  1. Simple plots in R --- (click here)
  2. Grammar of graphics --- (click here)
  3. How ggplot2 works (in a nutshell) --- (click here)
  4. Visualising a categorical variable --- (click here)
  5. Visualising a quantitative variable --- (click here)
  6. Visualising more than one variable --- (click here)
  7. More about visualisation? --- (click here)
2 / 71

1. Simple plots in R

3 / 71

Plots in base

The generic function plot() "knows" what to do (plot) with the input it receives.

# one quantitatieve variable
plot(Friends$fluency)

# one qualitative variables
plot(table(Friends$condition))

boxplot(
  Friends$fluency,
  main = "Boxplot of the variable 'fluency'", 
  col = "steelblue"
  )

hist(
  Friends$fluency,
  main = "Histogram of the variable 'fluency'",
  freq = FALSE,
  ylim = c(0, .08)
  )

4 / 71

2. Grammar of Graphics

5 / 71

A more theoretical approach to visualisation

  • Theoretical 'breakdown' of a visualisation into components (called layers)

  • One system to create different visualisations

  • At the heart of several modern graphical applications:

    • ggplot2
    • Tableau (Polaris)
    • Vega-Lite

Slide taken from slide show by Thomas Lin Pedersen

6 / 71

Key idea behind the Grammar of Graphics

Layers of a visualisation:
data
aesthetics
geometries
facets
statistics
coordinates
themes




Animation by Thomas de Beus

7 / 71

Grammar of Graphics is a bit like cake





Start by setting up the foundation with ggplot()

Specify ingredients (variables) with aes() and a flavour with scales

Create layers to plot with geoms

Style the cake with theme

Slide by Tanya Shapiro

8 / 71

Grammar of Graphics: data

  • Data is not only raw data, but can also be the results from an analysis
group country gender mean_age SD_age CI_lower CI_upper
BE Male BE Male 39 11.0 17.44 60.56
BE Female BE Female 41 13.2 15.13 66.87
BE Other BE Other 36 8.2 19.93 52.07
NL Male NL Male 37 12.0 13.48 60.52
NL Female NL Female 36 14.0 8.56 63.44
NL Other NL Other 31 7.2 16.89 45.11


  • Data has to be 'tidy'
9 / 71

Grammar of Graphics: aesthetics

  • Describe how variables in data are mapped to visual properties. For example:

    • variables mapped on x- and y-axis
    • variable that defines color or size of points
    • ...

  • Change appearances of aesthetics using scales

10 / 71

Grammar of Graphics: geometries

  • Geometrical shapes at the heart of visualisation. For example:
    • boxplot
    • line
    • ...

11 / 71

Grammar of Graphics: facets

  • Also called 'small multiples'

  • Define how much panels are shown and how they are arranged

12 / 71

Grammar of Graphics: statistics

  • Data might be tidy, but still in need of some statistical calculations. For example:

    • Calculate descriptive statistics to create a boxplot
    • Estimate a linear (or other) model to draw a regression line in a scatter plot

  • Often implicit done by ggplot2

13 / 71

3. How ggplot2 works (in a nutshell)

14 / 71

Time to get the penguins in...

Nice data set that can be used within R (Source: https://allisonhorst.github.io/palmerpenguins/articles/intro.html)

install.packages("palmerpenguins")
library(palmerpenguins)
data("penguins")

Artwork by @allison_horst

15 / 71

Time to get the penguins in...

Table: Table 1. Random sample of 10 observations from the Palmer Pinguins dataset

species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
Adelie Dream 39.6 18.8 190 4600 male 2007
Gentoo Biscoe 51.1 16.5 225 5250 male 2009
Gentoo Biscoe 45.2 15.8 215 5300 male 2008
Gentoo Biscoe 47.5 15.0 218 4950 female 2009
Adelie Dream 36.0 17.1 187 3700 female 2009
Adelie Dream 36.0 17.8 195 3450 female 2009
Gentoo Biscoe 43.8 13.9 208 4300 female 2008
Adelie Biscoe 39.6 20.7 191 3900 female 2009
Adelie Torgersen 46.0 21.5 194 4200 male 2007
Adelie Dream 36.8 18.5 193 3500 female 2009
16 / 71

The basics of ggplot2: data & aesthetics

Plot <- ggplot(
  ## Step 1: data
  data = penguins,
  ## Step 2: specify aesthetics (mapping)
  aes(
    x = flipper_length_mm,
    y = body_mass_g)
)
Plot

17 / 71

The basics of ggplot2: geometry

Plot <- ggplot(
  ## Step 1: data
  data = penguins,
  ## Step 2: specify aesthetics (mapping)
  aes(
    x = flipper_length_mm,
    y = body_mass_g)
) +
  ## Step 3: add geometry
  geom_point()
Plot

Not every component of the Grammar of Graphics needs to be defined. The other components have default values that are automatically applied.

18 / 71

The basics of ggplot2: facets

Plot <- ggplot(
  ## Step 1: data
  data = penguins,
  ## Step 2: specify aesthetics (mapping)
  aes(
    x = flipper_length_mm,
    y = body_mass_g)
) +
  ## Step 3: add geometry
  geom_point() +
  ## Step 4: define facets
  facet_wrap(~species)
Plot

19 / 71

The basics of ggplot2: theme

Plot <- ggplot(
  ## Step 1: data
  data = penguins,
  ## Step 2: specify aesthetics (mapping)
  aes(
    x = flipper_length_mm,
    y = body_mass_g)
) +
  ## Step 3: add geometry
  geom_point() +
  ## Step 4: define facets 
  facet_wrap(~species) +
  ## Step 5: set theme
  theme_minimal()
Plot

20 / 71

Building a visualisation by adding layers ...

21 / 71

Several geom_* options...

The cheatsheet: https://github.com/rstudio/cheatsheets/blob/main/data-visualization-2.1.pdf

22 / 71

4. Visualising a categorical variable

"The bar is open... Let's have a lollipop?"

23 / 71

Visualising a categorical variable?

There are many ways to visualise a categorical variable...

Can you think of some?

24 / 71

Visualising a categorical variable?

There are many ways to visualise a categorical variable...

Can you think of some?




Have a look at data to viz.com

24 / 71

Creating a barplot with geom_bar() or geom_col()

geom_bar()

geom_col()

25 / 71

Creating a barplot with geom_bar() or geom_col()

geom_bar()

geom_col() 

ggplot(
  penguins,
  aes(
    x = species
    )
  ) +
  geom_bar()
# stat_count() does the counting automatically
count_data <- 
  penguins %>%
  count(species, name = 'count')
ggplot(
  count_data,
  aes(
    x = species,
    y = count
    )
  ) +
  geom_col()
25 / 71

Creating a barplot with geom_bar()

  • Add color to barplot by defining an additional aesthetic: fill
ggplot(
  penguins,
  aes(
    x = species
    )
  ) +
  geom_bar(
    ## Additional aesthetic: "fill"-scale
    aes(fill = species) 
    )

26 / 71

Creating a barplot with geom_bar()

  • Determine fill-colors by adding scale_fill_manual()
ggplot(
  penguins,
  aes(
    x = species
    )
  ) +
  geom_bar(
    aes(fill = species)
    ) +
  ## Specify fill-colors
  scale_fill_manual( 
    values = c("darkorange", "purple", "cyan4") 
    )

27 / 71

Creating a barplot with geom_bar()

  • Add title, subtitle and change label of x-axis using labs()
ggplot(
  penguins,
  aes(
    x = species
    )
  ) +
  geom_bar(
    aes(fill = species)    ) +
  scale_fill_manual(
    values =
      c("darkorange","purple","cyan4")
    ) +
  ## Add title, subtitle and label x-axis
  labs(
    title = "Palmer penguins",
    subtitle = "n observations for species",
    x = ""
  )

28 / 71

Creating a barplot with geom_bar()

  • Add another theme using theme_minimal()
ggplot(
  penguins,
  aes(
    x = species
    )
  ) +
  geom_bar(
    aes(fill = species)
    ) +
  scale_fill_manual(
    values =
      c("darkorange","purple","cyan4")
    ) +
  labs(
    title = "Palmer penguins",
    subtitle = "n observations for species",
    x = ""
  ) +
  ## Choose theme
  theme_minimal()

29 / 71

Creating a barplot with geom_bar()

  • Flip x- and y-axis using coord_flip()
  • Remove legend using theme(legend.position = "none") 
ggplot(
  penguins,
  aes(
    x = species
    )
  ) +
  geom_bar(
    aes(fill = species)
    ) +
  scale_fill_manual(
    values = c("darkorange","purple","cyan4")
    ) +
  labs(
    title = "Palmer penguins",
    subtitle = "n observations for species",
    x = ""
  ) +  
  ## Flip x- and y-axis
  coord_flip( ) + 
  theme_minimal( ) +
  ## Remove legend
  theme(
    legend.position = "none"
  )

30 / 71

Creating a lollipop plot

Use two geoms: geom_point() and geom_segment() 

penguins %>% 
  count(species) %>% 
  ggplot(
    aes(
      x = species, 
      y = n)
    ) + 
  geom_point( 
    aes(col = species) 
  ) +  
  geom_segment(  
    aes(
      x = species,
      xend = species, 
      y = 0, 
      yend = n, 
      col = species
      )
    ) +

Re-use remainder of code! 

  scale_colour_manual(
    values = c("darkorange","purple","cyan4")
    ) +
  labs(
    title = "Palmer penguins",
    subtitle = "n observations for species",
    x = ""
  ) +  
  coord_flip( ) + 
  theme_minimal( ) +
  theme(
    legend.position = "none" 
  )

31 / 71

Creating a lollipop plot

  • Reorder 'bars' by creating a new variable using fct_reorder()
penguins %>% 
  count(species) %>% 
  ## Create an ordered factor
  mutate( 
    species_ord = fct_reorder(species,n) 
    ) %>% 
  ggplot(
    aes(x = species_ord,
        y = n)) + 
  geom_point( 
    aes(col = species) 
  ) + 
  geom_segment(  
    aes(
      x = species,
      xend = species, 
      y = 0, 
      yend = n,
      col = species
      ) 
    ) + 
  scale_colour_manual(
    values = c("darkorange","purple","cyan4")
    ) +
  labs(
    title = "Palmer penguins",
    subtitle = "n observations for species",
    x = ""
  ) +  
  coord_flip( ) + 
  theme_minimal( ) +
  theme(
    legend.position = "none"
    )

32 / 71

Chosing colors ...

http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/

33 / 71

Exercises [ggplot2] : part 1

  • You can find the qmd-file Exercises_ggplot2.qmd at the course website.

  • Download the qmd-file Exercises_ggplot2.qmd to your laptop

  • Open the file in RStudio

  • The file contains a set of coding assignments with empty code blocks

  • Now, we focus on part 1 of the exercises

  • Write the code (and test it by running it)

  • Stuck? No Worries!

    • We are there
    • Help each other
    • There is a solution key (at the website) (Exercises_ggplot2_solutions.qmd)
34 / 71

5. Visualising a quantitative variable

35 / 71

Visualising a quantitative variable?

There are many ways to visualise a quantitative variable...

Can you think of some?

36 / 71

Visualising a quantitative variable?

There are many ways to visualise a quantitative variable...

Can you think of some?


Taken from Fundamentals of Data Visualization by Claus Wilke

36 / 71

Creating a histogram with geom_histogram()

ggplot(
  penguins,
  aes(
    x = flipper_length_mm
    )
  ) +
  geom_histogram()

37 / 71

Creating a histogram with geom_histogram()

  • Change breaks on y-axis using scale_y_continuous() 
ggplot(
  penguins,
  aes(
    x = flipper_length_mm
    )
  ) +
  geom_histogram() +
  ## Specify breaks
  scale_y_continuous( 
    breaks = c(seq(0, 25, 5), 29) 
    )

38 / 71

Creating a histogram with geom_histogram()

  • Change transparency using argument alpha
  • Remove minor grid lines using argument panel.grid.minor = element_blank()
ggplot(
  penguins,
  aes(
    x = flipper_length_mm,
    fill = species
    )
  ) +
  geom_histogram(
    ## Change transparency of points
    alpha = .7 
  ) +
  ## Specify breaks
  scale_y_continuous( 
    breaks = c(seq(0, 25, 5), 29) 
    ) + 
  scale_fill_manual(
    values =
      c("darkorange","purple","cyan4")
    ) +
  labs(
    title = "Palmer penguins",
    subtitle = "Histogram of flipper length",
    x = "Flipper length"
  ) +
  theme_minimal() +
  theme(
    ## Remove minor grid lines
    panel.grid.minor = element_blank()
  )

39 / 71

Creating a density plot° with geom_density()

  • Replace geom_histogram() with geom_density() plot
ggplot(
  penguins,
  aes(
    x = flipper_length_mm,
    fill = species
    )
  ) +
  ## Use geom_density
  geom_density(
    alpha = .7
  ) +
  scale_fill_manual(
    values = c("darkorange","purple","cyan4")
    ) +
  labs(
    title = "Palmer penguins",
    subtitle = "Density plot of flipper length",
    x = "Flipper length"
  ) + 
  theme_minimal()

°surface below curve = 100%

40 / 71

Creating a density plot with geom_density()

  • Only colored lines by replacing argument fill with color
ggplot(
  penguins,
  aes(
    x = flipper_length_mm,
    color = species
    )
  ) +
  geom_density() + 
  ## Replace scale_color with scale_fill
  scale_color_manual( 
    values = c("darkorange","purple","cyan4") 
    ) + 
  labs(
    title = "Palmer penguins",
    subtitle = "Density plot of flipper length",
    x = "Flipper length"
  ) + 
  theme_minimal() +
  theme(
    panel.grid.minor = element_blank()
    )

41 / 71

Or even better ... geom_violin() + geom_jitter()

ggplot(
  penguins,
  aes(
    x = species,
    y = flipper_length_mm,
    ## Aesthetics fill and color applied to EVERY geom
    fill = species,
    color = species
    )
  ) +
  ## Use geom_violin and geom_jitter
  geom_violin(
    alpha = .65
  ) +
  geom_jitter(
    alpha = .7
  ) +
  scale_fill_manual(
    values = c("darkorange","purple","cyan4")
    ) +
  scale_color_manual(
    values = c("darkorange","purple","cyan4")
    ) +
  labs(
    title = "Palmer penguins",
    subtitle = "Density plot voor flipper lengte",
    y = "Flipper length",
    x = "",
  ) + 
  theme_minimal() +
  theme(
    legend.position = "none"
    )

42 / 71

Or even better geom_violin() + geom_jitter()

  • Put names of species on top using argument position="top"
  • Remove minor grid lines using argument panel.grid.major = element_blank()
ggplot(
  penguins,
  aes(
    x = species,
    y = flipper_length_mm,
    fill = species,
    color = species
    )
  ) +
  geom_violin(
    alpha = .65
  ) +
  geom_jitter(
    alpha = .7
  ) +
  scale_x_discrete(
    position = "top"
    ) +
  scale_fill_manual(
    values = c("darkorange","purple","cyan4")
    ) +
  scale_color_manual(
    values = c("darkorange","purple","cyan4")
    ) +
  labs(
    title = "Palmer penguins",
    subtitle = "Density plot of flipper length",
    y = "Flipper length",
    x = "",
  ) + 
  theme_minimal() + 
  theme(
    legend.position = "none",
    panel.grid.minor = element_blank(),
    ## Remove major grid lines
    panel.grid.major.x = element_blank()
    )

43 / 71

Or a rain cloud plot

Based on the tutorial of Cédric Scherer: https://www.cedricscherer.com/

44 / 71

Or a rain cloud plot (Appendix 1: How to grow a rain cloud plot?)

Additional package required: ggdist

library(ggdist)
ggplot(penguins, aes(x = species, y = flipper_length_mm)) + 
  stat_halfeye(
    adjust = .5, 
    width = .6, 
    .width = 0, 
    justification = -.2, 
    point_colour = NA
  ) + 
  geom_boxplot(
    width = .15, 
    outlier.shape = NA
  ) +
  geom_point(
    size = 1.3,
    alpha = .3,
    position = position_jitter(
      seed = 1, width = .1
  )) +
  labs(
    title = "Palmer penguins",
    subtitle = "Distribution of flipper length by species",
    x = "",
    y = "Flipper length"
  ) +
  coord_cartesian(xlim = c(1.2, NA), clip = "off") +
  coord_flip() +
  theme_minimal()
45 / 71

Exercises [ggplot2] : part 2

  • You can find the qmd-file Exercises_ggplot2.qmd at the course website.

  • Download this file to your laptop

  • Open the file in RStudio

  • The file contains a set of coding assignments with empty code blocks

  • Now, we focus on part 2 of the exercises

  • Write the code (and test it by running it)

  • Stuck? No Worries!

    • We are there
    • Help each other
    • There is a solution key (Exercises_ggplot2_solutions.qmd)
46 / 71

6. Visualising more than one variable

47 / 71

Visualising more than one variable?

There are many ways to visualise more than one variable... The choice depends (among other things) on the type of variables you want to plot:

  • only quantitative variables;
  • only qualitative variables;
  • or a combination of both

Can you think of an example of each?

48 / 71

Visualising more than one variable?

There are many ways to visualise more than one variable... The choice depends (among other things) on the type of variables you want to plot:

  • only quantitative variables;
  • only qualitative variables;
  • or a combination of both

Can you think of an example of each?


Have a look at data to viz.com

48 / 71

Visualising more than one variable





We focus today upon:

  • scatterplots (two numeric variables, two numeric variables and one categorical variable)
  • grouped barplots (two categorical variables)
49 / 71

Creating a scatterplot with geom_point()

ggplot(
  penguins,
  aes(
    x = body_mass_g,
    y = flipper_length_mm
    )
  ) +
  geom_point() +
  labs(
    title = "Palmer penguins",
    subtitle = "Relation of flipper length with body mass",
    y = "Flipper length",
    x = "Body mass",
  ) + 
  theme_minimal() + 
  theme(
    legend.position = "none"
    )

50 / 71

Creating a scatterplot with geom_point() and geom_smooth()

  • Add trend line using geom_smooth() 
ggplot(
  penguins,
  aes(
    x = body_mass_g,
    y = flipper_length_mm
    )
  ) +
  geom_point() +
  ## Add a trendline
  geom_smooth() +
  labs(
    title = "Palmer penguins",
    subtitle = "Relation of flipper length with body mass",
    y = "Flipper length",
    x = "Body mass"
  ) + 
  theme_minimal() + 
  theme(
    legend.position = "none"
    )

51 / 71

Creating a scatterplot with geom_point() and geom_smooth()

  • Linear trend line using argument method = "lm"
  • Remove confidence intervals using argument se = FALSE 
ggplot(
  penguins,
  aes(
    x = body_mass_g,
    y = flipper_length_mm
    )
  ) +
  geom_point() +
  geom_smooth( 
    ## Add linear trend line
    method = "lm", 
    ## Remove confidence band
    se = FALSE 
  ) + 
  labs(
    title = "Palmer penguins",
    subtitle = "Correlation between flipper length and body mass",
    y = "Flipper length",
    x = "Body mass",
  ) + 
  theme_minimal() + 
  theme(
    legend.position = "none"
    )

52 / 71

Creating a scatterplot with geom_point() and geom_smooth()

  • Add shape and color by specifying aesthetics: shape and color
  • Change legend position and background, position of plot title, and layout of (sub)title 
ggplot(
  penguins,
  aes(
    x = flipper_length_mm,
    y = body_mass_g
    )
  ) +
  geom_point(
    aes(
      color = species,
      shape = species
      ),
    size = 3,
    alpha = 0.8
    ) +
  geom_smooth(
    aes(color = species),
    se = F, 
    method = "lm"
    ) +
  theme_minimal() +
  scale_color_manual(
    values = c("darkorange","purple","cyan4")) +
  labs(
    title = "Palmer penguins",
    subtitle = "Correlation between flipper length and body mass",
    y = "Flipper length",
    x = "Body mass",
    color = "Species", 
    shape = "Species") + 
  theme(
    legend.position = c(0.2, 0.7), 
    legend.background = element_rect(fill = "white", color = NA),
    plot.title.position = "plot",
    plot.title = element_text(hjust = 0, face= "bold"),
    plot.subtitle = element_text(hjust = 0, face= "italic")
    )

53 / 71

Creating a scatterplot with geom_point() and geom_smooth()

  • Adds facets using facet_wrap() 
ggplot(
  penguins,
  aes(
    x = body_mass_g,
    y = flipper_length_mm
    )
  ) +
  geom_point( ) +
  geom_smooth(
    method = "lm",
    se = FALSE
  ) +
  facet_wrap(~species) + 
  labs(
    title = "Palmer penguins",
    subtitle = "Correlation between flipper length and body mass",
    y = "Flipper length",
    x = "Body mass",
  ) + 
  theme_minimal() + 
  theme(
    legend.position = "none",
    plot.title = element_text(hjust = 0, face= "bold"),
    plot.subtitle = element_text(hjust = 0, face= "italic")
    )

54 / 71

Creating a barplot of two variables (counts)

Grouped barplot of counts using
position_dodge()

Stacked barplot of counts using argument
position_stack()

55 / 71

Creating a barplot of two variables (counts)

Grouped barplot of counts using
position_dodge()

Stacked barplot of counts using argument
position_stack() 

penguins %>% 
  ggplot(
    aes(
      x = island,
      fill = species
      )
    ) +
  geom_bar(
    position = position_dodge()
    ) +
  theme_minimal()
penguins %>% 
  ggplot(
    aes(
      x = island,
      fill = species)
    ) +
  geom_bar(
    position = position_stack()
    ) +
  theme_minimal()
55 / 71

Creating a barplot of two variables (percentage)

  • Switch to relative frequencies using position_fill() 
penguins %>% 
  ggplot(
    aes(
      x = island,
      fill = species
      )
    ) +
  geom_bar(
    position = position_fill()
    ) +
  theme_minimal()

56 / 71

Creating a barplot of two variables (percentage)

  • Add texts to bars using geom_text()
  • Add "fill" colors using scale_fill_brewer()
  • Remove additional space using coord_cartesian(expand=FALSE) 
penguins %>% 
  ggplot(
    aes(
      x = island,
      fill = species
      )
    ) +
  geom_bar(
    position = position_fill(),
    alpha = .9
    ) +
  geom_text(
    aes(label = ..count..),
    stat = "count",
    colour = "white",
    position = position_fill(vjust = 0.5)
    ) +
  scale_fill_brewer(
    type = "qual", 
    palette = 6
  ) +
  scale_x_discrete(position = "top")+
  scale_y_continuous(breaks = c(0.5, 1)) +
  labs(
    title = "Palmer penguins",
    subtitle = "Distribution of penguin species by island",
    fill = "Species"
  ) +
  coord_cartesian(expand = FALSE) +
  theme_minimal() + 
  theme(
    plot.title = element_text(size = 20, face = "bold"),
    plot.subtitle = element_text(size = 16, face = "italic"),
    axis.title = element_blank(),
    axis.text.x = element_text(size = 14, face = "bold"),
    legend.title = element_text(face = "bold"),
    panel.grid.minor = element_blank(),
    panel.grid.major.x = element_blank()
    )

57 / 71

Chosing colors ...

  • Colors are not meaningless and ... they grab attention

  • R has several built-in color palettes. The functions scale_fill_brewer and scale_color_brewer use palettes of RColorBrewer. These palettes come in three 'flavours':

    • sequential: for ordered data ((for example, scoring low/mediocre/high on a likert scale)
    • qualitative: for nominal or categorical information (for example, different islands)
    • diverging: for ordered data with meaningful mid values ((for example, temperature or change in score)

58 / 71

Exercises [ggplot2] : part 3

  • You can find the qmd-file Exercises_ggplot2.qmd at the course website.

  • Download this file to your laptop

  • Open the file in RStudio

  • The file contains a set of coding assignments with empty code blocks

  • Now, we focus on part 3 of the exercises

  • Write the code (and test it by running it)

  • Stuck? No Worries!

    • We are there
    • Help each other
    • There is a solution key (Exercises_ggplot2_solutions.qmd)
59 / 71

7. More about visualisation?

60 / 71

The tip of the icebeRg...

Gallery of different types of visualisations accompanied by ggplot-code

Top50 ggplot visualisations...

library of graphs with ggplot-code

ggplot-extentions

61 / 71

Which visualisation to use?

Visual vocabulary


The Data Visualisation Catalogue



Research visuals: all the resources ...

Website of Cedric Scherer

62 / 71

In need of help?

Don't forget RStudio's help-function!

Google is your best fRiend...

Just google your question and you'll find code, examples, ...

Generative AI can help as well!

63 / 71

Appendix 1

How to grow a rain cloud plot?




Back to slide show...

64 / 71

Step 1

library(ggdist)
P1 <- 
  ggplot(
    penguins, 
    aes(
      x = species,
      y = flipper_length_mm
      )
    ) + 
  stat_halfeye() 
P1

65 / 71

Step 2

P1 <- 
  ggplot(
    penguins, 
    aes(
      x = species, 
      y = flipper_length_mm
      )
    ) + 
  stat_halfeye(
    adjust = .5, 
    width = .6, 
    .width = 0, 
    justification = -.2, 
    point_colour = NA
  ) 
P1

66 / 71

Step 3

P2 <- P1 + 
  geom_boxplot(
    width = .15, 
    outlier.shape = NA
  ) 
P2

67 / 71

Step 4

P3 <- P2 + 
  geom_point(
    size = 1.3,
    alpha = .3,
    position = position_jitter(
      seed = 1,
      width = .1
      )
    ) 
P3

68 / 71

Step 5

P4 <- P3 + 
  labs(
    title = "Palmer penguins",
    subtitle = "Distribution of flipper length by species",
    x = "",
    y = "Flipper length"
  ) 
P4

69 / 71

Step 6

P5 <- P4 +
  coord_cartesian(xlim = c(1.2, NA), clip = "off") +
  coord_flip() +
  theme_minimal() +
  theme(
    plot.title.position = "plot",
    plot.title = element_text(face = "bold"),
    plot.subtitle = element_text(face = "italic")
    )
P5

70 / 71

THE RAIN CLOUD PLOTS / Back to slide show...

71 / 71

Overview

  1. Simple plots in R --- (click here)
  2. Grammar of graphics --- (click here)
  3. How ggplot2 works (in a nutshell) --- (click here)
  4. Visualising a categorical variable --- (click here)
  5. Visualising a quantitative variable --- (click here)
  6. Visualising more than one variable --- (click here)
  7. More about visualisation? --- (click here)
2 / 71
Paused

Help

Keyboard shortcuts

↑, ←, Pg Up, k Go to previous slide
↓, →, Pg Dn, Space, j Go to next slide
Home Go to first slide
End Go to last slide
Number + Return Go to specific slide
b / m / f Toggle blackout / mirrored / fullscreen mode
c Clone slideshow
p Toggle presenter mode
t Restart the presentation timer
?, h Toggle this help
Esc Back to slideshow