Data visualisation

Bio300B Lecture 4

Richard J. Telford (Richard.Telford@uib.no)

Institutt for biovitenskap, UiB

1 September 2026

Data visualisation

• A picture is worth a thousand words
• Tell a story with figures
• Avoid common mistakes

“reflect the data, tell a story, and look professional” Wilke

ggplot2

• one of at least three schemes for graphics in R
• part of tidyverse

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, it takes care of the details.

ggplot in action

plot <- ggplot(data = penguins,     # Data
       mapping = aes(               # Aesthetics
         x = body_mass,    
         y = bill_len, 
         colour = species)) +
  geom_point() +                    # Geometries
  scale_colour_brewer(palette = "Set2") + # scales
  labs(x = "Body mass, g",          # labels
       y = "Bill length, mm", 
       colour = "Species") +
  theme_bw()                        # themes
                                    # Also facets
plot

ggplot in action

Data

Tibble or data frame with tidy data to be plotted.

Can process data within ggplot but usually best to do it first

Can add data to the whole plot or to individual geoms

penguin_summary <- penguins |> 
  summarise(body_mass = mean(body_mass, na.rm = TRUE), bill_len = mean(bill_len, na.rm = TRUE), .by = species)
ggplot(penguins, aes(x = body_mass, y = bill_len, colour = species)) +
  geom_point() +
  geom_text(aes(label = species), data = penguin_summary, colour = "black")

Aesthetics

mapping specifies which variables in the data should be mapped onto which aesthetics with aes()

Each geom takes different aesthetics

Common aesthetics

• x, y
• fill, colour, alpha
• shape, size
• linetype, linewidth
• group

Setting vs mapping

Mapping in aes()

ggplot(penguins, 
       aes(x = flipper_len, 
           fill = "blue")) +
geom_histogram()

Setting in the geom

ggplot(penguins, 
       aes(x = flipper_len)) +
geom_histogram(fill = "blue")

geoms

Use different geoms for different plot types

Important geoms

• geom_point()
• geom_boxplot()
• geom_histogram()
• geom_smooth()
• geom_line()
• geom_text()

Many geoms, some in extra packages

Geoms for distributions: Histogram

Count how many observations in each bin

ggplot(penguins, aes(x = flipper_len)) + geom_histogram(bins = 30)

Critical question - how many bins? Set with bins argument

viewof bins = Inputs.range(
  [ 1, 50 ], 
  { label: "Number of bins", step: 1, value: 30 }, 
)

viewof measure2 = Inputs.select(
  [ "flipper_len", "bill_len", "bill_dep", "body_mass" ],
  { label: "Measure" }
);

viewof species = Inputs.select(
  [ "Adelie", "Chinstrap", "Gentoo" ],
  { label: "Species" }
);

do_penguins_hist(species, measure2, bins);

Density

Smoothed histograms

ggplot(penguins, aes(x = flipper_len)) + geom_density(adjust = 1)

adjust argument adjusts bandwidth to control how smooth

viewof adjust = Inputs.range(
  [ 0.1, 2 ], 
  { label: "Adjust density", step: 0.1, value: 1 }, 
)

viewof measure = Inputs.select(
  [ "flipper_len", "bill_len", "bill_dep", "body_mass" ],
  { label: "Measure" }
);

do_penguins_density(measure, adjust);

Geoms to show many distributions

# standard plot base
base <- ggplot(penguins, aes(x = species, y = flipper_len))

p_prange <- base + stat_summary(fun = "mean", geom = "col")
p_box <- base + geom_boxplot(aes(fill = species))
p_vio <- base + geom_violin(aes(fill = species))
p_jit <- base + geom_jitter(aes(colour = species))
library(ggbeeswarm)
p_quasi <- base + geom_quasirandom(aes(colour = species))
p_quasi2 <- base + geom_violin(aes(fill = species), alpha = 0.3) +
  geom_quasirandom(aes(colour = species))

# Combine plots with patchwork
p_prange + labs(title= "geom_col()") + 
p_box + labs(title = "geom_boxplot()") +
  p_vio + labs(title = "geom_violin()") +
  p_jit + labs(title = "geom_jitter()") +
  p_quasi + labs(title = "geom_quasirandom()") +
  p_quasi2 + labs(title = "quasirandom & violin") +
  plot_layout(nrow = 2) &
  labs(y = "Flipper length, mm") &
  theme(axis.title.x = element_blank(), legend.position = "none")

Boxplots can mislead

p <- datasauRus::box_plots |> 
  pivot_longer(everything()) |> 
  ggplot(aes(x = name, y = value))

p1 <- p + geom_boxplot()
p2 <- p + geom_violin()

Show the raw data

Zhang et al (2023)

geoms for scatterplots

ggplot(penguins, aes(x = body_mass,  y = bill_len, colour = species)) +
  geom_point() +
  geom_smooth(method = "lm")

• geom_line() - join observations from left-right
• geom_path() - join observations from first to last in data

The group aesthetic

library(broom)

p1 <- co2 |> 
  tidy() |> 
  mutate(year = floor(index), month = round((index - year) * 12) + 1) |> 
  ggplot(aes(x = month, y = value)) +
  geom_line() +
  labs(x = "Month", y = expression(CO[2]~ppm), title = "Without group") +
  scale_x_continuous(breaks = 1:12, labels = substr(month.abb, 1, 1))
  
p2 <- p1 + aes(group = year) + labs(title = "With group")

p1 + p2

Choosing aesthetics for emphasis

p1 <- penguins |> 
  drop_na(sex, body_mass) |> 
  ggplot(aes(x = sex, y = body_mass, fill = species)) +
  geom_boxplot(show.legend = FALSE) +
  ggtitle("x = sex, fill = species")
p2 <- p1 + aes(x = species, fill = sex) +
    ggtitle("x = species, fill = sex")
p1 + p2

Choosing aesthetics for emphasis 2

p1 <- penguins |> 
  drop_na(sex, body_mass) |> 
  ggplot(aes(x = body_mass, y = flipper_len, colour = species, shape = sex)) +
  geom_point(show.legend = FALSE) +
  ggtitle("colour = species, shape = sex")
p2 <- p1 + aes(shape = species, colour = sex) + ggtitle("shape = species, colour = sex")
p1 + p2 

Scales

Control how

• variables are mapped onto the aesthetics
• axes breaks

All called scale_aesthetic_description

• scale_x_log()
• scale_y_reverse()
• scale_colour_viridis_c()
• scale_shape_manual()

Labels

• plot, axis and legend titles

ggplot(penguins, aes(x = body_mass, y = bill_len, colour = species)) +
  geom_point() +
  labs(x = "Body mass, g",
       y = "Bill length, mm", 
       colour = "Species", 
       title = "Bill length against body mass ") 

Facets

Split data into separate panels.

plot + facet_wrap(facets = vars(species))

facet_grid() for two dimensional arrays of subplots

diamonds |> ggplot(aes(x = carat, y = price)) +
  geom_point(data = diamonds |> select(carat, price), colour = "grey80") +
  geom_point() +
  facet_grid(rows = vars(color), cols = vars(clarity))

Themes

Change how non-data elements of the plot look

Entire themes

Themes

Can also change individual elements

plot + theme(legend.position = "top")

Removing elements

plot + theme(panel.grid = element_blank())

Colour & fills

Avoid primary colours

ggplot(penguins, aes(x = flipper_len, fill = species)) +
  geom_histogram() +
  scale_fill_manual(values = c("red", "green", "blue")) +
  labs(x = "Flipper length mm")

Colour deficient vision

den <- ggplot(penguins, aes(x = bill_len, fill = species)) +
  geom_density(alpha = 0.7)
den

colorBlindness::cvdPlot(den)

#End rainbow

Better colour scale

den <- ggplot(penguins, aes(x = bill_len, fill = species)) +
  geom_density(alpha = 0.7) +
  scale_fill_brewer(palette = "Set2")
den

colorBlindness::cvdPlot(den)

Using colour effectively

Choose an appropriate palette.

Qualitative palettes

RColorBrewer::display.brewer.all(type = "qual")

Sequential palettes

RColorBrewer::display.brewer.all(type = "seq")

Dividing palettes

RColorBrewer::display.brewer.all(type = "div")

Viridis

ggplot(penguins, aes(x = body_mass, y = flipper_len)) +
  geom_point(aes(colour = flipper_len)) +
  scale_colour_viridis_c()

Highlight

ggplot(penguins, aes(x = body_mass, y = flipper_len)) +
  geom_point(colour = "red") +
  gghighlight::gghighlight(species == "Chinstrap")

Redundant encoding

ggplot(penguins, 
       aes(x = body_mass,
           y = flipper_len,
           colour = species,
           shape = species)) +
  geom_point() 

Also colour and linetype/linewidth

Avoiding legends

library(directlabels)
direct.label(plot) 

Avoiding overplotting

Problem - points plot on top of each other.

More overplotting

Problem - too much data

Most common mistake in presentations

Solution

theme_bw(base_size = 18)

Summary

• If you can imagine it, you can plot it
• Whole ecosystem of packages to help

Further reading

Fundamentals of Data Visualization Wilke

ggplot2: Elegant Graphics for Data Analysis (3e) Wickham, Navarro, and Pedersen.