Distributions

Associations, correlation, PCA

Roland Krause

MADS6

Wednesday, 11 March 2026

Session aims

Learning objectives

Exploring a single distribution
Associations
Correlation plots
Principal component analysis

Exploratory plots

We aim to find a basis - No lables and titles

Material

Chapter Statistical summaries in ggplot2: Elegant Graphics for Data Analysis (3e)

Fundamentals of data visualisation

Consult the books for reference

We can only cover a fraction in class!

What are we visualising?

Amounts

x-y relationships

Trends

Proportions

Distributions

Uncertainty

Distributions

Many distributions are not “normal”

Power law distributions

Common in natural data with network characteristic

Also not so normal

Empirical cumulative distribution functions

gapminder_2002 <- gapminder |> filter(year == 2002) 

ggplot(gapminder_2002) +
  aes(x = pop) +
  stat_ecdf(geom = "step", pad = FALSE) +
  labs(title = "Global population by country" )

ECDF

No tuning parameter, possibly harder to interpret than a histogram

Log-transform

ggplot(gapminder_2002, 
       aes(pop)) +
  geom_histogram(fill = "#00A4E1"  ) +
  scale_x_log10(labels = scales::comma) + 
  labs(title = "Number of countries by population",
       x= "",
       y = NULL)

Tip

Use the log-transform in your plotting library. Don’t require your audience to compute!

gapminder_2002 |>
  mutate(log_pop = log10(pop)) |>
  ggplot(aes(log_pop)) +
  geom_histogram(fill = "#00A4E1") + 
  labs(title = "Number of countries by population",
       x= "Population (log_10)",
       y = NULL)

Transformed ECDF

ggplot(gapminder_2002) +
  aes(x = pop) +
  stat_ecdf(geom = "step", pad = FALSE) +
  scale_x_log10()

Plotting a normal distribution

s <- seq(0,10,0.025)

theoretical <-  
  tibble(x = s,
         y = dnorm(s,
                   mean(log10(gapminder_2002$pop)),
                   sd = sd(log10(gapminder_2002$pop))))

ggplot(theoretical) +
  aes(x, y) +
  geom_point() +
  geom_vline(xintercept = 
             mean(log10(gapminder_2002$pop)), 
             color = "#00a4e1", 
             size = 2)

How this should work in principle

 ggplot() + 
  geom_function(aes(colour = "Original prior"), fun = dnorm, args = list(
    mean =  mean(gapminder_2002$log_pop) , sd =  sd(gapminder_2002$log_pop)))

A randomized histogram

pop_random <-
  tibble(rnd = rnorm(
    n = 1000,
    mean = mean(log10(gapminder_2002$pop)),
    sd = sd(log10(gapminder_2002$pop))
  ) ,
  pop_a = 10 ** rnd)

ggplot(pop_random) +
  aes(pop_a) +
  geom_histogram(fill = "#00A4E1") +
  scale_x_log10()

Overlaying empirical and theoretical distribution

ggplot(pop_random) +
  aes(x = pop_a) + 
  # theoretical distribution from dnorm(x, mean , sd)
  stat_ecdf(geom = "step", pad = FALSE) +
  scale_x_log10() +
  stat_ecdf(
    data = gapminder_2002, 
    aes(x = pop),
    geom = "step",
    pad = FALSE,
    colour = "#00a4e1"
  )

Quantile-quantile plot

ggplot(gapminder_2002,
       aes(sample = log10(pop))) +
  stat_qq() + 
  stat_qq_line()

Displaying many distributions

Boxplots

gapminder |> 
  ggplot(aes(x = year, y = pop)) +
  geom_boxplot()

What are we missing?

glimpse(gapminder)

Rows: 1,704
Columns: 6
$ country   <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", …
$ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, …
$ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, …
$ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.8…
$ pop       <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12…
$ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, …

Year as date

gapminder |> 
  ggplot(aes(as.Date(as.character(year), format= "%Y"),  pop)) +
  geom_boxplot()

With factors

gapminder |> 
  ggplot(aes(as.factor(year),  pop)) +
  geom_boxplot()

Better with transform

gapminder |> 
  ggplot(aes(as.factor(year),  pop)) +
  geom_boxplot() +
  scale_y_log10()

Even better?

gapminder |> 
  ggplot(aes(as.factor(year),  pop)) +
  geom_violin() +
  scale_y_log10()  + 
  stat_summary(fun = "mean",
               geom = "crossbar", 
               colour = "#00a4e1")

Beeswarm plot

library(ggbeeswarm)
gapminder |> 
  ggplot(aes(as.factor(year),  pop)) +
    geom_violin() +
    geom_beeswarm(size = 0.5, colour = "#DC021B") +
  scale_y_log10()

Red dots

Red color plots in the lecture material represent undesirable solutions to the plotting problem.

Ridge plot (densities)

library(ggridges)
gapminder |> 
  # filtering self-join for countries with 
  # less than 10M people in 1952
  semi_join(gapminder |> 
               filter(year == 1952, 
                      pop < 10000000) |>
  select(country), join_by(country)) |> 
ggplot(aes(y = as.factor(year), x= pop)) +
  geom_density_ridges(fill = "#CDF6FF", 
                      color = "#00a4e1") +
  scale_x_sqrt()

Ridge plots are a convenient shorthand for multiple distributions

The overlap can be controlled.

Scatter plot

ggplot(swiss) +
  aes(Education, Agriculture) +
  geom_point() +
  labs(x = "Independent variable",
       y = "Dependent variable")

Back to the penguins

Code

ggplot(penguins, aes(x = bill_len, 
                     y = bill_dep, 
                     colour = sex)) +
  geom_point()

How to add bodymass and flipper length to this plot?

Adding another dimension

What is the relation between bodymass and bill depth?

Caution

Now four numeric variables. Not a good solution for mapping numeric variables to size and colour for analytical purposes.

Correlograms

penguins_clean <- penguins |> tidyr::drop_na()
 
  GGally::ggpairs(penguins_clean, 
                  columns = c(3:6,8),
                  progress = FALSE)

Complete features of GGally::ggpairs()

GGally::ggpairs(penguins,
                mapping = aes(colour = species),
                progress = FALSE) + 
  theme_bw(base_size = 8)

Heatmap

GGally::ggcorr(penguins_clean ) +
  guides(fill=guide_legend(title="All penguins")) -> peng_corr
peng_corr

Remember

Simpson paradox

Simpson’s paradox

Code

penguins_clean |> 
  nest(.by = species) |> 
  mutate(corrplot = map2(data, species, \(x, s) GGally::ggcorr(x, name =s))) |> 
  pull(corrplot) |> 
  patchwork::wrap_plots() -> pls

pls + (peng_corr + theme(plot.background = element_rect(colour = "red", linewidth = 2)))

All numeric variables except for year are highly correlated if the individual species are respected.

PCA

Principal component analysis

Dimensionality reduction

With many possibly correlated variables one wants to reduce the number of variables, potentially drop some.

Standardization/ normalization
Covariance Matrix
Eigen Decomposition Calculate eigenvalues (variance explained by components) and eigenvectors (directions of components).
Select Principal Components: Sort eigenvalues in descending order and select the top \(k\) components.
Project the original data onto the selected principal components.

Running PCA

penguins |> 
  tidyr::drop_na() |> 
  filter(species == "Adelie") -> adelie

  adelie |> 
    select(where(is.numeric), -year) -> 
    adelie_numeric
  
prcomp(adelie_numeric,  scale = TRUE) -> pca
pca

Standard deviations (1, .., p=4):
[1] 1.5250081 0.8403736 0.7833863 0.5953389

Rotation (n x k) = (4 x 4):
                   PC1         PC2         PC3        PC4
bill_len    -0.4879448  0.20838845 -0.78794300  0.3124580
bill_dep    -0.4944287  0.44731833  0.59986771  0.4422729
flipper_len -0.4382781 -0.86421656  0.12691004  0.2119809
body_mass   -0.5704055  0.09803219  0.05655442 -0.8135286

Principal component analysis in R

library(broom)
pca |> 
  # add original dataset back in
  augment(adelie) |>  
  ggplot(aes(x = .fittedPC1, 
             y = .fittedPC2, 
             shape = island, 
             color = sex)) + 
  geom_point(size = 3, alpha = 0.7)

Loadings

pca |> 
  tidy(matrix = "rotation")

# A tibble: 16 × 3
   column         PC   value
   <chr>       <dbl>   <dbl>
 1 bill_len        1 -0.488 
 2 bill_len        2  0.208 
 3 bill_len        3 -0.788 
 4 bill_len        4  0.312 
 5 bill_dep        1 -0.494 
 6 bill_dep        2  0.447 
 7 bill_dep        3  0.600 
 8 bill_dep        4  0.442 
 9 flipper_len     1 -0.438 
10 flipper_len     2 -0.864 
11 flipper_len     3  0.127 
12 flipper_len     4  0.212 
13 body_mass       1 -0.570 
14 body_mass       2  0.0980
15 body_mass       3  0.0566
16 body_mass       4 -0.814

Plotting the loading

# Nice arrow style
arrow_style <- arrow(
  angle = 20,
  ends = "first",
  type = "closed",
  length = grid::unit(8, "pt")
)

# plot rotation matrix
pca |> 
  tidy(matrix = "rotation") |> 
  pivot_wider(names_from = "PC", names_prefix = "PC", values_from = "value")  |> 
  ggplot(aes(PC1, PC2)) +
  geom_segment(xend = 0, yend = 0, arrow = arrow_style) +
  ggrepel::geom_text_repel(
    aes(label = column),
    hjust = 0.2,  nudge_x = -0.05, 
    color = "#00a4e1"
  ) +
  # Plot padding is often 
  xlim(-0.75, .25) + ylim(-1, 0.5)

#  coord_fixed()  # fix aspect ratio to 1:1

Variance explained

Eigenvalues

pca |> 
  tidy(matrix = "eigenvalues")

# A tibble: 4 × 4
     PC std.dev percent cumulative
  <dbl>   <dbl>   <dbl>      <dbl>
1     1   1.53   0.581       0.581
2     2   0.840  0.177       0.758
3     3   0.783  0.153       0.911
4     4   0.595  0.0886      1

Eigenvalues as bar chart

pca |> 
  tidy(matrix = "eigenvalues") %>%
  ggplot(aes(PC, percent)) +
  geom_col(fill = "#00a4e1")

Thank you for your attention!

Distributions

Session aims

Learning objectives

Material

What are we visualising?

Amounts

x-y relationships

Trends

Proportions

Distributions

Uncertainty

Distributions

Many distributions are not “normal”

Power law distributions

Also not so normal

Empirical cumulative distribution functions

Log-transform

Transformed ECDF

Plotting a normal distribution

A randomized histogram

Overlaying empirical and theoretical distribution

Quantile-quantile plot

Displaying many distributions

Boxplots

What are we missing?

Year as date

With factors

Better with transform

Even better?

Beeswarm plot

Ridge plot (densities)

Two and more related variables

Scatter plot

Back to the penguins

Adding another dimension

Correlograms

Complete features of GGally::ggpairs()

Heatmap

Simpson’s paradox

PCA

Principal component analysis

Dimensionality reduction

Running PCA

Principal component analysis in R

Loadings

Plotting the loading

Variance explained

Eigenvalues

Eigenvalues as bar chart

Thank you for your attention!