STATS 220

STATS 220Effective data visualisation👩‍🎨1 / 43

Graphical perception 👀2 / 43

Graphical perception 👀1. Preattentive processing2. Proximity3. Position vs angle4. Colour matters2 / 43

how human perceive a plot
given the same amount of info from plots, which data type help us to perceive more accurate info

Preattentive processing

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Preattentive processing

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Preattentive processing

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Have you noticed there are unusual data points? Can you locate them?
how about this one?

Preattentive processing colour > form (shape)

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Which plot helps you to distinguish the data points?
Which plot consumes your least attention?
viewers can sense certain features, b/f our mind starts to pay attention to any specific objs.

Proximity Make easy comparisons by grouping elements together

compare time use by categories within each country

compare time use by countries within each category

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left: it's not easy to compare how much time spent on sleep across countries

Position vs angle position > angle

Pie charts are BAD‼️

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"Other" and "Sleep", can u easily perceive these subtle differences from the pie charts?
But
A bar chart or dot chart is a preferable way of displaying this type of data.

Absolute vs relative positions absolute > relative

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absolute -> share the same base line. we compare their heights
right: 100% bar chart. its much harder to compare NZ to others, bc NZ is rel.
The eye is good at judging abs and bad at judging relative.

Colour matters8 / 43

Colour matters1. Colour spaces2. Colour scales3. Colour blindness8 / 43

3 ways to represent colour spacesRGB
HSV/HSL
HCL for humans
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RGB

Red (0-255): amount of red light
Green (0-255): amount of green light
Blue (0-255): amount of blue light

image credit: Claus O. Wilke

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for computer & screen

HSV

Hue (0-360): hue of the colour
Saturation (0-1): colourfulness relative to the brightness of the colour
Value (0-1): subjective perception of amount of light emitted

image credit: Claus O. Wilke

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HSL

Hue (0-360): hue of the colour
Lightness (0-1): brightness relative to the brightness of a illuminated white
Saturation (0-1): colourfulness relative to the brightness of the colour

image credit: Claus O. Wilke

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HCL aka polar LUV

Hue (0-360): hue of the colour
Chroma (0-180): degree of vividness of a colour
Luminance (0-100): amount of light emitted

image credit: Claus O. Wilke

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HCL: perceptually-based and device-independent

Encoding too much

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default ggplot2 colour scales -> not colour blind friendly
more than 7 qualitative colours, matching colours to categories are cumbersome
colour can be effective tool to enhance
choose colour wisely

Colour scales

3 fundamental use cases for colours in data visualisations:

use colour to distinguish groups of data from each other
use colour to represent data values
use colour to highlight

3 types of colour palettes ColorBrewer

Qualitative
Sequential
Diverging

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Qualitative palettes for categorical data with no intrinsic ordering

colorspace::hcl_palettes("Qualitative", plot = TRUE, n = 7)

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use colour to distinguish discrete items/groups, but doesn't give impression of an order
a finite set of specific colours that are chosen to look clearly distinct
no one single colour stands out relative to the others

Sequential palettes for ordered data from high to low

colorspace::hcl_palettes("Sequential", plot = TRUE, n = 7)

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heatmap: used colour to represent data values, like temperature
representing continuous/ordered values
colours indicate which data values are larger or smaller
the diff bt colours shows the diff b/t data values
seq colour needs to be perceived to vary uniformly across its entire range by changing hues

Diverging palettes for mid-range values and extremes at both ends

colorspace::hcl_palettes("Diverging", plot = TRUE, n = 7)

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vis the deviation of data values in one of 2 directions rel to a neutral midpoint
a straightforward eg is vis +/- values
think of a diverging scale as joining 2 seq sales at a common midpoint

Use colour palettes

time_use %>% 
  ggplot(aes(country, time_minutes)) +
  geom_col(
    aes(fill = category), 
    position = "dodge") +
  scale_fill_brewer(palette = "Dark2") +
  labs(y = "") +
  theme(legend.position = "bottom")

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Set custom colours

time_use %>% 
  ggplot(aes(country, time_minutes)) +
  geom_col(
    aes(fill = category),
    position = "dodge") +
  scale_fill_manual(
    values = c("#EF476F", "#FFD166",
               "#06D6A0", "#118AB2",
               "#073B4C", "grey")) +
  labs(y = "") +
  theme(legend.position = "bottom")

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Colour-vision deficiency

Red-green colour-vision deficiency (deuteranomaly & protanomaly) is the most common.

Blue-green colour-vision deficiency (tritanomaly) is rare but does occur.

ℹ️ Approximately 8% of males and 0.5% of females suffer from some sort of color-vision deficiency.

reference: Claus O. Wilke Fundamentals of Data Visualization

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A small prop of people with impaired colour vision have difficulty to distinguish certain types of colours

Choose colours using {colorspace}

colorspace::hclwizard()

colorspace::hcl_color_picker()

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ScalesControl how data is mapped to perceptual properties, and produce guides (axes and legends) which allow us to read the plot.
Important arguments: breaks, labels, and limits.
Naming scheme: scale_[aes]_[datatype]()
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scale	Description
scale_alpha, scale_alpha_continuous, scale_alpha_binned, scale_alpha_discrete	Alpha transparency scales
scale_x_binned, scale_y_binned	Positional scales for binning continuous data (x & y)
scale_colour_brewer, scale_fill_brewer, scale_colour_distiller, scale_fill_distiller	Sequential, diverging and qualitative colour scales from colorbrewer.org
scale_colour_continuous, scale_fill_continuous, scale_colour_binned, scale_fill_binned	Continuous and binned colour scales
scale_colour_discrete, scale_fill_discrete	Discrete colour scales

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Publication-ready visualisation 👩‍🎨25 / 43

Towards publication-ready visualisation

#MakingOf of last week's #TidyTuesday plot https://t.co/Jsg41id5Nw pic.twitter.com/dP6tSrjHy0
— Georgios Karamanis (@geokaramanis) March 22, 2021

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Exploratory data visualisation
For internal use only. Need to be able to create rapidly because your first attempt will never be the most revealing.
Iteration is crucial for developing multiple displays of your data.

Communication graphics
When you communicate your findings, you need to spend much time polishing your graphics to eliminate distractions and focus on the storytelling.
Iteration is crucial to ensure all the bits and pieces works well: labels, color choices, tick marks...

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Case study: COVID-19

covid19 <- read_csv("data/covid19-daily-cases.csv")
covid19

#> # A tibble: 15,677 x 3
#>   country_region date       confirmed
#>   <chr>          <date>         <dbl>
#> 1 Afghanistan    2020-03-01         1
#> 2 Afghanistan    2020-03-02         1
#> 3 Afghanistan    2020-03-03         2
#> 4 Afghanistan    2020-03-04         4
#> 5 Afghanistan    2020-03-05         4
#> 6 Afghanistan    2020-03-06         4
#> # … with 15,671 more rows

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COVID-19

- scale-y

Data as is

covid19 %>% 
  ggplot(aes(
    x = date, 
    y = confirmed, 
    colour = country_region)) +
  geom_line() +
  guides(colour = FALSE) # rm colour legend

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full screen of legends

COVID-19

- scale-y

Logarithmic scale

covid19 %>% 
  ggplot(aes(
    x = date, 
    y = log10(confirmed),
    colour = country_region)) +
  geom_line() +
  guides(colour = FALSE)

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perceive the rate of infections, slowing down

COVID-19

- scale-y

Logarithmic scale

covid19 %>% 
  ggplot(aes(
    x = date, 
    y = confirmed, 
    colour = country_region)) +
  geom_line() +
  guides(colour = FALSE) +
  scale_y_log10()

Rob J Hyndman's blog post on Why log ratios are useful for tracking COVID-19

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COVID-19
- scale-y
- scale-x
covid19_rel <- covid19 %>% 
  group_by(country_region) %>% 
  mutate(days = as.numeric(date - min(date))) %>% 
  ungroup()
covid19_rel

#> # A tibble: 15,677 x 4
#>   country_region date       confirmed  days
#>   <chr>          <date>         <dbl> <dbl>
#> 1 Afghanistan    2020-03-01         1     0
#> 2 Afghanistan    2020-03-02         1     1
#> 3 Afghanistan    2020-03-03         2     2
#> 4 Afghanistan    2020-03-04         4     3
#> 5 Afghanistan    2020-03-05         4     4
#> 6 Afghanistan    2020-03-06         4     5
#> # … with 15,671 more rows
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log(0) -> Inf

COVID-19

- scale-y

- scale-x

Relative days

covid19_rel %>% 
  ggplot(aes(
    x = days,
    y = confirmed, 
    colour = country_region)) +
  geom_line() +
  scale_y_log10() +
  guides(colour = FALSE)

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COVID-19

- scale-y

- scale-x

- highlight

Highlight New Zealand

covid19_nz <- covid19_rel %>% 
  filter(country_region == "New Zealand")
p_nz <- covid19_rel %>% 
  ggplot(aes(x = days, y = confirmed, 
    group = country_region)) +
  geom_line(colour = "grey", alpha = 0.5) +
  geom_line(colour = "#238b45", size = 1, data = covid19_nz) +
  scale_y_log10() +
  guides(colour = FALSE)
p_nz

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COVID-19

- scale-y

- scale-x

- highlight

- annotate

Label New Zealand

p_nz <- p_nz +
  geom_label(aes(
    x = max(days), y = max(confirmed),
    label = country_region), data = covid19_nz,
    colour = "#238b45", nudge_x = 3, nudge_y = .5)
p_nz

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COVID-19

- scale-y

- scale-x

- highlight

- annotate

- limits

Expand limits

p_nz <- p_nz +
  scale_y_log10(labels = scales::label_comma()) +
  xlim(c(0, 100))
p_nz

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COVID-19

- scale-y

- scale-x

- highlight

- annotate

- limits

- labels

Every figure needs the title

p_nz <- p_nz +
  labs(
    x = "Days since March 1",
    y = "Confirmed cases (on log10)",
    title = "Worldwide coronavirus confirmed cases",
    subtitle = "highlighting New Zealand",
    caption = "Data source: John Hopkins University, CSSE"
  )
p_nz

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COVID-19

- scale-y

- scale-x

- highlight

- annotate

- limits

- labels

- theme

Apply themes

# remotes::install_github("Financial-Times/ftplottools")
p_nz +
  ftplottools::ft_theme() +
  theme(
    plot.title.position = "plot", 
    plot.background = element_rect(fill = "#FFF1E0"))

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