Visualising Statistics:

The art of transforming scientific results into visual graphic

Rita Giordano

IUCr Crystallographic Computing School 2023 Melbourne

About Me

IUCr Computational school Mieres 2011

Outline

  • Data visualisation

  • Summary statistics

  • Storytelling for scientific results

  • Empathy, inclusion and accessibility in your visualisation

Data visualisation

Data Visualisation

Why we need to use it

image R. Giordano

Statistics in crystallography

Profile-fitting of three reflection from cubic insulin data. Source: “Crystallographic Statistics, progress and problem”, S. Ramaseshan, M. F. Richardson, A. J. C. Wilson

  • Bayesian statistics

  • Intensity statistics

  • Wilson statistics

Summary statistics

Shelx log file

Overview of mean, median and quantiles.

      Res            N_data          Chi_sq           I_sig      
 Min.   :1.390   Min.   : 1038   Min.   :0.4700   Min.   : 0.80  
 1st Qu.:1.670   1st Qu.: 7204   1st Qu.:0.5300   1st Qu.: 4.40  
 Median :2.120   Median :14077   Median :0.5400   Median :16.90  
 Mean   :2.815   Mean   :12629   Mean   :0.5673   Mean   :23.57  
 3rd Qu.:3.135   3rd Qu.:17550   3rd Qu.:0.5500   3rd Qu.:42.30  
 Max.   :7.450   Max.   :22436   Max.   :0.9000   Max.   :60.40  
    Complete         d_sig           CC1_2      
 Min.   :50.70   Min.   :0.670   Min.   :-3.80  
 1st Qu.:88.45   1st Qu.:0.990   1st Qu.:28.10  
 Median :93.80   Median :1.730   Median :66.60  
 Mean   :88.87   Mean   :2.270   Mean   :56.75  
 3rd Qu.:95.45   3rd Qu.:2.945   3rd Qu.:88.20  
 Max.   :96.30   Max.   :5.910   Max.   :97.70

Is it enough ?

NO!

Ansconbe quartet

In 1973 Francis Anscombe created four datasets.

# A tibble: 4 × 6
  set   mean_x mean_y std_dev_x std_dev_y corr_x_y
  <chr>  <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
1 1          9   7.50      3.32      2.03    0.816
2 2          9   7.50      3.32      2.03    0.816
3 3          9   7.5       3.32      2.03    0.816
4 4          9   7.50      3.32      2.03    0.817

Ansconbe quartet

The “DatasauRus” datasets

# A tibble: 13 × 6
   dataset    mean_x mean_y std_dev_x std_dev_y corr_x_y
   <chr>       <dbl>  <dbl>     <dbl>     <dbl>    <dbl>
 1 away         54.3   47.8      16.8      26.9  -0.0641
 2 bullseye     54.3   47.8      16.8      26.9  -0.0686
 3 circle       54.3   47.8      16.8      26.9  -0.0683
 4 dino         54.3   47.8      16.8      26.9  -0.0645
 5 dots         54.3   47.8      16.8      26.9  -0.0603
 6 h_lines      54.3   47.8      16.8      26.9  -0.0617
 7 high_lines   54.3   47.8      16.8      26.9  -0.0685
 8 slant_down   54.3   47.8      16.8      26.9  -0.0690
 9 slant_up     54.3   47.8      16.8      26.9  -0.0686
10 star         54.3   47.8      16.8      26.9  -0.0630
11 v_lines      54.3   47.8      16.8      26.9  -0.0694
12 wide_lines   54.3   47.8      16.8      26.9  -0.0666
13 x_shape      54.3   47.8      16.8      26.9  -0.0656

Visualising the datasaurus

Storytelling for scientific results

Data storytelling

image R. Giordano

  • We can use data to tell a story

  • Storytelling can help to better explain scientific results.

How can we better tell a story?

  1. Know your audience

  2. Analyse the data to find insights

  3. Find the right chart to represents your data

  4. Add annotation

  5. Don’t forget to include!

Audience

flowchart TD
    A[Audience] --> B(Know the subject)
    A[Audience] --> C(Don't know the subject)
    B --> D[Yes]
    D --> H{Know the type of chart?}
    H --> I[Yes]
    H --> J[No]
    B --> E[No]
    C --> F[Yes]
    C --> G[No]

flowchart TD
    A[Audience] --> B(Know the type of chart)
    A[Audience] --> C(Don't know the type if chart)
    B --> D[Yes]
    D --> H{Know the subject?}
    H --> I[Yes]
    H --> J[No]
    B --> E[No]
    C --> F[Yes]
    C --> G[No]

Anatomy of a visualisation

Expert or not expert, that is the question.

image R. Giordano

Anatomy of a visualisation

Expert in topic beginner in chart type

image R. Giordano

Anatomy of a visualisation

Beginner in topic expert in chart type

image R. Giordano

How to tell a different story

How to tell a different story

Empathy, inclusion and accessibility in your visualisation

Colour blindness

Colour blindness affect 1 in 12 men and 1 in 200 women in the world. There are different form:

  • Deuteranomaly: red-green colour blindness

  • Protonomaly: red-green colour blindness

  • Tritanomaly: blue-yellow colour blindness

  • Desaturated: reduction in colour vision to pure greyscale

Care to colour-blindness is to show empathy to others.

colourblind issue: example

How colourblindness perceive the colours

Colour blind friendly palette

{viridis} colour scales

https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html

Visual impairment: Alt-text

  • People with visual impairment use screen reader to decode images.

  • Screen reader read the Alt-Text of the image.

  • The Alt Text is an alternative text that is read from a narrator.

  • More details you will add more understanding will gain the person who is listening.

Conclusion

  • Keep always in mind your audience

  • Try to create visualisation that are accessible and clear

Thank you so much