LECTURE 1 - INTRO

Barrie Robison

2025-01-09

WHO AM I?

Barrie Robison

Department of Biological Sciences

Institute for Interdisicplinary Data Sciences

Polymorphic Games

University of Idaho

VISUALIZATION

Computers provide visual representations of datasets designed to help people carry out tasks more effectively.

Tamara Munzner

Department of Computer Science

Dr. Munzner’s version of this lecture

University of British Columbia

THE HUMAN

Why have a human in the loop?

Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.

  • We don’t need visualization when a trusted fully automatic solution exists.

  • Visualization is suitable when there is a need to augment human capabilities rather than replace people with computational decision-making methods.

THE REPRESENTATION

Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.

EXTERNAL REPRESENTATIONS: Replace cognition with perception.

IN CLASS EXERCISE

You have 5 minutes.

Find an example of a data visualization from your daily life. I may not be related to your research or discipline in any way. Be prepared to share it with the class (zoom screen share or email me a link) and discuss its purpose, efficacy, strengths, and weaknesses.

WHY DEPEND ON VISION?

Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.

  • The human visual system is a high-bandwidth channel to the brain.
  • Overview is possible due to background processing, providing the subjective experience of seeing everything simultaneously.
  • Significant processing occurs in parallel and pre-attentively.
  • What about sound? lower bandwidth and different semantics, overview not supported, subjective experience of sequential stream.
  • What about touch/haptics? impoverished record/replay capacity, only very low-bandwidth communication thus far.
  • What about taste, smell? no viable record/replay devices.

WHY REPRESENT (ALL THE) DATA?

Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively.

  • summaries lose information
  • details matter
  • confirm expected and find unexpected patterns
  • assess validity of statistical model
  • ANSCOMBE’S QUARTET is a fun example that we shall use to illustrate these points!

ANSCOMBE’S QUARTET

Code 
library(ggplot2)
library(grid)
library(gridExtra)
library(datasets)
library(tidyverse)
library(dplyr)
datasets::anscombe
   x1 x2 x3 x4    y1   y2    y3    y4
1  10 10 10  8  8.04 9.14  7.46  6.58
2   8  8  8  8  6.95 8.14  6.77  5.76
3  13 13 13  8  7.58 8.74 12.74  7.71
4   9  9  9  8  8.81 8.77  7.11  8.84
5  11 11 11  8  8.33 9.26  7.81  8.47
6  14 14 14  8  9.96 8.10  8.84  7.04
7   6  6  6  8  7.24 6.13  6.08  5.25
8   4  4  4 19  4.26 3.10  5.39 12.50
9  12 12 12  8 10.84 9.13  8.15  5.56
10  7  7  7  8  4.82 7.26  6.42  7.91
11  5  5  5  8  5.68 4.74  5.73  6.89

Anscombe’s Quartet

The four x-y pairs have identical summary statistics.

Code 
tidy_anscombe <- anscombe %>%
 pivot_longer(cols = everything(),
              names_to = c(".value", "set"),
              names_pattern = "(.)(.)")
tidy_anscombe_summary <- tidy_anscombe %>%
  group_by(set) %>%
  summarise(across(.cols = everything(),
                   .fns = lst(min,max,median,mean,sd,var),
                   .names = "{col}_{fn}"))
#> `summarise()` ungrouping output (override with `.groups` argument)

vars<-c("set", "x_mean", "x_var",  "y_mean", "y_var")
thing<- as.data.frame(tidy_anscombe_summary[vars])
knitr::kable(thing)
set x_mean x_var y_mean y_var
1 9 11 7.500909 4.127269
2 9 11 7.500909 4.127629
3 9 11 7.500000 4.122620
4 9 11 7.500909 4.123249

VIZ MATTERS

Code 
ggplot(tidy_anscombe,
       aes(x = x,
           y = y)) +
  geom_point() +
  geom_point(data = tidy_anscombe_summary, aes(x=x_mean, y = y_mean, color = "red", size = 5),
             show.legend = FALSE)+
  facet_wrap(~set) +
  geom_smooth(method = "lm", se = FALSE)

Learn more: TIDY ANSCOMBE

RESOURCE LIMITATIONS

Visualization designers must take into account three very different kinds of resource limitations:

  • Limitations of computers.
  • Limitations of humans.
  • Limitations of displays.

COMPUTATIONAL LIMITS

CPU time

System Memory

DISPLAY LIMITS

Pixels are precious and are the most constrained resource.

  • Information Density: ratio of space used to encode information vs unused whitespace.
  • There is a tradeoff between clutter and wasting space.
  • Designer must find the sweet spot between dense and sparse.

HUMAN LIMITS

  • Time
  • Memory
  • Attention

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