Interactive Graphics for Statistics
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1 Interactive Graphics for Statistics Principles & Examples
2 2 Graphics in Statistics: Diagnostics vs. Analysis Continuous Data
3 2 Graphics in Statistics: Diagnostics vs. Analysis Continuous Data
4 3 Graphics in Statistics: Diagnostics vs. Analysis Categorical Data
5 3 Graphics in Statistics: Diagnostics vs. Analysis Categorical Data
6 3 Graphics in Statistics: Diagnostics vs. Analysis Categorical Data
7 4 Graphics for Data Analysis is not based on formal theory but has proven to be very effective is not taught in standard curricula but used by most statisticians once they left college is supporting an interactive and iterative exploration of data thus needs interactive software tools is not well supported by most statistics software but there is software that helps
8 5 Interactive Graphical Tools for DA: History
9 5 Interactive Graphical Tools for DA: History 1973 PRIM-9 Tukey et al.
10 5 Interactive Graphical Tools for DA: History 1983 SPLOM Becker et al PRIM-9 Tukey et al.
11 5 Interactive Graphical Tools for DA: History 1983 SPLOM Becker et al PRIM-9 Tukey et al DataDesk Vellemann
12 5 Interactive Graphical Tools for DA: History 1983 SPLOM Becker et al XGobi Buja et al PRIM-9 Tukey et al DataDesk Vellemann
13 5 Interactive Graphical Tools for DA: History 1983 SPLOM Becker et al ggobi 1991 Swayne XGobi et al. Buja et al PRIM-9 Tukey et al DataDesk Vellemann
14 5 Interactive Graphical Tools for DA: History 1983 SPLOM Becker et al ggobi 1991 Swayne XGobi et al. Buja et al PRIM-9 Tukey et al DataDesk Vellemann 1993 MANET Unwin et al.
15 5 Interactive Graphical Tools for DA: History 1983 SPLOM Becker et al ggobi 1991 Swayne XGobi et al. Buja et al Mondrian Theus 136/ PRIM-9 Tukey et al DataDesk Vellemann 1993 MANET Unwin et al.
16 5 Interactive Graphical Tools for DA: History 1983 SPLOM Becker et al ggobi 1991 Swayne XGobi et al. Buja et al Mondrian Theus 136/ 437? 1973 PRIM-9 Tukey et al DataDesk Vellemann 1993 MANET Unwin et al StatStudio SAS
17 5 Interactive Graphical Tools for DA: History 1983 SPLOM Becker et al ggobi 1991 Swayne XGobi et al. Buja et al Mondrian Theus 136/ 437? PRIM-9 DataDesk MANET StatStudio Tukey et al. Vellemann Unwin et al. SAS
18 5 Interactive Graphical Tools for DA: History 1983 SPLOM Becker et al ggobi 1991 Swayne XGobi et al. Buja et al Mondrian Theus 136/ 437? 1973 PRIM-9 Tukey et al DataDesk Vellemann 1993 MANET Unwin et al StatStudio SAS
19 5 Interactive Graphical Tools for DA: History 1983 SPLOM Becker et al ggobi 1991 Swayne XGobi et al. Buja et al Mondrian Theus 136/ PRIM-9 Tukey et al.? DataDesk MANET StatStudio Vellemann Unwin et al. SAS
20 5 Interactive Graphical Tools for DA: History 1983 SPLOM Becker et al ggobi 1991 Swayne XGobi et al. Buja et al Mondrian Theus 136/ PRIM-9 Tukey et al.? DataDesk MANET StatStudio Vellemann Unwin et al. SAS
21 5 Interactive Graphical Tools for DA: History 1983 SPLOM Becker et al ggobi 1991 Swayne XGobi et al. Buja et al Mondrian Theus 136/ PRIM-9 Tukey et al DataDesk MANET Vellemann Unwin et al.? 2006 StatStudio SAS people forget far faster than they learn give them time!
22 6 On Hammers and Nails No one single tool is universal enough to cover every problem we might want to handle in data analysis use the best suited!?
23 6 On Hammers and Nails No one single tool is universal enough to cover every problem we might want to handle in data analysis use the best suited!? Iris data in a Fluctuation Diagram
24 6 On Hammers and Nails No one single tool is universal enough to cover every problem we might want to handle in data analysis use the best suited!? Iris data in a Fluctuation Diagram Titanic data in a scatterplot
25 7 What is Interactive Statistical Graphics about?
26 7 What is Interactive Statistical Graphics about? Direct manipulation is at the core (classical computational statistics was 90% algorithm and 10% interface Interactive Statistical Graphics is 10% algorithm and 90% interface!) Selection of subgroups, i.e., conditioning Modification of plot parameters
27 7 What is Interactive Statistical Graphics about? Direct manipulation is at the core (classical computational statistics was 90% algorithm and 10% interface Interactive Statistical Graphics is 10% algorithm and 90% interface!) Selection of subgroups, i.e., conditioning Modification of plot parameters What makes a graphics interactive? data can be selected (selection state is actually only one possible attribute of the data) support for highlighting objects can be queried
28 7 What is Interactive Statistical Graphics about? Direct manipulation is at the core (classical computational statistics was 90% algorithm and 10% interface Interactive Statistical Graphics is 10% algorithm and 90% interface!) Selection of subgroups, i.e., conditioning Modification of plot parameters What makes a graphics interactive? data can be selected (selection state is actually only one possible attribute of the data) support for highlighting objects can be queried Interactive Graphics Dynamic Graphics dynamic variation of plot parameters (animation) was the first interaction to be implemented 30 years ago, but is now out-dated.
29 8 Where did Linking go? Linking is used to (optionally) propagate attributes of the data Example:
30 8 Where did Linking go? Linking is used to (optionally) propagate attributes of the data Example: Set Dataset Attributes selection color Set Axes Mark: [0, 4, 0.25]
31 8 Where did Linking go? Linking is used to (optionally) propagate attributes of the data Example: Set Dataset Attributes selection color Get Get Set Axes Mark: [0, 4, 0.25]
32 9 More on Linking
33 9 More on Linking Linking is not restricted to the highlight state
34 9 More on Linking Linking is not restricted to the highlight state Case linking (transient) highlighting (persistent) color brushing symbols visibility
35 9 More on Linking Linking is not restricted to the highlight state Case linking (transient) highlighting (persistent) color brushing symbols visibility Axes linking continuous intervals categorical orderings
36 9 More on Linking Linking is not restricted to the highlight state Case linking (transient) highlighting (persistent) color brushing symbols visibility Axes linking continuous intervals categorical orderings Parameter linking
37 9 More on Linking Linking is not restricted to the highlight state Case linking (transient) highlighting (persistent) color brushing symbols visibility Axes linking continuous intervals categorical orderings Parameter linking Linking of attributes of the data (or any other incarnation of the data) is optionally.
38 9 More on Linking Linking is not restricted to the highlight state Case linking (transient) highlighting (persistent) color brushing symbols visibility Axes linking continuous intervals categorical orderings Parameter linking Linking of attributes of the data (or any other incarnation of the data) is optionally. Setting and/or getting of attributes may have constraints (group selections, cross-relation selections, )
39 10 More on Selections Different tools can be used (point, rectangle, slice, lasso, brush, ) most of them select parallel or orthogonal to variable axes. Successive selection steps must be combinable with boolean operators (single step selections can only select trivial sets). Multiple brushes can be used to define a sequence of selections. Painting (persistent brush selection) is actually covered by a selection sequence when all brushes are in OR mode. Prefer parametric selection over arbitrary selections. (How to communicate a lasso selection, or a selection in some high-dimensional rotated space?)
40 11 More on Highlighting Implementation of highlighting for glyph-based plots is trivial. Highlighting for rectangle based plots is still straight forward. The highlighting in a plot not necessarily is of the same type as the plot itself, but it must be faithful to the plots definition. Highlighting is a transient attribute of the data and usually compares to the complete sample, Color brushing defines a persistent group membership, and thus the groups should compare between each other. Not all plots can be used to implement (sensible) highlighting
41 12 More on Queries Graphics are good at communicating qualitative information but fail to give exact quantities need queries to get exact values. Interactive graphics often display very little scale information (cf. Tufte s data-ink-ratio ). The level of detail of a query should have optional granularities, e.g. scatterplot:
42 12 More on Queries Graphics are good at communicating qualitative information but fail to give exact quantities need queries to get exact values. Interactive graphics often display very little scale information (cf. Tufte s data-ink-ratio ). The level of detail of a query should have optional granularities, e.g. scatterplot: orientation
43 12 More on Queries Graphics are good at communicating qualitative information but fail to give exact quantities need queries to get exact values. Interactive graphics often display very little scale information (cf. Tufte s data-ink-ratio ). The level of detail of a query should have optional granularities, e.g. scatterplot: orientation standard
44 12 More on Queries Graphics are good at communicating qualitative information but fail to give exact quantities need queries to get exact values. Interactive graphics often display very little scale information (cf. Tufte s data-ink-ratio ). The level of detail of a query should have optional granularities, e.g. scatterplot: orientation standard extended
45 13 Interacting with Plots Typical manipulations comprise of: Selections Change of scale axes zoom (includes logical zooming) (order) Change of order (both manually and automatically) categories in a barchart variables in parallel coordinates variables in a mosaic plot Change of plot parameters, e.g. anchor point, bin width in histogram point size in scatterplots Important: Identify general concepts!
46 14 On the Interface The usability of an interactive, computer based system can be strongly improved by strict conventions. All interactions (selection, queries, ordering, zoom, ) MUST be implemented consistently across all plots and summaries.
47 14 On the Interface The usability of an interactive, computer based system can be strongly improved by strict conventions. All interactions (selection, queries, ordering, zoom, ) MUST be implemented consistently across all plots and summaries. single mouse-click keyboard shortcut slider, mouse-drag pop-up menu floating palette pull-down menu Amount of Information & Complexity Speed & Context Preservation modal dialog command line
48 15 Less can be more Software should offer a clearly defined (orthogonal) set of functions, which can be combined to get a wider range of functionality. Examples: Painting Mode is a brush in OR mode and covered by selection sequences Categorizing a continuous variable via a histogram Functions are less obvious for a novice user!
49 15 Less can be more Software should offer a clearly defined (orthogonal) set of functions, which can be combined to get a wider range of functionality. Examples: Painting Mode is a brush in OR mode and covered by selection sequences Categorizing a continuous variable via a histogram Functions are less obvious for a novice user!
50 15 Less can be more Software should offer a clearly defined (orthogonal) set of functions, which can be combined to get a wider range of functionality. Examples: Painting Mode is a brush in OR mode and covered by selection sequences Categorizing a continuous variable via a histogram Functions are less obvious for a novice user!
51 15 Less can be more Software should offer a clearly defined (orthogonal) set of functions, which can be combined to get a wider range of functionality. Examples: Painting Mode is a brush in OR mode and covered by selection sequences Categorizing a continuous variable via a histogram Functions are less obvious for a novice user!
52 15 Less can be more Software should offer a clearly defined (orthogonal) set of functions, which can be combined to get a wider range of functionality. Examples: Painting Mode is a brush in OR mode and covered by selection sequences Categorizing a continuous variable via a histogram Functions are less obvious for a novice user!
53 15 Less can be more Software should offer a clearly defined (orthogonal) set of functions, which can be combined to get a wider range of functionality. Examples: Painting Mode is a brush in OR mode and covered by selection sequences Categorizing a continuous variable via a histogram Functions are less obvious for a novice user!
54 15 Less can be more Software should offer a clearly defined (orthogonal) set of functions, which can be combined to get a wider range of functionality. Examples: Painting Mode is a brush in OR mode and covered by selection sequences Categorizing a continuous variable via a histogram Functions are less obvious for a novice user! 3 4 5
55 15 Less can be more Software should offer a clearly defined (orthogonal) set of functions, which can be combined to get a wider range of functionality. Examples: Painting Mode is a brush in OR mode and covered by selection sequences Categorizing a continuous variable via a histogram Functions are less obvious for a novice user! 3 4 5
56 16 Rendering Quality is an Issue! Most graphics systems (S, R, SAS, ) were designed at times where pen on paper was the rendering model. Drawing on a computer screen will introduce rounding problem; the size of small objects is usually far off from what it should be. Anti-aliased graphics and sub-pixel rendering are not only a matter of taste. Overplotting is not an inherent problem of computer graphics, but if software does not handle it decently, the interpretation of (glyph based) graphics can lead to wrong conclusions. (extremely misleading with color brushing!)
57 17 Conclusion
58 17 Conclusion Graphics for data analysis is still rare, as the focus of statistical education is often not on data analysis. it is not taught at universities (except for some misfits). there is not much continuity in tools yet. it still lacks possible formalization.
59 17 Conclusion Graphics for data analysis is still rare, as the focus of statistical education is often not on data analysis. it is not taught at universities (except for some misfits). there is not much continuity in tools yet. it still lacks possible formalization. Formalization can be done for some parts of the techniques involved (IMHO it does not make sense to go for a global model).
60 17 Conclusion Graphics for data analysis is still rare, as the focus of statistical education is often not on data analysis. it is not taught at universities (except for some misfits). there is not much continuity in tools yet. it still lacks possible formalization. Formalization can be done for some parts of the techniques involved (IMHO it does not make sense to go for a global model). Unfortunately statistical computing degraded to implementing yet another package for R not much hope for new software.
61 17 Conclusion Graphics for data analysis is still rare, as the focus of statistical education is often not on data analysis. it is not taught at universities (except for some misfits). there is not much continuity in tools yet. it still lacks possible formalization. Formalization can be done for some parts of the techniques involved (IMHO it does not make sense to go for a global model). Unfortunately statistical computing degraded to implementing yet another package for R not much hope for new software. Apart from a formal description in a mathematical sense, the way of teaching concepts and tools via case studies seems to be quite promising
62 18 Commercial Break
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