000 04917nam a22001577a 4500
008 231129b |||||||| |||| 00| 0 eng d
020 _a9789352138111
082 _a001.4226
_bWIL
100 _aWilke, Claus O.
_946546
245 _aFundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures
260 _bShroff Publishers and Distributors Pvt. Ltd.
_aMumbai
_c2019
300 _a370p
500 _a2. 1. Introduction 1. Ugly, Bad, and Wrong Figures 3. I. From Data to Visualization 4. 2. Visualizing Data: Mapping Data onto Aesthetics 1. Aesthetics and Types of Data 2. Scales Map Data Values onto Aesthetics 5. 3. Coordinate Systems and Axes 1. Cartesian Coordinates 2. Nonlinear Axes 3. Coordinate Systems with Curved Axes 6. 4. Color Scales 1. Color as a Tool to Distinguish 2. Color to Represent Data Values 3. Color as a Tool to Highlight 7. 5. Directory of Visualizations 1. Amounts 2. Distributions 3. Proportions 4. x–y relationships 5. Geospatial Data 6. Uncertainty 8. 6. Visualizing Amounts 1. Bar Plots 2. Grouped and Stacked Bars 3. Dot Plots and Heatmaps 9. 7. Visualizing Distributions: Histograms and Density Plots 1. Visualizing a Single Distribution 2. Visualizing Multiple Distributions at the Same Time 10. 8. Visualizing Distributions: Empirical Cumulative Distribution Functions and Q-Q Plots 1. Empirical Cumulative Distribution Functions 2. Highly Skewed Distributions 3. Quantile-Quantile Plots 11. 9. Visualizing Many Distributions at Once 1. Visualizing Distributions Along the Vertical Axis 2. Visualizing Distributions Along the Horizontal Axis 12. 10. Visualizing Proportions 1. A Case for Pie Charts 2. A Case for Side-by-Side Bars 3. A Case for Stacked Bars and Stacked Densities 4. Visualizing Proportions Separately as Parts of the Total 13. 11. Visualizing Nested Proportions 1. Nested Proportions Gone Wrong 2. Mosaic Plots and Treemaps 3. Nested Pies 4. Parallel Sets 14. 12. Visualizing Associations Among Two or More Quantitative Variables 1. Scatterplots 2. Correlograms 3. Dimension Reduction 4. Paired Data 15. 13. Visualizing Time Series and Other Functions of an Independent Variable 1. Individual Time Series 2. Multiple Time Series and Dose–Response Curves 3. Time Series of Two or More Response Variables 16. 14. Visualizing Trends 1. Smoothing 2. Showing Trends with a Defined Functional Form 3. Detrending and Time-Series Decomposition 17. 15. Visualizing Geospatial Data 1. Projections 2. Layers 3. Choropleth Mapping 4. Cartograms 18. 16. Visualizing Uncertainty 1. Framing Probabilities as Frequencies 2. Visualizing the Uncertainty of Point Estimates 3. Visualizing the Uncertainty of Curve Fits 4. Hypothetical Outcome Plots 19. II. Principles of Figure Design 20. 17. The Principle of Proportional Ink 1. Visualizations Along Linear Axes 2. Visualizations Along Logarithmic Axes 3. Direct Area Visualizations 21. 18. Handling Overlapping Points 1. Partial Transparency and Jittering 2. 2D Histograms 3. Contour Lines 22. 19. Common Pitfalls of Color Use 1. Encoding Too Much or Irrelevant Information 2. Using Nonmonotonic Color Scales to Encode Data Values 3. Not Designing for Color-Vision Deficiency 23. 20. Redundant Coding 1. Designing Legends with Redundant Coding 2. Designing Figures Without Legends 24. 21. Multipanel Figures 1. Small Multiples 2. Compound Figures 25. 22. Titles, Captions, and Tables 1. Figure Titles and Captions 2. Axis and Legend Titles 3. Tables 26. 23. Balance the Data and the Context 1. Providing the Appropriate Amount of Context 2. Background Grids 3. Paired Data 4. Summary 27. 24. Use Larger Axis Labels 28. 25. Avoid Line Drawings 29. 26. Don’t Go 3D 1. Avoid Gratuitous 3D 2. Avoid 3D Position Scales 3. Appropriate Use of 3D Visualizations 30. III. Miscellaneous Topics 31. 27. Understanding the Most Commonly Used Image File Formats 1. Bitmap and Vector Graphics 2. Lossless and Lossy Compression of Bitmap Graphics 3. Converting Between Image Formats 32. 28. Choosing the Right Visualization Software 1. Reproducibility and Repeatability 2. Data Exploration Versus Data Presentation 3. Separation of Content and Design 33. 29. Telling a Story and Making a Point 1. What Is a Story? 2. Make a Figure for the Generals 3. Build Up Toward Complex Figures 4. Make Your Figures Memorable 5. Be Consistent but Don’t Be Repetitive 34. Annotated Bibliography 1. Thinking About Data and Visualization 2. Programming Books 3. Statistics Texts 4. Historical Texts 5. Books on Broadly Related Topics 35. Technical Notes 36. References 37. Index
600 _aComputer Engineering
942 _2ddc
_m001.4226
_kWIL
_cLB
999 _c147942
_d147942