O'Reilly logo

Stay ahead with the world's most comprehensive technology and business learning platform.

With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

Start Free Trial

No credit card required

Displaying Time Series, Spatial, and Space-Time Data with R

Book Description

Code and Methods for Creating High-Quality Data GraphicsA data graphic is not only a static image, but it also tells a story about the data. It activates cognitive processes that are able to detect patterns and discover information not readily available with the raw data. This is particularly true for time series, spatial, and space-time datasets.F

Table of Contents

  1. Preliminaries
    1. Chapter 1 Introduction
      1. 1.1 What This Book Is About
      2. 1.2 What You Will Not Find in This Book
      3. 1.3 How to Read This Book
        1. 1.3.1 Website and Code Repository
      4. 1.4 R Graphics
        1. 1.4.1 lattice
        2. 1.4.2 ggplot2
        3. 1.4.3 Comparison between lattice and ggplot2
      5. 1.5 Packages
      6. 1.6 Software Used to Write This Book
      7. 1.7 About the Author
      8. 1.8 Acknowledgments
  2. Part I Time Series
    1. Chapter 2 Displaying Time Series: Introduction
      1. 2.1 Packages
        1. 2.1.1 zoo
        2. 2.1.2 xts
      2. 2.2 Further Reading
    2. Chapter 3 Time on the Horizontal Axis
      1. 3.1 Time Graph of Different Meteorological Variables
        1. 3.1.1 Annotations to Enhance the Time Graph
      2. 3.2 Time Series of Variables with the Same Scale
        1. 3.2.1 Aspect Ratio and Rate of Change
        2. 3.2.2 The Horizon Graph
        3. 3.2.3 Time Graph of the Differences between a Time Series and a Reference
        4. 3.2.4 Interaction with gridSVG
      3. 3.3 Stacked Graphs
        1. 3.3.1 Panel and Prepanel Functions to Implement the ThemeRiver with xyplot
        1. Figure 3.1
        2. Figure 3.2
        3. Figure 3.3
        4. Figure 3.4
        5. Figure 3.5
        6. Figure 3.6
        7. Figure 3.7
        8. Figure 3.8
        9. Figure 3.9
        10. Figure 3.10
        11. Figure 3.11
        12. Figure 3.12
        13. Figure 3.13
        14. Figure 3.14
    3. Chapter 4 Time as a Conditioning or Grouping Variable
      1. 4.1 Scatterplot Matrix: Time as a Grouping Variable
        1. 4.1.1 Hexagonal Binning
      2. 4.2 Scatterplot with Time as a Conditioning Variable
        1. Figure 4.1
        2. Figure 4.2
        3. Figure 4.3
        4. Figure 4.4
        5. Figure 4.5
    4. Chapter 5 Time as a Complementary Variable
      1. 5.1 Polylines
      2. 5.2 Choosing Colors
      3. 5.3 Labels to Show Time Information
      4. 5.4 Country Names: Positioning Labels
      5. 5.5 A Panel for Each Year
        1. 5.5.1 Using Variable Size to Encode an Additional Variable
      6. 5.6 Traveling Bubbles
        1. Figure 5.1
        2. Figure 5.2
        3. Figure 5.3
        4. Figure 5.4
        5. Figure 5.5
        6. Figure 5.6
        7. Figure 5.7
        8. Figure 5.8
        9. Figure 5.9
        10. Figure 5.10
    5. Chapter 6 About the Data
      1. 6.1 SIAR
        1. 6.1.1 Daily Data of Different Meteorological Variables
        2. 6.1.2 Solar Radiation Measurements from Different Locations
      2. 6.2 Unemployment in the United States
      3. 6.3 Gross National Income and CO2 Emissions
        1. Figure 6.1
  3. Part II Spatial Data
    1. Chapter 7 Displaying Spatial Data: Introduction
      1. 7.1 Packages
        1. 7.1.1 sp
        2. 7.1.2 raster
        3. 7.1.3 rasterVis
        4. 7.1.4 maptools
        5. 7.1.5 rgdal
        6. 7.1.6 gstat
        7. 7.1.7 maps
      2. 7.2 Further Reading
    2. Chapter 8 Thematic Maps
      1. 8.1 Proportional Symbol Mapping
        1. 8.1.1 Introduction
        2. 8.1.2 Proportional Symbol with spplot
        3. 8.1.3 Optimal Classification and Sizes to Improve Discrimination
        4. 8.1.4 Spatial Context with Underlying Layers and Labels
          1. 8.1.4.1 Static Image
          2. 8.1.4.2 Vector Data
        5. 8.1.5 Spatial Interpolation
        6. 8.1.6 Export to Other Formats
          1. 8.1.6.1 GeoJSON and OpenStreetMap
          2. 8.1.6.2 Keyhole Markup Language
        7. 8.1.7 Additional Information with Tooltips and Hyperlinks
      2. 8.2 Choropleth Maps
        1. 8.2.1 Administrative Boundaries
        2. 8.2.2 Map
        3. 8.2.3 Categorical and Quantitative Variables Combined in a Multivariate Choropleth Map
      3. 8.3 Raster Maps
        1. 8.3.1 Quantitative Data
          1. 8.3.1.1 Hill Shading
          2. 8.3.1.2 Excursus: 3D Visualization
          3. 8.3.1.3 Diverging Palettes
        2. 8.3.2 Categorical Data
        3. 8.3.3 Multivariate Legend
      4. 8.4 Vector Fields
        1. 8.4.1 Arrow Plot
        2. 8.4.2 Streamlines
        1. Figure 8.1
        2. Figure 8.2
        3. Figure 8.3
        4. Figure 8.4
        5. Figure 8.5
        6. Figure 8.6
        7. Figure 8.7
        8. Figure 8.8
        9. Figure 8.9
        10. Figure 8.10
        11. Figure 8.11
        12. Figure 8.12
        13. Figure 8.13
        14. Figure 8.14
        15. Figure 8.15
        16. Figure 8.16
        17. Figure 8.17
        18. Figure 8.18
        19. Figure 8.19
        20. Figure 8.20
        21. Figure 8.21
        22. Figure 8.22
        23. Figure 8.23
        24. Figure 8.24
        25. Figure 8.25
        26. Figure 8.26
        27. Figure 8.27
        28. Figure 8.28
    3. Chapter 9 Reference and Physical Maps
      1. 9.1 Physical Maps
        1. 9.1.1 Retrieving Data
        2. 9.1.2 Intersection of Shapefiles and Elevation Model
        3. 9.1.3 Labels
        4. 9.1.4 Overlaying Layers of Information
      2. 9.2 OpenStreetMap with Hill Shade Layers
        1. 9.2.1 Retrieving Data from OpenStreetMap
        2. 9.2.2 Hill Shading
        3. 9.2.3 Overlaying Layers of Information
        1. Figure 9.1
        2. Figure 9.2
        3. Figure 9.3
    4. Chapter 10 About the Data
      1. 10.1 Air Quality in Madrid
        1. 10.1.1 Data Arrangement
        2. 10.1.2 Combine Data and Spatial Locations
      2. 10.2 Spanish General Elections
      3. 10.3 CM SAF
      4. 10.4 Land Cover and Population Rasters
  4. Part III Space-Time Data
    1. Chapter 11 Displaying Spatiotemporal Data: Introduction
      1. 11.1 Packages
        1. 11.1.1 spacetime
        2. 11.1.2 raster
        3. 11.1.3 rasterVis
      2. 11.2 Further Reading
    2. Chapter 12 Spatiotemporal Raster Data
      1. 12.1 Introduction
        1. 12.1.1 Data
      2. 12.2 Level Plots
      3. 12.3 Graphical Exploratory Data Analysis
      4. 12.4 Space-Time and Time Series Plots
      5. 12.5 Animation
        1. 12.5.1 Data
        2. 12.5.2 Spatial Context: Administrative Boundaries
        3. 12.5.3 Producing the Frames and the Movie
        4. 12.5.4 Static Image
        1. Figure 12.1
        2. Figure 12.2
        3. Figure 12.3
        4. Figure 12.4
        5. Figure 12.5
        6. Figure 12.6
        7. Figure 12.7
        8. Figure 12.8
        9. Figure 12.9
    3. Chapter 13 Spatiotemporal Point Observations
      1. 13.1 Introduction
      2. 13.2 Data and Spatial Information
      3. 13.3 Graphics with spacetime
      4. 13.4 Animation
        1. 13.4.1 Initial Snapshot
        2. 13.4.2 Intermediate States to Create the Animation
        3. 13.4.3 Time Reference: Progress Bar
        4. 13.4.4 Time Reference: A Time Series Plot
        1. Figure 13.1
        2. Figure 13.2
        3. Figure 13.3
        4. Figure 13.4
        5. Figure 13.5
  5. Bibliography