Calendar-based graphics for visualizing people's daily schedules

10/23/2018 ∙ by Earo Wang, et al. ∙ 0

Calendars are broadly used in society to display temporal information, and events. This paper describes a new R package with functionality to organize and display temporal data, collected on sub-daily resolution, into a calendar layout. The function `frame_calendar` uses linear algebra on the date variable to restructure data into a format lending itself to calendar layouts. The user can apply the grammar of graphics to create plots inside each calendar cell, and thus the displays synchronize neatly with ggplot2 graphics. The motivating application is studying pedestrian behavior in Melbourne, Australia, based on counts which are captured at hourly intervals by sensors scattered around the city. Faceting by the usual features such as day and month, was insufficient to examine the behavior. Making displays on a monthly calendar format helps to understand pedestrian patterns relative to events such as work days, weekends, holidays, and special events. The layout algorithm has several format options and variations. It is implemented in the R package sugrrants.

READ FULL TEXT VIEW PDF
POST COMMENT

Comments

There are no comments yet.

Authors

page 4

page 9

page 24

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

1 Introduction

2 Creating a calendar display

3 Case study

4 Discussion

Acknowledgements

References

  • (1)
  • Chang et al. (2018) Chang, W., Cheng, J., Allaire, J., Xie, Y. & McPherson, J. (2018), shiny: Web Application Framework for R. R package version 1.1.0.
    https://CRAN.R-project.org/package=shiny
  • City of Melbourne (2017) City of Melbourne (2017), Pedestrian Volume in Melbourne.
    http://www.pedestrian.melbourne.vic.gov.au
  • Cleveland & McGill (1984) Cleveland, W. S. & McGill, R. (1984), ‘Graphical perception: Theory, experimentation, and application to the development of graphical methods’, Journal of the American Statistical Association 79(387), 531–554.
  • Hafen (2018) Hafen, R. (2018), geofacet: ‘ggplot2’ Faceting Utilities for Geographical Data. R package version 0.1.9.
    https://CRAN.R-project.org/package=geofacet
  • Hofmann et al. (2017) Hofmann, H., Wickham, H. & Kafadar, K. (2017), ‘Letter-value plots: Boxplots for large data’, Journal of Computational and Graphical Statistics 26(3), 469–477.
  • Jacobs (2017) Jacobs, J. (2017), ggcal: Calendar Plot Using ‘ggplot2’. R package version 0.1.0.
    https://github.com/jayjacobs/ggcal
  • Kothari & Ather (2016) Kothari, A. & Ather (2016), ggTimeSeries: Nicer Time Series Visualisations with ggplot syntax. R package version 0.1.
    https://github.com/Ather-Energy/ggTimeSeries
  • Lam et al. (2007) Lam, H., Munzner, T. & Kincaid, R. (2007), ‘Overview use in multiple visual information resolution interfaces’, IEEE Transactions on Visualization and Computer Graphics 13(6), 1278–1285.
  • Sievert (2018) Sievert, C. (2018), plotly for R.
    https://plotly-book.cpsievert.me
  • Van Wijk & Van Selow (1999) Van Wijk, J. J. & Van Selow, E. R. (1999), Cluster and calendar based visualization of time series data, in ‘Information Visualization, 1999. INFOVIS 1999 Proceedings. IEEE Symposium on’, IEEE, pp. 4–9.
  • Wang (2018) Wang, E. (2018), wanderer4melb: Shiny App for Wandering Around the Downtown Melbourne 2016. R package version 0.1.0.
    https://github.com/earowang/wanderer4melb
  • Wang et al. (2018) Wang, E., Cook, D. & Hyndman, R. J. (2018), sugrrants: Supporting Graphs for Analysing Time Series. R package version 0.1.6.
    https://pkg.earo.me/sugrrants
  • Wickham (2009) Wickham, H. (2009), ggplot2: Elegant Graphics for Data Analysis, Springer-Verlag New York, New York, NY.
  • Wickham (2014) Wickham, H. (2014), ‘Tidy data’, Journal of Statistical Software 59(10), 1–23.
  • Wickham (2017) Wickham, H. (2017), tidyverse: Easily Install and Load the ’Tidyverse’. R package version 1.2.1.
    https://CRAN.R-project.org/package=tidyverse
  • Wickham et al. (2018) Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C. & Woo, K. (2018), ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. http://ggplot2.tidyverse.org, https://github.com/tidyverse/ggplot2.
  • Wickham et al. (2012) Wickham, H., Hofmann, H., Wickham, C. & Cook, D. (2012), ‘Glyph-maps for visually exploring temporal patterns in climate data and models’, Environmetrics 23(5), 382–393.
  • Wilkinson (2005) Wilkinson, L. (2005), The Grammar of Graphics (Statistics and Computing), Springer-Verlag New York, Inc., Secaucus, NJ.