Preface

This is a collaborative writing project as part of the course MSIS 2629 “Data Visualization” at Santa Clara University. The purpose of the class reader is to collaboratively engage with and reflect on data visualizations, to establish a solid theoretical background, and to collect useful practices and showcases. More information on the background of this project is available in the syllabus.

The following text explains how we organize ourselves.

0.1 References

EVERY reference must be included in the book.bib file. This file uses the BibTeX notation (Learn how to use BibTeX here). Most literature search engines allow you to export the reference information in BibTeX. For websites we use the following minimal notation (you may add further information - usually the more the better is a good strategy):

@misc{great_viz,
  author = {{A great visualizer}},
  year = {1982},
  title = {A ficticious web page title},
  howpublished = {\url{http://great_viz_org/}},
  note = {Accessed: 2018-04-26}
}

Particularly important is the note field. Websites change frequently, so links will break. If we do this correctly, [@great_viz] will produce (visualizer 1982).

0.2 Images

Images should not be loaded from external website because the links may change. Instead download a version of the image and create a reference that contains the link to the image. For example the following image is a deceptive visualization (the bars do start at zero).

An Example of a deceptive visualization

Source: (Halper 2012) referenced in (Andalde 2014)

The citation for the image looks like this.

@misc{halper_2012,
  author={Halper, Daniel},
  year={2012},
  title = {Over 100 Million Now Receiving Federal Welfare},
  url={https://www.weeklystandard.com/daniel-halper/over-100-million-now-receiving-federal-welfare},
  note = {Accessed: 2018-04-26}
}

You have probably found this image through a different website that explains the visualization. For example the following website explains some problematic aspects of this visualization:

@misc{andale_2014,
  author={Andalde, Stephanie},
  year={2014},
  title = {Misleading Graphs: Real Life Examples},
  url={http://www.statisticshowto.com/misleading-graphs/},
  note = {Accessed: 2018-04-26}

0.3 Basic Guidelines

Figures and tables with captions will be placed in figure and table environments, respectively.

par(mar = c(4, 4, .1, .1))
plot(pressure, type = 'b', pch = 19)

Figure 0.1: Here is a nice figure!

Reference a figure by its code chunk label with the fig: prefix, e.g., see Figure 0.1. Similarly, you can reference tables generated from knitr::kable(), e.g., see Table 0.1.

knitr::kable(
 head(iris, 20), caption = 'Here is a nice table!',
 booktabs = TRUE
)

Table 0.1: Here is a nice table!
Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	Species
5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa
5.4	3.9	1.7	0.4	setosa
4.6	3.4	1.4	0.3	setosa
5.0	3.4	1.5	0.2	setosa
4.4	2.9	1.4	0.2	setosa
4.9	3.1	1.5	0.1	setosa
5.4	3.7	1.5	0.2	setosa
4.8	3.4	1.6	0.2	setosa
4.8	3.0	1.4	0.1	setosa
4.3	3.0	1.1	0.1	setosa
5.8	4.0	1.2	0.2	setosa
5.7	4.4	1.5	0.4	setosa
5.4	3.9	1.3	0.4	setosa
5.1	3.5	1.4	0.3	setosa
5.7	3.8	1.7	0.3	setosa
5.1	3.8	1.5	0.3	setosa

You can write citations, too. For example, we are using the bookdown package (Xie 2018) in this sample book, which was built on top of R Markdown and knitr (Xie 2015).

References

Andalde, Stephanie. 2014. “Misleading Graphs: Real Life Examples.” http://www.statisticshowto.com/misleading-graphs/.

Halper, Daniel. 2012. “Over 100 Million Now Receiving Federal Welfare.” https://www.weeklystandard.com/daniel-halper/over-100-million-now-receiving-federal-welfare.

visualizer, A great. 1982. “A ficticious web page title.” http://great{\_}viz{\_}org.

Xie, Yihui. 2015. Dynamic Documents with {R} and knitr. 2nd ed. Boca Raton, Florida: CRC. http://yihui.name/knitr/.

Xie, Yihui. 2018. Bookdown: Authoring Books and Technical Documents with R Markdown. https://CRAN.R-project.org/package=bookdown.

A Reader on Data Visualization