DSBA 5122: Visual Analytics

# DSBA 5122: Visual Analytics
## Class 4: Time and Space
### Ryan Wesslen
### September 16, 2019

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# In class assignment: 30 minutes

2. Create a new RStudio.Cloud Project. Share that workspace with your partner.

3. Install `devtools` and `socviz` by running:

```r
install.packages('devtools') # install devtools
devtools::install_github("kjhealy/socviz")
```

4. Create a new .Rmd project. Load `socviz` and `tidyverse`. Look at the data frame `organdata`.

]
.pull-right[
## Bootstrapped Mean C.I.'s

1. Read the first examples of this page: [`stat_summary`](https://ggplot2.tidyverse.org/reference/stat_summary.html).

2. (Optional) Read up on why bootstrapping works [StackExchange](https://stats.stackexchange.com/questions/26088/explaining-to-laypeople-why-bootstrapping-works)

3. Change "05-tables-and-labels-14" plot in  [`05_tables_and_labels.Rmd`](https://rstudio.cloud/spaces/22733/project/160035) to a bootstrapped mean 95% confidence interval. Modify parameters by looking at [`?geom_pointrange`](https://ggplot2.tidyverse.org/reference/geom_linerange.html).

4. (Bonus): Google "tidy bootstrapping". Is there a better (tidy) way to do it?
]

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# Time: Wilke Chapters 13-14

<blockquote class="twitter-tweet tw-align-center" width="50%"><p lang="en" dir="ltr">In this week&#39;s <a href="https://twitter.com/hashtag/tidytuesday?src=hash&amp;ref_src=twsrc%5Etfw">#tidytuesday</a> screencast, I analyze and predict trends in US dairy consumption over time<br><br>In the process I learn to do tidy forecasting with the sweep and timetk packages from <a href="https://twitter.com/bizScienc?ref_src=twsrc%5Etfw">@bizscienc</a> 📈 📉<a href="https://t.co/RyS50Ss6Vx">https://t.co/RyS50Ss6Vx</a> <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> <a href="https://t.co/Qlf5nfJx1u">pic.twitter.com/Qlf5nfJx1u</a></p>&mdash; David Robinson (@drob) <a href="https://twitter.com/drob/status/1090336749778337792?ref_src=twsrc%5Etfw">January 29, 2019</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

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# Visualizing one temporal variable

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# Visualizing one temporal variable

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# Visualizing one temporal variable

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# Visualizing one temporal variable

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# Visualizing Indexes

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# Visualizing Indexes: Detrending

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# Visualizing two temporal variables

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# Visualizing two temporal variables

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# Keeling Curve: What do you notice?

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# Trend + Seasonal + Noise

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# Ratios to log

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# Ratios to log

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# Spatial/Maps: Wilke Chap. 15 and Healy Chap. 7

<blockquote class="twitter-tweet tw-align-center" width="50%"><p lang="en" dir="ltr">Putting Hawaii and Alaska into their place. <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> <a href="https://twitter.com/hashtag/dataviz?src=hash&amp;ref_src=twsrc%5Etfw">#dataviz</a> <a href="https://twitter.com/hashtag/gganimate?src=hash&amp;ref_src=twsrc%5Etfw">#gganimate</a> <a href="https://t.co/JOMWlVv3A9">pic.twitter.com/JOMWlVv3A9</a></p>&mdash; Claus Wilke (@ClausWilke) <a href="https://twitter.com/ClausWilke/status/1026190740370731009?ref_src=twsrc%5Etfw">August 5, 2018</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>

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# Scale

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# Scale

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# Projections

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# Projections

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# Projections

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# Map encoding

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# UVA's Racial Dot Map

<https://demographics.virginia.edu/DotMap/index.html>

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# Chloropleths

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# Chloropleths

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# Chloropleths

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# Chloropleths

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# Cartograms

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# Cartograms

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# Small multiples: Time & Space