Tidyverse Workshop

<div class="bg-black white"  style="width:45%;right:0;bottom:0;padding-left:5px;border: solid 4px white;margin: auto;">
<i class="fas fa-exclamation-circle"></i> These slides are viewed best by Chrome and occasionally need to be refreshed if elements did not load properly. See here for <a href=session1.pdf>PDF <i class="fas fa-file-pdf"></i></a>. 
</div>

---

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# <span style="text-shadow: 2px 2px 30px white;">Tidyverse Workshop</span>

<br><br>
## <span style="color:yellow;text-shadow: 2px 2px 30px black;">Data visualisation with `ggplot2` <img src='images/ggplot2.png' height = '50px'></span>

:::

::: center shade_black animated bounceInUp slower

Presented by Emi Tanaka

School of Mathematics and Statistics

<img src="assets/USydLogo-white.svg" style="width:200px"><br>
<span><i class="fas  fa-envelope faa-float animated "></i></span>  dr.emi.tanaka@gmail.com
<span><i class="fab  fa-twitter faa-float animated faa-fast "></i></span>  @statsgen

.bottom_abs.width100.bg-black[
1st Dec 2019 @ Biometrics by the Botanic Gardens | Adelaide, Australia
]

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---

# 🎯 Aim: draw beautiful plots like this using (layered) grammar of graphics

::: grid

::: item

:::

::: item

:::

---

#  Basic structure of ggplot: 3 🔑 components

::: { .info-box .pos width: 60%; margin-left: 20%; font-size: 24pt; }

1. **data**, 
2. a set of **aesthetic** mappings between variables in the data and visual properties, and
3. at least one **layer** which describes how to render each observation.

:::

::: footnote
Reference: Wickham (2015) ggplot2 Elegant Graphics for Data Analysis
:::

---

# 🗃 Data:  .blue[Classic iris dataset]

`iris` is a built-in dataset in R - type `iris` to your console and press <kbd>Enter</kbd>.

```r
skimr::skim(iris)
```

```
  Skim summary statistics
   n obs: 150 
   n variables: 5 
  
  ── Variable type:factor ─────────────────────────────────────────
   variable missing complete   n n_unique                       top_counts ordered
    Species       0      150 150        3 set: 50, ver: 50, vir: 50, NA: 0   FALSE
  
  ── Variable type:numeric ────────────────────────────────────────
       variable missing complete   n mean   sd  p0 p25  p50 p75 p100     hist
   Petal.Length       0      150 150 3.76 1.77 1   1.6 4.35 5.1  6.9 ▇▁▁▂▅▅▃▁
    Petal.Width       0      150 150 1.2  0.76 0.1 0.3 1.3  1.8  2.5 ▇▁▁▅▃▃▂▂
   Sepal.Length       0      150 150 5.84 0.83 4.3 5.1 5.8  6.4  7.9 ▂▇▅▇▆▅▂▂
    Sepal.Width       0      150 150 3.06 0.44 2   2.8 3    3.3  4.4 ▁▂▅▇▃▂▁▁
```

<img src="images/iris.png" width = "530px" style = "position:absolute;top:20%;right:10%;"/>
<div class = "font_small" style = "position:absolute;top:300px;right:8%;width:530px;">Image source: <a href="http://suruchifialoke.com/2016-10-13-machine-learning-tutorial-iris-classification/">suruchifialoke.com</a>
</div>

---

# Aesthestic mappings: `aethestic = column`

::: paddings

<br>

* `Sepal.Length` is mapped to the `x` coordinate
* `Sepal.Width` is mapped to the `y` coordinate
* `Species` is mapped to the `color`

:::

---

# Layer

<br>

::: { .info-box .pos width: 70%; margin-left: 15%; font-size: 24pt; }

Each layer has a 
* `geom` - the geometric object to use display the data,
* `stat` - statisitcal transformations to use on the data, 
* `data` and `mapping` which is usually inherited from `ggplot` object,

:::

::: paddings

Further specifications are provided by `position` adjustment, `show_legend` and so on.

:::

---

# Hidden argument names in ggplot

---

::: grid font_smaller

::: item

```r
ggplot(iris, aes(Species))
```

```r
ggplot(iris, aes(Species, Sepal.Length))
```

:::

::: item

* No need to write explicitly write out `data = `, `mapping = `, `x = `, and `y = ` each time in `ggplot`.
* `ggplot` code in the wild often omit these argument names.
* But position needs to be correct if argument name not specified!
* If no layer is specified, then plot is `geom_blank()`.

:::

---

# Example layer: geom_point()

The `<layer>` is usually created by a function preceded by `geom_` in its name.

```r
ggplot(iris, aes(Species, Sepal.Length)) +
* geom_point()
```

<br>
is a shorthand for 
<br>

```r
ggplot(iris, aes(Species, Sepal.Length)) +
* layer(geom = "point",
*       stat = "identity", position = "identity",
*       params = list(na.rm = FALSE))
```

---

# Different geometric objects

```r
p <- ggplot(iris, aes(Species, Sepal.Length))
```

::: grid

::: item

```r
p + geom_violin()
```

:::

::: item

```r
p + geom_boxplot()
```

:::

::: item

```r
p + geom_point()
```

:::

---

# `geom`

<div id="htmlwidget-7abf64ae30d49491d425" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-7abf64ae30d49491d425">{"x":{"filter":"none","data":[["geom_abline, geom_hline, geom_vline","geom_bar, geom_col","geom_bin2d","geom_blank","geom_boxplot","geom_contour","geom_count","geom_density","geom_density_2d, geom_density2d","geom_dotplot","geom_errorbarh","geom_hex","geom_freqpoly, geom_histogram","geom_jitter","geom_crossbar, geom_errorbar, geom_linerange, geom_pointrange","geom_map","geom_path, geom_line, geom_step","geom_point","geom_polygon","geom_qq_line, geom_qq","geom_quantile","geom_ribbon, geom_area","geom_rug","geom_segment, geom_curve","geom_smooth","geom_spoke","geom_label, geom_text","geom_raster, geom_rect, geom_tile","geom_violin","geom_sf, geom_sf_label, geom_sf_text"],["Reference lines: horizontal, vertical, and diagonal","Bar charts","Heatmap of 2d bin counts","Draw nothing","A box and whiskers plot (in the style of Tukey)","2d contours of a 3d surface","Count overlapping points","Smoothed density estimates","Contours of a 2d density estimate","Dot plot","Horizontal error bars","Hexagonal heatmap of 2d bin counts","Histograms and frequency polygons","Jittered points","Vertical intervals: lines, crossbars & errorbars","Polygons from a reference map","Connect observations","Points","Polygons","A quantile-quantile plot","Quantile regression","Ribbons and area plots","Rug plots in the margins","Line segments and curves","Smoothed conditional means","Line segments parameterised by location, direction and distance","Text","Rectangles","Violin plot","Visualise sf objects"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>geom<\/th>\n      <th>Description<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"tp","ordering":false,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>

---

# Statistical transformation

```r
g <- ggplot(iris, aes(Species, Sepal.Length)) + geom_boxplot()
```

::: { .grid grid: 1fr / 300px 900px; }

::: item

<p></p>

:::

::: item

* The `y`-axis is not the raw data!
* It is plotting a statistical transformation of the `y`-values. 
* Under the hood, data is transformed (including `x` factor input to numerical values).

```r
layer_data(g, 1)
```

```
    ymin lower middle upper ymax outliers notchupper notchlower x PANEL group ymin_final ymax_final  xmin  xmax xid newx new_width weight colour  fill size alpha shape linetype
  1  4.3 4.800    5.0   5.2  5.8            5.089378   4.910622 1     1     1        4.3        5.8 0.625 1.375   1    1      0.75      1 grey20 white  0.5    NA    19    solid
  2  4.9 5.600    5.9   6.3  7.0            6.056412   5.743588 2     1     2        4.9        7.0 1.625 2.375   2    2      0.75      1 grey20 white  0.5    NA    19    solid
  3  5.6 6.225    6.5   6.9  7.9      4.9   6.650826   6.349174 3     1     3        4.9        7.9 2.625 3.375   3    3      0.75      1 grey20 white  0.5    NA    19    solid
```
<p></p>

:::

---

# Statistical transformation: stat_bin

* For `geom_histogram`, default is `stat = "bin"`.
* For `stat_bin`, default is `geom = "bar"`.
* .pink[Every `geom` has a `stat` and vice versa].

```r
p <- ggplot(iris, aes(Sepal.Length)) 
```

::: { .grid  grid: 1fr / 1fr 1fr 1fr; }

::: item

```r
p + geom_histogram()
```

:::

::: item

```r
p + stat_bin(geom = "bar")
```

:::

::: item

```r
p + stat_bin(geom = "line")
```

:::

---

# Using statistical transformations

To map an aesthestic to computed statistical variable (say called .pink[`var`]), you can refer to it by either .pink[`stat(var)`] or .pink[`..var..`].

---

::: grid

::: item

`stat = "bin"`

```
            x count   density
  1  4.344828     4 0.2148148
  2  4.468966     1 0.0537037
  3  4.593103     4 0.2148148
  4  4.717241     2 0.1074074
  5  4.841379    11 0.5907407
  6  4.965517    10 0.5370370
  7  5.089655     9 0.4833333
  8  5.213793     4 0.2148148
  9  5.337931     7 0.3759259
  10 5.462069     7 0.3759259
  11 5.586207     6 0.3222222
  12 5.710345     8 0.4296296
  13 5.834483     7 0.3759259
  14 5.958621     9 0.4833333
  15 6.082759     6 0.3222222
  16 6.206897     4 0.2148148
  17 6.331034     9 0.4833333
  18 6.455172    12 0.6444444
  19 6.579310     2 0.1074074
  20 6.703448     8 0.4296296
  21 6.827586     3 0.1611111
  22 6.951724     5 0.2685185
  23 7.075862     1 0.0537037
  24 7.200000     3 0.1611111
  25 7.324138     1 0.0537037
  26 7.448276     1 0.0537037
  27 7.572414     1 0.0537037
  28 7.696552     4 0.2148148
  29 7.820690     0 0.0000000
  30 7.944828     1 0.0537037
```
<p></p>

:::

::: item

```r
p + geom_histogram(aes(y = stat(density) ))
```

```r
p + geom_histogram(aes(y = ..density.. ))
```

:::

---

# `stat`

<div id="htmlwidget-0f12fab1c80819c4d7d7" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-0f12fab1c80819c4d7d7">{"x":{"filter":"none","data":[["stat_count","stat_bin_2d, stat_bin2d","stat_boxplot","stat_contour","stat_sum","stat_density","stat_density_2d, stat_density2d","stat_bin_hex, stat_binhex","stat_bin","stat_qq_line, stat_qq","stat_quantile","stat_smooth","stat_spoke","stat_ydensity","stat_sf","stat_ecdf","stat_ellipse","stat_function","stat_identity","stat_sf_coordinates","stat_summary_bin, stat_summary","stat_summary_2d, stat_summary2d, stat_summary_hex","stat_unique","update_stat_defaults"],["Bar charts","Heatmap of 2d bin counts","A box and whiskers plot (in the style of Tukey)","2d contours of a 3d surface","Count overlapping points","Smoothed density estimates","Contours of a 2d density estimate","Hexagonal heatmap of 2d bin counts","Histograms and frequency polygons","A quantile-quantile plot","Quantile regression","Smoothed conditional means","Line segments parameterised by location, direction and distance","Violin plot","Visualise sf objects","Compute empirical cumulative distribution","Compute normal confidence ellipses","Compute function for each x value","Leave data as is","Extract coordinates from 'sf' objects","Summarise y values at unique/binned x","Bin and summarise in 2d (rectangle & hexagons)","Remove duplicates","Modify geom/stat aesthetic defaults for future plots"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>stat<\/th>\n      <th>Description<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"tp","ordering":false,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>

---

# Add multiple layers

::: grid font_smaller

::: item

Each layer inherits mapping and data from `ggplot` by default.

```r
ggplot(data = iris, aes(x = Species, y = Sepal.Length)) +
  geom_violin() + 
  geom_boxplot() + 
  geom_point()
```
<p></p>

:::

::: item

:::

---

# Order of the layers matters!

Boxplot and violin plot order are switched around.

::: { .grid .font_smaller grid: 250px 250px / 1fr 1fr; }

::: item

```r
ggplot(data = iris, aes(x = Species, y = Sepal.Length)) +
* geom_violin() +
* geom_boxplot() +
  geom_point()
```

:::

::: item

:::

::: item

```r
ggplot(data = iris, aes(x = Species, y = Sepal.Length)) +
* geom_boxplot() +
* geom_violin() +
  geom_point()
```

:::

::: item

:::

---

# Layer-specific data and aesthestic mappings

::: grid

::: item

For each layer, aesthestic and/or data can be overwritten.

```r
ggplot(iris, aes(Species, Sepal.Length)) +
  geom_violin(aes(fill = Species)) +
  geom_boxplot(data = filter(iris, Species=="setosa")) +
  geom_point(data = filter(iris, Species=="setosa"),
             aes(y = Sepal.Width))
```

:::

::: item

:::

---

# Facetting

```r
g <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) + geom_point()
```

::: { .grid border-top: dashed 3px black; }

::: item

```r
g
```

```r
g + facet_wrap(~Species)
```

:::

::: item

```r
g + facet_grid(cut(Petal.Length, 3) ~ Species)
```

:::

---

background-color: #e5e5e5

::: { .grid grid: 1fr / 3fr 1fr;}

::: item

:::

::: item

HELP!

* RStudio > Help > Cheatsheets
* [R4DS Community Slack](https://www.rfordatasci.com/)
* [Twitter with hastag #rstats](https://twitter.com/search?q=%23rstats)
* [RStudio Community](https://community.rstudio.com/)
* [Stackoverflow](https://stackoverflow.com/questions/tagged/ggplot)

:::

---

# Recreate-the-plot Game

```r
colnames(iris)
```

```
  [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"
```
<p></p>

::: { .grid grid: 1fr / 1fr 2fr;}

::: item

:::

::: item

What are the `mapping`s and `geom`s?

* `x = ?`
* `y = ?`
* `color = ?`
* `fill = ?`
* `geom_???`
* other ?

:::

---

Open and go through:

`challenge-01-recreate-ggplot.Rmd`

<br>

For answers go to (but don't look until trying!):

`challenge-01-recreate-ggplot-solution.Rmd`

---

# Scales

---

# Diamonds data

```r
skimr::skim(diamonds)
```

```
  Skim summary statistics
   n obs: 53940 
   n variables: 10 
  
  ── Variable type:factor ─────────────────────────────────────────
   variable missing complete     n n_unique                                    top_counts ordered
    clarity       0    53940 53940        8  SI1: 13065, VS2: 12258, SI2: 9194, VS1: 8171    TRUE
      color       0    53940 53940        7           G: 11292, E: 9797, F: 9542, H: 8304    TRUE
        cut       0    53940 53940        5 Ide: 21551, Pre: 13791, Ver: 12082, Goo: 4906    TRUE
  
  ── Variable type:integer ────────────────────────────────────────
   variable missing complete     n   mean      sd  p0 p25  p50     p75  p100     hist
      price       0    53940 53940 3932.8 3989.44 326 950 2401 5324.25 18823 ▇▃▂▁▁▁▁▁
  
  ── Variable type:numeric ────────────────────────────────────────
   variable missing complete     n  mean   sd   p0   p25   p50   p75  p100     hist
      carat       0    53940 53940  0.8  0.47  0.2  0.4   0.7   1.04  5.01 ▇▅▁▁▁▁▁▁
      depth       0    53940 53940 61.75 1.43 43   61    61.8  62.5  79    ▁▁▁▃▇▁▁▁
      table       0    53940 53940 57.46 2.23 43   56    57    59    95    ▁▅▇▁▁▁▁▁
          x       0    53940 53940  5.73 1.12  0    4.71  5.7   6.54 10.74 ▁▁▁▇▇▃▁▁
          y       0    53940 53940  5.73 1.14  0    4.72  5.71  6.54 58.9  ▇▁▁▁▁▁▁▁
          z       0    53940 53940  3.54 0.71  0    2.91  3.53  4.04 31.8  ▇▃▁▁▁▁▁▁
```
<p></p>

::: {.pos font-size:80pt; top: 14%; right: 14%;}

💎

:::

---

# Scales

* Scales control the mapping from .blue[data] to .blue[aesthetics].

```r
g <- ggplot(diamonds, aes(carat, price) ) + geom_hex()
```

::: grid

::: item

```r
g + scale_y_continuous() +
  scale_x_continuous()
```

:::

::: item

```r
g + scale_x_reverse() +
  scale_y_continuous(trans="log10")
```

:::

::: item

```r
g + scale_y_log10() + 
  scale_x_sqrt()
```

:::

---

# `scale`

<div id="htmlwidget-f54babd2cb688ae4f9ac" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-f54babd2cb688ae4f9ac">{"x":{"filter":"none","data":[["scale_alpha, scale_alpha_continuous, scale_alpha_discrete, scale_alpha_ordinal, scale_alpha_datetime, scale_alpha_date","scale_colour_brewer, scale_fill_brewer, scale_colour_distiller, scale_fill_distiller, scale_color_brewer, scale_color_distiller","scale_colour_continuous, scale_fill_continuous","scale_x_continuous, scale_y_continuous, scale_x_log10, scale_y_log10, scale_x_reverse, scale_y_reverse, scale_x_sqrt, scale_y_sqrt","scale_x_date, scale_y_date, scale_x_datetime, scale_y_datetime, scale_x_time, scale_y_time","scale_x_discrete, scale_y_discrete","scale_colour_gradient, scale_fill_gradient, scale_colour_gradient2, scale_fill_gradient2, scale_colour_gradientn, scale_fill_gradientn, scale_colour_datetime, scale_colour_date, scale_fill_datetime, scale_fill_date, scale_color_continuous, scale_color_gradient, scale_color_gradient2","scale_colour_grey, scale_fill_grey, scale_color_grey","scale_colour_hue, scale_fill_hue, scale_colour_discrete, scale_fill_discrete, scale_color_discrete, scale_color_hue","scale_colour_identity, scale_fill_identity, scale_shape_identity, scale_linetype_identity, scale_alpha_identity, scale_size_identity, scale_discrete_identity, scale_continuous_identity, scale_color_identity","scale_linetype, scale_linetype_continuous, scale_linetype_discrete","scale_colour_manual, scale_fill_manual, scale_size_manual, scale_shape_manual, scale_linetype_manual, scale_alpha_manual, scale_discrete_manual, scale_color_manual","scale_shape, scale_shape_discrete, scale_shape_ordinal, scale_shape_continuous","scale_size, scale_size_continuous, scale_radius, scale_size_discrete, scale_size_ordinal, scale_size_area, scale_size_datetime, scale_size_date","scale_type","scale_colour_viridis_d, scale_fill_viridis_d, scale_colour_viridis_c, scale_fill_viridis_c, scale_colour_ordinal, scale_fill_ordinal, scale_color_viridis_d, scale_color_viridis_c"],["Alpha transparency scales","Sequential, diverging and qualitative colour scales from colorbrewer.org","Continuous colour scales","Position scales for continuous data (x & y)","Position scales for date/time data","Position scales for discrete data","Gradient colour scales","Sequential grey colour scales","Evenly spaced colours for discrete data","Use values without scaling","Scale for line patterns","Create your own discrete scale","Scales for shapes, aka glyphs","Scales for area or radius","Determine default scale type","Viridis colour scales from viridisLite"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>scales<\/th>\n      <th>Description<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"tp","ordering":false,"pageLength":4,"autoWidth":true,"columnDefs":[{"width":"400px","targets":0}],"order":[],"orderClasses":false,"lengthMenu":[4,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>

---

# Guide: an axis or a legend

* The scale creates a .blue[guide]: an .blue[axis] or .blue[legend].
* So to modify these you generally use .blue[`scale_*`] or other handy functions (`guides`, `labs`, `xlab`, `ylab` and so on).

---

# Modify axis

```r
g + 
  scale_y_continuous(name = "Price", 
                     breaks = c(0, 10000),
                     labels = c("0", "More\n than\n 10K")) + 
  geom_hline(yintercept = 10000, color = "red", size = 2)
```

---

# Nicer formatting functions in `scales` 📦

```r
g + 
  scale_y_continuous(
*   label = scales::dollar_format()
  )
```

---

# Modifying legend

```r
g + 
  scale_fill_continuous(
    breaks = c(0, 10, 100, 1000, 4000),
    trans = "log10"
  )
```

---

# Removing legend

```r
g + 
  scale_fill_continuous(
    guide = "none"
  )
```

---

# Alternative control of guides

```r
g + 
  ylab("Price") + # Changes the y axis label
  labs(x = "Carat", # Changes the x axis label
       fill = "Count") # Changes the legend name 
```

```r
g + guides(fill = "none") # remove the legend
```

---

Open and go through:

`challenge-02-ggplot-scales.Rmd`

<br>

For answers go to (but again don't look until trying!):

`challenge-02-ggplot-scales-solution.Rmd`

---

# Themes

---

# How to change the look?

::: grid

::: item

:::

::: item

:::

---

# `theme`: modify the *look* of texts

---

# `element_text()`

::: { .grid grid: 1fr / 1fr 1fr; }

::: item

```r
ggplot(diamonds, aes(carat, price)) + geom_hex() +
 labs(title = "Diamond") +
 theme(axis.title.x = element_text(size = 30,
                                 color = "red",
                                 face = "bold",
                                 angle = 10,
                                 family = "Fira Code"),
       legend.title = element_text(size = 25,
                                   color = "#ef42eb", 
                                   margin = margin(b = 5)),
       plot.title = element_text(size = 35,
                                 face = "bold",
                                 family = "Nunito",
                                 color = "blue"
                                ))
```
<p></p>

:::

::: item

<p></p>

:::

---

# `theme`: modify the *look* of the lines

---

# `element_line()`

::: { .grid grid: 1fr / 1fr 1fr; }

::: item

```r
ggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point() +
 theme(axis.line.y = element_line(color = "black",
                                  size = 1.2,
                                  arrow = grid::arrow()),
       axis.line.x = element_line(linetype = "dashed", 
                                  color = "brown",
                                  size = 1.2),
       axis.ticks = element_line(color = "red", size = 1.1),
       axis.ticks.length = unit(3, "mm"),
       panel.grid.major = element_line(color = "blue", 
                                       size = 1.2),
       panel.grid.minor = element_line(color = "#0080ff",
                                       size = 1.2,
                                       linetype = "dotted"))
```
<p></p>

:::

::: item

<p></p>

:::

---

# `theme`: modify the *look* of the <br>rectangular regions

---

# `element_line()`

::: { .grid grid: 1fr / 1fr 1fr; }

::: item

```r
ggplot(iris, aes(Sepal.Length, Sepal.Width)) + 
 geom_point(aes(color = Species)) +
 theme(
  legend.background = element_rect(fill = "#fff6c2", 
                                  color = "black",
                               linetype = "dashed"),
  legend.key = element_rect(fill = "grey", color = "brown"),
  panel.background = element_rect(fill = "#005F59",
                          color = "red", size = 3),
  panel.border = element_rect(color = "black", 
                              fill = "transparent",
                    linetype = "dashed", size = 3),
  plot.background = element_rect(fill = "#a1dce9",
                                color = "black",
                                 size = 1.3),
       legend.position = "bottom")
```
<p></p>

:::

::: item

<p></p>

:::

---

Open and go through:

`challenge-03-ggplot-themes.Rmd`

<br>

For answers go to:

`challenge-03-ggplot-themes-solution.Rmd`

---

background-color: #e5e5e5

# Session Information

:::: scroll-350

```r
devtools::session_info()
```

```
  ─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
   setting  value                       
   version  R version 3.6.0 (2019-04-26)
   os       macOS Mojave 10.14.6        
   system   x86_64, darwin15.6.0        
   ui       X11                         
   language (EN)                        
   collate  en_AU.UTF-8                 
   ctype    en_AU.UTF-8                 
   tz       Australia/Adelaide          
   date     2019-12-03                  
  
  ─ Packages ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
   package     * version    date       lib source                              
   anicon        0.1.0      2019-05-28 [1] Github (emitanaka/anicon@377aece)   
   assertthat    0.2.1      2019-03-21 [1] CRAN (R 3.6.0)                      
   backports     1.1.4      2019-04-10 [1] CRAN (R 3.6.0)                      
   broom         0.5.2      2019-04-07 [1] CRAN (R 3.6.0)                      
   callr         3.3.1      2019-07-18 [1] CRAN (R 3.6.0)                      
   cellranger    1.1.0      2016-07-27 [1] CRAN (R 3.6.0)                      
   cli           1.1.0      2019-03-19 [1] CRAN (R 3.6.0)                      
   colorspace    1.4-1      2019-03-18 [1] CRAN (R 3.6.0)                      
   countdown     0.2.0      2019-05-30 [1] Github (gadenbuie/countdown@c8e8710)
   crayon        1.3.4      2017-09-16 [1] CRAN (R 3.6.0)                      
   crosstalk     1.0.0      2016-12-21 [1] CRAN (R 3.6.0)                      
   desc          1.2.0      2018-05-01 [1] CRAN (R 3.6.0)                      
   devtools      2.0.2      2019-04-08 [1] CRAN (R 3.6.0)                      
   digest        0.6.22     2019-10-21 [1] CRAN (R 3.6.0)                      
   dplyr       * 0.8.3      2019-07-04 [1] CRAN (R 3.6.0)                      
   DT            0.6        2019-05-09 [1] CRAN (R 3.6.0)                      
   ellipsis      0.2.0.9000 2019-08-03 [1] Github (r-lib/ellipsis@27e0846)     
   emo           0.0.0.9000 2019-06-03 [1] Github (hadley/emo@02a5206)         
   evaluate      0.14       2019-05-28 [1] CRAN (R 3.6.0)                      
   forcats     * 0.4.0      2019-02-17 [1] CRAN (R 3.6.0)                      
   fs            1.3.1      2019-05-06 [1] CRAN (R 3.6.0)                      
   generics      0.0.2      2018-11-29 [1] CRAN (R 3.6.0)                      
   ggplot2     * 3.2.1      2019-08-10 [1] CRAN (R 3.6.0)                      
   glue          1.3.1.9000 2019-10-24 [1] Github (tidyverse/glue@71eeddf)     
   gtable        0.3.0      2019-03-25 [1] CRAN (R 3.6.0)                      
   haven         2.1.0      2019-02-19 [1] CRAN (R 3.6.0)                      
   here          0.1        2017-05-28 [1] CRAN (R 3.6.0)                      
   hexbin        1.27.3     2019-05-14 [1] CRAN (R 3.6.0)                      
   hms           0.5.1      2019-08-23 [1] CRAN (R 3.6.0)                      
   htmltools     0.4.0      2019-10-04 [1] CRAN (R 3.6.0)                      
   htmlwidgets   1.3        2018-09-30 [1] CRAN (R 3.6.0)                      
   httpuv        1.5.2      2019-09-11 [1] CRAN (R 3.6.0)                      
   httr          1.4.1      2019-08-05 [1] CRAN (R 3.6.0)                      
   icon          0.1.0      2019-05-28 [1] Github (ropenscilabs/icon@a510f88)  
   jsonlite      1.6        2018-12-07 [1] CRAN (R 3.6.0)                      
   knitr         1.25       2019-09-18 [1] CRAN (R 3.6.0)                      
   labeling      0.3        2014-08-23 [1] CRAN (R 3.6.0)                      
   later         1.0.0      2019-10-04 [1] CRAN (R 3.6.0)                      
   lattice       0.20-38    2018-11-04 [1] CRAN (R 3.6.0)                      
   lazyeval      0.2.2      2019-03-15 [1] CRAN (R 3.6.0)                      
   lifecycle     0.1.0      2019-08-01 [1] CRAN (R 3.6.0)                      
   lubridate     1.7.4      2018-04-11 [1] CRAN (R 3.6.0)                      
   magrittr      1.5        2014-11-22 [1] CRAN (R 3.6.0)                      
   memoise       1.1.0      2017-04-21 [1] CRAN (R 3.6.0)                      
   mime          0.7        2019-06-11 [1] CRAN (R 3.6.0)                      
   modelr        0.1.4      2019-02-18 [1] CRAN (R 3.6.0)                      
   munsell       0.5.0      2018-06-12 [1] CRAN (R 3.6.0)                      
   nlme          3.1-140    2019-05-12 [1] CRAN (R 3.6.0)                      
   pillar        1.4.2      2019-06-29 [1] CRAN (R 3.6.0)                      
   pkgbuild      1.0.3      2019-03-20 [1] CRAN (R 3.6.0)                      
   pkgconfig     2.0.3      2019-09-22 [1] CRAN (R 3.6.0)                      
   pkgload       1.0.2      2018-10-29 [1] CRAN (R 3.6.0)                      
   plyr          1.8.4      2016-06-08 [1] CRAN (R 3.6.0)                      
   png         * 0.1-7      2013-12-03 [1] CRAN (R 3.6.0)                      
   prettyunits   1.0.2      2015-07-13 [1] CRAN (R 3.6.0)                      
   processx      3.4.1      2019-07-18 [1] CRAN (R 3.6.0)                      
   promises      1.1.0      2019-10-04 [1] CRAN (R 3.6.0)                      
   ps            1.3.0      2018-12-21 [1] CRAN (R 3.6.0)                      
   purrr       * 0.3.2      2019-03-15 [1] CRAN (R 3.6.0)                      
   R6            2.4.0      2019-02-14 [1] CRAN (R 3.6.0)                      
   Rcpp          1.0.2      2019-07-25 [1] CRAN (R 3.6.0)                      
   readr       * 1.3.1      2018-12-21 [1] CRAN (R 3.6.0)                      
   readxl        1.3.1      2019-03-13 [1] CRAN (R 3.6.0)                      
   remotes       2.0.4      2019-04-10 [1] CRAN (R 3.6.0)                      
   reshape2      1.4.3      2017-12-11 [1] CRAN (R 3.6.0)                      
   rlang         0.4.0.9000 2019-08-03 [1] Github (r-lib/rlang@b0905db)        
   rmarkdown     1.16       2019-10-01 [1] CRAN (R 3.6.0)                      
   rprojroot     1.3-2      2018-01-03 [1] CRAN (R 3.6.0)                      
   rstudioapi    0.10       2019-03-19 [1] CRAN (R 3.6.0)                      
   rvest         0.3.4      2019-05-15 [1] CRAN (R 3.6.0)                      
   scales        1.0.0      2018-08-09 [1] CRAN (R 3.6.0)                      
   sessioninfo   1.1.1      2018-11-05 [1] CRAN (R 3.6.0)                      
   shiny         1.3.2      2019-04-22 [1] CRAN (R 3.6.0)                      
   skimr         1.0.6      2019-05-27 [1] CRAN (R 3.6.0)                      
   stringi       1.4.3      2019-03-12 [1] CRAN (R 3.6.0)                      
   stringr     * 1.4.0      2019-02-10 [1] CRAN (R 3.6.0)                      
   testthat      2.2.1      2019-07-25 [1] CRAN (R 3.6.0)                      
   tibble      * 2.1.3      2019-06-06 [1] CRAN (R 3.6.0)                      
   tidyr       * 1.0.0      2019-09-11 [1] CRAN (R 3.6.0)                      
   tidyselect    0.2.5      2018-10-11 [1] CRAN (R 3.6.0)                      
   tidyverse   * 1.2.1      2017-11-14 [1] CRAN (R 3.6.0)                      
   usethis       1.5.0      2019-04-07 [1] CRAN (R 3.6.0)                      
   vctrs         0.2.0.9000 2019-08-03 [1] Github (r-lib/vctrs@11c34ae)        
   whisker       0.3-2      2013-04-28 [1] CRAN (R 3.6.0)                      
   withr         2.1.2      2018-03-15 [1] CRAN (R 3.6.0)                      
   xaringan      0.9        2019-03-06 [1] CRAN (R 3.6.0)                      
   xfun          0.10       2019-10-01 [1] CRAN (R 3.6.0)                      
   xml2          1.2.0      2018-01-24 [1] CRAN (R 3.6.0)                      
   xtable        1.8-4      2019-04-21 [1] CRAN (R 3.6.0)                      
   yaml          2.2.0      2018-07-25 [1] CRAN (R 3.6.0)                      
   zeallot       0.1.0      2018-01-28 [1] CRAN (R 3.6.0)                      
  
  [1] /Library/Frameworks/R.framework/Versions/3.6/Resources/library
```

<p></p>
:::

These slides are licensed under <br><center><a href="https://creativecommons.org/licenses/by-sa/3.0/au/"><img src="images/cc.svg" style="height:2em;"/><img src="images/by.svg" style="height:2em;"/><img src="images/sa.svg" style="height:2em;"/></a></center>