<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>

---

count: false
background-image: url(images/bg6.jpg)
background-size: cover
class: hide-slide-number title-slide

:::::::::: { .grid grid: 50% 50% / 1fr;} 
::: item center

# <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
]

:::

::::::::::

---

# 🎯 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>

Notes for current slide

Notes for next slide

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1/42

Tidyverse Workshop

Data visualisation with `ggplot2`

Presented by Emi Tanaka

School of Mathematics and Statistics

dr.emi.tanaka@gmail.com @statsgen

1st Dec 2019 @ Biometrics by the Botanic Gardens | Adelaide, Australia

1/42

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

2/42

Basic structure of ggplot: 3 🔑 components

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

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

3/42

🗃 Data: Classic iris dataset

iris is a built-in dataset in R - type iris to your console and press Enter.

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 ▁▂▅▇▃▂▁▁

Image source: suruchifialoke.com

4/42

Aesthestic mappings: `aethestic = column`

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

5/42

Layer

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,

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

6/42

Hidden argument names in ggplot

ggplot(iris, aes(Species))

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

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().

7/42

Example layer: geom_point()

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

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

is a shorthand for

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

8/42

Different geometric objects

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

p + geom_violin()

p + geom_boxplot()

p + geom_point()

9/42

`geom`

geom	Description
geom_abline, geom_hline, geom_vline	Reference lines: horizontal, vertical, and diagonal
geom_bar, geom_col	Bar charts
geom_bin2d	Heatmap of 2d bin counts
geom_blank	Draw nothing
geom_boxplot	A box and whiskers plot (in the style of Tukey)
geom_contour	2d contours of a 3d surface
geom_count	Count overlapping points
geom_density	Smoothed density estimates
geom_density_2d, geom_density2d	Contours of a 2d density estimate
geom_dotplot	Dot plot

Previous1 2 3Next

10/42

Statistical transformation

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

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).

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

11/42

Statistical transformation: stat_bin

For geom_histogram, default is stat = "bin".
For stat_bin, default is geom = "bar".
Every geom has a stat and vice versa.

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

p + geom_histogram()

p + stat_bin(geom = "bar")

p + stat_bin(geom = "line")

12/42

Using statistical transformations

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

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 + geom_histogram(aes(y = stat(density) ))

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

13/42

`stat`

stat	Description
stat_count	Bar charts
stat_bin_2d, stat_bin2d	Heatmap of 2d bin counts
stat_boxplot	A box and whiskers plot (in the style of Tukey)
stat_contour	2d contours of a 3d surface
stat_sum	Count overlapping points
stat_density	Smoothed density estimates
stat_density_2d, stat_density2d	Contours of a 2d density estimate
stat_bin_hex, stat_binhex	Hexagonal heatmap of 2d bin counts
stat_bin	Histograms and frequency polygons
stat_qq_line, stat_qq	A quantile-quantile plot

Previous1 2 3Next

14/42

Add multiple layers

Each layer inherits mapping and data from ggplot by default.

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

15/42

Order of the layers matters!

Boxplot and violin plot order are switched around.

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

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

16/42

Layer-specific data and aesthestic mappings

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

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))

17/42

Facetting

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

g + facet_wrap(~Species)

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

18/42

HELP!

19/42

Recreate-the-plot Game

colnames(iris)

  [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"

What are the mappings and geoms?

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

20/42

Open and go through:

challenge-01-recreate-ggplot.Rmd

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

challenge-01-recreate-ggplot-solution.Rmd

20:00

21/42

Scales22/42

Diamonds data

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  ▇▃▁▁▁▁▁▁

💎

23/42

Scales

Scales control the mapping from data to aesthetics.

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

g + scale_y_continuous() +
  scale_x_continuous()

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

g + scale_y_log10() + 
  scale_x_sqrt()

24/42

`scale`

scales	Description
scale_alpha, scale_alpha_continuous, scale_alpha_discrete, scale_alpha_ordinal, scale_alpha_datetime, scale_alpha_date	Alpha transparency scales
scale_colour_brewer, scale_fill_brewer, scale_colour_distiller, scale_fill_distiller, scale_color_brewer, scale_color_distiller	Sequential, diverging and qualitative colour scales from colorbrewer.org
scale_colour_continuous, scale_fill_continuous	Continuous colour scales
scale_x_continuous, scale_y_continuous, scale_x_log10, scale_y_log10, scale_x_reverse, scale_y_reverse, scale_x_sqrt, scale_y_sqrt	Position scales for continuous data (x & y)

Previous1 2 3 4Next

25/42

Guide: an axis or a legend

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

26/42

Modify axis

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)

27/42

Nicer formatting functions in `scales` 📦

g + 
  scale_y_continuous(
    label = scales::dollar_format()
  )

28/42

Modifying legend

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

29/42

Removing legend

g + 
  scale_fill_continuous(
    guide = "none"
  )

30/42

Alternative control of guides

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

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

31/42

Open and go through:

challenge-02-ggplot-scales.Rmd

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

challenge-02-ggplot-scales-solution.Rmd

15:00

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Themes33/42

How to change the look?

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`theme`: modify the look of texts

element_text()

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`element_text()`

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"
                                ))

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`theme`: modify the look of the lines

element_line()

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`element_line()`

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"))

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`theme`: modify the look of the
rectangular regions

element_rect()

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`element_line()`

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")

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Open and go through:

challenge-03-ggplot-themes.Rmd

For answers go to:

challenge-03-ggplot-themes-solution.Rmd

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Session Information

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

These slides are licensed under

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Tidyverse Workshop

Data visualisation with `ggplot2`

Presented by Emi Tanaka

School of Mathematics and Statistics

dr.emi.tanaka@gmail.com @statsgen

1st Dec 2019 @ Biometrics by the Botanic Gardens | Adelaide, Australia

1/42

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Tidyverse Workshop

Data visualisation with ggplot2

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

Basic structure of ggplot: 3 🔑 components

🗃 Data: Classic iris dataset

Aesthestic mappings: aethestic = column

Layer

Hidden argument names in ggplot

Example layer: geom_point()

Different geometric objects

geom

Statistical transformation

Statistical transformation: stat_bin

Using statistical transformations

stat

Add multiple layers

Order of the layers matters!

Layer-specific data and aesthestic mappings

Facetting

Recreate-the-plot Game

Scales

Diamonds data

Scales

scale

Guide: an axis or a legend

Modify axis

Nicer formatting functions in scales 📦

Modifying legend

Removing legend

Alternative control of guides

Themes

How to change the look?

theme: modify the look of texts

element_text()

theme: modify the look of the lines

element_line()

theme: modify the look of the rectangular regions

element_line()

Session Information

Tidyverse Workshop

Data visualisation with ggplot2

Help

Data visualisation with `ggplot2`

Aesthestic mappings: `aethestic = column`

`geom`

`stat`

`scale`

Nicer formatting functions in `scales` 📦

`theme`: modify the look of texts

`element_text()`

`theme`: modify the look of the lines

`element_line()`

`theme`: modify the look of the
rectangular regions

`element_line()`

Data visualisation with `ggplot2`