Data Visualisation with R Workshop Part 1

<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=part1-catalogue.pdf>PDF <i class="fas fa-file-pdf"></i></a>. 
</div>

<br>

---

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

.item.center[

# <span style="text-shadow: 2px 2px 30px white;">Data Visualisation with R
Workshop Part 1
</span>

]

.center.shade_black.animated.bounceInUp.slower[
<br><br>
## Catalogue

<br>

Presented by Emi Tanaka

Department of Econometrics and Business Statistics

<img src="images/monash-one-line-reversed.png" style="width:500px"><br>
<span><i class="fas  fa-envelope faa-float animated "></i></span>  emi.tanaka@monash.edu
<span><i class="fab  fa-twitter faa-float animated faa-fast "></i></span>  @statsgen

.bottom_abs.width100.bg-black[
21st Feb 2022 @ methods@manchester | Zoom
]

]

</div>

---

# Catalogue

]

???

* Distribution of a single _continuous_ variable can be studied with **histogram**, **density or frequency plot**, **boxplot**, **violin plot** or **dot plot**. 
* If you have a discrete or categorical variable, you may like to study this using a **barplot** (or sometimes called barchart). 
* We'll go through how to create these layers in `ggplot2` next.

---

# Illustrative data 💰 Wages

* Let's look at a survey of average hourly earnings in United States where each observation is an individual.

```r
data(CPSch3, package = "Ecdat")
str(CPSch3)
```

```
## 'data.frame':	11130 obs. of  3 variables:
##  $ year: int  1992 1992 1992 1992 1992 1992 1992 1992 1992 1992 ...
##  $ ahe : num  13 11.6 17.4 10.1 16.8 ...
##  $ sex : Factor w/ 2 levels "male","female": 1 1 1 2 1 2 2 1 1 1 ...
```

???

* Let's use a data to illustrate the different layers in `ggplot2`.
* This data contains: 
  * `year` = the survey year
  * `ahe` = the average hourly earnings
  * `sex` = the sex (male or female)

---

[.tag.absolute.right-1.bottom-1[<i class="fas fa-caret-right"></i> Back to catalogue]](#catalogue)

---

# A histogram with `geom_histogram()`

```r
ggplot(data = CPSch3, 
       mapping = aes(x = ahe)) +
* geom_histogram() +
  labs(x = "Average hourly earnings (US$)")
```

???

* By default `ggplot2` uses 30 bins (`bins = 30`) when drawing histograms. 
* It is often a good idea to try different number of bins (`bins`) or different bin widths (`bindwidth`) to see how the **shape** of the histogram changes. 
* Here we can see that the distribution for the average hourly earnings are slightly right skewed.
* The mode average hourly earning looks like it's about US$14. 
* To find more about how to change parameters of the layer, have a look at the corresponding documentation (`?geom_histogram`).

---

# Layer data

```r
g <- ggplot(data = CPSch3, 
       mapping = aes(x = ahe)) +
  geom_histogram()

*layer_data(g, 1)
```
.f4[

```
##       y count         x       xmin      xmax      density      ncount    ndensity flipped_aes PANEL
## 1    19    19  1.734720  0.8673601  2.602080 0.0009840768 0.014011799 0.014011799       FALSE     1
## 2    91    91  3.469440  2.6020802  4.336800 0.0047132101 0.067109145 0.067109145       FALSE     1
## 3   255   255  5.204160  4.3368004  6.071521 0.0132073470 0.188053097 0.188053097       FALSE     1
## 4   514   514  6.938881  6.0715206  7.806241 0.0266218681 0.379056047 0.379056047       FALSE     1
## 5   760   760  8.673601  7.8062407  9.540961 0.0393630735 0.560471976 0.560471976       FALSE     1
## 6  1143  1143 10.408321  9.5409609 11.275681 0.0591999908 0.842920354 0.842920354       FALSE     1
## 7  1356  1356 12.143041 11.2756811 13.010401 0.0702320101 1.000000000 1.000000000       FALSE     1
## 8  1232  1232 13.877761 13.0104012 14.745121 0.0638096139 0.908554572 0.908554572       FALSE     1
## 9  1137  1137 15.612481 14.7451214 16.479842 0.0588892297 0.838495575 0.838495575       FALSE     1
## 10  941   941 17.347202 16.4798416 18.214562 0.0487377002 0.693952802 0.693952802       FALSE     1
## 11  798   798 19.081922 18.2145617 19.949282 0.0413312272 0.588495575 0.588495575       FALSE     1
## 12  749   749 20.816642 19.9492819 21.684002 0.0387933448 0.552359882 0.552359882       FALSE     1
## 13  497   497 22.551362 21.6840021 23.418722 0.0257413783 0.366519174 0.366519174       FALSE     1
## 14  428   428 24.286082 23.4187222 25.153442 0.0221676256 0.315634218 0.315634218       FALSE     1
## 15  279   279 26.020802 25.1534424 26.888163 0.0144503915 0.205752212 0.205752212       FALSE     1
## 16  249   249 27.755523 26.8881625 28.622883 0.0128965859 0.183628319 0.183628319       FALSE     1
## 17  186   186 29.490243 28.6228827 30.357603 0.0096335943 0.137168142 0.137168142       FALSE     1
## 18  148   148 31.224963 30.3576029 32.092323 0.0076654406 0.109144543 0.109144543       FALSE     1
## 19  101   101 32.959683 32.0923230 33.827043 0.0052311453 0.074483776 0.074483776       FALSE     1
## 20   66    66 34.694403 33.8270432 35.561763 0.0034183722 0.048672566 0.048672566       FALSE     1
## 21   40    40 36.429123 35.5617634 37.296484 0.0020717407 0.029498525 0.029498525       FALSE     1
## 22   48    48 38.163844 37.2964835 39.031204 0.0024860889 0.035398230 0.035398230       FALSE     1
## 23   29    29 39.898564 39.0312037 40.765924 0.0015020120 0.021386431 0.021386431       FALSE     1
## 24   20    20 41.633284 40.7659239 42.500644 0.0010358704 0.014749263 0.014749263       FALSE     1
## 25   15    15 43.368004 42.5006440 44.235364 0.0007769028 0.011061947 0.011061947       FALSE     1
## 26   13    13 45.102724 44.2353642 45.970084 0.0006733157 0.009587021 0.009587021       FALSE     1
## 27    6     6 46.837444 45.9700844 47.704805 0.0003107611 0.004424779 0.004424779       FALSE     1
## 28    3     3 48.572165 47.7048045 49.439525 0.0001553806 0.002212389 0.002212389       FALSE     1
## 29    5     5 50.306885 49.4395247 51.174245 0.0002589676 0.003687316 0.003687316       FALSE     1
## 30    2     2 52.041605 51.1742448 52.908965 0.0001035870 0.001474926 0.001474926       FALSE     1
##    group ymin ymax colour   fill size linetype alpha
## 1     -1    0   19     NA grey35  0.5        1    NA
## 2     -1    0   91     NA grey35  0.5        1    NA
## 3     -1    0  255     NA grey35  0.5        1    NA
## 4     -1    0  514     NA grey35  0.5        1    NA
## 5     -1    0  760     NA grey35  0.5        1    NA
## 6     -1    0 1143     NA grey35  0.5        1    NA
## 7     -1    0 1356     NA grey35  0.5        1    NA
## 8     -1    0 1232     NA grey35  0.5        1    NA
## 9     -1    0 1137     NA grey35  0.5        1    NA
## 10    -1    0  941     NA grey35  0.5        1    NA
## 11    -1    0  798     NA grey35  0.5        1    NA
## 12    -1    0  749     NA grey35  0.5        1    NA
## 13    -1    0  497     NA grey35  0.5        1    NA
## 14    -1    0  428     NA grey35  0.5        1    NA
## 15    -1    0  279     NA grey35  0.5        1    NA
## 16    -1    0  249     NA grey35  0.5        1    NA
## 17    -1    0  186     NA grey35  0.5        1    NA
## 18    -1    0  148     NA grey35  0.5        1    NA
## 19    -1    0  101     NA grey35  0.5        1    NA
## 20    -1    0   66     NA grey35  0.5        1    NA
## 21    -1    0   40     NA grey35  0.5        1    NA
## 22    -1    0   48     NA grey35  0.5        1    NA
## 23    -1    0   29     NA grey35  0.5        1    NA
## 24    -1    0   20     NA grey35  0.5        1    NA
## 25    -1    0   15     NA grey35  0.5        1    NA
## 26    -1    0   13     NA grey35  0.5        1    NA
## 27    -1    0    6     NA grey35  0.5        1    NA
## 28    -1    0    3     NA grey35  0.5        1    NA
## 29    -1    0    5     NA grey35  0.5        1    NA
## 30    -1    0    2     NA grey35  0.5        1    NA
```
]

???

* A histogram by default shows the **count** on the y-axis. 
* `ggplot2` calculates other useful statistics as well (seen using `layer_data`) and sometimes you want to use these instead.

---

# Layer data

```r
ggplot(data = CPSch3, 
       mapping = aes(x = ahe)) +
  geom_histogram(aes(y = after_stat(density))) 
```

???

* In this plot, the y-axis is showing density intead of count. 
* This is signalled by using `y = after_stat(density)`. 
* This syntax is equivalent to `y = stat(density)` or `y = ..density..`, which was the old syntax to refer to computed statistics.

---

# A density plot with `geom_density()`

```r
ggplot(data = CPSch3, 
       mapping = aes(x = ahe)) +
* geom_density() +
  labs(x = "Average hourly earnings (US$)")
```

???

* A density plot is like a histogram where it reveals the distribution of a (continuous) numerical variable. 
* The y-axis is showing the density, which is esimated using a particular kernel function. 
* The estimated density can oversmooth or have problems in the tails -- so don't only rely on density plots to tell you the full picture of the data.

---

# A frequency polygon with `geom_freqpoly()`

```r
ggplot(data = CPSch3, 
       mapping = aes(x = ahe)) +
* geom_freqpoly() +
  labs(x = "Average hourly earnings (US$)")
```

???

* The default `geom_freqpoly` is showing the same statistic (`stat = "bin"`) as the default `geom_histogram` except one is using a bar (`geom = "bar"`) and the other is using a line (`geom = "path"`).

---

# A boxplot with `geom_boxplot()`

```r
ggplot(data = CPSch3, 
       mapping = aes(x = ahe)) +
* geom_boxplot() +
  labs(x = "Average hourly earnings (US$)")
```

???

* A boxplot depicts graphically the five number summary: minimum, first quartile, median, third quartile and maximum. 
* `geom_boxplot` is also useful in comparing distributions of multiple numerical variables, like below, where each variable corresponds to one boxplot.

---

# A violin plot with `geom_violin()`

```r
ggplot(data = CPSch3, 
       mapping = aes(x = ahe, y = "")) +
* geom_violin() +
  labs(x = "Average hourly earnings (US$)")
```

???

* A dummy is required for `y` here!
* A violin plot depicts a density estimate of a continous variable.

---

# A dotplot with `geom_dotplot()`

```r
ggplot(data = dplyr::sample_n(CPSch3, 200), 
       mapping = aes(x = ahe)) +
* geom_dotplot() +
  labs(x = "Average hourly earnings (US$)")
```

???

* A dotplot works best for small number of observations so this plot is based on 200 random samples of the data.

---

# A barplot with `geom_bar()` with categorical variables

```r
ggplot(data = CPSch3, 
       mapping = aes(x = sex)) +
* geom_bar()
```

???

* If you have a categorical variable, then you usually want to study the frequency of its categories. 
* Notice here that the `stat = "count"` is computing the frequencies for each category for you.

---

# A barplot with `geom_bar()` with discrete variables

```r
ggplot(data = filter(CPSch3, year!=1994), 
       mapping = aes(x = year)) +
* geom_bar()
```

???

* If you supply a numerical variable, you can see now that the x-axis scale is continuous. 
* The years are 2 years apart -- I filtered out 1994 on purpose so that it's easier to see the distance between bars are determined by the numerical value. 
* If you want to study each level in a discrete variable, then you may want to convert the discrete variable to a factor instead `x = factor(year)`.

* When the variable is a factor or character, the distances between the bars are equal and the labels correspond to that particular level.

---

# A barplot with `geom_col()`

```r
dfsum <- CPSch3 %>% 
  group_by(sex) %>% 
  tally()
dfsum
```

```
## # A tibble: 2 × 2
##   sex        n
##   <fct>  <int>
## 1 male    5956
## 2 female  5174
```
]
.w-60[

```r
ggplot(data = dfsum, 
       mapping = aes(x = sex, 
                     y = n)) +
* geom_col()
```

<img src="images/part1-catalogue/geom-col-1.png" style="display: block; margin: auto;" />
]
]

???

* `tally()` is just a shorthand for `summarise(n = n())`. 
* Sometimes your input data may already contain pre-computed counts. 
* In this case, you don't need `stat = "count"` to do the counting for you. 
* You can use `geom_col()` instead.
* This is essential a short hand for `geom_bar(stat = "identity")` where `stat = "identity"` means that you will take the value as supplied without any statistical tranformation.

---

# Illusrative data 🚢 ship accidents

```r
data(Accident, package = "Ecdat")
str(Accident)
```

```
## 'data.frame':	40 obs. of  5 variables:
##  $ type   : Factor w/ 5 levels "A","B","C","D",..: 1 1 1 1 1 1 1 1 2 2 ...
##  $ constr : Factor w/ 4 levels "C6064","C6569",..: 1 1 2 2 3 3 4 4 1 1 ...
##  $ operate: Factor w/ 2 levels "O6074","O7579": 1 2 1 2 1 2 1 2 1 2 ...
##  $ months : int  127 63 1095 1095 1512 3353 NA 2244 44882 17176 ...
##  $ acc    : int  0 0 3 4 6 18 NA 11 39 29 ...
```

???

* `type` is the ship type
* `constr` is year constructed
* `operate` is the year operated
* `months` is the service amount counted in months
* `acc` is the number of accidents.

---

# A stacked barplot with `geom_col()`

```r
ggplot(Accident, aes(type, acc, fill = constr, group = operate)) +
  geom_col(color = "black", position = "stack")
```

???

* Recall that `geom_col()` is the same as `geom_bar()` except `stat="identity"`, i.e. your input data should already be summarised as count data. 
* By default the values in `y` are stacked on top of another.
* The aesthetic `group` here breaks the count in two groups and stack one on top of the other (try running the code without `group = operate`).

---

# A grouped barplot with `geom_col()`

```r
ggplot(Accident, aes(type, acc, fill = constr)) +
  geom_col(color = "black", position = "dodge")
```

???

* Notice here we removed the `group = operate` -- this results in stacked bars for the same `type` and `fill`.
* Here the `x` values are recalculated so that the factor levels within the same group (as determined by `x`) can fit.

---

# Another grouped barplot with `geom_col()`

```r
ggplot(Accident, aes(type, acc, fill = constr, group = operate)) +
  geom_col(color = "black", position = "dodge2")
```

???

* Can you see what changed?
* `position = "dodge"` doesn't deal well when there is `fill` and `group` together but you can use `position = "dodge2"` that recalculates the `x` values in another way.

---

# Stacked percentage barplot with `geom_col()`

```r
ggplot(Accident, aes(type, acc, fill = constr, group = operate)) +
  geom_col(color = "black", position = "fill")
```

???

* If you want to compare the percentages between the different `x`, then `position = "fill"` can be handy.

---

# Illustrative data 💎 diamonds

* Let's have a look at the `diamonds` data which contains information about different attributes of diamonds

```r
data(diamonds, package = "ggplot2")
diamonds
```

```
## # A tibble: 53,940 × 10
##    carat cut       color clarity depth table price     x     y     z
##    <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
##  1  0.23 Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43
##  2  0.21 Premium   E     SI1      59.8    61   326  3.89  3.84  2.31
##  3  0.23 Good      E     VS1      56.9    65   327  4.05  4.07  2.31
##  4  0.29 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63
##  5  0.31 Good      J     SI2      63.3    58   335  4.34  4.35  2.75
##  6  0.24 Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48
##  7  0.24 Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47
##  8  0.26 Very Good H     SI1      61.9    55   337  4.07  4.11  2.53
##  9  0.22 Fair      E     VS2      65.1    61   337  3.87  3.78  2.49
## 10  0.23 Very Good H     VS1      59.4    61   338  4     4.05  2.39
## # … with 53,930 more rows
```

---

# A scatterplot with `geom_point()`

```r
ggplot(data = diamonds,
       mapping = aes(x = carat, y = price)) +
  geom_point()
```

* Scatter plot of price vs carat of diamonds
* Each point correponds to a dimaond

???

* A scatterplot is useful in visualising two numerical variables 
* Each point corresponds to an observational unit 
* If you have other variables, you may like to map this to other aesthetic values, such as `shape` or `color`.
* When there is a lot of observations, it may be useful to use `alpha` to control the transparency of the points.

---

# A hexagonal 2D heatmap with `geom_hex()`

```r
ggplot(data = diamonds,
       mapping = aes(x = carat, y = price)) +
  geom_hex()
```

* A hexagon shows the count of observations within the region.

???

* A hex plot is useful if there is a lot of _overplotting_ in your scatterplot.
* **_Overplotting_** in data visualisation means that the data or labels overlap in the data visualisation making it harder to distinguish individual data points. * Overplotting typically occurs when you have a large dataset.
* A hex plot overcomes the issue by showing the number of observations within a hexagonal area.
* The count is shown by the `fill` in the resulting data visualisation.

---

# A rectangular 2D heatmap with `geom_bin_2d()`

```r
ggplot(data = diamonds,
       mapping = aes(x = carat, y = price)) +
  geom_bin_2d()
```

???

* `geom_bin_2d()` is the same as `geom_hex()` but the shape is a rectangle instead of a hexagon.

---

# Count of overlapping points with `geom_count()`

```r
ggplot(data = diamonds,
       mapping = aes(x = carat, y = price)) +
  geom_count()
```

???

* `geom_count()` counts the number of points in the same location
* The count is then mapped to the aesthetic `size`.

---

# Contour of a 2D density with `geom_density_2d()`

```r
ggplot(data = diamonds,
       mapping = aes(x = carat, y = price)) +
  geom_density_2d()
```

???

* If you have want to visualise the joint density of two variables then `geom_density_2d()` is useful. 
* The axis shows the density of the corresponding variable.
* The 2D density is estimated using `MASS::kde2d()`.

---

# Contour of a 2D density with `geom_density_2d_filled()`

```r
ggplot(data = diamonds,
       mapping = aes(x = carat, y = price)) +
  geom_density_2d_filled()
```

???

* `geom_density_2d()` draws the contour line 
* `geom_density_2d_filled()` draws the contour bands

---

# Illustrative data ⛰️ volcano

* The data contains topographic information on Auckland's Maunga Whau Volcano

```r
volcanod <- data.frame(row = as.vector(row(volcano)),
                       col = as.vector(col(volcano)),
                       value = as.vector(volcano))

volcanod
```

```
##     row col value
## 1     1   1   100
## 2     2   1   101
## 3     3   1   102
## 4     4   1   103
## 5     5   1   104
## 6     6   1   105
## 7     7   1   105
## 8     8   1   106
## 9     9   1   107
## 10   10   1   108
## 11   11   1   109
## 12   12   1   110
## 13   13   1   110
## 14   14   1   111
## 15   15   1   114
## 16   16   1   116
## 17   17   1   118
## 18   18   1   120
## 19   19   1   120
## 20   20   1   121
## 21   21   1   122
## 22   22   1   122
## 23   23   1   123
## 24   24   1   124
## 25   25   1   123
## 26   26   1   123
## 27   27   1   120
## 28   28   1   118
## 29   29   1   117
## 30   30   1   115
## 31   31   1   114
## 32   32   1   115
## 33   33   1   113
## 34   34   1   111
## 35   35   1   110
## 36   36   1   109
## 37   37   1   108
## 38   38   1   108
## 39   39   1   107
## 40   40   1   107
## 41   41   1   107
## 42   42   1   108
## 43   43   1   109
## 44   44   1   110
## 45   45   1   111
## 46   46   1   111
## 47   47   1   112
## 48   48   1   113
## 49   49   1   113
## 50   50   1   114
## 51   51   1   115
## 52   52   1   115
## 53   53   1   114
## 54   54   1   113
## 55   55   1   112
## 56   56   1   111
## 57   57   1   111
## 58   58   1   112
## 59   59   1   112
## 60   60   1   112
## 61   61   1   113
## 62   62   1   114
## 63   63   1   114
## 64   64   1   115
## 65   65   1   115
## 66   66   1   116
## 67   67   1   116
## 68   68   1   117
## 69   69   1   117
## 70   70   1   116
## 71   71   1   114
## 72   72   1   112
## 73   73   1   109
## 74   74   1   106
## 75   75   1   104
## 76   76   1   102
## 77   77   1   101
## 78   78   1   100
## 79   79   1   100
## 80   80   1    99
## 81   81   1    99
## 82   82   1    99
## 83   83   1    99
## 84   84   1    98
## 85   85   1    98
## 86   86   1    97
## 87   87   1    97
## 88    1   2   100
## 89    2   2   101
## 90    3   2   102
## 91    4   2   103
## 92    5   2   104
## 93    6   2   105
## 94    7   2   106
## 95    8   2   107
## 96    9   2   108
## 97   10   2   109
## 98   11   2   110
## 99   12   2   110
## 100  13   2   111
## 101  14   2   113
## 102  15   2   115
## 103  16   2   118
## 104  17   2   120
## 105  18   2   121
## 106  19   2   122
## 107  20   2   124
## 108  21   2   125
## 109  22   2   125
## 110  23   2   126
## 111  24   2   127
## 112  25   2   128
## 113  26   2   127
## 114  27   2   124
## 115  28   2   121
## 116  29   2   120
## 117  30   2   118
## 118  31   2   116
## 119  32   2   114
## 120  33   2   113
## 121  34   2   112
## 122  35   2   112
## 123  36   2   110
## 124  37   2   109
## 125  38   2   109
## 126  39   2   108
## 127  40   2   108
## 128  41   2   108
## 129  42   2   109
## 130  43   2   110
## 131  44   2   111
## 132  45   2   112
## 133  46   2   112
## 134  47   2   113
## 135  48   2   114
## 136  49   2   114
## 137  50   2   115
## 138  51   2   116
## 139  52   2   116
## 140  53   2   115
## 141  54   2   114
## 142  55   2   113
## 143  56   2   112
## 144  57   2   112
## 145  58   2   113
## 146  59   2   113
## 147  60   2   114
## 148  61   2   114
## 149  62   2   115
## 150  63   2   115
## 151  64   2   116
## 152  65   2   116
## 153  66   2   117
## 154  67   2   117
## 155  68   2   118
## 156  69   2   118
## 157  70   2   117
## 158  71   2   115
## 159  72   2   113
## 160  73   2   111
## 161  74   2   107
## 162  75   2   105
## 163  76   2   103
## 164  77   2   102
## 165  78   2   101
## 166  79   2   101
## 167  80   2   100
## 168  81   2   100
## 169  82   2   100
## 170  83   2    99
## 171  84   2    99
## 172  85   2    98
## 173  86   2    98
## 174  87   2    97
## 175   1   3   101
## 176   2   3   102
## 177   3   3   103
## 178   4   3   104
## 179   5   3   105
## 180   6   3   105
## 181   7   3   106
## 182   8   3   107
## 183   9   3   108
## 184  10   3   109
## 185  11   3   110
## 186  12   3   111
## 187  13   3   113
## 188  14   3   115
## 189  15   3   117
## 190  16   3   118
## 191  17   3   120
## 192  18   3   122
## 193  19   3   125
## 194  20   3   126
## 195  21   3   127
## 196  22   3   128
## 197  23   3   129
## 198  24   3   130
## 199  25   3   131
## 200  26   3   131
## 201  27   3   128
## 202  28   3   125
## 203  29   3   121
## 204  30   3   120
## 205  31   3   118
## 206  32   3   116
## 207  33   3   114
## 208  34   3   113
## 209  35   3   113
## 210  36   3   111
## 211  37   3   111
## 212  38   3   111
## 213  39   3   111
## 214  40   3   110
## 215  41   3   109
## 216  42   3   110
## 217  43   3   111
## 218  44   3   112
## 219  45   3   113
## 220  46   3   113
## 221  47   3   114
## 222  48   3   115
## 223  49   3   115
## 224  50   3   116
## 225  51   3   117
## 226  52   3   117
## 227  53   3   116
## 228  54   3   115
## 229  55   3   114
## 230  56   3   114
## 231  57   3   113
## 232  58   3   113
## 233  59   3   114
## 234  60   3   114
## 235  61   3   115
## 236  62   3   115
## 237  63   3   116
## 238  64   3   116
## 239  65   3   117
## 240  66   3   118
## 241  67   3   118
## 242  68   3   119
## 243  69   3   119
## 244  70   3   118
## 245  71   3   116
## 246  72   3   114
## 247  73   3   112
## 248  74   3   108
## 249  75   3   105
## 250  76   3   103
## 251  77   3   103
## 252  78   3   102
## 253  79   3   101
## 254  80   3   101
## 255  81   3   100
## 256  82   3   100
## 257  83   3    99
## 258  84   3    99
## 259  85   3    98
## 260  86   3    98
## 261  87   3    97
## 262   1   4   101
## 263   2   4   102
## 264   3   4   103
## 265   4   4   104
## 266   5   4   105
## 267   6   4   106
## 268   7   4   107
## 269   8   4   108
## 270   9   4   109
## 271  10   4   110
## 272  11   4   111
## 273  12   4   113
## 274  13   4   115
## 275  14   4   117
## 276  15   4   117
## 277  16   4   118
## 278  17   4   121
## 279  18   4   123
## 280  19   4   126
## 281  20   4   128
## 282  21   4   130
## 283  22   4   130
## 284  23   4   131
## 285  24   4   132
## 286  25   4   133
## 287  26   4   134
## 288  27   4   131
## 289  28   4   129
## 290  29   4   125
## 291  30   4   122
## 292  31   4   120
## 293  32   4   118
## 294  33   4   116
## 295  34   4   114
## 296  35   4   113
## 297  36   4   112
## 298  37   4   112
## 299  38   4   113
## 300  39   4   112
## 301  40   4   112
## 302  41   4   111
## 303  42   4   112
## 304  43   4   113
## 305  44   4   113
## 306  45   4   114
## 307  46   4   115
## 308  47   4   116
## 309  48   4   116
## 310  49   4   117
## 311  50   4   117
## 312  51   4   118
## 313  52   4   118
## 314  53   4   116
## 315  54   4   116
## 316  55   4   115
## 317  56   4   115
## 318  57   4   115
## 319  58   4   114
## 320  59   4   114
## 321  60   4   115
## 322  61   4   116
## 323  62   4   116
## 324  63   4   117
## 325  64   4   117
## 326  65   4   118
## 327  66   4   118
## 328  67   4   119
## 329  68   4   119
## 330  69   4   120
## 331  70   4   120
## 332  71   4   117
## 333  72   4   115
##  [ reached 'max' / getOption("max.print") -- omitted 4974 rows ]
```

???

* The original data is a matrix and we must wrangle in the form ready for `ggplot` first.

---

# A 2D contour plot with `geom_contour()`

```r
ggplot(data = volcanod, 
       mapping = aes(x = col, y = row, z = value)) + 
  geom_contour()
```

---

# A 2D contour plot with `geom_contour_filled()`

```r
ggplot(data = volcanod, 
       mapping = aes(x = col, y = row, z = value)) + 
  geom_contour_filled()
```

---

# Tile plots with `geom_tile()`

```r
ggplot(volcanod, aes(col, row, fill = value)) +
 geom_tile()
```

---

# Raster plots with `geom_raster()`

```r
ggplot(volcanod, aes(col, row, fill = value)) +
 geom_raster()
```

* A high performance special case of `geom_tile()` for when the tiles are the same size

---

# Rectangular plots with `geom_rect()`

```r
ggplot(volcanod, aes(xmin = col - 0.5, xmax = col + 0.5,
                     ymin = row - 0.5, ymax = row + 0.5, fill = value)) +
 geom_rect()
```

* A reparameterised version of `geom_tile()`

---

# Illustrative data 📈 economics

```r
data(economics, package = "ggplot2")
economics
```

```
## # A tibble: 574 × 6
##    date         pce    pop psavert uempmed unemploy
##    <date>     <dbl>  <dbl>   <dbl>   <dbl>    <dbl>
##  1 1967-07-01  507. 198712    12.6     4.5     2944
##  2 1967-08-01  510. 198911    12.6     4.7     2945
##  3 1967-09-01  516. 199113    11.9     4.6     2958
##  4 1967-10-01  512. 199311    12.9     4.9     3143
##  5 1967-11-01  517. 199498    12.8     4.7     3066
##  6 1967-12-01  525. 199657    11.8     4.8     3018
##  7 1968-01-01  531. 199808    11.7     5.1     2878
##  8 1968-02-01  534. 199920    12.3     4.5     3001
##  9 1968-03-01  544. 200056    11.7     4.1     2877
## 10 1968-04-01  544  200208    12.3     4.6     2709
## # … with 564 more rows
```

---

# Line plot with `geom_line()`

```r
ggplot(economics, aes(date, unemploy)) + 
  geom_line()
```

---

# `geom_path()`

```r
ggplot(economics, aes(unemploy/pop, psavert)) +
  geom_path()
```

---

# `geom_step()`

```r
ggplot(economics, aes(unemploy/pop, psavert)) +
  geom_step()
```

---

# Drawing maps

* Drawing maps require the map data

```r
world <- map_data("world")
world
```

```
##          long      lat group order      region subregion
## 1   -69.89912 12.45200     1     1       Aruba      <NA>
## 2   -69.89571 12.42300     1     2       Aruba      <NA>
## 3   -69.94219 12.43853     1     3       Aruba      <NA>
## 4   -70.00415 12.50049     1     4       Aruba      <NA>
## 5   -70.06612 12.54697     1     5       Aruba      <NA>
## 6   -70.05088 12.59707     1     6       Aruba      <NA>
## 7   -70.03511 12.61411     1     7       Aruba      <NA>
## 8   -69.97314 12.56763     1     8       Aruba      <NA>
## 9   -69.91181 12.48047     1     9       Aruba      <NA>
## 10  -69.89912 12.45200     1    10       Aruba      <NA>
## 12   74.89131 37.23164     2    12 Afghanistan      <NA>
## 13   74.84023 37.22505     2    13 Afghanistan      <NA>
## 14   74.76738 37.24917     2    14 Afghanistan      <NA>
## 15   74.73896 37.28564     2    15 Afghanistan      <NA>
## 16   74.72666 37.29072     2    16 Afghanistan      <NA>
## 17   74.66895 37.26670     2    17 Afghanistan      <NA>
## 18   74.55899 37.23662     2    18 Afghanistan      <NA>
## 19   74.37217 37.15771     2    19 Afghanistan      <NA>
## 20   74.37617 37.13735     2    20 Afghanistan      <NA>
## 21   74.49796 37.05722     2    21 Afghanistan      <NA>
## 22   74.52646 37.03066     2    22 Afghanistan      <NA>
## 23   74.54140 37.02217     2    23 Afghanistan      <NA>
## 24   74.43106 36.98369     2    24 Afghanistan      <NA>
## 25   74.19473 36.89688     2    25 Afghanistan      <NA>
## 26   74.03887 36.82573     2    26 Afghanistan      <NA>
## 27   74.00185 36.82310     2    27 Afghanistan      <NA>
## 28   73.90781 36.85293     2    28 Afghanistan      <NA>
## 29   73.76914 36.88848     2    29 Afghanistan      <NA>
## 30   73.73183 36.88779     2    30 Afghanistan      <NA>
## 31   73.41113 36.88169     2    31 Afghanistan      <NA>
## 32   73.11680 36.86856     2    32 Afghanistan      <NA>
## 33   72.99374 36.85161     2    33 Afghanistan      <NA>
## 34   72.76621 36.83501     2    34 Afghanistan      <NA>
## 35   72.62286 36.82959     2    35 Afghanistan      <NA>
## 36   72.53135 36.80200     2    36 Afghanistan      <NA>
## 37   72.43115 36.76582     2    37 Afghanistan      <NA>
## 38   72.32696 36.74239     2    38 Afghanistan      <NA>
## 39   72.24980 36.73472     2    39 Afghanistan      <NA>
## 40   72.15674 36.70088     2    40 Afghanistan      <NA>
## 41   72.09560 36.63374     2    41 Afghanistan      <NA>
## 42   71.92070 36.53418     2    42 Afghanistan      <NA>
## 43   71.82227 36.48608     2    43 Afghanistan      <NA>
## 44   71.77266 36.43184     2    44 Afghanistan      <NA>
## 45   71.71641 36.42656     2    45 Afghanistan      <NA>
## 46   71.62051 36.43647     2    46 Afghanistan      <NA>
## 47   71.54590 36.37769     2    47 Afghanistan      <NA>
## 48   71.46328 36.29326     2    48 Afghanistan      <NA>
## 49   71.31260 36.17119     2    49 Afghanistan      <NA>
## 50   71.23291 36.12178     2    50 Afghanistan      <NA>
## 51   71.18506 36.04209     2    51 Afghanistan      <NA>
## 52   71.22021 36.00068     2    52 Afghanistan      <NA>
## 53   71.34287 35.93853     2    53 Afghanistan      <NA>
## 54   71.39756 35.88018     2    54 Afghanistan      <NA>
## 55   71.42754 35.83374     2    55 Afghanistan      <NA>
## 56   71.48359 35.71460     2    56 Afghanistan      <NA>
## 57   71.51904 35.59751     2    57 Afghanistan      <NA>
## 58   71.57198 35.54683     2    58 Afghanistan      <NA>
## 59   71.58740 35.46084     2    59 Afghanistan      <NA>
## 60   71.60059 35.40791     2    60 Afghanistan      <NA>
## 61   71.57198 35.37041     2    61 Afghanistan      <NA>
## 62   71.54551 35.32851     2    62 Afghanistan      <NA>
## 63   71.54551 35.28886     2    63 Afghanistan      <NA>
## 64   71.57725 35.24800     2    64 Afghanistan      <NA>
## 65   71.60527 35.21177     2    65 Afghanistan      <NA>
## 66   71.62051 35.18301     2    66 Afghanistan      <NA>
## 67   71.60166 35.15068     2    67 Afghanistan      <NA>
## 68   71.54551 35.10141     2    68 Afghanistan      <NA>
## 69   71.51709 35.05112     2    69 Afghanistan      <NA>
## 70   71.45508 34.96694     2    70 Afghanistan      <NA>
## 71   71.35811 34.90962     2    71 Afghanistan      <NA>
## 72   71.29414 34.86773     2    72 Afghanistan      <NA>
## 73   71.22578 34.77954     2    73 Afghanistan      <NA>
## 74   71.11328 34.68159     2    74 Afghanistan      <NA>
## 75   71.06563 34.59961     2    75 Afghanistan      <NA>
## 76   71.01631 34.55464     2    76 Afghanistan      <NA>
## 77   70.96562 34.53037     2    77 Afghanistan      <NA>
## 78   70.97891 34.48628     2    78 Afghanistan      <NA>
## 79   71.02295 34.43115     2    79 Afghanistan      <NA>
## 80   71.09570 34.36943     2    80 Afghanistan      <NA>
## 81   71.09238 34.27324     2    81 Afghanistan      <NA>
## 82   71.08906 34.20406     2    82 Afghanistan      <NA>
## 83   71.09131 34.12027     2    83 Afghanistan      <NA>
## 84   71.05156 34.04971     2    84 Afghanistan      <NA>
## 85   70.84844 33.98188     2    85 Afghanistan      <NA>
## 86   70.65401 33.95229     2    86 Afghanistan      <NA>
## 87   70.41573 33.95044     2    87 Afghanistan      <NA>
## 88   70.32568 33.96113     2    88 Afghanistan      <NA>
## 89   70.25361 33.97598     2    89 Afghanistan      <NA>
## 90   69.99473 34.05181     2    90 Afghanistan      <NA>
## 91   69.88965 34.00727     2    91 Afghanistan      <NA>
## 92   69.86806 33.89766     2    92 Afghanistan      <NA>
## 93   70.05664 33.71987     2    93 Afghanistan      <NA>
## 94   70.13418 33.62075     2    94 Afghanistan      <NA>
## 95   70.21973 33.45469     2    95 Afghanistan      <NA>
## 96   70.28418 33.36904     2    96 Afghanistan      <NA>
## 97   70.26113 33.28901     2    97 Afghanistan      <NA>
## 98   70.09023 33.19810     2    98 Afghanistan      <NA>
## 99   69.92012 33.11250     2    99 Afghanistan      <NA>
## 100  69.70371 33.09473     2   100 Afghanistan      <NA>
## 101  69.56777 33.06416     2   101 Afghanistan      <NA>
## 102  69.50156 33.02007     2   102 Afghanistan      <NA>
## 103  69.45312 32.83281     2   103 Afghanistan      <NA>
## 104  69.40459 32.76426     2   104 Afghanistan      <NA>
## 105  69.40537 32.68272     2   105 Afghanistan      <NA>
## 106  69.35947 32.59033     2   106 Afghanistan      <NA>
## 107  69.28994 32.53057     2   107 Afghanistan      <NA>
## 108  69.24140 32.43354     2   108 Afghanistan      <NA>
## 109  69.25654 32.24946     2   109 Afghanistan      <NA>
## 110  69.27930 31.93682     2   110 Afghanistan      <NA>
## 111  69.18691 31.83809     2   111 Afghanistan      <NA>
## 112  69.08311 31.73848     2   112 Afghanistan      <NA>
## 113  68.97343 31.66738     2   113 Afghanistan      <NA>
## 114  68.86895 31.63423     2   114 Afghanistan      <NA>
## 115  68.78233 31.64643     2   115 Afghanistan      <NA>
## 116  68.71367 31.70806     2   116 Afghanistan      <NA>
## 117  68.67324 31.75972     2   117 Afghanistan      <NA>
## 118  68.59766 31.80298     2   118 Afghanistan      <NA>
## 119  68.52071 31.79414     2   119 Afghanistan      <NA>
## 120  68.44326 31.75449     2   120 Afghanistan      <NA>
## 121  68.31982 31.76767     2   121 Afghanistan      <NA>
## 122  68.21397 31.80737     2   122 Afghanistan      <NA>
## 123  68.16103 31.80298     2   123 Afghanistan      <NA>
## 124  68.13017 31.76328     2   124 Afghanistan      <NA>
## 125  68.01719 31.67798     2   125 Afghanistan      <NA>
## 126  67.73985 31.54819     2   126 Afghanistan      <NA>
## 127  67.62675 31.53877     2   127 Afghanistan      <NA>
## 128  67.57822 31.50649     2   128 Afghanistan      <NA>
## 129  67.59756 31.45332     2   129 Afghanistan      <NA>
## 130  67.64706 31.40996     2   130 Afghanistan      <NA>
## 131  67.73350 31.37925     2   131 Afghanistan      <NA>
## 132  67.73789 31.34395     2   132 Afghanistan      <NA>
## 133  67.66152 31.31299     2   133 Afghanistan      <NA>
## 134  67.59639 31.27769     2   134 Afghanistan      <NA>
## 135  67.45283 31.23462     2   135 Afghanistan      <NA>
## 136  67.28730 31.21782     2   136 Afghanistan      <NA>
## 137  67.11592 31.24292     2   137 Afghanistan      <NA>
## 138  67.02773 31.30024     2   138 Afghanistan      <NA>
## 139  66.92432 31.30561     2   139 Afghanistan      <NA>
## 140  66.82929 31.26367     2   140 Afghanistan      <NA>
## 141  66.73135 31.19453     2   141 Afghanistan      <NA>
## 142  66.62422 31.04604     2   142 Afghanistan      <NA>
## 143  66.59580 31.01997     2   143 Afghanistan      <NA>
## 144  66.56680 30.99658     2   144 Afghanistan      <NA>
## 145  66.49736 30.96455     2   145 Afghanistan      <NA>
## 146  66.39717 30.91221     2   146 Afghanistan      <NA>
## 147  66.34687 30.80278     2   147 Afghanistan      <NA>
## 148  66.28691 30.60791     2   148 Afghanistan      <NA>
## 149  66.30097 30.50298     2   149 Afghanistan      <NA>
## 150  66.30547 30.32114     2   150 Afghanistan      <NA>
## 151  66.28184 30.19346     2   151 Afghanistan      <NA>
## 152  66.23848 30.10962     2   152 Afghanistan      <NA>
## 153  66.24717 30.04351     2   153 Afghanistan      <NA>
## 154  66.31338 29.96856     2   154 Afghanistan      <NA>
## 155  66.28691 29.92002     2   155 Afghanistan      <NA>
## 156  66.23125 29.86572     2   156 Afghanistan      <NA>
## 157  66.17706 29.83559     2   157 Afghanistan      <NA>
## 158  65.96162 29.77891     2   158 Afghanistan      <NA>
## 159  65.66621 29.70132     2   159 Afghanistan      <NA>
## 160  65.47099 29.65156     2   160 Afghanistan      <NA>
## 161  65.18047 29.57763     2   161 Afghanistan      <NA>
## 162  65.09550 29.55947     2   162 Afghanistan      <NA>
## 163  64.91895 29.55278     2   163 Afghanistan      <NA>
## 164  64.82734 29.56416     2   164 Afghanistan      <NA>
## 165  64.70351 29.56714     2   165 Afghanistan      <NA>
## 166  64.52110 29.56450     2   166 Afghanistan      <NA>
## 167  64.39375 29.54433     2   167 Afghanistan      <NA>
##  [ reached 'max' / getOption("max.print") -- omitted 99172 rows ]
```

---

# Drawing world map with `geom_polygon()`

```r
world <- map_data("world")
ggplot(world, aes(long, lat, group = group)) +
  geom_polygon()
```

---

# Other layers

* There are more layers in `ggplot`!
* There are layers also beginning with `stat_`.
* There are also many extension packages to draw more plots: https://exts.ggplot2.tidyverse.org/gallery/ 
* It is normal to google and copy-and-paste code that you find on the web. 
* `ggplot2` is also [continually updated](https://github.com/tidyverse/ggplot2/blob/main/NEWS.md) so some new features may be added in the future.

---

# Session Information

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

```
## ─ Session info  3️⃣  🧛🏻  👨🏾   ─────────────────────────────────────────────────────────────────────────
##  hash: keycap: 3, vampire: light skin tone, man: medium-dark skin tone
## 
##  setting  value
##  version  R version 4.1.2 (2021-11-01)
##  os       macOS Big Sur 11.6.2
##  system   x86_64, darwin17.0
##  ui       RStudio
##  language (EN)
##  collate  en_AU.UTF-8
##  ctype    en_AU.UTF-8
##  tz       Australia/Melbourne
##  date     2022-02-20
##  rstudio  2022.02.0-preview+392 Prairie Trillium (desktop)
##  pandoc   2.17.1.1 @ /usr/local/bin/ (via rmarkdown)
## 
## ─ Packages ──────────────────────────────────────────────────────────────────────────────────────────
##  package     * version date (UTC) lib source
##  anicon        0.1.0   2021-11-30 [1] Github (emitanaka/anicon@0b756df)
##  assertthat    0.2.1   2019-03-21 [1] CRAN (R 4.1.0)
##  backports     1.3.0   2021-10-27 [1] CRAN (R 4.1.0)
##  bitops        1.0-7   2021-04-24 [1] CRAN (R 4.1.0)
##  broom         0.7.10  2021-10-31 [1] CRAN (R 4.1.0)
##  bslib         0.3.1   2021-10-06 [1] CRAN (R 4.1.0)
##  cachem        1.0.6   2021-08-19 [1] CRAN (R 4.1.0)
##  callr         3.7.0   2021-04-20 [1] CRAN (R 4.1.0)
##  cellranger    1.1.0   2016-07-27 [1] CRAN (R 4.1.0)
##  class         7.3-19  2021-05-03 [1] CRAN (R 4.1.2)
##  classInt      0.4-3   2020-04-07 [1] CRAN (R 4.1.0)
##  cli           3.2.0   2022-02-14 [1] CRAN (R 4.1.2)
##  codetools     0.2-18  2020-11-04 [1] CRAN (R 4.1.2)
##  colorspace    2.0-2   2021-06-24 [1] CRAN (R 4.1.0)
##  crayon        1.5.0   2022-02-14 [1] CRAN (R 4.1.2)
##  crosstalk     1.2.0   2021-11-04 [1] CRAN (R 4.1.0)
##  data.table    1.14.2  2021-09-27 [1] CRAN (R 4.1.0)
##  datasauRus  * 0.1.4   2018-09-20 [1] CRAN (R 4.1.0)
##  DBI           1.1.1   2021-01-15 [1] CRAN (R 4.1.0)
##  dbplyr        2.1.1   2021-04-06 [1] CRAN (R 4.1.0)
##  desc          1.4.0   2021-09-28 [1] CRAN (R 4.1.0)
##  devtools      2.4.2   2021-06-07 [1] CRAN (R 4.1.0)
##  digest        0.6.29  2021-12-01 [1] CRAN (R 4.1.0)
##  dplyr       * 1.0.7   2021-06-18 [1] CRAN (R 4.1.0)
##  DT            0.20    2021-11-15 [1] CRAN (R 4.1.0)
##  e1071         1.7-9   2021-09-16 [1] CRAN (R 4.1.0)
##  ellipsis      0.3.2   2021-04-29 [1] CRAN (R 4.1.0)
##  evaluate      0.14    2019-05-28 [1] CRAN (R 4.1.0)
##  fansi         1.0.2   2022-01-14 [1] CRAN (R 4.1.2)
##  farver        2.1.0   2021-02-28 [1] CRAN (R 4.1.0)
##  fastmap       1.1.0   2021-01-25 [1] CRAN (R 4.1.0)
##  forcats     * 0.5.1   2021-01-27 [1] CRAN (R 4.1.0)
##  fs            1.5.2   2021-12-08 [1] CRAN (R 4.1.0)
##  gapminder   * 0.3.0   2017-10-31 [1] CRAN (R 4.1.0)
##  generics      0.1.2   2022-01-31 [1] CRAN (R 4.1.2)
##  gganimate   * 1.0.7   2020-10-15 [1] CRAN (R 4.1.0)
##  ggmap       * 3.0.0   2019-02-05 [1] CRAN (R 4.1.0)
##  ggplot2     * 3.3.5   2021-06-25 [1] CRAN (R 4.1.0)
##  ggthemes    * 4.2.4   2021-01-20 [1] CRAN (R 4.1.0)
##  glue          1.6.1   2022-01-22 [1] CRAN (R 4.1.2)
##  gridExtra     2.3     2017-09-09 [1] CRAN (R 4.1.0)
##  gtable        0.3.0   2019-03-25 [1] CRAN (R 4.1.0)
##  haven         2.4.3   2021-08-04 [1] CRAN (R 4.1.0)
##  hexbin        1.28.2  2021-01-08 [1] CRAN (R 4.1.0)
##  highr         0.9     2021-04-16 [1] CRAN (R 4.1.0)
##  hms           1.1.1   2021-09-26 [1] CRAN (R 4.1.0)
##  htmltools     0.5.2   2021-08-25 [1] CRAN (R 4.1.0)
##  htmlwidgets   1.5.4   2021-09-08 [1] CRAN (R 4.1.0)
##  httpuv        1.6.4   2021-12-14 [1] CRAN (R 4.1.0)
##  httr          1.4.2   2020-07-20 [1] CRAN (R 4.1.0)
##  icon          0.1.0   2021-11-30 [1] Github (emitanaka/icon@8458546)
##  isoband       0.2.5   2021-07-13 [1] CRAN (R 4.1.0)
##  jpeg          0.1-9   2021-07-24 [1] CRAN (R 4.1.0)
##  jquerylib     0.1.4   2021-04-26 [1] CRAN (R 4.1.0)
##  jsonlite      1.7.3   2022-01-17 [1] CRAN (R 4.1.2)
##  KernSmooth    2.23-20 2021-05-03 [1] CRAN (R 4.1.2)
##  knitr         1.37    2021-12-16 [1] CRAN (R 4.1.0)
##  labeling      0.4.2   2020-10-20 [1] CRAN (R 4.1.0)
##  later         1.3.0   2021-08-18 [1] CRAN (R 4.1.0)
##  lattice       0.20-45 2021-09-22 [1] CRAN (R 4.1.2)
##  lazyeval      0.2.2   2019-03-15 [1] CRAN (R 4.1.0)
##  leaflet     * 2.0.4.1 2021-01-07 [1] CRAN (R 4.1.0)
##  lifecycle     1.0.1   2021-09-24 [1] CRAN (R 4.1.0)
##  lubridate   * 1.8.0   2021-10-07 [1] CRAN (R 4.1.0)
##  magick        2.7.3   2021-08-18 [1] CRAN (R 4.1.0)
##  magrittr      2.0.2   2022-01-26 [1] CRAN (R 4.1.2)
##  maps          3.4.0   2021-09-25 [1] CRAN (R 4.1.0)
##  MASS          7.3-54  2021-05-03 [1] CRAN (R 4.1.2)
##  memoise       2.0.0   2021-01-26 [1] CRAN (R 4.1.0)
##  mime          0.12    2021-09-28 [1] CRAN (R 4.1.0)
##  modelr        0.1.8   2020-05-19 [1] CRAN (R 4.1.0)
##  munsell       0.5.0   2018-06-12 [1] CRAN (R 4.1.0)
##  oz            1.0-21  2016-12-08 [1] CRAN (R 4.1.0)
##  ozmaps      * 0.4.5   2021-08-03 [1] CRAN (R 4.1.0)
##  pillar        1.7.0   2022-02-01 [1] CRAN (R 4.1.2)
##  pkgbuild      1.2.0   2020-12-15 [1] CRAN (R 4.1.0)
##  pkgconfig     2.0.3   2019-09-22 [1] CRAN (R 4.1.0)
##  pkgload       1.2.3   2021-10-13 [1] CRAN (R 4.1.0)
##  plotly      * 4.10.0  2021-10-09 [1] CRAN (R 4.1.0)
##  plyr          1.8.6   2020-03-03 [1] CRAN (R 4.1.0)
##  png           0.1-7   2013-12-03 [1] CRAN (R 4.1.0)
##  prettyunits   1.1.1   2020-01-24 [1] CRAN (R 4.1.0)
##  processx      3.5.2   2021-04-30 [1] CRAN (R 4.1.0)
##  progress      1.2.2   2019-05-16 [1] CRAN (R 4.1.0)
##  promises      1.2.0.1 2021-02-11 [1] CRAN (R 4.1.0)
##  prompt        1.0.1   2021-03-12 [1] CRAN (R 4.1.0)
##  proxy         0.4-26  2021-06-07 [1] CRAN (R 4.1.0)
##  ps            1.6.0   2021-02-28 [1] CRAN (R 4.1.0)
##  purrr       * 0.3.4   2020-04-17 [1] CRAN (R 4.1.0)
##  R6            2.5.1   2021-08-19 [1] CRAN (R 4.1.0)
##  Rcpp          1.0.8   2022-01-13 [1] CRAN (R 4.1.2)
##  readr       * 2.1.2   2022-01-30 [1] CRAN (R 4.1.2)
##  readxl        1.3.1   2019-03-13 [1] CRAN (R 4.1.0)
##  remotes       2.4.1   2021-09-29 [1] CRAN (R 4.1.0)
##  reprex        2.0.1   2021-08-05 [1] CRAN (R 4.1.0)
##  RgoogleMaps   1.4.5.3 2020-02-12 [1] CRAN (R 4.1.0)
##  rjson         0.2.20  2018-06-08 [1] CRAN (R 4.1.0)
##  rlang         1.0.1   2022-02-03 [1] CRAN (R 4.1.2)
##  rmarkdown     2.11    2021-09-14 [1] CRAN (R 4.1.0)
##  rprojroot     2.0.2   2020-11-15 [1] CRAN (R 4.1.0)
##  rstudioapi    0.13    2020-11-12 [1] CRAN (R 4.1.0)
##  rvest         1.0.2   2021-10-16 [1] CRAN (R 4.1.0)
##  s2            1.0.7   2021-09-28 [1] CRAN (R 4.1.0)
##  sass          0.4.0   2021-05-12 [1] CRAN (R 4.1.0)
##  scales        1.1.1   2020-05-11 [1] CRAN (R 4.1.0)
##  servr         0.24    2021-11-16 [1] CRAN (R 4.1.2)
##  sessioninfo   1.2.1   2021-11-02 [1] CRAN (R 4.1.0)
##  sf            1.0-4   2021-11-14 [1] CRAN (R 4.1.0)
##  sp            1.4-6   2021-11-14 [1] CRAN (R 4.1.0)
##  stringi       1.7.6   2021-11-29 [1] CRAN (R 4.1.0)
##  stringr     * 1.4.0   2019-02-10 [1] CRAN (R 4.1.0)
##  testthat      3.1.0   2021-10-04 [1] CRAN (R 4.1.0)
##  tibble      * 3.1.6   2021-11-07 [1] CRAN (R 4.1.0)
##  tidyr       * 1.1.4   2021-09-27 [1] CRAN (R 4.1.0)
##  tidyselect    1.1.1   2021-04-30 [1] CRAN (R 4.1.0)
##  tidyverse   * 1.3.1   2021-04-15 [1] CRAN (R 4.1.0)
##  tweenr        1.0.2   2021-03-23 [1] CRAN (R 4.1.0)
##  tzdb          0.2.0   2021-10-27 [1] CRAN (R 4.1.0)
##  units         0.7-2   2021-06-08 [1] CRAN (R 4.1.0)
##  usethis       2.1.5   2021-12-09 [1] CRAN (R 4.1.0)
##  utf8          1.2.2   2021-07-24 [1] CRAN (R 4.1.0)
##  vctrs         0.3.8   2021-04-29 [1] CRAN (R 4.1.0)
##  viridis     * 0.6.2   2021-10-13 [1] CRAN (R 4.1.0)
##  viridisLite * 0.4.0   2021-04-13 [1] CRAN (R 4.1.0)
##  withr         2.4.3   2021-11-30 [1] CRAN (R 4.1.0)
##  wk            0.5.0   2021-07-13 [1] CRAN (R 4.1.0)
##  xaringan      0.22    2021-06-23 [1] CRAN (R 4.1.0)
##  xfun          0.29    2021-12-14 [1] CRAN (R 4.1.0)
##  xml2          1.3.2   2020-04-23 [1] CRAN (R 4.1.0)
##  yaml          2.2.2   2022-01-25 [1] CRAN (R 4.1.2)
## 
##  [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
## 
## ─────────────────────────────────────────────────────────────────────────────────────────────────────
```
]

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>