Data Visualisation with R Workshop Part 1

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</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<br>Workshop Part 1</span>

]

.center.shade_black.animated.bounceInUp.slower[
<br><br>
## Grammar of Graphics

<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[
6th Dec 2021 @ Statistical Society of Australia NSW Branch | Zoom
]

]

</div>

---

# <i class="fas fa-key"></i> Why grammar of graphics for data visualisation?

---

# <i class="fas fa-chart-bar"></i> Constructing plots with R: `base` version

```r
df
```

```
## # A tibble: 3 × 2
##   duty      perc
##   <chr>    <dbl>
## 1 Teaching    40
## 2 Research    40
## 3 Admin       20
```

[.tag[<i class="fas fa-caret-right"></i> `ggplot2` version]](#ggplot-graphics)

]
.item.border-right[
Stacked barplot

```r
barplot(as.matrix(df$perc),
        legend = df$duty)
```

![](images/part1-session1/barplot-1.png)

]
.item[
Pie chart

```r
pie(df$perc, labels = df$duty)
```

![](images/part1-session1/pie-1.png)

]
]

.footnote[
R Core Team (2020) R: A Language and Environment for Statistical Computing https://www.R-project.org/
]

* **Single purpose functions** to generate "named plots"
* **Input** varies, here it is vector or matrix

]

---

<br>
How are **barplots** different to a **pie chart**? 🤔

![](images/part1-session1/unstacked-barplot-1.png)

Don't they all **depict the same information**? 🤨

]
.item.center[

![](images/part1-session1/barplot-1.png)
![](images/part1-session1/pie-1.png)

]
]

---

# .circle-big[1]

# Grammar of graphics

.footnote.monash-bg-blue[
Wickham (2016) ggplot2: Elegant Graphics for Data Analysis. *Springer-Verlag New York*
]

---

#  Basic structure of ggplot

.info-box[
1. **data** as `data.frame` (or `tibble`), 
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. 
]

---

# <img src="images/penguins-logo.png" width="50px" style="vertical-align: middle;"> Palmer penguins

`penguins` data is from the `palmerpenguins` 📦

```r
library(palmerpenguins)
glimpse(penguins)
```

```
## Rows: 344
## Columns: 8
## $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
## $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
## $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
## $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
## $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
## $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
## $ sex               <fct> male, female, female, NA, female, male, female, male…
## $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…
```
]

.footnote[
Horst AM, Hill AP, Gorman KB (2020). palmerpenguins: Palmer Archipelago (Antarctica) penguin data. R package version 0.1.0. https://allisonhorst.github.io/palmerpenguins/ <br>
Gorman KB, Williams TD, Fraser WR (2014). Ecological sexual dimorphism and environmental variability within a community of Antarctic penguins (genus Pygoscelis). PLoS ONE 9(3):e90081.
]

<div class="tag center animated rubberBand" style="position:absolute;top:2%;left:12%;transform:rotate(-3deg);">
demo data for today
</div>

---

# Aesthestic mappings
.center[
aesthestic = column in data
]

```r
ggplot(data = penguins, 
       mapping = aes(x = bill_depth_mm, y = bill_length_mm, color = species)) + 
  geom_point()
```

* `bill_depth_mm` is mapped to the `x` coordinate
* `bill_length_mm` is mapped to the `y` coordinate
* `species` is mapped to the `color`

]
]

---

# Hidden argument names in ggplot

---

```r
ggplot(penguins, aes(species))
```

![](images/part1-session1/unnamed-chunk-4-1.png)

```r
ggplot(penguins, aes(species, bill_length_mm))
```

![](images/part1-session1/unnamed-chunk-5-1.png)

]

* No need to write explicitly write out `data = `, `mapping = `, `x = `, and `y = ` each time in `ggplot`.
{{content}}

]

--
* `ggplot` code in the wild often omit these argument names.
{{content}}
--
* But position needs to be correct if argument names are not specified!
{{content}}
--
* If no layer is specified, then the plot is `geom_blank()`.

---

# Layer

.info-box[
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,

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

---

# Example layer: geom_point()

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

```r
ggplot(penguins, aes(bill_depth_mm, bill_length_mm)) +
* geom_point()
```

<br>
is a shorthand for 
<br>

```r
ggplot(penguins, aes(bill_depth_mm, bill_length_mm)) +
* layer(geom = "point",
*       stat = "identity", position = "identity",
*       params = list(na.rm = FALSE))
```

---

# Different geometric objects

```r
p <- ggplot(penguins, aes(species, bill_length_mm))
```

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

![](images/part1-session1/unnamed-chunk-9-1.png)

]
.item[

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

![](images/part1-session1/boxplot-1.png)

]
.item[

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

![](images/part1-session1/unnamed-chunk-10-1.png)

]]

---

# `geom`

<div id="htmlwidget-b1d572b16718e294af6b" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-b1d572b16718e294af6b">{"x":{"filter":"none","vertical":false,"data":[["geom_abline, geom_hline, geom_vline","geom_bar, geom_col","geom_bin_2d, geom_bin2d","geom_blank","geom_boxplot","geom_contour, geom_contour_filled","geom_count","geom_density","geom_density_2d, geom_density2d, geom_density_2d_filled, geom_density2d_filled","geom_dotplot","geom_errorbarh","geom_function","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","Draw a function as a continuous curve","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(penguins, aes(species, bill_length_mm)) + geom_boxplot()
```

![](images/part1-session1/boxplot-1.png)

]
.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 flipped_aes
## 1 32.1 36.75  38.80 40.750 46.0            39.31431   38.28569 1       FALSE
## 2 40.9 46.35  49.55 51.075 58.0            50.45532   48.64468 2       FALSE
## 3 40.9 45.30  47.30 49.550 55.9     59.6   47.90547   46.69453 3       FALSE
##   PANEL group ymin_final ymax_final  xmin  xmax xid newx new_width weight
## 1     1     1       32.1       46.0 0.625 1.375   1    1      0.75      1
## 2     1     2       40.9       58.0 1.625 2.375   2    2      0.75      1
## 3     1     3       40.9       59.6 2.625 3.375   3    3      0.75      1
##   colour  fill size alpha shape linetype
## 1 grey20 white  0.5    NA    19    solid
## 2 grey20 white  0.5    NA    19    solid
## 3 grey20 white  0.5    NA    19    solid
```

]
</div>

---

# 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(penguins, aes(bill_length_mm)) 
```

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

![](images/part1-session1/unnamed-chunk-14-1.png)

]
.item[

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

![](images/part1-session1/unnamed-chunk-15-1.png)

]

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

![](images/part1-session1/unnamed-chunk-16-1.png)

]
</div>

---

# 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..`].

---

`stat = "bin"`

```
##           x count     density
## 1  32.24138     1 0.003083466
## 2  33.18966     2 0.006166932
## 3  34.13793     6 0.018500797
## 4  35.08621     7 0.021584264
## 5  36.03448    18 0.055502392
## 6  36.98276    13 0.040085061
## 7  37.93103    22 0.067836257
## 8  38.87931    18 0.055502392
## 9  39.82759    19 0.058585859
## 10 40.77586    21 0.064752791
## 11 41.72414    13 0.040085061
## 12 42.67241    15 0.046251994
## 13 43.62069    13 0.040085061
## 14 44.56897     9 0.027751196
## 15 45.51724    27 0.083253589
## 16 46.46552    30 0.092503987
## 17 47.41379    14 0.043168527
## 18 48.36207    15 0.046251994
## 19 49.31034    18 0.055502392
## 20 50.25862    25 0.077086656
## 21 51.20690    16 0.049335460
## 22 52.15517     9 0.027751196
## 23 53.10345     4 0.012333865
## 24 54.05172     2 0.006166932
## 25 55.00000     1 0.003083466
## 26 55.94828     2 0.006166932
## 27 56.89655     0 0.000000000
## 28 57.84483     1 0.003083466
## 29 58.79310     0 0.000000000
## 30 59.74138     1 0.003083466
```
]

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

![](images/part1-session1/unnamed-chunk-18-1.png)

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

]
]

---

# `stat`

<div id="htmlwidget-1f6da18d8f52c1c03f04" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-1f6da18d8f52c1c03f04">{"x":{"filter":"none","vertical":false,"data":[["stat_count","stat_bin_2d, stat_bin2d","stat_boxplot","stat_contour, stat_contour_filled","stat_sum","stat_density","stat_density_2d, stat_density2d, stat_density_2d_filled","stat_function","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_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","Draw a function as a continuous curve","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 data ellipses","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>

---

# <i class="fas fa-chart-pie"></i> Constructing plots with R: `ggplot2` version

```r
df
```

```
## # A tibble: 3 × 2
##   duty      perc
##   <chr>    <dbl>
## 1 Teaching    40
## 2 Research    40
## 3 Admin       20
```

[.tag[<i class="fas fa-caret-right"></i> `base` version]](#base-graphics)

]
.item.border-right[
Stacked barplot

```r
ggplot(df, aes(x = "", # dummy
               y = perc, 
               fill = duty)) + 
  geom_col()
```

![](images/part1-session1/ggbarplot-1.png)

]

<div class="item" style="opacity:0;">
Pie chart

</div>
]

.footnote[
Wilkinson (2005) The Grammar of graphics. *Statistics and Computing. Springer, 2nd edition.*

Wickham (2008) Practical Tools for Exploring Data and Models. *PhD Thesis Chapter 3: A layered grammar of graphics*.

Wickham (2010) A Layered Grammar of Graphics, *Journal of Computational and Graphical Statistics, 19:1, 3-28*

]

---

# <i class="fas fa-chart-pie"></i> Constructing plots with R: `ggplot2` version

```r
df
```

```
## # A tibble: 3 × 2
##   duty      perc
##   <chr>    <dbl>
## 1 Teaching    40
## 2 Research    40
## 3 Admin       20
```

[.tag[<i class="fas fa-caret-right"></i> `base` version]](#base-graphics)

]
.item.border-right[
Stacked barplot

```r
ggplot(df, aes(x = "", # dummy
               y = perc, 
               fill = duty)) + 
  geom_col()
```

![](images/part1-session1/ggbarplot-1.png)

]

```r
ggplot(df, aes(x = "", # dummy
               y = perc, 
               fill = duty)) + 
  geom_col() + 
* coord_polar(theta = "y")
```

![](images/part1-session1/ggpie-1.png)

]
]

.footnote[
Wilkinson (2005) The Grammar of graphics. *Statistics and Computing. Springer, 2nd edition.*

Wickham (2008) Practical Tools for Exploring Data and Models. *PhD Thesis Chapter 3: A layered grammar of graphics*.

Wickham (2010) A Layered Grammar of Graphics, *Journal of Computational and Graphical Statistics, 19:1, 3-28*

]

.corner-box[
The difference between a **stacked barplot** and a **pie chart** is that the coordinate system have been transformed from **Cartesian coordinate** to **polar coordinate**.
]

---

# <i class="fas fa-puzzle-piece"></i> How do we create this barplot in `ggplot`?

```r
df
```

```
## # A tibble: 3 × 2
##   duty      perc
##   <chr>    <dbl>
## 1 Teaching    40
## 2 Research    40
## 3 Admin       20
```

```r
barplot(df$perc, names.arg = df$duty)
```

![](images/part1-session1/unstacked-barplot-1.png)

]
.item[

```r
ggplot(data = df, 
       aes(x = ..., y = ...)) + 
  geom_...
```

]
]

---

# <i class="fas fa-puzzle-piece"></i> How do we create this barplot in `ggplot`?

```r
df
```

```
## # A tibble: 3 × 2
##   duty      perc
##   <chr>    <dbl>
## 1 Teaching    40
## 2 Research    40
## 3 Admin       20
```

```r
barplot(df$perc, names.arg = df$duty)
```

![](images/part1-session1/unstacked-barplot-1.png)

]
.item[

```r
ggplot(data = df, 
       aes(x = duty, y = perc)) + 
  geom_col()
```

![](images/part1-session1/unstacked-ggbarplot-1.png)

]
]

---

# <i class="fas fa-puzzle-piece"></i> What graph will this yield?

```r
df2
```

```
## # A tibble: 6 × 3
##   duty      perc type    
##   <chr>    <dbl> <chr>   
## 1 Teaching    40 standard
## 2 Research    40 standard
## 3 Admin       20 standard
## 4 Teaching    80 teaching
## 5 Research     0 teaching
## 6 Admin       20 teaching
```

]
.item.border-right[

```r
g <- ggplot(df2, 
*       aes(x = type,
            y = perc, 
            fill = duty)) + 
        geom_col()
g
```

]
.item[

```r
*g + coord_polar("y")
```

]
]

---

# <i class="fas fa-puzzle-piece"></i> What graph will this yield?

```r
df2
```

]
.item.border-right[

```r
g <- ggplot(df2, 
*       aes(x = type,
            y = perc, 
            fill = duty)) + 
        geom_col()
g
```

![](images/part1-session1/barplot2-1.png)

]
.item[

```r
*g + coord_polar("y")
```

]
]

---

# <i class="fas fa-puzzle-piece"></i> What graph will this yield?

```r
df2
```

]
.item.border-right[

```r
g <- ggplot(df2, 
*       aes(x = type,
            y = perc, 
            fill = duty)) + 
        geom_col()
g
```

![](images/part1-session1/barplot2-1.png)

]
.item[

```r
*g + coord_polar("y")
```

![](images/part1-session1/pie2-1.png)

]
]

```r
g + coord_polar("x")
```

![](images/part1-session1/pie2x-1.png)

---

# <i class="fas fa-code"></i> Open `part1-exercise-01.Rmd`

---

# Session Information

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

```
## ─ Session info  🦵🏾  🛌🏻  👎🏽   ─────────────────────────────────────────────────
##  hash: leg: medium-dark skin tone, person in bed: light skin tone, thumbs down: medium skin tone
## 
##  setting  value
##  version  R version 4.1.2 (2021-11-01)
##  os       macOS Big Sur 10.16
##  system   x86_64, darwin17.0
##  ui       X11
##  language (EN)
##  collate  en_AU.UTF-8
##  ctype    en_AU.UTF-8
##  tz       Australia/Melbourne
##  date     2021-12-06
##  pandoc   2.11.4 @ /Applications/RStudio.app/Contents/MacOS/pandoc/ (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)
##  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)
##  cli              3.1.0   2021-10-27 [1] CRAN (R 4.1.0)
##  colorspace       2.0-2   2021-06-24 [1] CRAN (R 4.1.0)
##  countdown        0.3.5   2021-11-30 [1] Github (gadenbuie/countdown@a544fa4)
##  crayon           1.4.2   2021-10-29 [1] CRAN (R 4.1.0)
##  crosstalk        1.2.0   2021-11-04 [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           0.6.28  2021-09-23 [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)
##  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            0.5.0   2021-05-25 [1] CRAN (R 4.1.0)
##  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               1.5.0   2020-07-31 [1] CRAN (R 4.1.0)
##  generics         0.1.1   2021-10-25 [1] CRAN (R 4.1.0)
##  ggplot2        * 3.3.5   2021-06-25 [1] CRAN (R 4.1.0)
##  glue             1.5.0   2021-11-07 [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)
##  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)
##  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)
##  jquerylib        0.1.4   2021-04-26 [1] CRAN (R 4.1.0)
##  jsonlite         1.7.2   2020-12-09 [1] CRAN (R 4.1.0)
##  knitr            1.36    2021-09-29 [1] CRAN (R 4.1.0)
##  labeling         0.4.2   2020-10-20 [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)
##  magrittr         2.0.1   2020-11-17 [1] CRAN (R 4.1.0)
##  memoise          2.0.0   2021-01-26 [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)
##  palmerpenguins * 0.1.0   2020-07-23 [1] CRAN (R 4.1.0)
##  pillar           1.6.4   2021-10-18 [1] CRAN (R 4.1.0)
##  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)
##  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)
##  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             1.0.7   2021-07-07 [1] CRAN (R 4.1.0)
##  readr          * 2.1.0   2021-11-11 [1] CRAN (R 4.1.0)
##  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)
##  rlang            0.4.12  2021-10-18 [1] CRAN (R 4.1.0)
##  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)
##  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)
##  sessioninfo      1.2.1   2021-11-02 [1] CRAN (R 4.1.0)
##  stringi          1.7.5   2021-10-04 [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)
##  tzdb             0.2.0   2021-10-27 [1] CRAN (R 4.1.0)
##  usethis          2.1.3   2021-10-27 [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)
##  whisker          0.4     2019-08-28 [1] CRAN (R 4.1.0)
##  withr            2.4.2   2021-04-18 [1] CRAN (R 4.1.0)
##  xaringan         0.22    2021-06-23 [1] CRAN (R 4.1.0)
##  xfun             0.28    2021-11-04 [1] CRAN (R 4.1.0)
##  xml2             1.3.2   2020-04-23 [1] CRAN (R 4.1.0)
##  yaml             2.2.1   2020-02-01 [1] CRAN (R 4.1.0)
## 
##  [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library
## 
## ──────────────────────────────────────────────────────────────────────────────
```
]

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