STATS 220

# STATS 220
## Data visualisation📊

---

.pull-left[
<br>
> The greatest value of a picture is when it forces us to notice what we never expected to see.
> <br> -- [John W. Tukey](https://en.wikipedia.org/wiki/John_Tukey)
]
.pull-right[
<br>
.center[<img src="img/eda.jpg" height="100%">]
]

???

* John Tukey, one of the most well-known stat
* invent boxplot, stem-and-leaf (pencil and paper)
* coined the term EDA

---

## numbers vs plots

```r
dino
```

```
#> # A tibble: 142 x 2
#>       x     y
#>   <dbl> <dbl>
#> 1  55.4  97.2
#> 2  51.5  96.0
#> 3  46.2  94.5
#> 4  42.8  91.4
#> 5  40.8  88.3
#> 6  38.7  84.9
#> # … with 136 more rows
```
]

???

* humans digest info quicker through eyes than reading tables. -> ytb, tt
* numbers on their own don't make sense to us.

---

## numbers vs plots

???

* simple stats cannot reveal full pic.
* all xy data tbl share the same mean/sd/corr, but different str in data...

---

## Why data visualisation?📊

> A picture is worth a thousand words. -- Henrik Ibsen

1. Data visualisation communicates information much quicker than numerical tables.
2. Data visualisation can reveal unexpected structures in data; it is not surprising that data visualisation is one of the key tools in exploratory data analysis.
3. Data plot is usually more eye-catching even if you lose accuracy of the information.

---

## Charts 🥊 Graphics

???

* When we talk about data vis, we use plots, charts, graphics interchangablely
* But when comes to stats, there's difference bt.
* What do we mean by graphics, how to make graphics

---

## A toy example

<br>
.pull-left[

```r
sci_tbl
```

```
#> # A tibble: 4 x 2
#>   dept             count
#>   <chr>            <int>
#> 1 Physics             12
#> 2 Mathematics          8
#> 3 Statistics          20
#> 4 Computer Science    23
```
]
.pull-right[
* `dept`: discrete/categorical
* `count`: quantitative/numeric
<hr>
What types of plots can we make?
1. bar plot for counts
2. pie chart for proportions
]

???

---

## Named charts

```r
barplot(as.matrix(sci_tbl$count), 
  legend = sci_tbl$dept)
```

<img src="figure/base-barplot-1.png" width="480" style="display: block; margin: auto;" />
]
.pull-right[
* Pie chart

```r
pie(sci_tbl$count, 
  labels = sci_tbl$dept)
```

<img src="figure/base-pieplot-1.png" width="480" style="display: block; margin: auto;" />
]

???

* default r functions
* one-off functions
* What's the fundamental difference bt bar and pie

---

## Seems convenient, but ...

<br>
.pull-left[
.x[
* a limited set of named charts
* single purpose functions
* inconsistent inputs
]
]
.pull-right[

```r
barplot(as.matrix(sci_tbl$count), 
  legend = sci_tbl$dept)
```

```r
pie(sci_tbl$count, 
  labels = sci_tbl$dept)
```
]

???

---

.pull-left[
<br>
<br>
.center[<img src="img/gg.jpg" width="300px">]
]
.pull-right[
> Grammar makes language expressive. A language consisting of words and no grammar (statement = word) expresses only as many ideas as there are words. By specifying how words are combined in statements, a grammar expands a language’s scope.
]

???

* a book blew my mind, changed the view to look at stat graphics.
* we can easily run out of names
* we can generate many types of new graphics by combine components following the grammar.
* this books lays the theoretical foundation to {ggplot2}, {tab}, {vega-lite}

---

???

decomposed to

---

### The *grammar of graphics* takes us beyond a limited set of .blue[charts (words)] to an almost unlimited world of .blue[graphical forms (statements)]. 
<hr>
### *{ggplot2}* provides a cohesive system for declaratively creating elegant graphics, based on The Grammar of Graphics.

???

* extends gg, and layered gg.
* provides a cohesive and declarative grammar to create graphics

---

```r
library(ggplot2)
ggplot(data = sci_tbl) +
  geom_bar(
    aes(x = "", y = count, fill = dept),
    stat = "identity"
  )
```

<img src="figure/gg-col-1.png" width="480" style="display: block; margin: auto;" />
]
.pull-right[

```r
ggplot(data = sci_tbl) +
  geom_bar(
    aes(x = "", y = count, fill = dept),
    stat = "identity"
  ) +
* coord_polar(theta = "y")
```

<img src="figure/gg-pie-1.png" width="480" style="display: block; margin: auto;" />
]

???

* Back to our question, the difference
* The difference is plotting bars on the polar coord

---

## A graphing template

```r
ggplot(data = <DATA>, mapping = aes(<MAPPINGS>)) +
  layer(geom = <GEOM>, stat = <STAT>, position = <POSITION>) +
  layer(geom = <GEOM>, stat = <STAT>, position = <POSITION>)
```

1. `data`: tibble/data.frame.
2. `mapping`: .brown[aes]thetic mappings between data variables and visual elements, via `aes()`.
3. `layer()`: a graphical layer is a combination of data, stat and geom with a potential position adjustment.
  * `geom`: geometric elements to render each data observation.
  * `stat`: statistical transformations applied to the data prior to plotting.
  * `position`: position adjustment, such as "identity", "stack", "dodge" etc.

???

* geom: points, bars, lines, text
* stat: "identity", leave as is, boxplot, five numbers
* `+`: layer + layer
* When you think about a graphic to make:
  + which geom to represent the data
  + any stats to be used

---

## Layers: a bar chart 📊

```r
ggplot(data = sci_tbl, mapping = aes(x = dept, y = count)) +
  layer(geom = "bar", stat = "identity", position = "identity")
```

---

## Aesthetic mapping: positional

```r
*p <- ggplot(sci_tbl, aes(x = dept, y = count))
p
```

???

* `ggplot()` initialise the plot
* save a ggplot obj to a symbol

---

## Geoms .small[(a shorthand to `layer()`)]

```r
p + 
  geom_bar(stat = "identity")
```

<img src="figure/gg-bar-1.png" width="480" style="display: block; margin: auto;" />
]
.pull-right[

```r
p + 
  geom_col()
```
* `stat = "identity"` leaves data as is.
* `geom_col()` is a shortcut to `geom_bar(stat = "identity")`.

**Generally, we use `geom_*()` instead of `layer()` in practice.**
]

???

* auto complete for `geom_*()`

---

## Geoms

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

<img src="figure/gg-point-1.png" width="480" style="display: block; margin: auto;" />
]
]
.pull-right[
.small[

```r
p +
  geom_segment(aes(xend = dept, y = 0, yend = count))
```

<img src="figure/gg-seg-1.png" width="480" style="display: block; margin: auto;" />
]
]

???

* We don't have to stick with bar
* we can use points/vertical lines
* `geom_segment()`: more aes

---

## Composite geoms: lollipop 🍭 = points + segments

```r
p +
  geom_point() +
  geom_segment(aes(xend = dept, y = 0, yend = count))
```

---

## Geom [catalogue](https://ggplot2.tidyverse.org/reference/index.html#section-layers)

<div id="htmlwidget-2c6a05cb6c29c43a641e" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-2c6a05cb6c29c43a641e">{"x":{"filter":"none","data":[["geom_abline, geom_hline, geom_vline","geom_bar, geom_col","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,"pageLength":5,"width":"80%","order":[],"autoWidth":false,"orderClasses":false,"lengthMenu":[5,10,25,50,100]}},"evals":[],"jsHooks":[]}</script>

---

## Stats

```r
sci_tbl
```

```
#> # A tibble: 4 x 2
#>   dept             count
#>   <chr>            <int>
#> 1 Physics             12
#> 2 Mathematics          8
#> 3 Statistics          20
#> 4 Computer Science    23
```
]
.pull-right[
* Disaggregated

```r
sci_tbl0
```

```
#> # A tibble: 63 x 1
#>   dept   
#>   <chr>  
#> 1 Physics
#> 2 Physics
#> 3 Physics
#> 4 Physics
#> 5 Physics
#> 6 Physics
#> # … with 57 more rows
```
]

---

## Stats

```r
ggplot(sci_tbl, aes(x = dept, y = count)) +
  geom_bar(stat = "identity")
```

<img src="figure/gg-bar-asis-1.png" width="480" style="display: block; margin: auto;" />
]
]
.pull-right[
.small[

```r
ggplot(sci_tbl0, aes(x = dept)) +
  geom_bar(stat = "count")
```

<img src="figure/gg-bar-c-1.png" width="480" style="display: block; margin: auto;" />
]
]

---

## Aesthetic mapping: visual

```r
p +
  geom_col(aes(colour = dept))
```

<img src="figure/gg-bar-col-1.png" width="480" style="display: block; margin: auto;" />
]
.pull-right[

```r
p +
  geom_col(aes(fill = dept))
```

<img src="figure/gg-bar-fill-1.png" width="480" style="display: block; margin: auto;" />
]

---

## Mapping .brown[variables] / Setting .brown[constants]

```r
p +
  geom_col(aes(fill = dept))
```

<img src="figure/gg-bar-fill2-1.png" width="480" style="display: block; margin: auto;" />
]
.pull-right[

```r
p +
  geom_col(fill = "#756bb1")
```

<img src="figure/gg-bar-fill-str-1.png" width="480" style="display: block; margin: auto;" />
]

???

* bar -> rect -> 2d
  + stroke + fill
+ auto legend

---

## Mapping .brown[variables] + Setting .brown[constants]

```r
p +
  geom_col(aes(fill = dept), colour = "#000000")
```

---

## Visual aesthetics

.pull-left[
* `colour`/`color`, `fill`:
  + named colours, e.g. `"red"`
  + RGB specification, e.g. `"#756bb1"`
* `alpha`: opacity between 0 and 1
* `shape`:
  * an integer between 0 and 25
  * a single string, e.g. `"triangle open"`
* `linetype`: 
  * an integer between 0 and 6
  * a single string, e.g. `"dashed"`
* `size`, `radius`: a numerical value (in millimetres)
]
.pull-right[
<br>
<br>
<img src="https://ggplot2.tidyverse.org/reference/scale_shape-6.png" width="100%">
]

---

## Your turn

Describe a bubble chart in terms of grammar of graphics.

???

gg: grammar of graphics
{ggplot2}: the second version

---

## Coords

.pull-left[
* Coordinate systems
  + `coord_cartesian()` (default)
  + ~~`coord_flip()`~~ (deprecated; now you can simply swap `x` and `y`)
  + `coord_map()`
  + `coord_polar()`
]
.pull-right[

```r
p +
  geom_col(aes(fill = dept)) +
* coord_polar(theta = "y")
```

<img src="figure/gg-arc-1.png" width="600" style="display: block; margin: auto;" />
]

???

live demo:
* `ggplot()`
* `ggplot(data)`
* `ggplot(data, aes())`
* inherit aes
+ layers
* swap x and y

---

## Themes: modify the look

.pull-left[
* [Built-in ggplot themes](https://ggplot2.tidyverse.org/reference/ggtheme.html)
  + `theme_grey()`/`theme_gray()`
  + `theme_bw()`, `theme_linedraw()`
  + `theme_light()`, `theme_dark()`
  + `theme_minimal()`, `theme_classic()`
  + `theme_void()`
]
.pull-right[

```r
p +
  geom_col(aes(fill = dept)) +
* theme_bw()
```

<img src="figure/gg-theme-bw-1.png" width="600" style="display: block; margin: auto;" />
]

???

* start with p11

---

## Themes: modify the look

.pull-left[
* Many R packages provide themes.
  + [{ggthemes}](https://github.com/jrnold/ggthemes)
  + [{ggthemr}](https://github.com/Mikata-Project/ggthemr)
  + [{hrbrthemes}](https://cinc.rud.is/web/packages/hrbrthemes/)
  + [{ggtech}](https://github.com/ricardo-bion/ggtech)
]
.pull-right[

```r
library(ggthemes)
p +
  geom_col(aes(fill = dept)) +
* theme_economist()
```

<img src="figure/ggthemes-1.png" width="480" style="display: block; margin: auto;" />
]

???

* to be able to modify the theme is the 1st step to make pub-ready plot
* in an organisation, use a uniform theme across
* if you want to make your first R package, contributing a theme would be a good start.

---

## Modify the look of *texts* with `element_text()`

???

* tons of args in `themes()` for fine tune
* I'll quickly go through these args, but dive more deeply in week 7 for effective data vis.

---

## Modify the look of *texts* with `element_text()`

```r
p +
  geom_col(aes(fill = dept)) +
  theme(axis.text.x = element_text(angle = 30, vjust = 0.1))
```

---

## Modify the look of *lines* with `element_line()`

---

## Modify the look of *regions* with `element_rect()`

---

## Small multiples .small[(or trellis/faceting plots)]

### 🌟 the idea of conditioning on the values taken on by one or more of the variables in a data set

???

---

```r
mpg
```

```
#> # A tibble: 234 x 11
#>   manufacturer model displ  year   cyl trans     drv     cty
#>   <chr>        <chr> <dbl> <int> <int> <chr>     <chr> <int>
#> 1 audi         a4      1.8  1999     4 auto(l5)  f        18
#> 2 audi         a4      1.8  1999     4 manual(m… f        21
#> 3 audi         a4      2    2008     4 manual(m… f        20
#> 4 audi         a4      2    2008     4 auto(av)  f        21
#> 5 audi         a4      2.8  1999     6 auto(l5)  f        16
#> 6 audi         a4      2.8  1999     6 manual(m… f        18
#> # … with 228 more rows, and 3 more variables: hwy <int>,
#> #   fl <chr>, class <chr>
```
]

---

```r
p_mpg <- ggplot(mpg, aes(displ, cty)) + 
  geom_point(aes(colour = drv))
p_mpg
```

<img src="figure/gg-mpg-1.png" width="600" style="display: block; margin: auto;" />
]

---

```r
p_mpg +
  facet_grid(rows = vars(drv))
  # facet_grid(~ drv)
```

<img src="figure/gg-facet-rows-1.png" width="600" style="display: block; margin: auto;" />
]

???

* looking at conditional distribution
* `grid` -> 2d matrix layout

---

```r
p_mpg +
  facet_grid(cols = vars(drv))
  # facet_grid(drv ~ .)
```

<img src="figure/gg-facet-cols-1.png" width="720" style="display: block; margin: auto;" />
]

---

```r
p_mpg +
  facet_grid(rows = vars(drv), cols = vars(cyl))
  # facet_grid(cyl ~ drv)
```

<img src="figure/gg-facet-grid-1.png" width="840" style="display: block; margin: auto;" />
]

---

```r
p_mpg +
  facet_wrap(vars(drv, cyl), ncol = 3)
  # facet_wrap(~ drv + cyl, ncol = 3)
```

<img src="figure/gg-facet-wrap-1.png" width="720" style="display: block; margin: auto;" />
]

---

# .large[.orange[Exploratory data visualisation]]

---

```r
movies <- as_tibble(jsonlite::read_json(
  "https://vega.github.io/vega-editor/app/data/movies.json",
  simplifyVector = TRUE))
movies
```

```
#> # A tibble: 3,201 x 16
#>   Title                US_Gross Worldwide_Gross US_DVD_Sales
#>   <chr>                   <int>           <dbl>        <int>
#> 1 The Land Girls         146083          146083           NA
#> 2 First Love, Last Ri…    10876           10876           NA
#> 3 I Married a Strange…   203134          203134           NA
#> 4 Let's Talk About Sex   373615          373615           NA
#> 5 Slam                  1009819         1087521           NA
#> 6 Mississippi Mermaid     24551         2624551           NA
#> # … with 3,195 more rows, and 12 more variables:
#> #   Production_Budget <int>, Release_Date <chr>,
#> #   MPAA_Rating <chr>, Running_Time_min <int>,
#> #   Distributor <chr>, Source <chr>, Major_Genre <chr>,
#> #   Creative_Type <chr>, Director <chr>,
#> #   Rotten_Tomatoes_Rating <int>, IMDB_Rating <dbl>,
#> #   IMDB_Votes <int>
```
]

---

```r
skimr::skim(movies)
```

```
#> ── Data Summary ────────────────────────
#>                            Values
#> Name                       movies
#> Number of rows             3201  
#> Number of columns          16    
#> _______________________          
#> Column type frequency:           
#>   character                8     
#>   numeric                  8     
#> ________________________         
#> Group variables            None  
#> 
#> ── Variable type: character ────────────────────────────────────────────────────
#>   skim_variable n_missing complete_rate   min   max empty n_unique whitespace
#> 1 Title                 1         1.00      1    66     0     3176          0
#> 2 Release_Date          7         0.998     8    11     0     1603          0
#> 3 MPAA_Rating         605         0.811     1     9     0        7          0
#> 4 Distributor         232         0.928     3    33     0      174          0
#> 5 Source              365         0.886     6    29     0       18          0
#> 6 Major_Genre         275         0.914     5    19     0       12          0
#> 7 Creative_Type       446         0.861     7    23     0        9          0
#> 8 Director           1331         0.584     7    27     0      550          0
#> 
#> ── Variable type: numeric ──────────────────────────────────────────────────────
#>   skim_variable          n_missing complete_rate        mean           sd
#> 1 US_Gross                       7         0.998 44002085.    62555311.  
#> 2 Worldwide_Gross                7         0.998 85343400.   149947343.  
#> 3 US_DVD_Sales                2637         0.176 34901547.    45895122.  
#> 4 Production_Budget              1         1.00  31069171.    35585913.  
#> 5 Running_Time_min            1992         0.378      110.          20.2 
#> 6 Rotten_Tomatoes_Rating       880         0.725       54.3         28.1 
#> 7 IMDB_Rating                  213         0.933        6.28         1.25
#> 8 IMDB_Votes                   213         0.933    29909.       44938.  
#>         p0       p25        p50        p75         p100 hist 
#> 1      0   5493221.  22019466.  56091762.   760167650   ▇▁▁▁▁
#> 2      0   8031285.  31168926.  97283797   2767891499   ▇▁▁▁▁
#> 3 618454   9906211.  20331558.  37794216.   352582053   ▇▁▁▁▁
#> 4    218   6575000   20000000   42000000    300000000   ▇▁▁▁▁
#> 5     46        95        107        121          222   ▁▇▃▁▁
#> 6      1        30         55         80          100   ▅▆▆▇▇
#> 7      1.4       5.6        6.4        7.2          9.2 ▁▁▅▇▂
#> 8     18      4828.     15106      35810.      519541   ▇▁▁▁▁
```
]
]

---

.left-column[
## case study
### - import
### - skim
### - vis
]
.right-column[
* Data analysis starts with questions (a.k.a. curiosity).
.center[
<img src="https://rstudio-education.github.io/tidyverse-cookbook/images/data-science-workflow.png" width="100%">
]
]

???

* eda: wandering around a city as a tourist: sometimes have destinations, sometimes no.
* once you started off with one question, more questions on the way.
* There's no best single plot for the data.
* Make as many plots as possible, as quick as possible to get most facets of the data
* end up with nothing
* for internal use, don't need to polish

---

.left-column[
## case study
### - import
### - skim
### - vis
]
.right-column[
<i class="far fa-question-circle"></i> Are movies ratings consistent b/t IMDB & Rotten Tomatoes
.small[

```r
ggplot(movies, aes(x = IMDB_Rating, y = Rotten_Tomatoes_Rating)) +
  geom_point(size = 0.5, alpha = 0.5) +
  geom_smooth(method = "gam") +
  theme(aspect.ratio = 1)
```

<img src="figure/movies-gam-1.png" width="420" style="display: block; margin: auto;" />
]
]

---

.left-column[
## case study
### - import
### - skim
### - vis
]
.right-column[
<i class="far fa-question-circle"></i> Are movies ratings consistent b/t IMDB & Rotten Tomatoes
.small[

```r
ggplot(movies, aes(x = IMDB_Rating, y = Rotten_Tomatoes_Rating)) +
  geom_hex() +
  theme(aspect.ratio = 1)
```

<img src="figure/movies-hex-1.png" width="480" style="display: block; margin: auto;" />
]
]

???

* live demo: for rendering speed

---

.left-column[
## case study
### - import
### - skim
### - vis
]
.right-column[
<i class="far fa-question-circle"></i> The popularity of major genre
.small[

```r
ggplot(movies, aes(y = Major_Genre)) +
  geom_bar()
```

<img src="figure/movies-bar-1.png" width="600" style="display: block; margin: auto;" />
]
]

---

.left-column[
## case study
### - import
### - skim
### - vis
]
.right-column[
<i class="far fa-question-circle"></i> The likeness of major genre
.small[

```r
ggplot(movies) +
  geom_boxplot(aes(x = IMDB_Rating, y = Major_Genre))
```

<img src="figure/movies-boxplot-1.png" width="600" style="display: block; margin: auto;" />
]
]

---

.left-column[
## case study
### - import
### - skim
### - vis
]
.right-column[
<i class="far fa-question-circle"></i> The likeness of major genre
.small[

```r
ggplot(movies) +
  geom_density(aes(x = IMDB_Rating, fill = Major_Genre))
```

<img src="figure/movies-density-1.png" width="720" style="display: block; margin: auto;" />
]
]

???

* overlapping
* could use facet

---

.left-column[
## case study
### - import
### - skim
### - vis
]
.right-column[
<i class="far fa-question-circle"></i> The likeness of major genre
.small[

```r
library(ggridges)
ggplot(movies, aes(x = IMDB_Rating, y = Major_Genre)) +
  geom_density_ridges(aes(fill = Major_Genre))
```

<img src="figure/movies-ridges-1.png" width="840" style="display: block; margin: auto;" />
]
]

---

## {ggplot2}-ext 📦

> {ggplot2} now has an official extension mechanism. This means that others can now easily create their own stats, geoms and positions, and provide them in other packages. This should allow the ggplot2 community to flourish, even as less development work happens in ggplot2 itself.

➡️ <https://exts.ggplot2.tidyverse.org/gallery/>

???

* {ggplot2} has been around for more than 10 yrs.
* {ggplot2} is extensible

---

.pull-left[
[<img src="https://gganimate.com/reference/figures/logo.png" width="80px">](https://gganimate.com)
```r
library(gganimate)
ggplot(mtcars, aes(factor(cyl), mpg)) +
  geom_boxplot() +
  # Here comes the gganimate code
  transition_states(
    gear,
    transition_length = 2,
    state_length = 1
  ) +
  enter_fade() +
  exit_shrink() +
  ease_aes('sine-in-out')
```
]
.pull-right[
<br>
<br>
<br>
<img src="https://gganimate.com/reference/figures/README-unnamed-chunk-2-1.gif" width="100%">
]

---

.left-column[
[<img src="https://github.com/slowkow/ggrepel/raw/master/man/figures/logo.svg" width="80px">](https://ggrepel.slowkow.com/index.html)
]
.right-column[
<br>
<br>
<br>
<img src="https://ggrepel.slowkow.com/articles/examples_files/figure-html/comparison-1.png" width="100%">
]

---

## The R Graph Gallery

???

* good resources for d3.js

---

.left-column[
## To be continued ...
]
.right-column[
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">NEW: the Thursday 19 March update of our coronavirus mortality trajectories tracker<br><br>• Italy now has more Covid-19 deaths than China’s total<br>• UK remains on a steeper mortality curve than Italy, while Britain remains far from lockdown<br><br>Live version here: <a href="https://t.co/VcSZISFxzF">https://t.co/VcSZISFxzF</a> <a href="https://t.co/QvByzSj6QX">pic.twitter.com/QvByzSj6QX</a></p>&mdash; John Burn-Murdoch (@jburnmurdoch) <a href="https://twitter.com/jburnmurdoch/status/1240723388336877569?ref_src=twsrc%5Etfw">March 19, 2020</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
]

???

* a tragic yr cos of covid-19
* a blast yr for data vis
* log scale

---

## Reading

.pull-left[
.center[[<img src="https://d33wubrfki0l68.cloudfront.net/b88ef926a004b0fce72b2526b0b5c4413666a4cb/24a30/cover.png" height="520px">](https://r4ds.had.co.nz)]
]
.pull-right[
* [Data visualisation](https://r4ds.had.co.nz/data-visualisation.html)
* [{ggplot2} cheatsheet](https://github.com/rstudio/cheatsheets/raw/master/data-visualization-2.1.pdf)
]