NHS Workshop Introduction to ggplot

class: title-slide, right, top
background-image: url(data:image/png;base64,#img/sunrise.png)
background-position: 90% 75%, 75% 75%
background-size:cover

**Eugene Hickey**<br>
January 21st 2021
]

.palegrey[.left[.footnote[Graphic by [Elaine Hickey](https://photos.google.com/photo/AF1QipMjKNoaxyne8nte4HmxA6Th9-4fUfSbl_mx-_1G)]]]

???

Welcome to the workshop on ggplot.

Where we'll show you how to create impressive data visualisations.

---

# Picturing Data Different Ways with ggplot

### We're going to set out some of the options for looking at data

### these depend on what kind of data you have

### and what you want to investigate

Lots of these come from [Top 50 Visualizations in R](http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html#5.%20Composition)

---

<ol>
<li> Visualising Amounts
<li> Visualising Proportions
<li><span style="color: red;"> Visualising Distributions </span>
<li><span style="color: red;"> Visualising Relationships </span>
<li><span style="color: blue;"> Visualising Time Series </span>
<li><span style="color: blue;"> Visualising Groups </span>
<li><span style="color: blue;"> Visualising Networks </span>
<li><span style="color: blue;"> Visualising Spatial Data </span>

Items in <span style="color: red;">red</span> we'll cover this afternoon. In <span style="color: blue;">blue</span> will have to wait for a future workshop.

---
class: inverse

# Visualising Distributions

- histograms
- density plots
- boxplot
- violin plot
- ridge plots

---

```r
*basketball
```
]
 
.panel2-histogram-auto[

```
## # A tibble: 3,366 x 8
##    name     year_start year_end position height weight birth_date   college     
##    <chr>         <dbl>    <dbl> <chr>     <dbl>  <dbl> <chr>        <chr>       
##  1 Kareem ~       1970     1989 C          218.  102.  April 16, 1~ University ~
##  2 Mahmoud~       1991     2001 G          185.   73.5 March 9, 19~ Louisiana S~
##  3 Tariq A~       1998     2003 F          198.  101.  November 3,~ San Jose St~
##  4 Shareef~       1997     2008 F          206.  102.  December 11~ University ~
##  5 Tom Abe~       1977     1981 F          201.   99.8 May 6, 1954  Indiana Uni~
##  6 Forest ~       1957     1957 G          190.   81.6 July 27, 19~ Western Ken~
##  7 John Ab~       1947     1948 F          190.   88.5 February 9,~ Salem Inter~
##  8 Alex Ac~       2006     2009 G          196.   83.9 January 21,~ Pepperdine ~
##  9 Don Ack~       1954     1954 G          183.   83.0 September 4~ Long Island~
## 10 Bud Act~       1968     1968 F          198.   95.3 January 11,~ Hillsdale C~
## # ... with 3,356 more rows
```
]

---
count: false

```r
basketball %>%
* ggplot(aes(weight))
```
]
 
.panel2-histogram-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/histogram_auto_02_output-1.png)
]

---
count: false

```r
basketball %>%
  ggplot(aes(weight)) +
* geom_histogram(fill = "firebrick4",
*                bins = 50)
```
]
 
.panel2-histogram-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/histogram_auto_03_output-1.png)
]

---
count: false

```r
basketball %>%
  ggplot(aes(weight)) +
  geom_histogram(fill = "firebrick4",
                 bins = 50) +
* labs(x = "weight (kg)",
*      y = "",
*      caption = "@Data from Kaggle",
*      title = "Weight of NBA Players")
```
]
 
.panel2-histogram-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/histogram_auto_04_output-1.png)
]

---

```r
*basketball
```
]
 
.panel2-histogram-dodge-auto[

---
count: false

```r
basketball %>%
* ggplot(aes(weight,
*            fill = position))
```
]
 
.panel2-histogram-dodge-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/histogram-dodge_auto_02_output-1.png)
]

---
count: false

```r
basketball %>%
  ggplot(aes(weight,
             fill = position)) +
* geom_histogram(bins = 20,
*                position = "dodge")
```
]
 
.panel2-histogram-dodge-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/histogram-dodge_auto_03_output-1.png)
]

---
count: false

```r
basketball %>%
  ggplot(aes(weight,
             fill = position)) +
  geom_histogram(bins = 20,
                 position = "dodge") +
* labs(x = "weight (kg)",
*      y = "",
*      caption = "@Data from Kaggle",
*      title = "Weight of NBA Players by Position")
```
]
 
.panel2-histogram-dodge-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/histogram-dodge_auto_04_output-1.png)
]

---

```r
*basketball
```
]
 
.panel2-density-auto[

---
count: false

```r
basketball %>%
* ggplot(aes(weight,
*            col = position))
```
]
 
.panel2-density-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/density_auto_02_output-1.png)
]

---
count: false

```r
basketball %>%
  ggplot(aes(weight,
             col = position)) +
* stat_density(geom = "line",
*              position = "identity")
```
]
 
.panel2-density-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/density_auto_03_output-1.png)
]

---
count: false

```r
basketball %>%
  ggplot(aes(weight,
             col = position)) +
  stat_density(geom = "line",
               position = "identity") +
* labs(x = "weight (kg)",
*      y = "",
*      caption = "@Data from Kaggle",
*      title = "Weight of NBA Players by Position")
```
]
 
.panel2-density-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/density_auto_04_output-1.png)
]

---
count: false

```r
basketball %>%
  ggplot(aes(weight,
             col = position)) +
  stat_density(geom = "line",
               position = "identity") +
  labs(x = "weight (kg)",
       y = "",
       caption = "@Data from Kaggle",
       title = "Weight of NBA Players by Position") +
* geom_rug()
```
]
 
.panel2-density-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/density_auto_05_output-1.png)
]

---

```r
*basketball
```
]
 
.panel2-boxplots-auto[

---
count: false

```r
basketball %>%
*  ggplot(aes(x = position,
*             y = weight,
*             colour = position))
```
]
 
.panel2-boxplots-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/boxplots_auto_02_output-1.png)
]

---
count: false

```r
basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position)) +
*  geom_boxplot(show.legend = F)
```
]
 
.panel2-boxplots-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/boxplots_auto_03_output-1.png)
]

---
count: false

```r
basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position)) +
   geom_boxplot(show.legend = F) +
*  labs(y = "weight (kg)",
*       x = "position",
*       caption = "@Data from Kaggle",
*       title = "Weight of NBA Players by Position")
```
]
 
.panel2-boxplots-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/boxplots_auto_04_output-1.png)
]

---
count: false

```r
basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position)) +
   geom_boxplot(show.legend = F) +
   labs(y = "weight (kg)",
        x = "position",
        caption = "@Data from Kaggle",
        title = "Weight of NBA Players by Position") +
* geom_jitter(size = 0.4,
*             alpha = 0.2,
*             show.legend = F)
```
]
 
.panel2-boxplots-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/boxplots_auto_05_output-1.png)
]

```r
*basketball
```
]
 
.panel2-violins-auto[

---
count: false

```r
basketball %>%
*  ggplot(aes(x = position,
*             y = weight,
*             colour = position))
```
]
 
.panel2-violins-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/violins_auto_02_output-1.png)
]

---
count: false

```r
basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position)) +
*  geom_violin(show.legend = F)
```
]
 
.panel2-violins-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/violins_auto_03_output-1.png)
]

---
count: false

```r
basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position)) +
   geom_violin(show.legend = F) +
*  labs(x = "position",
*       y = "weight (kg)",
*       caption = "@Data from Kaggle",
*       title = "Weight of NBA Players by Position")
```
]
 
.panel2-violins-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/violins_auto_04_output-1.png)
]

---
count: false

```r
basketball %>%
   ggplot(aes(x = position,
              y = weight,
              colour = position)) +
   geom_violin(show.legend = F) +
   labs(x = "position",
        y = "weight (kg)",
        caption = "@Data from Kaggle",
        title = "Weight of NBA Players by Position") +
*  geom_jitter(size = 0.4,
*              alpha = 0.2,
*              show.legend = F)
```
]
 
.panel2-violins-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/violins_auto_05_output-1.png)
]

---

```r
*gapminder::gapminder
```
]
 
.panel2-ridges-auto[

```
## # A tibble: 1,704 x 6
##    country     continent  year lifeExp      pop gdpPercap
##    <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Afghanistan Asia       1952    28.8  8425333      779.
##  2 Afghanistan Asia       1957    30.3  9240934      821.
##  3 Afghanistan Asia       1962    32.0 10267083      853.
##  4 Afghanistan Asia       1967    34.0 11537966      836.
##  5 Afghanistan Asia       1972    36.1 13079460      740.
##  6 Afghanistan Asia       1977    38.4 14880372      786.
##  7 Afghanistan Asia       1982    39.9 12881816      978.
##  8 Afghanistan Asia       1987    40.8 13867957      852.
##  9 Afghanistan Asia       1992    41.7 16317921      649.
## 10 Afghanistan Asia       1997    41.8 22227415      635.
## # ... with 1,694 more rows
```
]

---
count: false

```r
gapminder::gapminder %>%
* ggplot(aes(x = lifeExp,
*            y = factor(year)))
```
]
 
.panel2-ridges-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/ridges_auto_02_output-1.png)
]

---
count: false

```r
gapminder::gapminder %>%
  ggplot(aes(x = lifeExp,
             y = factor(year))) +
* geom_density_ridges(fill = "firebrick4",
*                     colour = "firebrick4",
*                     alpha = 0.4)
```
]
 
.panel2-ridges-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/ridges_auto_03_output-1.png)
]

---
count: false

```r
gapminder::gapminder %>%
  ggplot(aes(x = lifeExp,
             y = factor(year))) +
  geom_density_ridges(fill = "firebrick4",
                      colour = "firebrick4",
                      alpha = 0.4) +
* theme_ridges()
```
]
 
.panel2-ridges-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/ridges_auto_04_output-1.png)
]

---
count: false

```r
gapminder::gapminder %>%
  ggplot(aes(x = lifeExp,
             y = factor(year))) +
  geom_density_ridges(fill = "firebrick4",
                      colour = "firebrick4",
                      alpha = 0.4) +
  theme_ridges() +
* labs(x = "Life Expectancy (years)",
*      y = "",
*      caption = "@Data Gapminder (WHO)")
```
]
 
.panel2-ridges-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/ridges_auto_05_output-1.png)
]

---
class: inverse

## Summary of Distributions

- hugely important
- great way to explore your data / introduce it to others
- make sure you show you data when possible
    - use *geom_rug()*
    - use *geom_jitter()*
    - if lots of points, then use *alpha* to mute them
    
---

<div class="shareagain" style="min-width:300px;margin:1em auto;">
<iframe src="https://eugene-hickey.shinyapps.io/histograms/" width="1600" height="900" style="border:2px solid currentColor;" loading="lazy" allowfullscreen></iframe>
<script>fitvids('.shareagain', {players: 'iframe'});</script>
</div>

---

# Visualising Relationships

- scatter plots
    - encircling
    - jittering
    - using colour / size / shape 
    - fitting lines
    - histograms and boxplots on the axes (and geom_rug())
- line plots
- correlation

---

```r

*stars
```
]
 
.panel2-encircleplot-auto[

```
##                 star magnitude  temp type
## 1                Sun       4.8  5840    G
## 2            SiriusA       1.4  9620    A
## 3            Canopus      -3.1  7400    F
## 4           Arcturus      -0.4  4590    K
## 5     AlphaCentauriA       4.3  5840    G
## 6               Vega       0.5  9900    A
## 7            Capella      -0.6  5150    G
## 8              Rigel      -7.2 12140    B
## 9           ProcyonA       2.6  6580    F
## 10        Betelgeuse      -5.7  3200    M
## 11           Achemar      -2.4 20500    B
## 12             Hadar      -5.3 25500    B
## 13            Altair       2.2  8060    A
## 14         Aldebaran      -0.8  4130    K
## 15             Spica      -3.4 25500    B
## 16           Antares      -5.2  3340    M
## 17         Fomalhaut       2.0  9060    A
## 18            Pollux       1.0  4900    K
## 19             Deneb      -7.2  9340    A
## 20        BetaCrucis      -4.7 28000    B
## 21           Regulus      -0.8 13260    B
## 22             Acrux      -4.0 28000    B
## 23            Adhara      -5.2 23000    B
## 24            Shaula      -3.4 25500    B
## 25         Bellatrix      -4.3 23000    B
## 26            Castor       1.2  9620    A
## 27            Gacrux      -0.5  3750    M
## 28      BetaCentauri      -5.1 25500    B
## 29    AlphaCentauriB       5.8  4730    K
## 30           AlNa'ir      -1.1 15550    B
## 31       Miaplacidus      -0.6  9300    A
## 32            Elnath      -1.6 12400    B
## 33           Alnilam      -6.2 26950    B
## 34            Mirfak      -4.6  7700    F
## 35           Alnitak      -5.9 33600    O
## 36             Dubhe       0.2  4900    K
## 37            Alioth       0.4  9900    A
## 38           Peacock      -2.3 20500    B
## 39     KausAustralis      -0.3 11000    B
## 40      ThetaScorpii      -5.6  7400    F
## 41             Atria      -0.1  4590    K
## 42            Alkaid      -1.7 20500    B
## 43      AlphaCrucisB      -3.3 20500    B
## 44             Avior      -2.1  4900    K
## 45 DeltaCanisMajoris      -8.0  6100    F
## 46            Alhena       0.0  9900    A
## 47        Menkalinan       0.6  9340    A
## 48           Polaris      -4.6  6100    F
## 49            Mirzam      -4.8 25500    B
## 50   DeltaVulpeculae       0.6  9900    A
## 51  *ProximaCentauri      15.5  2670    M
## 52   *AlphaCentauriB       5.8  4900    K
## 53     Barnard'sStar      13.2  2800    M
## 54           Wolf359      16.7  2670    M
## 55           HD93735      10.5  3200    M
## 56           *L726-8      15.5  2670    M
## 57           *UVCeti      16.0  2670    M
## 58          *SiriusA       1.4  9620    A
## 59          *SiriusB      11.2 14800   DA
## 60           Ross154      13.1  2800    M
## 61           Ross248      14.8  2670    M
## 62    EpsilonEridani       6.1  4590    K
## 63           Ross128      13.5  2800    M
## 64            L789-6      14.5  2670    M
## 65     *GXAndromedae      10.4  3340    M
## 66     *GQAndromedae      13.4  2670    M
## 67       EpsilonIndi       7.0  4130    K
## 68         *61CygniA       7.6  4130    K
## 69         *61CygniB       8.4  3870    K
## 70      *Struve2398A      11.2  3070    M
## 71      *Struve2398B      11.9  2940    M
## 72           TauCeti       5.7  5150    G
## 73         *ProcyonA       2.6  6600    F
## 74         *ProcyonB      13.0  9700   DF
## 75      Lacaille9352       9.6  3340    M
## 76            G51-I5      17.0  2500    M
## 77            YZCeti      14.1  2670    M
## 78         BD+051668      11.9  2800    M
## 79      Lacaille8760       8.7  3340    K
## 80      KapteynsStar      10.9  3480    M
## 81        *Kruger60A      11.9  2940    M
## 82        *Kruger60B      13.3  2670    M
## 83         BD-124523      12.1  2940    M
## 84          Ross614A      13.1  2800    M
## 85          Wolf424A      15.0  2670    M
## 86   vanMaanen'sStar      14.2 13000   DB
## 87         TZArietis      14.0  2800    M
## 88          HD225213      10.3  3200    M
## 89            Altair       2.2  8060    A
## 90          ADLeonis      11.0  2940    M
## 91       *40EridaniA       6.0  4900    K
## 92       *40EridaniB      11.1 10000   DA
## 93       *40EridaniC      12.8  2940    M
## 94      *70OphiuchiA       5.8  4950    K
## 95      *70OphiuchiB       7.5  3870    K
## 96        EVLacertae      11.7  2800    M
```
]

---
count: false

```r

stars %>%
* ggplot(aes(temp,
*            magnitude,
*            col = type))
```
]
 
.panel2-encircleplot-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/encircleplot_auto_02_output-1.png)
]

---
count: false

```r

stars %>%
  ggplot(aes(temp,
             magnitude,
             col = type)) +
* geom_point(show.legend = F)
```
]
 
.panel2-encircleplot-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/encircleplot_auto_03_output-1.png)
]

---
count: false

```r

stars %>%
  ggplot(aes(temp,
             magnitude,
             col = type)) +
  geom_point(show.legend = F) +
* geom_encircle(data = stars %>%
*                 dplyr::filter(type == "B" | (type == "M" & magnitude > 9)),
*               show.legend = F)
```
]
 
.panel2-encircleplot-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/encircleplot_auto_04_output-1.png)
]

---
count: false

```r

stars %>%
  ggplot(aes(temp,
             magnitude,
             col = type)) +
  geom_point(show.legend = F) +
  geom_encircle(data = stars %>%
                  dplyr::filter(type == "B" | (type == "M" & magnitude > 9)),
                show.legend = F) +
* scale_x_log10()
```
]
 
.panel2-encircleplot-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/encircleplot_auto_05_output-1.png)
]

---
count: false

```r

stars %>%
  ggplot(aes(temp,
             magnitude,
             col = type)) +
  geom_point(show.legend = F) +
  geom_encircle(data = stars %>%
                  dplyr::filter(type == "B" | (type == "M" & magnitude > 9)),
                show.legend = F) +
  scale_x_log10() +
* annotate("text",
*          x = c(15000, 5000),
*          y = c(-4, 14),
*          label = c("Type B Stars", "Faint Type M Stars"),
*          col = c("blue", "olivedrab3"),
*          family = "Ink Free",
*          size = 4,
*          fontface = 2)
```
]
 
.panel2-encircleplot-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/encircleplot_auto_06_output-1.png)
]

---
count: false

```r

stars %>%
  ggplot(aes(temp,
             magnitude,
             col = type)) +
  geom_point(show.legend = F) +
  geom_encircle(data = stars %>%
                  dplyr::filter(type == "B" | (type == "M" & magnitude > 9)),
                show.legend = F) +
  scale_x_log10() +
  annotate("text",
           x = c(15000, 5000),
           y = c(-4, 14),
           label = c("Type B Stars", "Faint Type M Stars"),
           col = c("blue", "olivedrab3"),
           family = "Ink Free",
           size = 4,
           fontface = 2) +
* scale_color_viridis_d()
```
]
 
.panel2-encircleplot-auto[
![](data:image/png;base64,#03-distributions-relationships_files/figure-html/encircleplot_auto_07_output-1.png)
]

---

```r
scatter <- Galton %>% 
  ggplot(aes(parent, child)) +
  geom_point()
jittered <- Galton %>% ggplot(aes(parent, child)) + geom_jitter(width = 0.4, height = 0.4)
scatter + plot_spacer() + jittered 
```

![](data:image/png;base64,#03-distributions-relationships_files/figure-html/jitter-1.png)

---

<div class="shareagain" style="min-width:300px;margin:1em auto;">
<iframe src="https://eugene-hickey.shinyapps.io/lunchtime-challenge/" width="1600" height="900" style="border:2px solid currentColor;" loading="lazy" allowfullscreen></iframe>
<script>fitvids('.shareagain', {players: 'iframe'});</script>
</div>