Data Visualization

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# Data Visualization
## Splitting graphs into facets
### Andrew Irwin, <a href="mailto:a.irwin@dal.ca" class="email">a.irwin@dal.ca</a>
### Math & Stats, Dalhousie University
### 2021-01-27 (updated: 2021-01-09)

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# Splitting a plot into facets

* Using one categorical variable

* Using two categorical variables

* Using quantitative variables

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# One categorical variable

* `facet_wrap( ~ variable)`

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```r
mtcars %>% ggplot(aes(x =wt, y=mpg)) + geom_point()
```

![](09-facets_files/figure-html/unnamed-chunk-1-1.png)

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```r
mtcars %>% ggplot(aes(x =wt, y=mpg)) + geom_point() +
  facet_wrap(~cyl)
```

![](09-facets_files/figure-html/unnamed-chunk-2-1.png)

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```r
penguins %>%
  ggplot(aes(x=body_mass_g, y= flipper_length_mm, color = sex)) +
  geom_point() + 
  facet_wrap( ~ species)
```

![](09-facets_files/figure-html/unnamed-chunk-3-1.png)

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# Using two categorical variables

* `facet_grid( y ~ x)`
* `facet_wrap( ~ x + y)`
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```r
mtcars %>% ggplot(aes(x =wt, y=mpg)) + geom_point() +
  facet_grid(gear~cyl)
```

![](09-facets_files/figure-html/unnamed-chunk-4-1.png)

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```r
mtcars %>% ggplot(aes(x =wt, y=mpg)) + geom_point() +
  facet_grid(gear~cyl, 
             labeller = labeller(.cols = function(x) paste(x, " cylinders"),
                                 .rows = label_both))
```

![](09-facets_files/figure-html/unnamed-chunk-5-1.png)

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```r
penguins %>%
  ggplot(aes(x=body_mass_g, y= flipper_length_mm, color = sex)) +
  geom_point() + 
  facet_wrap(~ species + island)
```

![](09-facets_files/figure-html/unnamed-chunk-6-1.png)

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```r
gapminder %>%
  ggplot(aes(x=year, y = lifeExp, group = country)) + 
  geom_line() +
  facet_grid( continent ~ . )
```

![](09-facets_files/figure-html/unnamed-chunk-7-1.png)

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```r
gapminder %>%
  ggplot(aes(x=year, y = lifeExp)) + 
  geom_line(aes(group = country), color = "gray40", size=0.25) +
  geom_smooth(method = "lm", color = "blue", se=FALSE) + 
  facet_grid( continent ~ . )
```

![](09-facets_files/figure-html/unnamed-chunk-8-1.png)

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# Splitting on a quantitative variable

* Start by converting the quantitative variable into a cateogorical one

For example:

```r
penguins %>% 
  mutate(bill_categories = cut_number(bill_length_mm, n = 4)) %>%
  select(species, bill_length_mm, bill_categories, everything())
```

```
## # A tibble: 344 x 9
##    species bill_length_mm bill_categories island bill_depth_mm flipper_length_…
##    <fct>            <dbl> <fct>           <fct>          <dbl>            <int>
##  1 Adelie            39.1 [32.1,39.2]     Torge…          18.7              181
##  2 Adelie            39.5 (39.2,44.5]     Torge…          17.4              186
##  3 Adelie            40.3 (39.2,44.5]     Torge…          18                195
##  4 Adelie            NA   <NA>            Torge…          NA                 NA
##  5 Adelie            36.7 [32.1,39.2]     Torge…          19.3              193
##  6 Adelie            39.3 (39.2,44.5]     Torge…          20.6              190
##  7 Adelie            38.9 [32.1,39.2]     Torge…          17.8              181
##  8 Adelie            39.2 [32.1,39.2]     Torge…          19.6              195
##  9 Adelie            34.1 [32.1,39.2]     Torge…          18.1              193
## 10 Adelie            42   (39.2,44.5]     Torge…          20.2              190
## # … with 334 more rows, and 3 more variables: body_mass_g <int>, sex <fct>,
## #   year <int>
```

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```r
penguins %>% 
  mutate(bill_categories = cut_number(bill_length_mm, n = 3)) %>%
  ggplot(aes(x= body_mass_g, y = flipper_length_mm, color = species)) + 
  geom_point() + 
  facet_wrap( ~ bill_categories)
```

![](09-facets_files/figure-html/unnamed-chunk-10-1.png)

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# Summary

* `facet_wrap()`, `facet_grid()`

* You can set the number of rows and columns with `nrow = Y` or `ncol=X`

* You can label facets with both the variable and value using `labeller = labeller(.rows = label_both)`

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# Further reading

* More examples with different data in the course notes

* R4DS [Section 3.5](https://r4ds.had.co.nz/data-visualisation.html#facets)

* Wilke [Multi-panel figures](https://clauswilke.com/dataviz/multi-panel-figures.html) discussing facets and other suggestions for combining different views of data together into one figure

* Healy [Section 4.3](https://socviz.co/groupfacettx.html#facet-to-make-small-multiples)

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# Task

Practice these skills by making the plots in Task 6.