Getting things into the right order

class: center, middle, title-slide

# Getting things into the right order
### Claus O. Wilke
### last updated: 2021-09-23

---

## Remember from "Visualizing amounts"

.small-font[
We can use `fct_relevel()` to manually order the bars in a bar plot
]

.tiny-font[

```r
ggplot(penguins, aes(y = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie"))) +
  geom_bar()
```
]

.center[
![](getting-things-in-order_files/figure-html/penguins-ordered-out-1.svg)
]

---

## Somewhat cleaner: mutate first, then plot

.tiny-font[

```r
penguins %>%
  mutate(species = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie")) %>%
  ggplot(aes(y = species)) +
  geom_bar()
```
]

.center[
![](getting-things-in-order_files/figure-html/penguins-ordered-pipe-out-1.svg)
]

---

## We order things in ggplot with factors

.tiny-font[

```r
penguins %>%
  mutate(species = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie")) %>%
  slice(1:30) %>%   # get first 30 rows
  pull(species)     # pull out just the `species` column
```

```
 [1] Adelie Adelie Adelie Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[11] Adelie Adelie Adelie Adelie Adelie Adelie Adelie Adelie Adelie Adelie
[21] Adelie Adelie Adelie Adelie Adelie Adelie Adelie Adelie Adelie Adelie
Levels: Chinstrap Gentoo Adelie
```
]

.small-font[
- The column `species` is a factor
]

.small-font[
- A factor is a categorical variable with defined categories called levels
]

.small-font[
- For factors, ggplot generally places visual elements in the order defined by the levels
]

---

## We order things in ggplot with factors

.tiny-font[

```r
penguins %>%
  mutate(species = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie")) %>%
  slice(1:30) %>%   # get first 30 rows
  pull(species)     # pull out just the `species` column
```

```r
# the order of factor levels is independent of the order of values in the table
penguins %>% 
  mutate(species = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie"))
```

```
# A tibble: 344 × 8
   species island    bill_length_mm bill_depth_mm flipper_length_mm body_mass_g
   <fct>   <fct>              <dbl>         <dbl>             <int>       <int>
 1 Adelie  Torgersen           39.1          18.7               181        3750
 2 Adelie  Torgersen           39.5          17.4               186        3800
 3 Adelie  Torgersen           40.3          18                 195        3250
 4 Adelie  Torgersen           NA            NA                  NA          NA
 5 Adelie  Torgersen           36.7          19.3               193        3450
 6 Adelie  Torgersen           39.3          20.6               190        3650
 7 Adelie  Torgersen           38.9          17.8               181        3625
 8 Adelie  Torgersen           39.2          19.6               195        4675
 9 Adelie  Torgersen           34.1          18.1               193        3475
10 Adelie  Torgersen           42            20.2               190        4250
# … with 334 more rows, and 2 more variables: sex <fct>, year <int>
```
]

---

## Manual ordering of factor levels: `fct_relevel()`

.tiny-font[

```r
penguins %>%
  mutate(species = fct_relevel(species)) %>%
  slice(1:30) %>%   # get first 30 rows
  pull(species)     # pull out just the `species` column
```

.small-font[
Default: alphabetic order
]
---

## Manual ordering of factor levels: `fct_relevel()`

.tiny-font[

```r
penguins %>%
  mutate(species = fct_relevel(species, "Gentoo")) %>%
  slice(1:30) %>%   # get first 30 rows
  pull(species)     # pull out just the `species` column
```

.small-font[
Move `"Gentoo"` in front, rest alphabetic
]

---

## Manual ordering of factor levels: `fct_relevel()`

.tiny-font[

```r
penguins %>%
  mutate(species = fct_relevel(species, "Chinstrap", "Gentoo")) %>%
  slice(1:30) %>%   # get first 30 rows
  pull(species)     # pull out just the `species` column
```

]

.small-font[
Move `"Chinstrap"` in front, then `"Gentoo"`, rest alphabetic
]

---

## Manual ordering of factor levels: `fct_relevel()`

.tiny-font[

```r
penguins %>%
  mutate(species = fct_relevel(species, "Chinstrap", "Adelie", "Gentoo")) %>%
  slice(1:30) %>%   # get first 30 rows
  pull(species)     # pull out just the `species` column
```

]

.small-font[
Use order `"Chinstrap"`, `"Adelie"`, `"Gentoo"`
]

---

## Manual ordering of factor levels: `fct_relevel()`

.tiny-font[

```r
penguins %>%
  mutate(species = fct_relevel(species, "Gentoo", "Chinstrap", "Adelie")) %>%
  slice(1:30) %>%   # get first 30 rows
  pull(species)     # pull out just the `species` column
```

]

.small-font[
Use order `"Gentoo"`, `"Chinstrap"`, `"Adelie"`
]

---

## The order of the y axis is from bottom to top

.tiny-font[

```r
penguins %>%
  mutate(species = fct_relevel(species, "Chinstrap", "Gentoo", "Adelie")) %>%
  ggplot(aes(y = species)) +
  geom_bar()
```
]

.center[
![](getting-things-in-order_files/figure-html/penguins-ordered-pipe2-out-1.svg)
]

---

## Reorder based on frequency: `fct_infreq()`

.tiny-font[

```r
penguins %>%
  mutate(species = fct_infreq(species)) %>%
  slice(1:30) %>%   # get first 30 rows
  pull(species)     # pull out just the `species` column
```

]

.small-font[
- Use the order defined by the number of penguins of different species
]

.small-font[
- The order is descending, from most frequent to least frequent
]

---

## Reorder based on frequency: `fct_infreq()`

.tiny-font[

```r
penguins %>%
  mutate(species = fct_infreq(species)) %>%
  ggplot(aes(y = species)) + geom_bar()
```

]

.center[
![](getting-things-in-order_files/figure-html/penguins-infreq-out-1.svg)
]

---

## Reverse order: `fct_rev()`

.tiny-font[

```r
penguins %>%
  mutate(species = fct_rev(fct_infreq(species))) %>%
  ggplot(aes(y = species)) + geom_bar()
```

]

.center[
![](getting-things-in-order_files/figure-html/penguins-infreq-rev-out-1.svg)
]

---

## Reorder based on numeric values: `fct_reorder()`

.tiny-font[

```r
penguins %>%
  count(species)
```

```
# A tibble: 3 × 2
  species       n
  <fct>     <int>
1 Adelie      152
2 Chinstrap    68
3 Gentoo      124
```

]

.tiny-font[

```r
penguins %>%
  count(species) %>%
  mutate(species = fct_reorder(species, n)) %>%
  pull(species)     # pull out just the `species` column
```

```
[1] Adelie    Chinstrap Gentoo   
Levels: Chinstrap Gentoo Adelie
```

]

.small-font[
The order is ascending, from smallest to largest value
]

---

## Reorder based on numeric values: `fct_reorder()`

.tiny-font[

```r
penguins %>%
  count(species) %>%
  mutate(species = fct_reorder(species, n)) %>%
  ggplot(aes(n, species)) + geom_col()
```

]

.center[
![](getting-things-in-order_files/figure-html/penguins-reorder-out-1.svg)
]

---

## Compare to see the difference

.xtiny-font.pull-left[

```r
penguins %>%
  count(species) %>% # summarize data
  mutate(species = fct_reorder(species, n))
```

```
# A tibble: 3 × 2
  species       n
  <fct>     <int>
1 Adelie      152
2 Chinstrap    68
3 Gentoo      124
```

]

.xtiny-font.pull-right[

```r
penguins %>% 
  # modify the original dataset, no summary
  mutate(species = fct_infreq(species))
```

---

## Compare to see the difference

.xtiny-font.pull-left[

```r
penguins %>%
  count(species) %>% # summarize data
  mutate(species = fct_reorder(species, n)) %>%
  ggplot(aes(n, species)) + geom_col()
```

![](getting-things-in-order_files/figure-html/penguins-geom-col-1.svg)

]

.xtiny-font.pull-right[

```r
penguins %>% 
  # modify the original dataset, no summary
  mutate(species = fct_infreq(species)) %>%
  ggplot(aes(y = species)) + geom_bar()
```

![](getting-things-in-order_files/figure-html/penguins-geom-bar-1.svg)
]

---

## Compare to see the difference

.xtiny-font.pull-left[

```r
penguins %>%
  count(species) %>% # summarize data
  mutate(species = fct_reorder(species, n)) %>%
  ggplot(aes(n, species)) + geom_col()
```

![](getting-things-in-order_files/figure-html/penguins-geom-col2-1.svg)

]

.xtiny-font.pull-right[

```r
penguins %>% 
  # modify the original dataset, no summary
  mutate(species = fct_infreq(species)) %>%
  ggplot(aes(y = fct_rev(species))) + geom_bar()
```

![](getting-things-in-order_files/figure-html/penguins-geom-bar2-1.svg)
]

[//]: # "segment ends here"

---

class: middle, center

# Ordering other plot elements

---

## The gapminder dataset: Life expectancy data

.tiny-font[

```r
library(gapminder)

gapminder
```

```
# A tibble: 1,704 × 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
```
]

---

## Life expectancy in the Americas in 2007

.tiny-font.pull-left[

```r
gapminder %>%
  filter(
    year == 2007,
    continent == "Americas"
  ) %>%
  ggplot(aes(lifeExp, country)) + 
  geom_point()
```
]

.pull-right[
![](getting-things-in-order_files/figure-html/gapminder-unordered-out-1.svg)
]

---

## Life expectancy in the Americas in 2007

.pull-left[
.tiny-font[

```r
gapminder %>%
  filter(
    year == 2007,
    continent == "Americas"
  ) %>%
  ggplot(aes(lifeExp, country)) + 
  geom_point()
```
]

.small-font[
Reminder:  
Default order is alphabetic, from bottom to top
]]

.pull-right[
![](getting-things-in-order_files/figure-html/gapminder-unordered2-out-1.svg)
]

---

## Life expectancy, ordered from highest to lowest

.pull-left[.tiny-font[

```r
gapminder %>%
  filter(
    year == 2007,
    continent == "Americas"
  ) %>%
  mutate(
    country = fct_reorder(country, lifeExp)
  ) %>%
  ggplot(aes(lifeExp, country)) + 
  geom_point()
```
]

.small-font[
Order is ascending from bottom to top
]]

.pull-right[
![](getting-things-in-order_files/figure-html/gapminder-ordered-out-1.svg)
]

---

## We can also order facets

.tiny-font[

```r
gapminder %>%
  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
  ggplot(aes(year, lifeExp)) + geom_line() +
  facet_wrap(vars(country), nrow = 1)
```
]

.center[
![](getting-things-in-order_files/figure-html/gapminder-europe-unordered-out-1.svg)
]

.small-font[
- Default ordering is alphabetic; there's no good reason for this ordering
]

---

## We can also order facets

.tiny-font[

```r
gapminder %>%
  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
  ggplot(aes(year, lifeExp)) + geom_line() +
  facet_wrap(vars(country), nrow = 1)
```
]

.center[
![](getting-things-in-order_files/figure-html/gapminder-europe-unordered2-out-1.svg)
]

.small-font[
- Let's apply `fct_reorder()` and see what happens
]

---

## We can also order facets

.tiny-font[

```r
gapminder %>%
  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
  mutate(country = fct_reorder(country, lifeExp)) %>% # default: order by median
  ggplot(aes(year, lifeExp)) + geom_line() +
  facet_wrap(vars(country), nrow = 1)
```
]

.center[
![](getting-things-in-order_files/figure-html/gapminder-europe1-out-1.svg)
]

.small-font[
- When the levels of a factor occur more than once, `fct_reorder()` applies a summary function
]

---

## We can also order facets

.tiny-font[

.center[
![](getting-things-in-order_files/figure-html/gapminder-europe2-out-1.svg)
]

.small-font[
- The default summary function is `median()`
]

---

## We can also order facets

.tiny-font[

```r
gapminder %>%
  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
  mutate(country = fct_reorder(country, lifeExp, median)) %>% # order by median
  ggplot(aes(year, lifeExp)) + geom_line() +
  facet_wrap(vars(country), nrow = 1)
```
]

.center[
![](getting-things-in-order_files/figure-html/gapminder-europe3-out-1.svg)
]

.small-font[
- We can also set the summary function explicitly
]

---

## Alternative orderings: By smallest value per facet

.tiny-font[

```r
gapminder %>%
  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
  mutate(country = fct_reorder(country, lifeExp, min)) %>% # order by minimum
  ggplot(aes(year, lifeExp)) + geom_line() +
  facet_wrap(vars(country), nrow = 1)
```
]

.center[
![](getting-things-in-order_files/figure-html/gapminder-europe4-out-1.svg)
]

---

## Alternative orderings: By largest value per facet

.tiny-font[

```r
gapminder %>%
  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
  mutate(country = fct_reorder(country, lifeExp, max)) %>% # order by maximum
  ggplot(aes(year, lifeExp)) + geom_line() +
  facet_wrap(vars(country), nrow = 1)
```
]

.center[
![](getting-things-in-order_files/figure-html/gapminder-europe5-out-1.svg)
]

---

## Alternative orderings: By smallest difference

.tiny-font[

```r
gapminder %>%
  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
  # order by custom function: here, difference between max and min
  mutate(country = fct_reorder(country, lifeExp, function(x) { max(x) - min(x) })) %>%
  ggplot(aes(year, lifeExp)) + geom_line() +
  facet_wrap(vars(country), nrow = 1)
```
]

.center[
![](getting-things-in-order_files/figure-html/gapminder-europe6-out-1.svg)
]

---

## Alternative orderings: By largest difference

.tiny-font[

```r
gapminder %>%
  filter(country %in% c("Norway", "Portugal", "Spain", "Austria")) %>%
  # order by custom function: here, difference between min and max
  mutate(country = fct_reorder(country, lifeExp, function(x) { min(x) - max(x) })) %>%
  ggplot(aes(year, lifeExp)) + geom_line() +
  facet_wrap(vars(country), nrow = 1)
```
]

.center[
![](getting-things-in-order_files/figure-html/gapminder-europe7-out-1.svg)
]

---

## Final example: Lumping factor levels together

.small-font[
Dataset: Flights out of New York City in 2013
]

.tiny-font[

```r
library(nycflights13)

flight_data <- flights %>% # take data on individual flights
  left_join(airlines) %>%  # add in full-length airline names
  select(name, carrier, flight, year, month, day, origin, dest) # pick columns of interest
```

```
Joining, by = "carrier"
```
]

.tiny-font[

```r
flight_data
```

```
# A tibble: 336,776 × 8
   name                     carrier flight  year month   day origin dest 
   <chr>                    <chr>    <int> <int> <int> <int> <chr>  <chr>
 1 United Air Lines Inc.    UA        1545  2013     1     1 EWR    IAH  
 2 United Air Lines Inc.    UA        1714  2013     1     1 LGA    IAH  
 3 American Airlines Inc.   AA        1141  2013     1     1 JFK    MIA  
 4 JetBlue Airways          B6         725  2013     1     1 JFK    BQN  
 5 Delta Air Lines Inc.     DL         461  2013     1     1 LGA    ATL  
 6 United Air Lines Inc.    UA        1696  2013     1     1 EWR    ORD  
 7 JetBlue Airways          B6         507  2013     1     1 EWR    FLL  
 8 ExpressJet Airlines Inc. EV        5708  2013     1     1 LGA    IAD  
 9 JetBlue Airways          B6          79  2013     1     1 JFK    MCO  
10 American Airlines Inc.   AA         301  2013     1     1 LGA    ORD  
# … with 336,766 more rows
```
]

---

## Flights out of New York City in 2013

.pull-left.tiny-font[

```r
flight_data %>%
  ggplot(aes(y = name)) + 
  geom_bar()
```
]

.pull-right[
![](getting-things-in-order_files/figure-html/flights-unordered-out-1.svg)
]

.small-font[
As (almost) always, the default alphabetic ordering is terrible
]

---

## Flights out of New York City in 2013

.pull-left.tiny-font[

```r
flight_data %>%
  mutate(
    name = fct_infreq(name)
  ) %>%
  ggplot(aes(y = fct_rev(name))) + 
  geom_bar()
```
]

.pull-right[
![](getting-things-in-order_files/figure-html/flights-ordered-out-1.svg)
]

.small-font[
Ordering by frequency is better, but do we need to show all airlines?
]

---