Lecture 17: More on ggplot

class: center, middle, inverse, title-slide

.title[
# Lecture 17: More on ggplot
]
.author[
### Robin Liu
]
.institute[
### UCSB
]
.date[
### 2022-07-20
]

---

# Prereq
For this lecture we need to install and load the `socviz` package.

```r
install.packages("socviz")
library(socviz)
```

https://socviz.co/

---
# Aesthetic mappings
Last time we set the aesthetic mappings at the top level: in the call to `ggplot`.

```r
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y=lifeExp))
p + geom_point(alpha = 0.3) +
    scale_x_log10(labels = scales::label_dollar())
```

---
# Aesthetic mappings
We can set the mapping directly in the geom.

```r
p <- ggplot(data = gapminder)
p + geom_point(mapping = aes(x = gdpPercap, y=lifeExp), alpha = 0.3) +
    scale_x_log10(labels = scales::label_dollar())
```

---
# Aesthetic mappings
But then further geoms do not *inherit* the mapping.

```r
p <- ggplot(data = gapminder)
p + geom_point(mapping = aes(x = gdpPercap, y=lifeExp), alpha = 0.3) +
    geom_smooth(method = "lm") +
    scale_x_log10(labels = scales::label_dollar())
```

```
## `geom_smooth()` using formula 'y ~ x'
```

```
## Error in `check_required_aesthetics()`:
## ! stat_smooth requires the following missing aesthetics: x and y
```

The `x` and `y` aesthetics are *required* in `geom_smooth`.

---
# Aesthetic mappings
Mapping `x` and `y` aesthetics in `geom_smooth`.

```r
p <- ggplot(data = gapminder)
p + geom_point(mapping = aes(x = gdpPercap, y=lifeExp), alpha = 0.3) +
    geom_smooth(mapping = aes(x = gdpPercap, y=lifeExp), method = "lm") +
    scale_x_log10(labels = scales::label_dollar())
```

---
# Aesthetic mappings
Better: mappings in the top layer are *inherited* by the geoms.
Less duplication.

```r
p <- ggplot(data = gapminder, mapping = aes(x = gdpPercap, y=lifeExp))
p + geom_point(alpha = 0.3) +
    geom_smooth(method = "lm") +
    scale_x_log10(labels = scales::label_dollar())
```

<img src="Lec17_files/figure-html/unnamed-chunk-7-1.png" style="display: block; margin: auto;" />
---
class: inverse, center, middle
# Grouped data
---
# Grouped data
Recall whats in `gapminder`

```r
head(gapminder, 5)
```

```
## # A tibble: 5 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.
```

---
# Grouped data

```r
gapminder |>
  filter(country == "Afghanistan") |>
  ggplot(mapping = aes(x = year, y = gdpPercap)) +
    geom_line()
```

---
# Grouped data

```r
gapminder |>
  ggplot(mapping = aes(x = year, y = gdpPercap)) +
  geom_line()
```

What happened?

---
# Grouped data
`ggplot` did not know about different countries in the previous plot.
We need to specify countries as a `group`.

```r
gapminder |>
  ggplot(mapping = aes(x = year, y = gdpPercap)) +
  geom_line(aes(group = country))
```

Plot is still a mess.

---
# Grouped data
Let's further split up the countries by continent.

```r
p <- ggplot(gapminder, mapping = aes(x = year, y = gdpPercap))
p + geom_line(aes(group = country)) +
*  facet_wrap(~ continent) 
```

---
# Grouped data
**Very** useful visualization tool: facet the data by a factor.
Allows us to compare data among groups on the same scale.

---
# Facets
Facets are especially effective for data with many factors.
[General Social Survey 2016](https://rdrr.io/github/kjhealy/socviz/man/gss_sm.html)

```r
# library(socviz)
gss_sm
```

```
## # A tibble: 2,867 x 32
##     year    id ballot       age childs sibs   degree race  sex   region income16
##    <dbl> <dbl> <labelled> <dbl>  <dbl> <labe> <fct>  <fct> <fct> <fct>  <fct>   
##  1  2016     1 1             47      3 2      Bache~ White Male  New E~ $170000~
##  2  2016     2 2             61      0 3      High ~ White Male  New E~ $50000 ~
##  3  2016     3 3             72      2 3      Bache~ White Male  New E~ $75000 ~
##  4  2016     4 1             43      4 3      High ~ White Fema~ New E~ $170000~
##  5  2016     5 3             55      2 2      Gradu~ White Fema~ New E~ $170000~
##  6  2016     6 2             53      2 2      Junio~ White Fema~ New E~ $60000 ~
##  7  2016     7 1             50      2 2      High ~ White Male  New E~ $170000~
##  8  2016     8 3             23      3 6      High ~ Other Fema~ Middl~ $30000 ~
##  9  2016     9 1             45      3 5      High ~ Black Male  Middl~ $60000 ~
## 10  2016    10 3             71      4 1      Junio~ White Male  Middl~ $60000 ~
## # ... with 2,857 more rows, and 21 more variables: relig <fct>, marital <fct>,
## #   padeg <fct>, madeg <fct>, partyid <fct>, polviews <fct>, happy <fct>,
## #   partners <fct>, grass <fct>, zodiac <fct>, pres12 <labelled>,
## #   wtssall <dbl>, income_rc <fct>, agegrp <fct>, ageq <fct>, siblings <fct>,
## #   kids <fct>, religion <fct>, bigregion <fct>, partners_rc <fct>, obama <dbl>
```

---
# Facets

```r
p <- ggplot(data = gss_sm, mapping = aes(y = degree))
p + geom_bar()
```

---
# Facets

```r
p <- ggplot(data = gss_sm, mapping = aes(y = degree))
p + geom_bar() +
*  facet_wrap(~ race) 
```

---
# Facets

```r
p <- ggplot(data = gss_sm, mapping = aes(y = degree))
p + geom_bar() +
*  facet_grid(sex ~ race) 
```

---
# Facets
<img src="Lec17_files/figure-html/unnamed-chunk-18-1.png" style="display: block; margin: auto;" />

<div class="countdown" id="timer_62d832e3" style="right:0;bottom:0;" data-warnwhen="0">
<code class="countdown-time"><span class="countdown-digits minutes">02</span><span class="countdown-digits colon">:</span><span class="countdown-digits seconds">00</span></code>
</div>
---
# geom_bar vs geom_col

```r
gss_sm |>
  ggplot(gss_sm, mapping = aes(y = fct_infreq(degree))) +
*  geom_bar() 
```

---
# geom_bar vs geom_col

```r
gss_sm |>
  group_by(degree) |>
  summarize(count = n()) |>
  ggplot(mapping = aes(x = count, y = reorder(degree, count, decreasing = T))) +
*  geom_col() 
```

---
# group_by, summarize
A complicated `group_by`:

```r
(rel_by_region <- gss_sm |>
    group_by(bigregion, religion) |>
    summarize(N = n()) |>
    mutate(freq = N / sum(N),
           pct = round((freq*100), 0)))
```

```
## # A tibble: 24 x 5
## # Groups:   bigregion [4]
##    bigregion religion       N    freq   pct
##    <fct>     <fct>      <int>   <dbl> <dbl>
##  1 Northeast Protestant   158 0.324      32
##  2 Northeast Catholic     162 0.332      33
##  3 Northeast Jewish        27 0.0553      6
##  4 Northeast None         112 0.230      23
##  5 Northeast Other         28 0.0574      6
##  6 Northeast <NA>           1 0.00205     0
##  7 Midwest   Protestant   325 0.468      47
##  8 Midwest   Catholic     172 0.247      25
##  9 Midwest   Jewish         3 0.00432     0
## 10 Midwest   None         157 0.226      23
## # ... with 14 more rows
```

---
# group_by, summarize

```r
p <- ggplot(rel_by_region, mapping = aes(x = bigregion, y = pct, fill = religion))
p + geom_col() +
  labs(x = "Region", y = "Percent", fill = "Religion")
```

We used `geom_col()` since the percentages are given.

---
# Dodged barplot

```r
p <- ggplot(rel_by_region, mapping = aes(x = bigregion, y = pct, fill = religion))
p + geom_col(position = "dodge") +
  labs(x = "Region", y = "Percent", fill = "Religion") +
  theme(legend.position = "top")
```

---
# Dodged barplot

```r
p <- ggplot(rel_by_region, mapping = aes(y = religion, x = pct, fill = religion))
p + geom_col(position = "dodge") +
  labs(x = "Percent", y = NULL, fill = "Religion") +
  guides(fill = "none") +
  facet_grid(~ bigregion) +
  theme_minimal()
```

---
# Organ data example

```r
set.seed(100)
organdata |> select(1:6) |> slice_sample(n = 5)
```

```
## # A tibble: 5 x 6
##   country        year       donors   pop pop_dens   gdp
##   <chr>          <date>      <dbl> <int>    <dbl> <int>
## 1 Switzerland    1995-01-01   13    7041     17.1 26304
## 2 Germany        1993-01-01   13.9 81156     22.7 19983
## 3 Germany        NA           NA      NA     NA      NA
## 4 United Kingdom 1996-01-01   13.6 58139     23.9 20839
## 5 Switzerland    1999-01-01   14.4  7144     17.3 28562
```

---
# Organ data example

```r
p <- ggplot(data = organdata,
            mapping = aes(x = year, y = donors))
p + geom_line(aes(group = country)) + facet_wrap(~ country)
```

```
## Warning: Removed 34 row(s) containing missing values (geom_path).
```

---
# Organ data Cleveland dotplot demo

```r
(by_country <- organdata |> group_by(consent_law, country) |>
                            summarize(donors_mean = mean(donors, na.rm = T),
                                      donors_sd = sd(donors, na.rm = T)))
```

```
## # A tibble: 17 x 4
## # Groups:   consent_law [2]
##    consent_law country        donors_mean donors_sd
##    <chr>       <chr>                <dbl>     <dbl>
##  1 Informed    Australia             10.6     1.14 
##  2 Informed    Canada                14.0     0.751
##  3 Informed    Denmark               13.1     1.47 
##  4 Informed    Germany               13.0     0.611
##  5 Informed    Ireland               19.8     2.48 
##  6 Informed    Netherlands           13.7     1.55 
##  7 Informed    United Kingdom        13.5     0.775
##  8 Informed    United States         20.0     1.33 
##  9 Presumed    Austria               23.5     2.42 
## 10 Presumed    Belgium               21.9     1.94 
## 11 Presumed    Finland               18.4     1.53 
## 12 Presumed    France                16.8     1.60 
## 13 Presumed    Italy                 11.1     4.28 
## 14 Presumed    Norway                15.4     1.11 
## 15 Presumed    Spain                 28.1     4.96 
## 16 Presumed    Sweden                13.1     1.75 
## 17 Presumed    Switzerland           14.2     1.71
```

---
# Organ data Cleveland dotplot demo

---
# Organ data Cleveland dotplot demo

---
# Plot text directly

```r
(by_country <- organdata |> group_by(consent_law, country) |>
                            summarize(donors_mean = mean(donors, na.rm = T),
                                      roads_mean = mean(roads, na.rm = T)))
```

```
## # A tibble: 17 x 4
## # Groups:   consent_law [2]
##    consent_law country        donors_mean roads_mean
##    <chr>       <chr>                <dbl>      <dbl>
##  1 Informed    Australia             10.6      105. 
##  2 Informed    Canada                14.0      109. 
##  3 Informed    Denmark               13.1      102. 
##  4 Informed    Germany               13.0      113. 
##  5 Informed    Ireland               19.8      118. 
##  6 Informed    Netherlands           13.7       76.1
##  7 Informed    United Kingdom        13.5       67.9
##  8 Informed    United States         20.0      155. 
##  9 Presumed    Austria               23.5      150. 
## 10 Presumed    Belgium               21.9      155. 
## 11 Presumed    Finland               18.4       93.6
## 12 Presumed    France                16.8      156. 
## 13 Presumed    Italy                 11.1      122. 
## 14 Presumed    Norway                15.4       70.0
## 15 Presumed    Spain                 28.1      161. 
## 16 Presumed    Sweden                13.1       72.3
## 17 Presumed    Switzerland           14.2       96.4
```

---
# Plot text directly

```r
p <- ggplot(data = by_country,
            mapping = aes(x = roads_mean, y = donors_mean))
p + geom_point() + geom_text(mapping = aes(label = country))
```

---
# Plot text directly

```r
library(ggrepel)
p <- ggplot(data = by_country,
            mapping = aes(x = roads_mean, y = donors_mean))
p + geom_point() + geom_text_repel(mapping = aes(label = country))
```

---
# Plot text directly

```r
library(ggrepel)
p <- ggplot(data = by_country,
            mapping = aes(x = roads_mean, y = donors_mean))
p + geom_point() + geom_text_repel(data = filter(by_country, donors_mean > 22),
                                   mapping = aes(label = country))
```

---
class: inverse, middle, center
# Other aesthetics
---
# Mapping other aesthetics

```r
(by_country <- organdata |> group_by(consent_law, country) |>
                            summarize(donors_mean = mean(donors, na.rm = T),
                                      roads_mean = mean(roads, na.rm = T),
                                      pop_mean = mean(pop, na.rm = T),
                                      external_mean = mean(external, na.rm = T)))
```

```
## # A tibble: 17 x 6
## # Groups:   consent_law [2]
##    consent_law country        donors_mean roads_mean pop_mean external_mean
##    <chr>       <chr>                <dbl>      <dbl>    <dbl>         <dbl>
##  1 Informed    Australia             10.6      105.    18318.          393 
##  2 Informed    Canada                14.0      109.    29608.          411.
##  3 Informed    Denmark               13.1      102.     5257.          532.
##  4 Informed    Germany               13.0      113.    80255.          391.
##  5 Informed    Ireland               19.8      118.     3674.          394 
##  6 Informed    Netherlands           13.7       76.1   15548.          286.
##  7 Informed    United Kingdom        13.5       67.9   58187.          288.
##  8 Informed    United States         20.0      155.   269330.          530 
##  9 Presumed    Austria               23.5      150.     7927.          507.
## 10 Presumed    Belgium               21.9      155.    10153.          542.
## 11 Presumed    Finland               18.4       93.6    5112.          722.
## 12 Presumed    France                16.8      156.    58056.          603.
## 13 Presumed    Italy                 11.1      122.    57360.          369.
## 14 Presumed    Norway                15.4       70.0    4386.          423.
## 15 Presumed    Spain                 28.1      161.    39666.          377.
## 16 Presumed    Sweden                13.1       72.3    8789.          396.
## 17 Presumed    Switzerland           14.2       96.4    7037.          488.
```

---
# Other aesthetics

```r
p <- ggplot(data = by_country,
            mapping = aes(x = roads_mean, y = external_mean,
                          color = donors_mean, size = pop_mean,
                          shape = consent_law))
p + geom_point() +
  scale_size_area(max_size = 8) +
  scale_colour_distiller(palette = "YlGnBu")
```

---
# Discrete color palettes

```r
organdata |>
  group_by(world, country) |>
  summarize(donors_mean = mean(donors, na.rm = T)) |>
  ggplot(mapping = aes(x = donors_mean, y = country, fill = world)) +
    geom_col() +
    scale_fill_brewer(type = "qual")
```

---
# Summary
- Faceting by a discrete variable
- the `group` aesthetic

Each `geom_` function requires certain aesthetics.
- `geom_point()` requires `x` and `y`
- `geom_pointrange()` requires `x`, `y`, but also `x_min` and `x_max`
- Different geoms can use the same aesthetics: `x`, `y`, `color`, and `fill`.

Explore the different geoms and pick one suitable for your project.

https://r-graph-gallery.com/ggplot2-package.html

---
# For fun
https://twitter.com/accidental__aRt

https://github.com/djnavarro/jasmines

https://www.data-imaginist.com/art