class: center, top
background-image: url("ggplot2.png")
background-size: fill
# Joys of ggplot

---

## Why ggplot?

**Make good looking graphs faster in fewer lines of code**

- "Grammar of Graphics"
  - Makes a standardized syntax for making graphs

- Why not base?

- Anything you can do in base, you can do in ggplot
---

# Why ggplot?

.pull-left[  
      - Pros:
      - "language" instead of cluttered toolbox
      - Very fast data exploration
      - Savable and reproducible
      - Gets you to the fun part faster
]
.pull-right[
  - Cons:
      - Something new to learn
      - Hard to go "out of bounds"
        - But not much is out of bounds
      - Really nerdy bar fights
      - Not as version-stable
      ]

---

## ggplot Basics

```r
ggplot(DATA) + 
  geom_TYPE(mapping = aes(MAPPING))
```

- `ggplot` creates a blank "coordinate system"
- `geom_`'s are layered on
- `DATA` and "aesthetics"(`aes`) are shared across `geom`'s

---

## ggplot Basics
![:scale 50%](ggplot2.jpg)

---

## ggplot Basics

* Let's start with the "diamonds" data set

<div id="htmlwidget-5deccb6a7f66596476c3" style="width:100%;height:auto;" class="datatables html-widget"></div>
<script type="application/json" data-for="htmlwidget-5deccb6a7f66596476c3">{"x":{"filter":"none","data":[["1","2","3","4","5","6","7","8","9","10"],[0.23,0.21,0.23,0.29,0.31,0.24,0.24,0.26,0.22,0.23],["Ideal","Premium","Good","Premium","Good","Very Good","Very Good","Very Good","Fair","Very Good"],["E","E","E","I","J","J","I","H","E","H"],["SI2","SI1","VS1","VS2","SI2","VVS2","VVS1","SI1","VS2","VS1"],[61.5,59.8,56.9,62.4,63.3,62.8,62.3,61.9,65.1,59.4],[55,61,65,58,58,57,57,55,61,61],[326,326,327,334,335,336,336,337,337,338],[3.95,3.89,4.05,4.2,4.34,3.94,3.95,4.07,3.87,4],[3.98,3.84,4.07,4.23,4.35,3.96,3.98,4.11,3.78,4.05],[2.43,2.31,2.31,2.63,2.75,2.48,2.47,2.53,2.49,2.39]],"container":"<table class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>carat<\/th>\n <th>cut<\/th>\n <th>color<\/th>\n <th>clarity<\/th>\n <th>depth<\/th>\n <th>table<\/th>\n <th>price<\/th>\n <th>x<\/th>\n <th>y<\/th>\n <th>z<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[1,5,6,7,8,9,10]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>

---

## ggplot Basics

.pull-left[What makes a diamond expensive?

```r
ggplot(data = diamonds) + 
  geom_histogram(aes(price)) 
```
]
.pull-right[

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

]

---

## ggplot Basics

.pull-left[
Why in the world is

```r
ggplot(data = diamonds) + 
  geom_histogram(aes(price)) 
```

better than

```r
hist(diamonds$price)
```
]
.pull-right[
![](learn_ggplot_files/figure-html/unnamed-chunk-7-1.png)

]

---

# The power of `aes`

.pull-left[
- `aes` stands for aesthetics

- Allows you to map data to graphics

```r
ggplot(data = diamonds) +
  geom_point(aes(x = carat, y = price),
  color = 'red')
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-9-1.png)

]

---

# The power of `aes`

.pull-left[

```r
ggplot(data = diamonds) +
  geom_point(aes(x = carat, y = price,
  color = 'red'))
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-11-1.png)

]

---

# The power of `aes`

.pull-left[

```r
ggplot(data = diamonds) +
geom_point(aes(x = carat, y = price,
color = clarity))
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-13-1.png)

]

---

## The power of `aes`
.pull-left[
- You can map different variables to different aesthetics

```r
ggplot(data = diamonds) +
 geom_point(aes(
 x = carat,
 y = price,
 color = clarity,
 shape = cut
 ),
 size = 4, 
 alpha = 0.5)
```
]
.pull-right[
![](learn_ggplot_files/figure-html/unnamed-chunk-15-1.png)
]
---

## The power of `aes`

.pull-left[
- Or the same variable to different aesthetics

```r
ggplot(data = diamonds) +
 geom_point(aes(
 x = carat,
 y = price,
 color = clarity,
 shape = clarity
 ),
 size = 4,
 alpha = 0.5)
```
]
.pull-right[
![](learn_ggplot_files/figure-html/unnamed-chunk-17-1.png)

]
---

## ggplot Basics

- 90% of your time in ggplot is spent in

- Pass data to `ggplot`
  
  - Decide on a `geom` to represent the data
  
  - Decide on `aes` mappings to visualize additional things
  
  - Make prettier

---

## ggplot for Data Exploration
.pull-left[
- What makes an expensive diamond?

```r
diamonds %>%
  mutate(numeric_cut = as.numeric(cut)) %>%
  ggplot() +
  geom_boxplot(aes(cut, price)) +
  geom_smooth(aes(numeric_cut, price)
  , method = 'lm',
  se = F) +
  scale_y_continuous(labels = scales::dollar)
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-19-1.png)
]

---

## ggplot for Data Exploration
.pull-left[

```r
diamonds %>%
sample_n(1500) %>%
ggplot() +
geom_point(
shape = 21,
size = 4,
alpha = 0.75,
aes(carat, price, fill = cut)
) +
scale_y_continuous(labels = scales::dollar)
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-21-1.png)
]

---

## ggplot for Data Exploration
.pull-left[

```r
diamonds %>%
  mutate(numeric_cut = as.numeric(cut)) %>%
  ggplot() +
  geom_boxplot(aes(cut, carat)) +
  geom_smooth(aes(numeric_cut, carat), method = 'lm',
  se = F)
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-23-1.png)
]

---

## Intermediate ggplot

Let's move over to the `gapminder` data set

```r
library(gapminder)

DT::datatable(gapminder::gapminder)
```

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class=\"display\">\n <thead>\n <tr>\n <th> <\/th>\n <th>country<\/th>\n <th>continent<\/th>\n <th>year<\/th>\n <th>lifeExp<\/th>\n <th>pop<\/th>\n <th>gdpPercap<\/th>\n <\/tr>\n <\/thead>\n<\/table>","options":{"columnDefs":[{"className":"dt-right","targets":[3,4,5,6]},{"orderable":false,"targets":0}],"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>

---

## Alternative `geoms`
![](ggplot_cheat.png)

---

## Alternative `geoms`

.pull-left[
There are lots of different `geoms` we can use

```r
gapminder %>% 
  ggplot() + 
  geom_bar(aes(x = continent))
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-26-1.png)

]
---

## geoms

- `geom_bar` is a little confusing

- By default, it tallies counts of a thing
  - This is the technical definition of a barplot
  
- A lot of the time, we want to make "bars" of x,y data

```r
gapminder %>% 
  group_by(continent) %>% 
  summarise(num_countries = length(unique(country)))
```

```
## # A tibble: 5 x 2
## continent num_countries
## <fct> <int>
## 1 Africa 52
## 2 Americas 25
## 3 Asia 33
## 4 Europe 30
## 5 Oceania 2
```
---

## geom_bar

.pull-left[

```r
gapminder %>%
  group_by(continent) %>%
  summarise(num_countries =
              length(unique(country))) %>%
  ggplot() +
  geom_bar(aes(continent, num_countries), stat = 'identity')
```
]

.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-29-1.png)

]
---

## `geom_col` is easier

.pull-left[

```r
gapminder %>%
  group_by(continent) %>%
  summarise(num_countries =
              length(unique(country))) %>%
  ggplot() +
  geom_col(aes(continent, num_countries))
```
]

.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-31-1.png)

]
---

## Layering `geom`'s

.pull-left[

```r
a = gapminder %>% 
  ggplot() + 
  geom_line(aes(year,lifeExp))
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-33-1.png)

]

---

## Layering `geom`'s

.pull-left[

```r
a <- gapminder %>% 
 ggplot() + 
 geom_line(aes(year,lifeExp, color = country))
```
]
.pull-right[
![](learn_ggplot_files/figure-html/unnamed-chunk-35-1.png)

]
---

## Layering `geom`'s

.pull-left[

```r
a <- gapminder %>% 
 ggplot() + 
 geom_line(aes(year,lifeExp, color = country),
 show.legend = F)
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-37-1.png)

]

---

## Layering `geom`'s

.pull-left[

- Keep on `+`ing

```r
a <- gapminder::gapminder %>%
 ggplot() +
 geom_line(aes(year, lifeExp, color = country),
 show.legend = F) +
 geom_point(aes(year, lifeExp, color = country),
 show.legend = F,
 size = 4)
```
]
.pull-right[
![](learn_ggplot_files/figure-html/unnamed-chunk-39-1.png)

]

---

## Layering `geom`'s

.pull-left[

- "global" aesthetics save a lot of typing

```r
a <- gapminder::gapminder %>%
 ggplot(aes(year, lifeExp, color = country)) +
 geom_line(show.legend = F) +
 geom_point(show.legend = F,
 size = 4)
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-41-1.png)

]

---

## Layering `geom`'s

.pull-left[

- You can mix and match global and "local" aesthetics

```r
a <- gapminder::gapminder %>%
 ggplot(aes(year, lifeExp, color = country)) +
 geom_line() +
 geom_point(aes(size = pop)) +
 scale_size_continuous(range = c(4, 12)) +
 scale_color_discrete(guide = F)
```
]
.pull-right[
![](learn_ggplot_files/figure-html/unnamed-chunk-43-1.png)

]

---

## Layering geom's

.pull-left[
- You can also combine more complex `geoms`

```r
a <- gapminder::gapminder %>% 
 ggplot(aes(continent, lifeExp)) + 
 geom_boxplot() + 
 ggbeeswarm::geom_beeswarm(
 aes(color = gdpPercap)) 
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-45-1.png)
]
---

## Modifying aesthetics

.pull-left[

- `scale_AESTHETIC_TYPE` controls aesthetic attributes
  - `scale_color_discrete/brewer` controls discrete color scales
  - `scale_color_continuous/gradient` controls continuous color scales

```r
library(ggbeeswarm)
library(gapminder)
a <- gapminder %>% 
 ggplot(aes(continent, lifeExp)) + 
 geom_boxplot(aes(fill = continent)) + 
 geom_beeswarm(aes(color = gdpPercap))
```
] 
.pull-right[
![](learn_ggplot_files/figure-html/unnamed-chunk-47-1.png)

]

---

## Modifying aesthetics

.pull-left[

- `scale_AESTHETIC_TYPE` controls aesthetic attributes
  - `scale_color_discrete/brewer` controls discrete color scales
  - `scale_color_continuous/gradient` controls continuous color scales
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-48-1.png)
]

---

## Modifying aesthetics
.pull-left[
- `scale_AESTHETIC_TYPE` controls aesthetic attributes
  - `scale_color_discrete/brewer` controls discrete color scales
  - `scale_color_continuous/gradient` controls continuous color scales

```r
 gapminder::gapminder %>% 
 ggplot(aes(continent, lifeExp)) + 
 geom_boxplot(aes(fill = continent)) + 
 ggbeeswarm::geom_beeswarm(aes(color = gdpPercap)) +
 scale_fill_brewer(palette = 'Spectral')
```
]
.pull-right[
![](learn_ggplot_files/figure-html/unnamed-chunk-50-1.png)

]

---

## Modifying aesthetics

.pull-left[

```r
a <- gapminder::gapminder %>% 
 ggplot(aes(continent, lifeExp)) + 
 geom_boxplot(aes(fill = continent)) + 
 ggbeeswarm::geom_beeswarm(aes(color =
 log(gdpPercap))) +
 scale_fill_brewer(palette = 'Spectral') +
 scale_color_gradient(low = 'red',
 high = 'blue')
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-52-1.png)

]

---

## Faceting

- So far, we've covered the basic ways to

- Use different `geom`'s
  
  - Define those `geom`'s aesthetics (`aes`)
  
  - Modify aesthetics
  
- Can think of x,y, and aesthetics as "dimensions"

- We can get one more layer of dimensions using `facets`

---

## Facets

![](learn_ggplot_files/figure-html/unnamed-chunk-54-1.png)

---

## Facets

.pull-left[

- `facet_wrap(~VARIABLE)`

- Automatically grids by `VARIABLE`

```r
gapminder %>% 
  ggplot(aes(year,lifeExp,
        color = country)) + 
  geom_line(show.legend = F) + 
  facet_wrap(~continent)
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-56-1.png)
]
---

## Facets

- `facet_grid()` allows for more control

- `facet_grid( . ~ VARIABLE)`
  - Rows

- `facet_grid( Variable ~ .)`
  - Columns

- `facet_grid(VARIABLE_1 ~ VARIABLE_2)`
  - matrix

---

## facet_grid( . ~ VARIABLE)

![](learn_ggplot_files/figure-html/unnamed-chunk-57-1.png)

---

## facet_grid( VARIABLE ~ .)

![](learn_ggplot_files/figure-html/unnamed-chunk-58-1.png)

---

## facet_grid( VARIABLE ~ ., scales = 'free_y')

![](learn_ggplot_files/figure-html/unnamed-chunk-59-1.png)

---

## facet_grid( VARIABLE_1 ~ Variable_2)

![](learn_ggplot_files/figure-html/unnamed-chunk-60-1.png)

---

## Themes

- `geom`, `aes`, `facet` allows us to quickly plot many dimensions of data

- So far though, you've probable though "eh, but these don't look too good"

- `theme` to the rescue!

- Beauty of `ggplot`: focus on making it look good, not making it exist

- You layer "themes" like any other `geom`

```r
gapminder %>% 
  ggplot(aes(year,lifeExp, color = country)) + 
  geom_line(show.legend = F) + 
  theme(text = element_text(size = 42, family = 'Times New Roman'))
```

---

## Themes

![](learn_ggplot_files/figure-html/unnamed-chunk-62-1.png)

---

## Themes

- You can control just about every aspect of a graph through `theme`

- syntax: `theme(thing = element_thing(attributes))`

- `theme(axis.text.x = element_text(size = 24, angle = 45, color = 'red'))`

- `theme(plot.background = element_rect(color = 'red'))`

---

## theme(axis.text.x = element_text(size = 24, angle = 45, color = 'red',vjust = 0.5))

![](learn_ggplot_files/figure-html/unnamed-chunk-63-1.png)

---

## theme(panel.background = element_rect(fill = 'black'))

![](learn_ggplot_files/figure-html/unnamed-chunk-64-1.png)

---

## Theme

- There are also lots of pre-built themes that you can add on

- `theme_light`

- `theme_bw`

- `theme_solarized2`

`ggthemes` package has a lot of extras

---

## theme_light()

![](learn_ggplot_files/figure-html/unnamed-chunk-65-1.png)

---

## theme_classic()

![](learn_ggplot_files/figure-html/unnamed-chunk-66-1.png)

---

## theme_economist()

![](learn_ggplot_files/figure-html/unnamed-chunk-67-1.png)

---

## Saving

- The other reallllly nice thing about ggplot is that you can save objects

- base `plot`:

- Print to a 'device'
  - Saved either as static item...
  - Or save data and rerun script

- `ggplot`

- Can be stored as a variable
    - `my_plot = ggplot() +...`
  - Can then call up `my_plot` later
  - Or save to list/object and load it later
  - Can still save to pdf, whatever through
    - pdf()
    - ggsave(file = "PATH.pdf", plot = my_plot)

---

## Saving

.pull-left[

- Saving is great for papers

- Create a "base" version of your graph

- Use themes to modify graphs for different uses

```r
my_plot <- 
 gapminder %>% 
 filter(year == max(year)) %>% 
 ggplot(aes(gdpPercap, lifeExp)) + 
 geom_point(size = 4)
```
]
.pull-right[

![](learn_ggplot_files/figure-html/unnamed-chunk-69-1.png)

]

---

## Submit to Journal of Stuffy Academics

```r
boring_theme <- 
 theme_classic(base_size = 16,
 base_family = 'Andale Mono')
```

---

## Submit to Journal of Stuffy Academics

```r
my_plot + 
  boring_theme
```

![](learn_ggplot_files/figure-html/unnamed-chunk-72-1.png)

---

## Party Theme

```r
party_theme <- 
 xkcd::theme_xkcd() + 
 theme(text = element_text(size = 54),
 panel.background = element_rect(fill = 'green'))
```

```r
my_plot +
  party_theme +
  scale_x_log10(labels = scales::comma) +
  geom_point(
  aes(fill = country),
  shape = 21,
  show.legend = F,
  size = 4
  ) +
  xlab('GDP Per Capita') +
  ylab('Life Expectancy')
```

---

## Party Theme

![](learn_ggplot_files/figure-html/unnamed-chunk-75-1.png)

---

## Fancy Things

- Since `ggplot` has a standardized API, people can write extensions

- Allows `ggplot` objects to be passed to all kinds of other things

---

## ggplotly

```r
plotly_plot <- my_plot +
 geom_point(aes(fill = country, key = continent), shape = 21,
 size = 4,
 show.legend = F) + 
 scale_x_continuous(labels = scales::dollar) + 
 theme_fivethirtyeight(base_size = 14) + 
 theme(axis.title = element_text())
```

---

## ggplotly(plotly_plot)

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