BANL 6100: Business Analytics

.title[
# BANL 6100: Business Analytics
]
.subtitle[
## Data Visualization
]
.author[
### Mehmet Balcilar </br> <a href="mailto:mbalcilar@newhaven.edu" class="email">mbalcilar@newhaven.edu</a>
]
.institute[
### Univeristy of New Haven
]
.date[
### 2023-09-28 (updated: 2023-10-02)
]

---

# Data Visualization using ggplot2

---
class: middle

> “The simple graph has brought more information to the data analyst’s mind than any other device.” <br> <br> --- John Tukey

---

## A Brief History

Based on [The Grammar of Graphics](https://smile.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448) by Leland Wilkinson (2000).

ggplot releases:

+ [ggplot1](https://github.com/hadley/ggplot1) was released in April, 2006.
  + __Do NOT use this release.__
  + Hadley uses it as a guide for API design in R.
+ [ggplot2](https://github.com/tidyverse/ggplot2) was released in June, 2007.
  + Feature freeze in February 2014.
  + The official extension mechanism was added in December 2015.

---

## The Layered Grammar of Graphics

If you're interested in the theoretical underpinnings of ggplot2:
<p>
[The Layered Grammar of Graphics](https://vita.had.co.nz/papers/layered-grammar.html) by Hadley Wickham (2010).

+ Contrasts the ggplot2 approach with the earlier approach by Wilkenson
+ Introduces the concept of layers
+ Added to the existing R programming language

---

## The Benefits of using a Grammar

> “A grammar of graphics is a tool that enables us to concisely describe the components of a graphic.” <br> <br> --- Hadley Wickham

The most important reason is the grammar:

+ The MIT "language gives us power over problems" approach to problem solving
+ See relationships between graphs, components and data that might not be obvious otherwise
+ A higher level of abstraction than pen and paper approaches
+ Provides a concise way of thinking about the connection between data and graphs

---

## Why Should I use ggplot2?

+ Charts are more elegant than those found in base R
+ Well supported; Provides a wide variety of charts and themes
+ Well designed API (OOP plus FP plus DSL)
+ Building charts in layers is quite intuitive

> **“The transferrable skills from ggplot2 are not the idiosyncracies of plotting syntax, but a powerful way of thinking about visualisation, as a way of** mapping between variables and the visual properties of geometric objects **that you can perceive.”** <br> <br> **---  Hadley Wickham**

---

## Why Should I use ggplot2?

### My personal reasons

- .hl[Functional] data visualization

1. Wrangle data
    2. Map data to visual elements
    3. Tweak scales, guides, axis, labels, theme

- Easy to .hl[reason] about how data drives visualization

- Easy to .hl[iterate]

- Easy to be .hl[consistent]

### .hl[Plus:] **stunning figures**

---

---

### Load the tidyverse

```r
library(tidyverse)
```

```
## ── Attaching core tidyverse packages ────────────────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.3     ✔ readr     2.1.4
## ✔ forcats   1.0.0     ✔ stringr   1.5.0
## ✔ ggplot2   3.4.3     ✔ tibble    3.2.1
## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.2     
## ── Conflicts ──────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
```

---
### Other packages you'll need for this adventure

We'll use an excerpt of the [gapminder](http://www.gapminder.org/data/) dataset provided by the [`gapminder` package](https://github.com/jennybc/gapminder) by Jenny Bryan.

<https://github.com/jennybc/gapminder>

```r
## install.packages("gapminder")
library(gapminder)
```

---
layout: false
class: inverse center middle text-white

---

## What is a grammar of graphics?

#### How is the drawing on the right connected to data?

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/guess-data-from-plot-0-1.png" width="100%" />
]

---
layout: true
## Guess the data behind this plot?

- Manufacturer
- Car Type (Class)
- City MPG
- Highway MPG
]

---

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/guess-data-from-plot-2-1.png" width="100%" />
]

---

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/guess-data-from-plot-3-1.png" width="100%" />
]

---

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/guess-data-from-plot-1-1.png" width="100%" />
]

---

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/guess-data-from-plot-4-1.png" width="100%" />
]

---

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/guess-data-from-plot-5-1.png" width="100%" />
]

---

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/guess-data-from-plot-6-1.png" width="100%" />
]

---

.right-plot[
<table>
 <thead>
  <tr>
   <th style="text-align:left;"> manufacturer </th>
   <th style="text-align:left;"> class </th>
   <th style="text-align:right;"> cty </th>
   <th style="text-align:right;"> hwy </th>
   <th style="text-align:left;"> model </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> audi </td>
   <td style="text-align:left;"> compact </td>
   <td style="text-align:right;"> 21 </td>
   <td style="text-align:right;"> 30 </td>
   <td style="text-align:left;"> a4 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> audi </td>
   <td style="text-align:left;"> compact </td>
   <td style="text-align:right;"> 17 </td>
   <td style="text-align:right;"> 25 </td>
   <td style="text-align:left;"> a4 quattro </td>
  </tr>
  <tr>
   <td style="text-align:left;"> ford </td>
   <td style="text-align:left;"> suv </td>
   <td style="text-align:right;"> 12 </td>
   <td style="text-align:right;"> 18 </td>
   <td style="text-align:left;"> expedition 2wd </td>
  </tr>
  <tr>
   <td style="text-align:left;"> ford </td>
   <td style="text-align:left;"> suv </td>
   <td style="text-align:right;"> 13 </td>
   <td style="text-align:right;"> 19 </td>
   <td style="text-align:left;"> explorer 4wd </td>
  </tr>
  <tr>
   <td style="text-align:left;"> toyota </td>
   <td style="text-align:left;"> suv </td>
   <td style="text-align:right;"> 16 </td>
   <td style="text-align:right;"> 20 </td>
   <td style="text-align:left;"> 4runner 4wd </td>
  </tr>
  <tr>
   <td style="text-align:left;"> toyota </td>
   <td style="text-align:left;"> compact </td>
   <td style="text-align:right;"> 22 </td>
   <td style="text-align:right;"> 31 </td>
   <td style="text-align:left;"> camry solara </td>
  </tr>
  <tr>
   <td style="text-align:left;"> toyota </td>
   <td style="text-align:left;"> compact </td>
   <td style="text-align:right;"> 28 </td>
   <td style="text-align:right;"> 37 </td>
   <td style="text-align:left;"> corolla </td>
  </tr>
  <tr>
   <td style="text-align:left;"> toyota </td>
   <td style="text-align:left;"> suv </td>
   <td style="text-align:right;"> 13 </td>
   <td style="text-align:right;"> 18 </td>
   <td style="text-align:left;"> land cruiser wagon 4wd </td>
  </tr>
</tbody>
</table>
]

---
layout: false
## How do we express visuals in words?

--
.font120[
- **.hlb[Geom]etric objects** that appear on the plot
]

--
.font120[
- **.hlb[Aes]thetic mappings** from data to visual component
]

--
.font120[
- **.hlb[Stat]istics** transform data on the way to visualization
]

--
.font120[
- **.hlb[Coord]inates** organize location of geometric objects
]

--
.font120[
- **.hlb[Scale]s** define the range of values for aesthetics
]

--
.font120[
- **.hlb[Facet]s** group into subplots
]

---
layout: true
## gg is for Grammar of Graphics

```r
ggplot(data)
```
]

---

1. Each variable forms a .hl[column]

2. Each observation forms a .hl[row]

3. Each observational unit forms a table
]

1. What information do I want to use in my visualization?

1. Is that data contained in .hl[one column/row] for a given data point?
]

---

.right-column[
<table>
 <thead>
  <tr>
   <th style="text-align:left;"> country </th>
   <th style="text-align:right;"> 1997 </th>
   <th style="text-align:right;"> 2002 </th>
   <th style="text-align:right;"> 2007 </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Canada </td>
   <td style="text-align:right;"> 30.30584 </td>
   <td style="text-align:right;"> 31.90227 </td>
   <td style="text-align:right;"> 33.39014 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> China </td>
   <td style="text-align:right;"> 1230.07500 </td>
   <td style="text-align:right;"> 1280.40000 </td>
   <td style="text-align:right;"> 1318.68310 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> United States </td>
   <td style="text-align:right;"> 272.91176 </td>
   <td style="text-align:right;"> 287.67553 </td>
   <td style="text-align:right;"> 301.13995 </td>
  </tr>
</tbody>
</table>
]

```r
tidy_pop <- gather(messy_pop, 'year', 'pop', -country)
```

<table>
 <thead>
  <tr>
   <th style="text-align:left;"> country </th>
   <th style="text-align:left;"> year </th>
   <th style="text-align:right;"> pop </th>
  </tr>
 </thead>
<tbody>
  <tr>
   <td style="text-align:left;"> Canada </td>
   <td style="text-align:left;"> 1997 </td>
   <td style="text-align:right;"> 30.306 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> China </td>
   <td style="text-align:left;"> 1997 </td>
   <td style="text-align:right;"> 1230.075 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> United States </td>
   <td style="text-align:left;"> 1997 </td>
   <td style="text-align:right;"> 272.912 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Canada </td>
   <td style="text-align:left;"> 2002 </td>
   <td style="text-align:right;"> 31.902 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> China </td>
   <td style="text-align:left;"> 2002 </td>
   <td style="text-align:right;"> 1280.400 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> United States </td>
   <td style="text-align:left;"> 2002 </td>
   <td style="text-align:right;"> 287.676 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> Canada </td>
   <td style="text-align:left;"> 2007 </td>
   <td style="text-align:right;"> 33.390 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> China </td>
   <td style="text-align:left;"> 2007 </td>
   <td style="text-align:right;"> 1318.683 </td>
  </tr>
  <tr>
   <td style="text-align:left;"> United States </td>
   <td style="text-align:left;"> 2007 </td>
   <td style="text-align:right;"> 301.140 </td>
  </tr>
</tbody>
</table>
]

---
layout: true
## gg is for Grammar of Graphics

```r
+ aes()
```

]
---

- year

- pop

- country
]

---

- year → **x**

- pop → **y**

- country → *shape*, *color*, etc.
]

---

```r
aes(
  x = year,
  y = pop,
  color = country
)
```
]

---
layout: true
## gg is for Grammar of Graphics

```r
+ geom_*()
```
]

---

<img src="6-Visualization-with-ggplot_files/figure-html/geom_demo-1.png" width="650px" />
]

---

.right-column[
Here are the [some of the most widely used geoms](https://eric.netlify.com/2017/08/10/most-popular-ggplot2-geoms/)

.font70.center[
| Type | Function |
|:----:|:--------:|
| Point | `geom_point()` |
| Line | `geom_line()` |
| Bar | `geom_bar()`, `geom_col()` |
| Histogram | `geom_histogram()` |
| Regression | `geom_smooth()` |
| Boxplot | `geom_boxplot()` |
| Text | `geom_text()` |
| Vert./Horiz. Line | `geom_{vh}line()` |
| Count | `geom_count()` |
| Density | `geom_density()` |

<https://eric.netlify.com/2017/08/10/most-popular-ggplot2-geoms/>
]
]

---

See <http://ggplot2.tidyverse.org/reference/> for many more options

```
##  [1] "geom_abline"            "geom_area"              "geom_bar"              
##  [4] "geom_bin_2d"            "geom_bin2d"             "geom_blank"            
##  [7] "geom_boxplot"           "geom_col"               "geom_contour"          
## [10] "geom_contour_filled"    "geom_count"             "geom_crossbar"         
## [13] "geom_curve"             "geom_density"           "geom_density_2d"       
## [16] "geom_density_2d_filled" "geom_density2d"         "geom_density2d_filled" 
## [19] "geom_dotplot"           "geom_errorbar"          "geom_errorbarh"        
## [22] "geom_freqpoly"          "geom_function"          "geom_hex"              
## [25] "geom_histogram"         "geom_hline"             "geom_jitter"           
## [28] "geom_label"             "geom_line"              "geom_linerange"        
## [31] "geom_map"               "geom_path"              "geom_point"            
## [34] "geom_pointrange"        "geom_polygon"           "geom_qq"               
## [37] "geom_qq_line"           "geom_quantile"          "geom_raster"           
## [40] "geom_rect"              "geom_ribbon"            "geom_rug"              
## [43] "geom_segment"           "geom_sf"                "geom_sf_label"         
## [46] "geom_sf_text"           "geom_smooth"            "geom_spoke"            
## [49] "geom_step"              "geom_text"              "geom_tile"             
## [52] "geom_violin"            "geom_vline"
```
]
]
--

.right-column[
<img src="images/geom.gif" width="200px" style="float: right; margin-right: 100px; margin-top: -25px;">
Or just start typing `geom_` in RStudio
]

---
layout: true
## Our first plot!
---

```r
ggplot(tidy_pop)
```
]

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/first-plot1a-out-1.png" width="100%" />
]

---

```r
ggplot(tidy_pop) +
* aes(x = year,
*     y = pop)
```
]

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/first-plot1b-out-1.png" width="100%" />
]

---

```r
ggplot(tidy_pop) +
  aes(x = year,
      y = pop) +
* geom_point()
```
]

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/first-plot1c-out-1.png" width="100%" />
]

---

```r
ggplot(tidy_pop) +
  aes(x = year,
      y = pop,
*     color = country) +
  geom_point()
```
]

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/first-plot1-out-1.png" width="100%" />
]

---

```r
ggplot(tidy_pop) +
  aes(x = year,
      y = pop,
      color = country) +
  geom_point() +
* geom_line()
```

.font80[
```r
geom_path: Each group consists
of only one observation. 
Do you need to adjust the 
group aesthetic?
```
]
]

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/first-plot2-fake-out-1.png" width="100%" />
]
---

```r
ggplot(tidy_pop) +
  aes(x = year,
      y = pop,
      color = country) +
  geom_point() +
  geom_line(
*   aes(group = country))
```
]

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/first-plot2-out-1.png" width="100%" />
]

---

```r
g <- ggplot(tidy_pop) +
  aes(x = year,
      y = pop,
      color = country) +
  geom_point() +
  geom_line(
    aes(group = country))

g
```
]

.right-plot[
<img src="6-Visualization-with-ggplot_files/figure-html/first-plot3-out-1.png" width="100%" />
]

---
layout: true
## gg is for Grammar of Graphics

```r
+ geom_*()
```
]
---

- `data` Geoms can have their own data
    - Has to map onto global coordinates

- `map` Geoms can have their own aesthetics
    - Inherits global aesthetics
    - Have geom-specific aesthetics
        - `geom_point` needs `x` and `y`, optional `shape`, `color`, `size`, etc.
        - `geom_ribbon` requires `x`, `ymin` and `ymax`, optional `fill`
    - `?geom_ribbon`
]

---

- `stat` Some geoms apply further transformations to the data
    - All respect `stat = 'identity'`
    - Ex: `geom_histogram` uses `stat_bin()` to group observations
    
- `position` Some adjust location of objects
    - `'dodge'`, `'stack'`, `'jitter'`
]

---
layout: true
## gg is for Grammar of Graphics

```r
+facet_wrap()

+facet_grid()
```
]
---

```r
g + facet_wrap(~ country)
```

<img src="6-Visualization-with-ggplot_files/figure-html/geom_facet-1.png" width="90%" />
]

---

```r
g + facet_grid(continent ~ country)
```

<img src="6-Visualization-with-ggplot_files/figure-html/geom_grid-1.png" width="90%" />
]

---
layout: true
## gg is for Grammar of Graphics

```r
+ labs()
```
]
---

```r
g + labs(x = "Year", y = "Population")
```

<img src="6-Visualization-with-ggplot_files/figure-html/labs-ex-1.png" width="90%" />
]

---
layout: true
## gg is for Grammar of Graphics

```r
+ coord_*()
```
]
---

```r
g + coord_flip()
```

<img src="6-Visualization-with-ggplot_files/figure-html/coord-ex-1.png" width="90%" />
]

---

```r
g + coord_polar()
```

<img src="6-Visualization-with-ggplot_files/figure-html/coord-ex2-1.png" width="90%" />
]

---
layout: true
## gg is for Grammar of Graphics

```r
+ scale_*_*()
```
]
---

What parameter do you want to adjust? → `<aes>` <br>
What type is the parameter? → `<type>`

- I want to change my discrete x-axis<br>`scale_x_discrete()`
- I want to change range of point sizes from continuous variable<br>`scale_size_continuous()`
- I want to rescale y-axis as log<br>`scale_y_log10()`
- I want to use a different color palette<br>`scale_fill_discrete()`<br>`scale_color_manual()`
]

---

```r
g + scale_color_manual(values = c("peru", "pink", "plum"))
```

<img src="6-Visualization-with-ggplot_files/figure-html/scale_ex1-1.png" width="90%" />
]

---

```r
g + scale_y_log10()
```

<img src="6-Visualization-with-ggplot_files/figure-html/scale_ex2-1.png" width="90%" />
]

---

```r
g + scale_x_discrete(labels = c("MCMXCVII", "MMII", "MMVII"))
```

<img src="6-Visualization-with-ggplot_files/figure-html/scale_ex4-1.png" width="90%" />
]

---
layout: true
## gg is for Grammar of Graphics

```r
+ theme()
```
]

---

A few "starter" themes ship with the package

- `g + theme_bw()`
- `g + theme_dark()`
- `g + theme_gray()`
- `g + theme_light()`
- `g + theme_minimal()`

]

---

- Global options: `line`, `rect`, `text`, `title`
- `axis`: x-, y- or other axis title, ticks, lines
- `legend`: Plot legends
- `panel`: Actual plot area
- `plot`: Whole image
- `strip`: Facet labels
]
---

- `element_blank()` removes the element
- `element_line()`
- `element_rect()`
- `element_text()`
]

---

```r
g + theme_bw()
```

<img src="6-Visualization-with-ggplot_files/figure-html/unnamed-chunk-1-1.png" width="90%" />
]

---

```r
g + theme_minimal() + theme(text = element_text(family = "Palatino"))
```

<img src="6-Visualization-with-ggplot_files/figure-html/unnamed-chunk-2-1.png" width="90%" />
]
]

---

```r
my_theme <- theme_bw() +
  theme(
    text = element_text(family = "Palatino", size = 12),
    panel.border = element_rect(colour = 'grey80'), 
    panel.grid.minor = element_blank()
  )

theme_set(my_theme)
```

All plots will now use this theme!
]

---

```r
g
```

<img src="6-Visualization-with-ggplot_files/figure-html/unnamed-chunk-3-1.png" width="90%" />
]

---

```r
g + theme(legend.position = 'bottom')
```

<img src="6-Visualization-with-ggplot_files/figure-html/unnamed-chunk-4-1.png" width="90%" />
]

---
layout: false
## Save Your Work

To save your plot, use **ggsave**

```r
ggsave(
  filename = "my_plot.png",
  plot = my_plot,
  width = 10,
  height = 8,
  dpi = 100,
  device = "png"
)
```

---
layout: false
count: hide
class: fullscreen, inverse, top, left, text-white
background-image: url(images/super-grover.jpg)

# "Live" Coding

```r
library(gapminder)
```

---
## head(gapminder)

```
## # A tibble: 6 × 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.
```

---
## glimpse(gapminder)

```
Rows: 1,704
Columns: 6
$ country   <fct> "Afghanistan", "Afghanistan", "Afghanistan", "Afghanistan", "Afghanist…
$ continent <fct> Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia…
$ year      <int> 1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, 2002, 2007…
$ lifeExp   <dbl> 28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.854, 40.822, 41.674…
$ pop       <int> 8425333, 9240934, 10267083, 11537966, 13079460, 14880372, 12881816, 13…
$ gdpPercap <dbl> 779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 786.1134, 978.0114, …
```

Let's start with `lifeExp` vs `gdpPercap`

---
class: fullscreen
layout: true
---

```r
ggplot(gapminder) +
  aes(x = gdpPercap,
      y = lifeExp)
```
]

Add points...

---

```r
ggplot(gapminder) +
  aes(x = gdpPercap,
      y = lifeExp) +
* geom_point()
```
]

How can I tell countries apart?

---

```r
ggplot(gapminder) +
  aes(x = gdpPercap,
      y = lifeExp,
*     color = continent) +
  geom_point()
```
]

GDP is squished together on the left

---

```r
ggplot(gapminder) +
  aes(x = gdpPercap,
      y = lifeExp,
      color = continent) +
  geom_point() +
* scale_x_log10()
```
]

Still lots of overlap in the countries...

---

```r
ggplot(gapminder) +
  aes(x = gdpPercap,
      y = lifeExp,
      color = continent) +
  geom_point() +
  scale_x_log10() +
* facet_wrap(~ continent) +
* guides(color = FALSE)
```

No need for color legend thanks to facet titles
]

Lots of overplotting due to point size

---

```r
ggplot(gapminder) +
  aes(x = gdpPercap,
      y = lifeExp,
      color = continent) +
* geom_point(size = 0.25) +
  scale_x_log10() +
  facet_wrap(~ continent) +
  guides(color = FALSE)
```
]

Is there a trend?

---

```r
ggplot(gapminder) +
  aes(x = gdpPercap,
      y = lifeExp,
      color = continent) +
* geom_line() +
  geom_point(size = 0.25) +
  scale_x_log10() +
  facet_wrap(~ continent) +
  guides(color = FALSE)
```
]

Okay, that line just connected all of the points sequentially...

---

```r
ggplot(gapminder) +
  aes(x = gdpPercap,
      y = lifeExp,
      color = continent) +
  geom_line(
*   aes(group = country)
  ) +
  geom_point(size = 0.25) +
  scale_x_log10() +
  facet_wrap(~ continent) +
  guides(color = FALSE)
```

.font200.center[&#x1F914;]
]

&#x1F4A1; We need time on x-axis!

---

```r
ggplot(gapminder) +
* aes(x = year,
*     y = gdpPercap,
      color = continent) +
  geom_line(
    aes(group = country)
  ) +
  geom_point(size = 0.25) +
* scale_y_log10() +
  facet_wrap(~ continent) +
  guides(color = FALSE)
```
]

Can't see x-axis labels, though

---

```r
ggplot(gapminder) +
  aes(x = year,
      y = gdpPercap,
      color = continent) +
  geom_line(
    aes(group = country)
  ) +
  geom_point(size = 0.25) +
  scale_y_log10() +
* scale_x_continuous(breaks =
*   seq(1950, 2000, 25)
* ) +
  facet_wrap(~ continent) +
  guides(color = FALSE)
```
]

What about life expectancy?

---

```r
ggplot(gapminder) +
  aes(x = year, 
*     y = lifeExp,
      color = continent) +
  geom_line(
    aes(group = country)
  ) +
  geom_point(size = 0.25) +
* #scale_y_log10() +
  scale_x_continuous(breaks = 
    seq(1950, 2000, 25)
  ) +  
  facet_wrap(~ continent) +
  guides(color = FALSE)
```
]

Okay, let's add a trend line

---

```r
ggplot(gapminder) +
  aes(x = year, 
      y = lifeExp,
      color = continent) +
  geom_line(
    aes(group = country)
  ) +
  geom_point(size = 0.25) +
* geom_smooth() +
  scale_x_continuous(breaks = 
    seq(1950, 2000, 25)
  ) +  
  facet_wrap(~ continent) +
  guides(color = FALSE)
```
]

De-emphasize individual countries

---

```r
ggplot(gapminder) +
  aes(x = year, 
      y = lifeExp,
      color = continent) +
  geom_line(
    aes(group = country),
*   color = "grey75"
  ) +
  geom_point(size = 0.25) +
  geom_smooth() + 
  scale_x_continuous(breaks = 
    seq(1950, 2000, 25)
  ) +  
  facet_wrap(~ continent) +
  guides(color = FALSE)
```
]

Points are still in the way

---

```r
ggplot(gapminder) +
  aes(x = year, 
      y = lifeExp,
      color = continent) +
  geom_line(
    aes(group = country),
    color = "grey75"
  ) +
* #geom_point(size = 0.25) +
  geom_smooth() + 
  scale_x_continuous(breaks = 
    seq(1950, 2000, 25)
  ) +  
  facet_wrap(~ continent) +
  guides(color = FALSE)
```
]

Let's compare continents

---

```r
ggplot(gapminder) +
  aes(x = year, 
      y = lifeExp,
      color = continent) +
  geom_line(
    aes(group = country),
    color = "grey75"
  ) +
  geom_smooth() + 
  # scale_x_continuous(
  #   breaks = 
  #     seq(1950, 2000, 25)
  # ) +  
* # facet_wrap(~ continent) +
  guides(color = FALSE)
```
]

Wait, what color is each continent?

---

```r
ggplot(gapminder) +
  aes(x = year, 
      y = lifeExp,
      color = continent) +
  geom_line(
    aes(group = country),
    color = "grey75"
  ) +
  geom_smooth() + 
* theme(
* legend.position = "bottom"
* )
```
]

Let's try the minimal theme

---

```r
ggplot(gapminder) +
  aes(x = year, 
      y = lifeExp,
      color = continent) +
  geom_line(
    aes(group = country),
    color = "grey75"
  ) +
  geom_smooth() + 
* theme_minimal() +
  theme(
  legend.position = "bottom"
  )
```
]

Fonts are kind of big

---

```r
ggplot(gapminder) +
  aes(x = year, 
      y = lifeExp,
      color = continent) +
  geom_line(
    aes(group = country),
    color = "grey75"
  ) +
  geom_smooth() + 
  theme_minimal( 
*   base_size = 8) +
  theme(
  legend.position = "bottom"
  )
```
]

Cool, let's switch gears

---

```r
americas <- 
  gapminder %>% 
  filter(
    country %in% c(
      "United States",
      "Canada",
      "Mexico",
      "Ecuador"
    )
  )
```

Let's look at four countries in more detail. How do their populations compare to each other?
]

```
## # A tibble: 48 × 6
##    country continent  year lifeExp      pop gdpPercap
##    <fct>   <fct>     <int>   <dbl>    <int>     <dbl>
##  1 Canada  Americas   1952    68.8 14785584    11367.
##  2 Canada  Americas   1957    70.0 17010154    12490.
##  3 Canada  Americas   1962    71.3 18985849    13462.
##  4 Canada  Americas   1967    72.1 20819767    16077.
##  5 Canada  Americas   1972    72.9 22284500    18971.
##  6 Canada  Americas   1977    74.2 23796400    22091.
##  7 Canada  Americas   1982    75.8 25201900    22899.
##  8 Canada  Americas   1987    76.9 26549700    26627.
##  9 Canada  Americas   1992    78.0 28523502    26343.
## 10 Canada  Americas   1997    78.6 30305843    28955.
## # ℹ 38 more rows
```
]

---

```r
ggplot(americas) +
  aes(
    x = year,
    y = pop
  ) +
  geom_col()
```
]

Yeah, but how many people are in each country?

---

```r
ggplot(americas) +
  aes(
    x = year,
    y = pop,
*   fill = country
  ) +
  geom_col()
```
]

Bars are "stacked", can we separate?

---

```r
ggplot(americas) +
  aes(
    x = year,
    y = pop,
    fill = country
  ) +
  geom_col(
*   position = "dodge"
  )
```

`position = "dodge"` places objects _next to each other_ instead of overlapping
]

&#x1F913; What is scientific notation anyway?

---

```r
ggplot(americas) +
  aes(
    x = year,
*   y = pop / 10^6,
    fill = country
  ) +
  geom_col(
    position = "dodge" 
  )
```

ggplot aesthetics can take expressions!
]

Might be easier to see countries individually

---

```r
ggplot(americas) +
  aes(
    x = year,
    y = pop / 10^6,
    fill = country
  ) +
  geom_col(
    position = "dodge" 
  ) +
* facet_wrap(~ country) +
* guides(fill = FALSE)
```
]

Let range of y-axis vary in each plot

---

```r
ggplot(americas) +
  aes(
    x = year,
    y = pop / 10^6,
    fill = country
  ) +
  geom_col(
    position = "dodge" 
  ) +
  facet_wrap(~ country,
*   scales = "free_y") +
  guides(fill = FALSE)
```
]

What about life expectancy again?

---

```r
ggplot(americas) +
  aes(
    x = year,
*   y = lifeExp,
    fill = country
  ) +
  geom_col(
    position = "dodge" 
  ) +
  facet_wrap(~ country,
    scales = "free_y") +
  guides(fill = FALSE)
```
]

This should really be &#x1F4C8; instead of &#x1F4CA;

---

```r
ggplot(americas) +
  aes(
    x = year,
    y = lifeExp,
    fill = country
  ) +
* geom_line() +
  facet_wrap(~ country,
    scales = "free_y") +
  guides(fill = FALSE)
```
]

&#x1F4CA; are **fill**ed, &#x1F4C8; are **color**ed

---

```r
ggplot(americas) +
  aes(
    x = year,
    y = lifeExp,
*   color = country
  ) +
  geom_line() +
  facet_wrap(~ country,
    scales = "free_y") +
* guides(color = FALSE)
```
]

Altogether now!

---

```r
ggplot(americas) +
  aes(
    x = year,
    y = lifeExp,
    color = country
  ) +
  geom_line()
```
]

--
.right-plot[
Okay, changing gears again. What is range of life expectancy in Americas?
]
---

```r
gapminder %>% 
  filter(
    continent == "Americas"
* ) %>%
* ggplot() +
  aes(
    x = year,
    y = lifeExp
  )
```

You can pipe into `ggplot()`!  
Just watch for `%>%` changing to `+`
]

Boxplot for life expectancy range

---

```r
gapminder %>% 
  filter(
    continent == "Americas"
  ) %>%
  ggplot() +
  aes(
    x = year,
    y = lifeExp
  ) +
* geom_boxplot()
```
]

Why not boxplots by year?

---

```r
gapminder %>% 
  filter(
    continent == "Americas"
  ) %>%
* mutate(
*   year = factor(year)
* ) %>%
  ggplot() +
  aes(
    x = year,
    y = lifeExp
  ) +
  geom_boxplot()
```
]

OK, what about global life expectancy?

---

```r
gapminder %>% 
  # filter(
  #   continent == "Americas"
  # ) %>%
  mutate(
    year = factor(year)
  ) %>% 
  ggplot() +
  aes(
    x = year,
    y = lifeExp
  ) +
  geom_boxplot()
```
]

Can we have cute little boxplots for each continent?

---

```r
gapminder %>% 
  mutate(
    year = factor(year)
  ) %>% 
  ggplot() +
  aes(
    x = year,
    y = lifeExp,
*   fill = continent
  ) +
  geom_boxplot()
```
]

Hard to read years, let's rotate &#x1F504;

---

```r
gapminder %>% 
  mutate(
    year = factor(year)
  ) %>% 
  ggplot() +
  aes(
    x = year,
    y = lifeExp,
    fill = continent
  ) +
  geom_boxplot() +
* coord_flip()
```
]

Use `dplyr` to group by decade

---

```r
gapminder %>% 
  mutate(
*   decade = floor(year / 10),
*   decade = decade * 10,
*   decade = factor(decade)
  ) %>% 
  ggplot() +
  aes(
*   x = decade,
    y = lifeExp,
    fill = continent
  ) +
  geom_boxplot() +
  coord_flip()
```
]

Let's hide Oceania, sorry &#x1F1E6;&#x1F1FA;&#x1F1F3;&#x1F1FF;&#x1F1EE;&#x1F1E9;&#x1F1EB;&#x1F1EF;&#x1F1F5;&#x1F1EC;

---

```r
g <- gapminder %>% 
* filter(
*   continent != "Oceania"
* ) %>%
  mutate(
    decade = floor(year / 10) * 10, decade = factor(decade)      
  ) %>% 
  ggplot() +
  aes(
    x = decade,
    y = lifeExp,
    fill = continent
  ) +
  geom_boxplot() +
  coord_flip()
```
]

---

```r
g +
  theme_minimal(8) +
  labs(
    y = "Life Expectancy",
    x = "Decade",
    fill = NULL,
    title = "Life Expectancy by Continent and Decade",
    caption = "gapminder.org"
  )
```

Note `x` and `y` are _original_ aesthetics, `coord_flip()` happens _after_.

Remove labels by setting `= NULL`.
]

---
layout: false
class: inverse, center, middle

# Level up

```r
Inspired by "The Best Stats You've Ever Seen" by Hans Rosling

http://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen
```

---
## Create the initial layout

```r
g_hr <- 
  ggplot(gapminder) +
  aes(x = gdpPercap, y = lifeExp, size = pop, color = country) +
  geom_point() +
  facet_wrap(~year)
```

---
## Hide the guides

```r
g_hr <- 
  ggplot(gapminder) +
  aes(x = gdpPercap, y = lifeExp, size = pop, color = country) +
  geom_point() +
  facet_wrap(~year) +
  guides(color = FALSE, size = FALSE)
```

---
## Adjust scales of x-axis, color, and size

```r
g_hr <- 
  g_hr +
  scale_x_log10(breaks = c(10^3, 10^4, 10^5), labels = c("1k", "10k", "100k")) +
  scale_color_manual(values = gapminder::country_colors) +
  scale_size(range = c(0.5, 12))
```

---
## Tweak Anotations

```r
g_hr <- 
  g_hr +
  labs(
    x = "GDP per capita",
    y = "Life Expectancy"
  ) +
  theme_minimal(base_family = "Fira Sans") +
  theme(
    strip.text = element_text(size = 16, face = "bold"),
    panel.border = element_rect(fill = NA, color = "grey40"),
    panel.grid.minor = element_blank()
  )
```

.plot-callout.top-right[
![](6-Visualization-with-ggplot_files/figure-html/hans-rosling-3-1.png)
]

---
## The final code and plot

```r
ggplot(gapminder) +
  aes(x = gdpPercap, y = lifeExp, size = pop, color = country) +
  geom_point() +
  facet_wrap(~year) +
  guides(color = FALSE, size = FALSE) +
  scale_x_log10(
    breaks = c(10^3, 10^4, 10^5), 
    labels = c("1k", "10k", "100k")) +
  scale_color_manual(values = gapminder::country_colors) +
  scale_size(range = c(0.5, 12)) +
  labs(
    x = "GDP per capita",
    y = "Life Expectancy") +
  theme_minimal(14, base_family = "Fira Sans") +
  theme(
    strip.text = element_text(size = 16, face = "bold"),
    panel.border = element_rect(fill = NA, color = "grey40"),
    panel.grid.minor = element_blank())
```
]

---
class: fullscreen
background-image: url(6-Visualization-with-ggplot_files/figure-html/hans-rosling-final-1.png)
background-size: cover

---
## Special Bonus: Animated!

```r
# library(devtools)
# install_github("thomasp85/gganimate")
library(gganimate)

# Same plot without facet_wrap()
g_hra + 
  transition_states(year, 1, 0) + 
  ggtitle("{closest_state}")
```
]
]

---
layout: false
class: inverse, middle, center

# g is for Goodbye
---
layout: true
## Stack Exchange is Awesome
---

![](images/stack-exchange-search.png)

---

---
layout: false

## ggplot2 Extensions: exts.ggplot2.tidyverse.org/gallery

---
## ggplot2 and beyond

### Learn more

- **ggplot2 docs:** <http://ggplot2.tidyverse.org/>

- **R4DS - Data visualization:** <http://r4ds.had.co.nz/data-visualisation.html>

- **Hadley Wickham's ggplot2 book:** <https://www.amazon.com/dp/0387981403/>

### Noteworthy RStudio Add-Ins

- [esquisse](https://github.com/dreamRs/esquisse): Interactively build ggplot2 plots

- [ggplotThemeAssist](https://github.com/calligross/ggthemeassist): Customize your ggplot theme interactively

- [ggedit](https://github.com/metrumresearchgroup/ggedit): Layer, scale, and theme editing