ggplot2.Rmd

---
title: "Visualisation with ggplot2"
subtitle: "https://privefl.github.io/R-presentation/ggplot2.html"
author: "Florian Privé"
date: "October 19, 2017"
output:
  xaringan::moon_reader:
    includes:
      after_body: include_twitter.html
    lib_dir: libs
    nature:
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
---

```{r setup, include=FALSE}
options(htmltools.dir.version = FALSE)
knitr::opts_chunk$set(cache = TRUE, fig.align = "center", dev = "svg")
```

class: center, top, inverse
background-image: url(http://hexb.in/vector/ggplot2.svg)
background-position: 50% 80%
background-size: 40%

# http://ggplot2.tidyverse.org/

---

class: center, middle, inverse

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

---

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# Introduction

---

## What does *ggplot2* stand for?

--

### A __Grammar of Graphics__!

--

```
ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(
     mapping = aes(<MAPPINGS>),
     stat = <STAT>, 
     position = <POSITION>
  ) +
  <COORDINATE_FUNCTION> +
  <FACET_FUNCTION>
```

--

</br>

#### You can uniquely describe any plot as a combination of these 7 parameters.

---

## How long have you known **ggplot2**?

<br>
<br>
<br>

--

<blockquote class="twitter-tweet" data-lang="en" align="center"><p lang="en" dir="ltr">Happy 10th birthday ggplot2! 🎉🎂📊📈10 years ago today the first version was accepted to CRAN: <a href="https://t.co/tiXIkqnCcA">https://t.co/tiXIkqnCcA</a></p>&mdash; Hadley Wickham (@hadleywickham) <a href="https://twitter.com/hadleywickham/status/873556949207535616">10 juin 2017</a></blockquote>

---

## Why use ggplot2?

</br>

- Automatic legends, colors, etc.

- Easy superposition, facetting, etc.

- Nice rendering (yet, I don't like the default grey theme).

- **Store any ggplot2 object for modification or future recall.** Super useful for packages.

- Lots of users (less bugs, much help on Stack Overflow).

- Lots of extensions.

- Nice saving option.

---

class: center, middle, inverse

# Tidy data?

---

## Untidy data

```{r, include=FALSE}
library(tidyverse)
fertilityData <- structure(list(Country = structure(c(2L, 5L, 6L, 
9L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L), .Label = c("Abkhazia", 
"Afghanistan", "Akrotiri and Dhekelia", "Åland", "Albania", 
"Algeria", "American Samoa", "Andorra", "Angola", "Anguilla", 
"Antigua and Barbuda", "Argentina", "Armenia", "Aruba", "Australia", 
"Austria", "Azerbaijan", "Bahamas", "Bahrain", "Bangladesh", 
"Barbados", "Belarus", "Belgium", "Belize", "Benin", "Bermuda", 
"Bhutan", "Bolivia", "Bosnia and Herzegovina", "Botswana", "Brazil", 
"British Virgin Islands", "Brunei", "Bulgaria", "Burkina Faso", 
"Burundi", "Cambodia", "Cameroon", "Canada", "Cape Verde", "Cayman Islands", 
"Central African Republic", "Chad", "Channel Islands", "Chile", 
"China", "Christmas Island", "Cocos Island", "Colombia", "Comoros", 
"Congo, Dem. Rep.", "Congo, Rep.", "Cook Is", "Costa Rica", "Cote d'Ivoire", 
"Croatia", "Cuba", "Cyprus", "Czech Republic", "Czechoslovakia", 
"Denmark", "Djibouti", "Dominica", "Dominican Republic", "East Germany", 
"Ecuador", "Egypt", "El Salvador", "Equatorial Guinea", "Eritrea", 
"Eritrea and Ethiopia", "Estonia", "Ethiopia", "Faeroe Islands", 
"Falkland Is (Malvinas)", "Fiji", "Finland", "France", "French Guiana", 
"French Polynesia", "Gabon", "Gambia", "Georgia", "Germany", 
"Ghana", "Gibraltar", "Greece", "Greenland", "Grenada", "Guadeloupe", 
"Guam", "Guatemala", "Guernsey", "Guinea", "Guinea-Bissau", "Guyana", 
"Haiti", "Holy See", "Honduras", "Hong Kong, China", "Hungary", 
"Iceland", "India", "Indonesia", "Iran", "Iraq", "Ireland", "Isle of Man", 
"Israel", "Italy", "Jamaica", "Japan", "Jersey", "Jordan", "Kazakhstan", 
"Kenya", "Kiribati", "Kosovo", "Kuwait", "Kyrgyz Republic", "Lao", 
"Latvia", "Lebanon", "Lesotho", "Liberia", "Libya", "Liechtenstein", 
"Lithuania", "Luxembourg", "Macao, China", "Macedonia, FYR", 
"Madagascar", "Malawi", "Malaysia", "Maldives", "Mali", "Malta", 
"Marshall Islands", "Martinique", "Mauritania", "Mauritius", 
"Mayotte", "Mexico", "Micronesia, Fed. Sts.", "Moldova", "Monaco", 
"Mongolia", "Montenegro", "Montserrat", "Morocco", "Mozambique", 
"Myanmar", "Namibia", "Nauru", "Nepal", "Netherlands", "Netherlands Antilles", 
"New Caledonia", "New Zealand", "Ngorno-Karabakh", "Nicaragua", 
"Niger", "Nigeria", "Niue", "Norfolk Island", "North Korea", 
"North Yemen (former)", "Northern Cyprus", "Northern Mariana Islands", 
"Norway", "Oman", "Pakistan", "Palau", "Panama", "Papua New Guinea", 
"Paraguay", "Peru", "Philippines", "Pitcairn", "Poland", "Portugal", 
"Puerto Rico", "Qatar", "Reunion", "Romania", "Russia", "Rwanda", 
"Samoa", "San Marino", "Sao Tome and Principe", "Saudi Arabia", 
"Senegal", "Serbia", "Serbia and Montenegro", "Serbia excluding Kosovo", 
"Seychelles", "Sierra Leone", "Singapore", "Slovak Republic", 
"Slovenia", "Solomon Islands", "Somalia", "Somaliland", "South Africa", 
"South Korea", "South Ossetia", "South Yemen (former)", "Spain", 
"Sri Lanka", "St. Barthélemy", "St. Helena", "St. Kitts and Nevis", 
"St. Lucia", "St. Martin", "St. Vincent and the Grenadines", 
"St.-Pierre-et-Miquelon", "Sudan", "Suriname", "Svalbard", "Swaziland", 
"Sweden", "Switzerland", "Syria", "Taiwan", "Tajikistan", "Tanzania", 
"Thailand", "Timor-Leste", "Togo", "Tokelau", "Tonga", "Transnistria", 
"Trinidad and Tobago", "Tunisia", "Turkey", "Turkmenistan", "Turks and Caicos Islands", 
"Tuvalu", "Uganda", "Ukraine", "United Arab Emirates", "United Kingdom", 
"United Korea (former)\n", "United States", "Uruguay", "USSR", 
"Uzbekistan", "Vanuatu", "Venezuela", "Vietnam", "Virgin Islands (U.S.)", 
"Wallis et Futuna", "West Bank and Gaza", "West Germany", "Western Sahara", 
"Yemen", "Yugoslavia", "Zambia", "Zimbabwe"), class = "factor"), 
    `1800` = c(7, 4.6, 6.99, 6.93, 5, 6.8, 7.8, 5.64, 6.5, 5.1, 
    8.1, 5.9), `1801` = c(7, 4.6, 6.99, 6.93, 5, 6.8, 7.8, 5.64, 
    6.48, 5.1, 8.1, 5.9), `1802` = c(7, 4.6, 6.99, 6.93, 4.99, 
    6.8, 7.81, 5.64, 6.46, 5.1, 8.1, 5.9), `1803` = c(7, 4.6, 
    6.99, 6.93, 4.99, 6.8, 7.81, 5.64, 6.44, 5.1, 8.1, 5.9), 
    `1804` = c(7, 4.6, 6.99, 6.93, 4.99, 6.8, 7.81, 5.64, 6.42, 
    5.1, 8.1, 5.9), `1805` = c(7, 4.6, 6.99, 6.93, 4.98, 6.8, 
    7.82, 5.64, 6.4, 5.1, 8.1, 5.9)), .Names = c("Country", 
"1800", "1801", "1802", "1803", "1804", "1805"), row.names = c(NA, 
-12L), class = c("tbl_df", "tbl", "data.frame"))
```

```{r}
fertilityData
```


The variables are the **country**, **year** and **fertility rate**.

---

## Tidy data

```{r, echo=FALSE}
knitr::include_graphics("http://r4ds.had.co.nz/images/tidy-1.png")
```

It is easier to work and reason with

- operations

- manipulation

- visualization

<br>

Learn more at http://tidyr.tidyverse.org/articles/tidy-data.html.

---

### Tidy the previous data

```{r, cache=FALSE, message=FALSE}
library(tidyverse)
```

```{r}
(fertilityTidy <- fertilityData %>%
   gather(key = "Year", value = "Fertility", -Country) %>%
   mutate(Year = as.integer(Year)))
```

---

### This is easier to plot

```{r, out.width = "80%", fig.asp = 0.7}
ggplot(data = fertilityTidy) + 
  geom_point(mapping = aes(x = Year, y = Fertility))
```

---

class: center, middle, inverse

# Basics & Customization

---

### Custom theme I use: black and white and larger fonts

```{r}
myggplot <- function(..., coeff = 1) 
  bigstatsr:::MY_THEME(ggplot(...), coeff = 1.2 * coeff)
```

***

```{r, out.width = "68%", fig.asp = 0.7}
myggplot(data = fertilityTidy) + 
  geom_point(mapping = aes(x = Year, y = Fertility))
```

---

## Drop extra typing

```{r, out.width = "80%", fig.asp = 0.7}
myggplot(fertilityTidy) + 
  geom_point(aes(Year, Fertility))
```

---

## Add colors

```{r, out.width = "80%", fig.asp = 0.7}
myggplot(fertilityTidy) + 
  geom_point(aes(Year, Fertility, color = Country))
```

---

## Add lines: add one geom

```{r, out.width = "80%", fig.asp = 0.7}
myggplot(fertilityTidy) + 
  geom_point(aes(Year, Fertility, color = Country)) + 
  geom_line(aes(Year, Fertility, color = Country))
```

---

### Remove redundancy: move 'aes' to the top

#### So that the mapping is inherited by both geoms

```{r, out.width = "72%", fig.asp = 0.7}
myggplot(fertilityTidy, aes(Year, Fertility, color = Country)) + 
  geom_point() +
  geom_line()
```

---

### Larger points and lines

```{r out.width = "80%", fig.asp = 0.7}
myggplot(fertilityTidy, aes(Year, Fertility, color = Country)) + 
  geom_point(size = 4) +
  geom_line(size = 3)
```

---

### Futher customization: themes

```{r out.width = "90%", fig.asp = 0.7, fig.width=8}
myggplot(fertilityTidy, aes(Year, Fertility, color = Country)) + 
  geom_point(size = 4) +
  geom_line(size = 3) +
  theme(aspect.ratio = 0.8, legend.key.width = unit(3, "line"))
```

---

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# Layers 

---

## Iris: base dataset of R

### about plants

```{r}
as_tibble(iris)   ## print better
```

---

## Layers: example with geom_smooth()

```{r out.width = "80%", fig.asp = 0.7}
myggplot(iris) + 
  geom_point(aes(Petal.Length, Petal.Width, 
                 color = Species, shape = Species), size = 3)
```

---

### Geom_smooth on all: move x and y on top

```{r out.width = "75%", fig.asp = 0.7}
myggplot(iris, aes(Petal.Length, Petal.Width)) + 
  geom_point(aes(color = Species, shape = Species), size = 3) + 
  geom_smooth(color = "black")
```

---

### Points on top: change the order of layers

```{r out.width = "75%", fig.asp = 0.7}
myggplot(iris, aes(Petal.Length, Petal.Width)) + 
  geom_smooth(color = "black") + 
  geom_point(aes(color = Species, shape = Species), size = 3)
```

---

### Geom_smooth by group

```{r out.width = "75%", fig.asp = 0.7}
myggplot(iris, aes(Petal.Length, Petal.Width)) + 
  geom_smooth(aes(group = Species), color = "black") + 
  geom_point(aes(color = Species, shape = Species), size = 3)
```

---

### Or use color for both geoms

```{r out.width = "75%", fig.asp = 0.7}
myggplot(iris, aes(Petal.Length, Petal.Width, color = Species)) + 
  geom_smooth() + 
  geom_point(aes(shape = Species), size = 3)
```

---

## An important application

### Simpson's Paradox

<br>

```{r, echo=FALSE}
knitr::include_graphics("https://paulvanderlaken.files.wordpress.com/2017/09/simpsonsparadox.png?w=1080")
```

.footnote[Source: https://goo.gl/GycYod]

---

## Learn more

- [Chapter *Data Visualisation*](http://r4ds.had.co.nz/data-visualisation.html) of 

```{r, out.width="35%", echo=FALSE}
knitr::include_graphics("http://r4ds.had.co.nz/cover.png")
```

.footnote[Freely available online.]

---

## Find answers

### [Stack Overflow questions with tag `[ggplot2]`](https://stackoverflow.com/questions/tagged/ggplot2)

<blockquote class="twitter-tweet" data-lang="en" align="center"><p lang="en" dir="ltr">ok, hands up... when looking up code errors/questions with the intent of finding answers on Stack Overflow, do you:</p>&mdash; Sharla Gelfand (@sharlagelfand) <a href="https://twitter.com/sharlagelfand/status/915654253456318464?ref_src=twsrc%5Etfw">4 octobre 2017</a></blockquote>
<script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>

<br><center>e.g. google "larger points legend ggplot2"

---

## Using the Stack Overflow answer

```{r out.width = "75%", fig.asp = 0.7}
myggplot(iris) + 
  geom_point(aes(Petal.Length, Petal.Width, 
                 color = Species, shape = Species), size = 3) +
  guides(colour = guide_legend(override.aes = list(size = 10)))
```

---

## Go check the [**R Graph Gallery**](http://www.r-graph-gallery.com/portfolio/ggplot2-package/)

<br>

<br>

<br>

<blockquote class="twitter-tweet" data-cards="hidden" data-lang="en" align="center"><p lang="en" dir="ltr">🍾🍾 Today the <a href="https://twitter.com/hashtag/rstats?src=hash&amp;ref_src=twsrc%5Etfw">#rstats</a> graph gallery reached 1.000.000 visits! 🍾🍾 Thanks to all users &amp; contributors!<a href="https://t.co/94JzuHDJot">https://t.co/94JzuHDJot</a> <a href="https://t.co/xu4XIsqmut">pic.twitter.com/xu4XIsqmut</a></p>&mdash; The R Graph Gallery (@R_Graph_Gallery) <a href="https://twitter.com/R_Graph_Gallery/status/915092693977460741?ref_src=twsrc%5Etfw">3 octobre 2017</a></blockquote>
<script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>

---

class: center, middle, inverse

# More

---

### Coordinates

```{r out.width = "77%", fig.asp = 0.7}
myggplot(iris) + 
  geom_point(aes(Petal.Length, Petal.Width, 
                 color = Species, shape = Species), size = 3) + 
  scale_x_log10(breaks = 1:7)
```


---

### Facets

```{r out.width = "85%", fig.asp = 0.6}
myggplot(iris) + 
  geom_point(aes(Petal.Length, Petal.Width, 
                 color = Species, shape = Species), size = 3) + 
  facet_wrap(~Species) +   ## facet_grid for multiple variables
  theme(strip.text.x = element_text(size = rel(2.5)))
```

---

## Iterate over variables with **aes_string**

```{r}
(var <- names(iris)[1:4])
p_list <- list()
for (i in seq_along(var)) {
  p_list[[i]] <- myggplot(iris, coeff = 0.6) + 
    geom_density(aes_string(var[i], fill = "Species"), alpha = 0.6)
    
}

str(p_list, max.level = 1)
```

---

## Combine plots with [cowplot](https://cran.r-project.org/web/packages/cowplot/vignettes/introduction.html)

```{r out.width = "85%", fig.asp = 0.65}
cowplot::plot_grid(plotlist = p_list, ncol = 2, align = "hv",
                   labels = LETTERS[1:4], label_size = 15)
```

---

### Common legend

```{r out.width = "85%", fig.asp = 0.6}
lapply(p_list, function(p) p + theme(legend.position = "none")) %>%
  cowplot::plot_grid(plotlist = ., ncol = 2, align = "hv",
                   labels = LETTERS[1:4], label_size = 15) %>%
  cowplot::plot_grid(cowplot::get_legend(p_list[[1]]),
                     rel_widths = c(1, 0.3))
```

---

## Interactive plots

```{r, out.width="80%", fig.asp=0.7, cache=FALSE}
myggplot(iris, aes(Petal.Length, Petal.Width, 
                   color = Species, shape = Species)) + 
  geom_point(size = 3)
```

---

## Transform ggplot to plotly

```{r}
plotly::ggplotly(width = 700, height = 450)
```

---

### Add more infos

```{r}
plotly::ggplotly(
  last_plot() + aes(text = bigstatsr::asPlotlyText(iris)),
  tooltip = "text", width = 700, height = 420)
```



---

## Miscellaneous

- [Pie charts](https://guangchuangyu.github.io/2016/12/scatterpie-for-plotting-pies-on-ggplot/) but [others plots are often better](http://annkemery.com/pie-chart-guidelines/)

- [Spatial Visualization](https://cran.r-project.org/web/packages/ggmap/index.html)

- [Heatmaps](http://blog.aicry.com/r-heat-maps-with-ggplot2/)

- [Cookbook for R - Graphs](http://www.cookbook-r.com/Graphs/)

- [**Cheatsheet**](https://www.rstudio.com/wp-content/uploads/2016/11/ggplot2-cheatsheet-2.1.pdf)

- [Top 50 ggplot2 Visualizations](http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html)

- [**Viridis color palette**](https://cran.r-project.org/web/packages/viridis/vignettes/intro-to-viridis.html)

- [**An RStudio addin for ggplot2 theme tweaking**](https://github.com/calligross/ggthemeassist)

- [**Publication Ready Plots**](http://www.sthda.com/english/rpkgs/ggpubr/)

- [Extensions](http://www.ggplot2-exts.org/)

---

class: center, middle, inverse

# Two last points from my experience

---

## Why I finally switched to using ggplot2?

```{r out.width = "70%", fig.asp = 0.75, results='hide', cache=FALSE}
library(bigstatsr)
X <- big_attachExtdata()
svd <- big_SVD(X, big_scale(), k = 10)
plot(svd, type = "scores")
```

---

### An object that the user can modify

```{r out.width = "60%", fig.asp = 0.7}
pop <- rep(c("POP1", "POP2", "POP3"), c(143, 167, 207))
last_plot() + 
  # add colors
  aes(color = pop) + labs(color = "Population") +
  ## change the place of the legend
  theme(legend.position = c(0.85, 0.2)) + 
  ## change the title and the label of the x-axis
  labs(title = "Yet another title", x = "with a new 'x' label")
```

---

## How I choose the size of a plot?

- I plot something, e.g.

```{r, eval = FALSE}
ggplot(iris, aes(Petal.Length, Petal.Width, 
                 color = Species, shape = Species)) + 
  geom_point(size = 3)
```

- I use the *Zoom* button of RStudio

- I resize the "Plot Zoom" windows till I'm satisfied

- Right-click on this window -> "Open Image"

- Then, I use the dimensions that are displayed at the top in `ggsave()` with `scale = 1/90` (calibrate the value for your computer), e.g. 
```{r, eval = FALSE}
ggsave("myggplot.pdf", scale = 1/90, width = 888, height = 725)
```

.footnote[In R Markdown, use chunk options `out.width` and `fig.asp`.]

---

class: center, middle, inverse

# Your turn

---

## Use this data..

```{r}
(df <- gapminder::gapminder %>%
   filter(year == 1992))
```

---

## ..to reproduce this plot

```{r, out.width = "100%", fig.asp = 0.7, echo = FALSE, fig.width = 5}
ggplot(df) + 
  geom_point(aes(gdpPercap, lifeExp, size = pop, 
                 color = continent)) +
  scale_x_log10(breaks = c(300, 1e3, 3e3, 10e3, 30e3)) + 
  labs(title = "Gapminder for 1992",
       x = "Gross Domestic Product (log scale)",
       y = "Life Expectancy at birth (years)",
       color = "Continent", size = "Population")
```

---

```{r, out.width = "78%", fig.asp = 0.7, fig.width = 5}
ggplot(df) + 
  geom_point(aes(gdpPercap, lifeExp, size = pop, 
                 color = continent)) +
  scale_x_log10(breaks = c(300, 1e3, 3e3, 10e3, 30e3)) + 
  labs(title = "Gapminder for 1992",
       x = "Gross Domestic Product (log scale)",
       y = "Life Expectancy at birth (years)",
       color = "Continent", size = "Population")
```

---

class: center, middle, inverse

# Thanks!

<br>

Presentation available at

https://privefl.github.io/R-presentation/ggplot2.html

<br>

Twitter and GitHub: [@privefl](https://twitter.com/privefl)

.footnote[Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan).]