Workshop 3: Introduction to ggplot2

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# Workshop 3: Introduction to ggplot2
## QCBS R Workshop Series
### Québec Centre for Biodiversity Science

---

## Introduction

- To follow along:

Code and .HTML available at http://qcbs.ca/wiki/r/workshop3

- Recommendation:
      * create your own new script
      * refer to provided code only if needed
      * avoid copy pasting or running the code directly from script

- ggplot2 is also hosted on Github: https://github.com/hadley/ggplot2

---
## Outline
1. Introduction
2. ggplot mechanics
3. Importance of data structure
4. Basic plot
5. Aesthetic
6. Geom
  * point, line, Histogram, box plot
  * Error bar, sgnificant value & linear regression
7. Fine tunning
  * Color
  * Theme
8. Miscellaneous

---
## Introduction : Why use R for plotting?
<div style="text-align:center">
<img src="images/Divided_reproducible workflow.png" height=85% width=85%>
</div>

---
## Introduction : Why use R for plotting?
<div style="text-align:center">
<img src="images/Divided_reproducible workflow2.png" height=85% width=85%>
</div>

---
## Introduction : Why use R for plotting?
Beautiful and flexible graphics!

<img src="workshop03-en_files/figure-html/unnamed-chunk-3-1.png" width="504" style="display: block; margin: auto;" />
---
## Introduction

- Have you created plots?
      * What kind of plot?
      * Which software?

- Have you plotted in R?
      * `base` R, `lattice`?
      * `ggplot2`?

---
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# ggplot2

---
## ggplot2

The `ggplot2` package lets you make beautiful and customizable plots of your data. It implements the grammar of graphics, an easy to use system for building plots.

---
## Introduction

Required packages

```r
install.packages("ggplot2")
library(ggplot2)
```

---
## Grammar of Graphics (GG)

A graphic is made of different layers:
  * aesthetics (`aes`)
  * transformation
  * geometries (`geoms`)
  * axis (coordinate system)
  * scales

---

```r
ggplot(data = iris,             # Data
       aes(x = Sepal.Length,    # Your X-value
           y = Sepal.Width,     # Your Y-value
           col = Species)) +    # Aesthetics
  geom_point() +                # Geometry
  geom_smooth(method = "lm") +  # Linear regression
  ggtitle("My fabulous graph")+ # Title
  theme(plot.title= element_text(color="red",         #|
                                 size=14,             #| Theme
                                 face="bold.italic")) #|
```

---
## Importance in data structure

```r
head(iris[,c("Sepal.Length","Sepal.Width","Species")], n = 5)
#   Sepal.Length Sepal.Width Species
# 1          5.1         3.5  setosa
# 2          4.9         3.0  setosa
# 3          4.7         3.2  setosa
# 4          4.6         3.1  setosa
# 5          5.0         3.6  setosa
```

`ggplot(data = iris, aes(`**x = Sepal.Length**`,` **y = Sepal.Width**`, `**col = Species**`))`

---
## A simple Example with iris database

.small[

.pull-left[

**Inheritance from ggplot**

```r
ggplot(data = iris,             # Data
      aes(x = Sepal.Length,    # Your X-value
          y = Sepal.Width)) +  # Your Y-value
 geom_point()                  # Geometry

```

**Storing ggplot in object**

```r

p <- ggplot(data = iris,
                   aes(x = Sepal.Length,
                       y = Sepal.Width))
q <- p + geom_point()
q   # Print your final plot

```
]

.pull-right[

**No inheritance from ggplot**

```r
ggplot() +
 geom_point(data = iris,
            aes(x = Sepal.Length,
                y = Sepal.Width))
```

**Adding layer from ggplot object**

```r
s <- ggplot()
s <- s +
 geom_point(data = iris,
            aes(x = Sepal.Length,
                y = Sepal.Width))
s
```
]

]

---
## GGplot dynamics: base layer

```r
ggplot() +
scale_x_continuous() +
scale_y_continuous()
```

---
## GGplot dynamics: Data layer

```r
ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  xlab("x = Sepal Lenght") +
  ylab("y = Sepal Width")
```

---
## GGplot dynamics: Gemoetric Layer

```r
ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  xlab("x = Sepal Lenght") + ylab("y = Sepal Width") +
  geom_point()
```

---
## Challenge #1 ![:cube]()

* Draw your 1rst (gg)plot:

---
## Solution Challenge 1# ![:cube]()

```r
ggplot(data = iris, aes(x = Petal.Length, y = Petal.Width)) +
  geom_point()
```

---
## Saving plots in RStudio

---
## Saving plots in code

`ggsave()` will write directly to your working directory all in one line of code and you can specify the name of the file and the dimensions of the plot:

.small[

```r
my1rstPlot <- ggplot(data = iris, aes(x = Petal.Length, y = Petal.Width)) + geom_point()
ggsave("my1rstPlot.pdf",
       my1rstPlot,
       height = 8.5,
       width = 11,
       units = "in")
```
]

.comment[Note that vector format (e.g., pdf, svg) are often preferable choice compared to raster format (jpeg, png, ...)]

.comment[Other methods to save image `?pdf` `?jpeg`]

---
## Aesthetic

Different aesthetic are available to help you distinguish different class, group & structure within your data:

---
## Example of aesthetic: color

.small[
.pull-left[

```r
#Without Color
ggplot(data = iris, aes(x = Sepal.Length,
                        y = Sepal.Width)) +
  geom_point()
```

<img src="workshop03-en_files/figure-html/unnamed-chunk-17-1.png" width="432" style="display: block; margin: auto;" />
]
.pull-right[

```r
ggplot(data = iris, aes(x = Sepal.Length,
                        y = Sepal.Width,
                        color = Species)) +
geom_point() + theme(legend.position = 'bottom')
```

<img src="workshop03-en_files/figure-html/unnamed-chunk-18-1.png" width="432" style="display: block; margin: auto;" />
]
]

---
## Challenge #2 ![:cube]()

Produce a colourful plot from built in data such as the `Iris`, `CO2` or the `msleep` dataset

<br>

---
## Solution to challenge #2 ![:cube]()

Example using `iris` database

```r
data(iris)
iris.plot <- ggplot(data = iris,
  aes(x = Petal.Length, y = Petal.Width, color = Species)) +
  geom_point()
iris.plot
```

---
## Solution to challenge #2 ![:cube]()

Example using `CO2` database

```r
data(CO2)
CO2.plot <- ggplot(data = CO2,
    aes(x = conc, y = uptake, colour = Treatment)) +
    geom_point()
CO2.plot
```

---
## Solution to challenge #2 ![:cube]()

Example using `msleep` database

```r
data(msleep)
msleep.plot <- ggplot(data = msleep,
    aes(x = log10(bodywt), y = awake, colour = vore, shape= conservation)) +
    geom_point()
msleep.plot
```

---
## Facet: `Iris`

```r
ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width,
                        color = Species)) +
  geom_point() +
  facet_grid(~Species, scales = "free")
```

---
## Facet: `CO2`

---
## Fine tuning - colours

Colour: Manual_colour

.small[

```r
iris.plot <- ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
            geom_point()
iris.plot + scale_colour_manual(values = c("setosa" = "red",
                                           "versicolor" = "darkgreen",
                                           "virginica"="blue"))
```
]
.pull-left[
**Classic**
<img src="workshop03-en_files/figure-html/unnamed-chunk-25-1.png" width="432" style="display: block; margin: auto;" />
]
.pull-right[
**Manual**
<img src="workshop03-en_files/figure-html/unnamed-chunk-26-1.png" width="432" style="display: block; margin: auto;" />
]

---
## Fine tuning - gradient colours for quantitative variable

*quantitavie variable: `Petal.Length`*

```r
iris.plot.petal <- ggplot(data=iris, aes(..., color = Petal.Length))
```

.pull-left[
<img src="workshop03-en_files/figure-html/unnamed-chunk-28-1.png" width="432" style="display: block; margin: auto;" />
]
.pull-right[
<img src="workshop03-en_files/figure-html/unnamed-chunk-29-1.png" width="432" style="display: block; margin: auto;" />
]

---
## Fine tuning - colours

Coulours theme

```r
install.packages("RColorBrewer")
require(RColorBrewer)
```

---
## Fine tuning - colours

Coulours theme: RColorBrewer

```r
iris.plot + scale_color_brewer(palette = "Dark2")
```

.pull-left[
**Classic**
<img src="workshop03-en_files/figure-html/unnamed-chunk-32-1.png" width="432" style="display: block; margin: auto;" />
]
.pull-right[
**Dark2**
<img src="workshop03-en_files/figure-html/unnamed-chunk-33-1.png" width="432" style="display: block; margin: auto;" />
]

---
## Fine tuning - colours

Blind color friendly: presentation purpose

```r
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
                "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
iris.plot + scale_color_manual(values=cbbPalette)
```

.pull-left[
**Classic**
<img src="workshop03-en_files/figure-html/unnamed-chunk-35-1.png" width="432" style="display: block; margin: auto;" />
]
.pull-right[
**Blind color friendly**
<img src="workshop03-en_files/figure-html/unnamed-chunk-36-1.png" width="432" style="display: block; margin: auto;" />
]

---
## Fine tuning - colours

black & white: grey scale for publication purpose

```r
iris.plot + scale_color_grey()
```

.pull-left[
**Defaut**
<img src="workshop03-en_files/figure-html/unnamed-chunk-38-1.png" width="432" style="display: block; margin: auto;" />
]
.pull-right[
**Grey scale**
<img src="workshop03-en_files/figure-html/unnamed-chunk-39-1.png" width="432" style="display: block; margin: auto;" />
]

---
## Fine tuning - theme

Theme: classic for publication purpose

```r
iris.plot + scale_color_grey() + theme_classic()
```

.pull-left[
**Defaut**
<img src="workshop03-en_files/figure-html/unnamed-chunk-41-1.png" width="432" style="display: block; margin: auto;" />
]
.pull-right[
**Classic**
<img src="workshop03-en_files/figure-html/unnamed-chunk-42-1.png" width="432" style="display: block; margin: auto;" />
]

---
## Challenge #3 ![:cube]()

Explore a new `geom()` and other plot elements with your own data or built in data

```r
data(msleep)
data(OrchardSprays)
```

---
## Solution challenge #3 ![:cube]()

```r
box.plot <- ggplot(data = OrchardSprays,
                  aes(x = treatment, y = decrease)) +
                  geom_boxplot()
box.plot
```

---
## Geom: GGplot

---
## Base plot

---
## Blank plot

.small[
.pull-left[
*GGplot2*

```r
ggplot(mtcars, aes(wt, mpg)) +
       geom_blank()
```

<img src="workshop03-en_files/figure-html/unnamed-chunk-45-1.png" width="360" style="display: block; margin: auto;" />
]

.pull-right[
*Base Plot*

```r
plot(mpg~wt, data = mtcars, type = "n")
```

<img src="workshop03-en_files/figure-html/unnamed-chunk-46-1.png" width="360" style="display: block; margin: auto;" />
]]

---
## Histogram

.small[
.pull-left[
*GGplot2*

```r
qplot(rnorm(100)) +
      geom_histogram()
```

<img src="workshop03-en_files/figure-html/unnamed-chunk-47-1.png" width="360" style="display: block; margin: auto;" />
]

.pull-right[
*Base Plot*

```r
hist(rnorm(100))
```

<img src="workshop03-en_files/figure-html/unnamed-chunk-48-1.png" width="360" style="display: block; margin: auto;" />
]]

---
## Barplot: value significance

.small[
.pull-left[
*GGplot2*

```r
qplot(rnorm(100)) +
      geom_histogram()
```

<img src="workshop03-en_files/figure-html/unnamed-chunk-49-1.png" width="360" style="display: block; margin: auto;" />
]

.pull-right[
*Base Plot*

```r
counts <- table(mtcars$gear)
barplot(counts, main="Car Distribution",
  	    xlab="Number of Gears", border=NA)
```

<img src="workshop03-en_files/figure-html/unnamed-chunk-50-1.png" width="360" style="display: block; margin: auto;" />
]]

---
## Plot: linear regression

---
## Plot with categories

---
## Boxplot with significant value only

---
## Violoin Plot

---
## Plot with errors bars

---
## Map

---
## Density Graph

---
## Dendogram

---
## Ordination: PCA

---
## Geom_point & Geom_line

.alert[put ggplot vs base plot]

```r
CO2.plot.facet.baline <- CO2.plot.facet + geom_line()
CO2.plot.facet.line <- CO2.plot.facet + geom_line(aes(group=Plant))
multiplot(CO2.plot.facet.baline, CO2.plot.facet.line)
```

---
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# Miscellaneous

---
## Miscellaneous
  * .alert[Put other Crazy R visualisation]
  * .alert[A time for a tour on r-graph-gallery.com]
  * .alert[Itroduce Dynamics representative]
  * .alert[May be let the choice to the current speaker to show its own tricks and/or own data visu]

---
# Miscellaneous : `qplot()` vs `ggplot()`

```r
qplot(data = iris,
      x = Sepal.Length,
      xlab = "Sepal Length (mm)",
      y = Sepal.Width,
      ylab = "Sepal Width (mm)",
      main = "Sepal dimensions")

ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point() +
  xlab("Sepal Length (mm)") +
  ylab("Sepal Width (mm)") +
  ggtitle("Sepal dimensions")
```
---
## Available elements

[Data Visualization with ggplot2 Cheat Sheet](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf)

---
## Additional resources

`help(package = ggplot)`

http://ggplot2.tidyverse.org/reference/

---
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# Merci d'avoir participé!

![:scale 50%](images/QCBS_logo.png)