The Grammar of {ggplot2}

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# **The Grammar of `{ggplot2}`** A graphics framework for elegant plotting in **`R`**
 
### **Cédric Scherer** Leibniz Institute for Zoo and Wildlife Research Berlin scherer@izw-berlin.de | cedricscherer.netlifiy.com | @CedScherer
 
### Image by Richard Strozynski

---
class: center, middle

### **`{ggplot2}`** is a system for declaratively creating graphics, based on "The Grammar of Graphics" (Wilkinson, 2005).
### You provide the data, tell **`{ggplot2}`** how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.

ggplot2 package description

---
class: middle

# **Advantages of `{ggplot2}`**

* consistent underlying grammar of graphics (Wilkinson, 2005)

* very flexible, layered plot specification

* theme system for polishing plot appearance
  
* active and helpful community  
  (e.g. [community.rstudio.com](wwww.community.rstudio.com), [R4DS Learning Community](https://www.rfordatasci.com), [Twitter](https://twitter.com/search?q=%23ggplot2&src=typed_query))

---
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# **The `{ggplot2}` Showcase**

---
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<img src="img/Fig2_disease-data_full_log.png" style="width: 53%"> Figure in Scherer et al. 2019 J. Anim. Ecol.

---
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<img src="img/2019-08-18_phone-use.png" style="width: 74%"> Contribution to #MakeoverMonday

---
class: inverse, center, middle

<img src="img/2019_19_StudentTeacher.png" style="width: 68%"> Contribution to #TidyTuesday

---
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<img src="img/2019_24_Meteorites.png" style="width: 68%"> Contribution to #TidyTuesday

---
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.pull-left[
<img src="img/georgos_bob_ross.png" style="width: 98%"> #TidyTuesday Contribution by Georgos Karamanis (github.com/gkaramanis/tidytuesday)
]

.pull-right[
<img src="img/jake_bird_collisions.png" style="width: 98%"> #TidyTuesday Contribution by Jake Kaupp (github.com/jkaupp/tidytuesdays)
]

---
class: inverse, center, middle

.pull-left[
 
<img src="img/swiss_map.png" style="width: 98%"> Bivariate Map by Timo Gossenbacher (timogrossenbacher.ch/2019/04/bivariate-maps-with-ggplot2-and-sf)
]

.pull-right[
<img src="img/europe_sprengler.jpeg" style="width: 98%"> European Elections by Torsten Sprenger (twitter.com/spren9er/status/1138000009306234880)
]

---
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<img src="img/bbc-gallery.png" style="width: 120%"> Collection of BBC Graphics (adapted from bbc.github.io/rcookbook)

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.pull-left[
<img src="img/gganimate-datasaurus.gif" style="width: 98%">
]

.pull-right[
<img src="img/gganimate-hurricane.gif" style="width: 98%">
]

Examples of the {gganimate} Package (github.com/thomasp85/gganimate/wiki)

---
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<img src="img/rayshader.gif" style="width: 120%"> Demonstration of the {rayshader} Package (tylermw.com/3d-maps-with-rayshader)

---
class: inverse, center, middle

# **The Setup**

---
## **The package**

**`{ggplot2}`** is a **data visualization package** for the statistical programming language **`R`** 
created by Hadley Wickham in 2005.

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

--
 **`{ggplot2}`** is part of the [**`{tidyverse}`**](https://blog.rstudio.org/2016/09/15/tidyverse-1-0-0/), a set of packages that work in harmony to manipulate and explore data.

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

**`{tidyverse}`** contains **`{ggplot2}`**, **`{dplyr}`**, **`{tidyr}`**, **`{readr}`**, **`{purrr}`**, **`{tibble}`** and some more packages.

---
## **The data**

We use data from the *National Morbidity and Mortality Air Pollution Study* (NMMAPS), filtered for the city of Chicago and the timespan January 1997 to December 2000.

```r
chic <- readr::read_csv("https://raw.githubusercontent.com/z3tt/ggplot-courses/main/data/chicago-nmmaps-custom.csv")
chic$year <- factor(chic$year, levels = as.character(1997:2000))
tibble::glimpse(chic)
```

```
## Observations: 1,461
## Variables: 10
## $ city <chr> "chic", "chic", "chic", "chic", "chic", "chic", "chic", "c...
## $ date <date> 1997-01-01, 1997-01-02, 1997-01-03, 1997-01-04, 1997-01-0...
## $ death <dbl> 137, 123, 127, 146, 102, 127, 116, 118, 148, 121, 110, 127...
## $ temp <dbl> 36.0, 45.0, 40.0, 51.5, 27.0, 17.0, 16.0, 19.0, 26.0, 16.0...
## $ dewpoint <dbl> 37.500, 47.250, 38.000, 45.500, 11.250, 5.750, 7.000, 17.7...
## $ pm10 <dbl> 13.052268, 41.948600, 27.041751, 25.072573, 15.343121, 9.3...
## $ o3 <dbl> 5.659256, 5.525417, 6.288548, 7.537758, 20.760798, 14.9408...
## $ time <dbl> 3654, 3655, 3656, 3657, 3658, 3659, 3660, 3661, 3662, 3663...
## $ season <fct> Winter, Winter, Winter, Winter, Winter, Winter, Winter, Wi...
## $ year <fct> 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997, 1997...
```

For more details about the data set check Roger Peng's book [Statistical Methods in Environmental Epidemiology with R](http://www.springer.com/de/book/9780387781662).

---
class: inverse, center, middle

# **The Structure of `{ggplot2}`** "The Grammar of Graphics"

---
## **The Structure of `{ggplot2}`**

1. **Data** → The raw data that you want to visualise

2. **Layers: `geom_` and `stat_`** → The geometric shapes and statistical summaries representing the data

3. **Aesthetics `aes()`** → Aesthetic mappings of the geometric and statistical objects

4. **Scales `scale_`** → Maps between the data and the aesthetic dimensions

5. **Coordinate system `coord_`** → Maps data into the plane of the data rectangle
 
6. **Facets `facet_`** → The arrangement of the data into a grid of plots
 
7. **Visual themes `theme() and theme_`** → The overall visual defaults of a plot

---
class: inverse, center, middle

# **1. Data `ggplot()`**

---

### **1. Data: `ggplot()`**

We need to specify data and the two variables we want to plot as **`aes`**tethics of the **`ggplot()`** call:

.pull-left[

```r
*ggplot(data = chic,
* mapping =
* aes(
* x = date,
* y = temp
* )
* )
```
There is only an empty panel because **`{ggplot2}` doesn't know *how* it should plot the data**.
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-data-vars-1.png)
]

---
### **1. Data: `ggplot()`**

We need to specify data and the two variables we want to plot as **`aes`**tethics of the **`ggplot()`** call:

.pull-left[
Since almost every **`ggplot()`** takes the same arguments (**`data, mapping = aes(x, y)`**), we can also write:

```r
*ggplot(chic, aes(date, temp))
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-data-vars2-1.png)
]

---

### **1. Data: `ggplot()`**

We need to specify data and the two variables we want to plot as **`aes`**tethics of the **`ggplot()`** call:

.pull-left[
Since almost every **`ggplot()`** takes the same arguments (**`data, mapping = aes(x, y)`**), we can also write:

```r
ggplot(chic, aes(date, temp))
```

... or add the **`aes`**thetics outside the **`ggplot`** function:

```r
ggplot(chic) +
* aes(date, temp)
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-data-vars4-1.png)
]

---
class: inverse, center, middle

# **2. Layers `geom_*()` and `stat_*()`**

---
### **2. Layers: `geom_*()` and `stat_*()`**

By adding one or multiple layers we can tell **`{ggplot2}`** *how* to represent the data. 
There are lots of build-in geometrics elements (**`geoms`**) and statistical transformations (**`stats`**):

<img src="img/ggplot-layers.png" style="width: 57%">Adapted from ggplot2.tidyverse.org/reference/

... and several more in extension packages, e.g. **`{ggforce}`**, **`{ggalt}`**, **`{ggridges}`**, **`{ggrepel}`**, **`{ggcorrplot}`**, **`{ggraph}`**, **`{ggdendro}`** & **`{ggalluvial}`**.

---
### **2. Layers — Geometries: `geom_point()`**

We can tell **`{ggplot2}`** to represent the data for example as a **scatterplot**:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
* geom_point()
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-geom-point-1.png)
]

---
### **2. Layers — Geometries: `geom_line()`**

... or a **line plot**:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
* geom_line()
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-geom-line-1.png)
]

---
### **2. Layers — Geometries: `geom_area()`**

... or an **area plot**:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
* geom_area()
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-geom-area-1.png)
]

---
### **2. Layers — Geometries: `geom_boxplot()`**

... or a **box and whiskers plot**:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
* geom_boxplot()
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-geom-box-1.png)
]

---
### **2. Layers — Geometries: `geom_boxplot()`**

... or a **box and whiskers plot**:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
* geom_boxplot()
```

What's going on here?!
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-geom-box2-1.png)
]

---
### **2. Layers — Geometries: `geom_boxplot()`**

We need to specify the  variable as **categorial** (**`year`**), not as **continuous** (**`date`**):

.pull-left[

```r
*ggplot(chic, aes(year, temp)) +
    geom_boxplot() 
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-geom-box-fct-1.png)
]

---
### **2. Layers — Geometries: Using Multiple `geom_*()`**

Other layers can be added to an existing plot - a line and a rug representation for example:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point() +
* geom_line() +
* geom_rug(sides = "r")
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-geom-rug-1.png)
]

---
### **2. Layers — Statistical transformations: `stat_summary()`**

A handful of layers with attention to the statistical transformation rather than the visual appearance:

.pull-left[

```r
ggplot(chic, aes(year, temp)) +
  geom_boxplot() +
* stat_summary(
*   fun.y = mean,
*   geom = "point"
*  )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-scale-stats-sum-1.png)
]

---
### **2. Layers — Statistical transformations: `stat_function()`**

**`stat_function()`** makes it easy to add a function to a plot, either continuous or discrete:

.pull-left[

```r
ggplot(tibble(x = c(-8, 8)), aes(x)) +
* stat_function(fun = dnorm) +
* stat_function(
*   fun = dcauchy,
*   geom = "point",
*   n = 75
* )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-stats-function-1.png)
]

---
### **2. Layers — Statistical transformations: `stat_ecdf()`**

You can also easily plot the empirical cumulative distribution function (ECDF) of a variable:

.pull-left[

```r
ggplot(chic, aes(temp)) +
* stat_ecdf(geom = "step")
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-stats-ecdf-1.png)
]

---
### **2. Layers — Statistical transformations: `stat_summary_hex()`**

With **`stat_summary_hex()`** data is divided into bins defined by **`x`** and **`y`** and then filled with values of **`z`**:

.pull-left[

```r
ggplot(chic, aes(date, temp, z = o3)) +
* stat_summary_hex()
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-stats-hex-1.png)
]

---
### **2. Layers — Statistical transformations: `stat_smooth()`**

You can directly add smoothed conditional means:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point() +
* stat_smooth()
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-stats-smooth-1.png)
]

---
### **2. Layers — Statistical transformations: `stat_smooth()`**

You can directly add smoothed conditional means:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point() +
* stat_smooth()
```

By default this adds a **LOESS** (locally weighted scatterplot smoothing, **`method = "loess"`**) or a **GAM** (generalized additive model, **`method = "gam"`**) depending on the number of data points (GAM in case of ≥ 1000 observations).
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-stats-smooth2-1.png)
]

---
### **2. Layers — Statistical transformations: `stat_smooth()`**

You can specify the fitting method and the formula:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point() +
* stat_smooth(
*   method = "gam",
*   formula = y ~ s(x, k = 100),
*   se = F
* )
```

Other methods such as **`method = "lm"`** for linear regressions and **`method = "glm"`** for generalized linear models are available as well.
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-stats-smooth-gam-1.png)
]

---
### **2. Layers — Statistical transformations: `stat_smooth()`**

You can specify the fitting method and the formula:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
    geom_point() +
    stat_smooth(
*       method = "lm",
*       formula = y ~ poly(x, 10),
        se = F
    )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-stats-smooth-lm-1.png)
]

---
class: inverse, center, middle

# **3. Aesthetics `aes()`**

---
### **3. Aesthetics: `aes()`**

Aesthetics of the geometric and statistical objects, such as

* **position** via **`x`**, **`y`**, **`xmin`**, **`xmax`**, **`ymin`**, **`ymax`**, ...

* **colors** via **`color`** and **`fill`**

* **transparency** via **`alpha`**

* **sizes** via **`size`** and **`width`**

* **shapes** via **`shape`**  and **`linetype`**

In general, everything which maps to the data needs to be wrapped in **`aes()`** while static arguments are placed outside the **`aes()`**.

e.g. 
**`geom_point(aes(color = season))`** to color points based on the variable **`season`** 
**`geom_point(color = "grey")`** to color all points in the same color

---
### **3. Aesthetics: `aes(color/fill/alpha/size/shape)`**

This way, we can  enlarge all points and add some transparency:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(
*   size = 4,
*   alpha = 0.2
  )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-aes-size-alpha-1.png)
]

---
### **3. Aesthetics: `aes(color/fill/alpha/size/shape)`**

... and change the color and the shape based on **`season`** and **`year`**:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(
*   aes(
*     color = season,
*     shape = year
*   ),
    size = 4,
    alpha = 0.2
  )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-aes-col-shape-1.png)
]

---
### **3. Aesthetics: `aes(color/fill/alpha/size/shape)`**

Alternatively, all **`aes`**thetics can be grouped together (and are then applied to all **`geoms`** and **`stats`**):

.pull-left[

```r
ggplot(
  chic, 
  aes(date, 
      temp,
*     color = season,
*     shape = year)
  ) +
  geom_point(
    size = 4,
    alpha = 0.2
  )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-aes-col-shape2-1.png)
]

---
### **3. Aesthetics: `aes(group)`**

You can create subsets of the data by specifying a grouping variable via **`group`**:

.pull-left[

```r
ggplot(chic, aes(temp)) +
  stat_ecdf(
*   aes(group = year),
    geom = "step"
  )
```

However, for most applications you can simply specify the grouping using visual aesthetics (**`color`**, **`fill`**, **`alpha`**, **`shape`**, **`linetype`**).
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-aes-group-1.png)
]

---
### **3. Aesthetics: `aes(group)`**

You can create subsets of the data by specifying a grouping variable via **`group`**:

.pull-left[

```r
ggplot(chic, aes(temp)) +
  stat_ecdf(
*   aes(color = year),
    geom = "step"
  )
```

However, for most applications you can simply specify the grouping using visual aesthetics (**`color`**, **`fill`**, **`alpha`**, **`shape`**, **`linetype`**).
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-aes-group-col-1.png)
]

---
class: inverse, center, middle

# **4. Scales `scale_*()`**

---
### **4. Scales: `scale_`**

One can use **`scale_*()`** to change properties of all the **aesthetic dimensions mapped to the data**.

Consequently, there are **`scale_*()`** functions for all aesthetics such as:

* **position** via **`scale_x_*()`** and **`scale_y_*()`**

* **colors** via **`scale_color_*()`** and **`scale_fill_*()`**

* **transparency** via **`scale_alpha_*()`**

* **sizes** via **`scale_size_*()`**

* **shapes** via **`scale_shape_*()`**  and **`scale_linetype_*()`**

... with extensions (**`*`**) such as
* **`continuous()`**, **`discrete()`**, **`reverse()`**, **`log10()`**, **`squrt()`**, **`date()`**, **`time()`** for axes
* **`continuous()`**, **`discrete()`**, **`manual()`**, **`gradient()`**, **`hue()`**, **`brewer()`** for colors and fills
* **`continuous()`**, **`discrete()`**, **`manual()`**, **`ordinal()`**, **`identity()`**, **`date()`** for transparencies
* **`continuous()`**, **`discrete()`**, **`manual()`**, **`ordinal()`**, **`identity()`**, **`area()`**, **`date()`** for sizes
* **`continuous()`**, **`discrete()`**, **`manual()`**, **`ordinal()`**, **`identity()`** for shapes and linetypes

---
### **4. Scales: `scale_x_*()` and `scale_y_*()`**

For example, to change the titles of the axes...

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
* scale_x_date(
*   name = NULL
* ) +
* scale_y_continuous(
*   name = "Temperature (°F)"
* )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-scale-axis-1.png)
]

---
### **4. Scales: `scale_x_*()` and `scale_y_*()`**

... and their properties such as the range, scaling, labels, and axis breaks:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_x_date(
    name = NULL, 
*   limits = c(
*     as.Date("1997-01-01"),
*     as.Date("1999-12-31")
*   )
  ) + 
* scale_y_log10(
    name = "Temperature (°F)",
*   breaks = c(1, 10, 100),
*   labels = scales::scientific
  )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-scale-axis2-1.png)
]

---
### **4. Scales: `scale_x_*()` and `scale_y_*()`**

With **`breaks`** and **`labels`** it's also possible to alter the labels without changing the data:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_x_date(
    name = NULL, 
    limits = c(
      as.Date("1997-01-01"),
      as.Date("1999-12-31")
    )
  ) + 
  scale_y_continuous(
*   name = "How I feel...",
*   breaks = c(0, 30, 60, 90),
*   labels = c(
*     '"Brrr, too cold!"',
*     '"Still freezing."',
*     '"Okay."',
*     '"Uff, too hot!"'
*   )
  )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-scale-labels-1.png)
]

---
### **4. Scales: `scale_x_*()` and `scale_y_*()`**

Some people are annoyed by the extra spacing around the data but we can remove that:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_x_date(
    name = NULL,
*   expand = c(0, 0)
  ) + 
  scale_y_continuous(
    name = "Temperature (°F)",
*   expand = c(0, 0)
  )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-scale-axis-expand-1.png)
]

---
### **4. Scales: `scale_color_*()` and `scale_fill_*()`**

Similarly, we can change properties of the other aesthetics. for example get rid of the default colors:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
* scale_color_manual(
*   values = c(
*     "firebrick",
*     "dodgerblue",
*     "darkorchid",
*     "goldenrod"
*   )
* )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-scale-color-1.png)
]

---
### **4. Scales: `scale_color_*()` and `scale_fill_*()`**

... and change the title of the legend:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_color_manual( 
    values = c( 
      "firebrick",
      "dodgerblue",
      "darkorchid",
      "goldenrod"
    ),
*   name = "Pretty sesons:"
  ) 
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-scale-color-legend-1.png)
]

---
### **4. Scales: `scale_color_*()` and `scale_fill_*()`**

... or remove the legend for a specific aesthetic:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
  scale_color_manual( 
    values = c( 
      "firebrick",
      "dodgerblue",
      "darkorchid",
      "goldenrod"
    ),
*   guide = FALSE
  ) 
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-scale-color-legend-off-1.png)
]

---
### **4. Scales: `scale_shape_*()`**

... or remove the legend for a **specific** aesthetic:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season,
*                shape = year)) +
  scale_color_manual( 
    values = c( 
      "firebrick",
      "dodgerblue",
      "darkorchid",
      "goldenrod"
    ),
*   guide = FALSE
  ) +
* scale_shape_discrete(
*   solid = FALSE,
*   name = "Year"
* )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-scale-color-legend-off+shape-1.png)
]

---
### **4. Scales: `scale_color_*()` and `scale_fill_*()`**

One can of course also use preset color palettes:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
* scale_color_brewer(
*   palette = "Set2",
    guide = FALSE
  ) 
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-scale-color-brewer-1.png)
]

---
### **4. Scales: `scale_color_*()` and `scale_fill_*()`**

One can of course also use preset color palettes:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
* scale_color_brewer(
*   palette = "Set2",
    guide = FALSE
  ) 
```

Several packages offer predefined palettes, e.g.:
* **`{viridis}`** for perceptually uniform palettes
* **`{rcartocolor}`** for map color schemes
* **`{LaCroixColoR}`** for vibrant summery colors
* **`{ggthemes}`** for colors of popular software & publishers

Check the [collection by Emil Hvitfeldt](https://github.com/EmilHvitfeldt/r-color-palettes) for a list of color palettes available in **`R`**.
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-scale-color-brewer2-1.png)
]

---
### **4. Scales: `scale_color_*()` and `scale_fill_*()`** — Color Palettes

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plots-viridis-1.png)

---
class: inverse, center, middle

# **5. Coordinate System `coord_*()`**

---
### **5. Coordinate System: `coord_*()`**

Coordinate systems combine the two position aesthetics (usually **`x`** and **`y`**) to produce a 2d position on the plot.

The meaning of the position aesthetics depends on the coordinate system used:

* Linear coordinate systems that preserve the shape of geoms: 
 + **`coord_cartesian()`**: the default with two fixed perpendicular oriented axes
 + **`coord_flip()`**: a Cartesian coordinate system with flipped axes
 + **`coord_fixed()`**: a Cartesian coordinate system with a fixed aspect ratio

* Non-linear coordinate systems that likely change the shapes: 
 + **`coord_map()`**: map projections
 + **`coord_polar()`**: a polar coordinate system
 + **`coord_trans()`**: arbitrary transformations to x and y positions

---
### **5. Coordinate System: `coord_cartesian()`**

**`coord_*()`** functions allow you to zoom in a plot:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_line() +
* coord_cartesian(
*   ylim = c(50, 70)
* )
```

Note that this does prevent a removal of the data points outside the plot area! 
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-coord-zoom-1.png)
]

---
### **5. Coordinate System: `coord_cartesian()`**

In case you want to remove those data points, use **`scale_y_continuous(limits = c(min, max))`**:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_line() +
* coord_cartesian(
*   ylim = c(50, 70)
* )
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-coord-zoom-again-1.png)
]

.pull-right[

```r
ggplot(chic, aes(date, temp)) +
  geom_line() +
* scale_y_continuous(
*   limits = c(50, 70)
* )
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-coord-lim-1.png)
]

---
### **5. Coordinate System: `coord_cartesian()`**

In case you want to remove those data points, use **`scale_y_continuous(limits = c(min, max))`**:

.pull-left[

```r
ggplot(chic, aes(year, temp)) +
  geom_boxplot()  +
* coord_cartesian(
*   ylim = c(50, 70)
* )
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-coord-zoom-box-1.png)
]

.pull-right[

```r
ggplot(chic, aes(year, temp)) +
  geom_boxplot()  +
* scale_y_continuous(
*   limits = c(50, 70)
* )
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-coord-lim-box-1.png)
]

---
### **5. Coordinate System: `coord_cartesian()`**

`coord_*()` is also used to *prevent clipping* of points and correct alignment of axis ticks:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point() +
* scale_x_date(
*   expand = c(0, 0)
* ) +
* coord_cartesian(ylim = c(0, 75))

ggplot(chic, aes(date, temp)) +
  geom_point() +
  scale_x_date(
    expand = c(0, 0)
  ) + 
  coord_cartesian(
    ylim = c(0, 75), 
*   clip = "off"
  )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-scale-axis-clip-1.png)![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-scale-axis-clip-2.png)
]

---
### **5. Coordinate System: `coord_flip()`**

**`coord_flip()`** allows you to flip a Cartesian coordinate system:

.pull-left[

```r
ggplot(chic, aes(season, temp)) +
  geom_col() +
* coord_flip()
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-coord-flip-1.png)
]

---
### **5. Coordinate System: `coord_polar()`**

You can easily transform a rectangular coordinate system into a polar one:

.pull-left[

```r
ggplot(chic, aes(season, temp)) +
  stat_summary(fun.y = mean,
              geom = "bar")
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-coord-polar-before-1.png)
]

.pull-right[

```r
ggplot(chic, aes(season, temp)) +
  stat_summary(fun.y = mean,
              geom = "bar") +
* coord_polar()
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-coord-polar-1.png)
]

---
### **5. Coordinate System: `coord_polar()`**

This way, we can create pie charts:

.pull-left[

```r
ggplot(chic, aes(factor(1), temp, 
                 fill = season)) +
  geom_col() +
* coord_polar() +
  scale_x_discrete(NULL, expand = c(0, 0)) +
  scale_y_continuous(NULL, expand = c(0, 0))
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-coord-pie1-1.png)
]

.pull-right[

```r
ggplot(chic, aes(factor(1), temp, 
                 fill = season)) +
  geom_col() +
* coord_polar(theta = "y") +
  scale_x_discrete(NULL, expand = c(0, 0)) +
  scale_y_continuous(NULL, expand = c(0, 0))
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-coord-pie2-1.png)
]

---
### **5. Coordinate System: `coord_trans()`**

**`coord_*()`** functions allow you to zoom in a plot:

.pull-left[

```r
ggplot(chic, aes(o3, temp)) +
  geom_point() +
* coord_trans(x = "log2")
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-coord-trans-1.png)
]

---
### **5. Coordinate System: `coord_map()`**

Maps often come as polygons and can be plotted via **`geom_polygon()`**:

.pull-left[

```r
(g_world <- 
 ggplot(map_data("world"),
 aes(long, lat, 
 group = group)) +
* geom_polygon(
* fill = "tan",
* color = "grey20"
* )
)
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-coord-map-noproj-1.png)
]

---
### **5. Coordinate System: `coord_map()`**

Since maps are displaying spherical data, we must project the data via **`coord_map()`**:

.pull-left[

```r
g_world +
* coord_map(xlim = c(-180,180))
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-coord-map-proj1-1.png)
]

.pull-right[

```r
g_world +
* coord_map("ortho")
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-coord-map-proj2-1.png)
]

---
class: inverse, center, middle

# **6. Facets `facet_*()`**

---
### **6. Facets: `facet_*()`**

Facetting generates small multiples each showing a different subset of the data:

<img src="img/position-facets.png" style="width: 75%">
Adapted from "ggplot2: Elegant graphics for data analysis" by Hadley Wickham

---
### **6. Facets: `facet_wrap()`**

**`facet_wrap()`** splits the data into small multiples based on *one grouping variable*:

.pull-left[

```r
ggplot(chic, aes(temp, o3)) +
  geom_point(aes(color = year)) +
* facet_wrap(~ season)
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-facet-wrap-1.png)
]

---
### **6. Facets: `facet_wrap()`**

**`facet_wrap()`** splits the data into small multiples based on *one grouping variable*:

.pull-left[

```r
ggplot(chic, aes(temp, o3)) +
  geom_point(aes(color = year)) +
  facet_wrap(~ season, 
*            scales = "free")
```

It is possible to change the axes range to scale free for each subset (for only one axis to scale free use **`"free_x"`** or **`"free_y"`**.
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-facet-wrap2-1.png)
]

---
### **6. Facets: `facet_grid()`**

**`facet_grid()`** spans a grid of each combination of *two grouping variables*:

.pull-left[

```r
ggplot(chic, aes(temp, o3)) +
  geom_point(aes(color = year)) +
* facet_grid(season ~ year)
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-facet-grid-1.png)
]

---
class: inverse, center, middle

# **7. Visual Themes `theme()` and `theme_*()`**

---
### **7. Visual Themes: `theme()` and `theme_*()`**

* Themes control the display of all non-data elements of the plot 
 
 + titles
 + labels
 + fonts
 + background
 + gridlines
 + legends
 + base size!

* Themes can be modified for each plot by calling **`theme()`** and **`element_`** functions

* Themes can be used to give plots a consistent customized look by defining a custom theme using **`theme_update()`** or **`theme_set()`**

---
### **7. Visual Themes: `theme()`**

To modify the theme of a plot use **`theme()`** in combination with **`element_*()`**:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point() +
* theme(
*   axis.text = element_text(
*     size = 15,
*     face = "bold",
*     color = "red"
*   ),
*   panel.grid.major.x = element_line(
*     linetype = "dotted",
*     color = "black"
*   ),
*   plot.background = element_rect(
*     fill = "dodgerblue",
*     color = "goldenrod"
*   )
* )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-theme-manual-1.png)
]

---
### **7. Visual Themes: `theme()`**

**`element_blank()`** can be used to remove certain theme elements:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point() +
* theme(
*   axis.title.x = element_blank(),
*   panel.grid.major.y = element_blank(),
*   panel.grid.minor = element_blank()
* )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-theme-blank-1.png)
]

---
### **7. Visual Themes: `theme()`**

Using the argument **`base_size()`** you can change the size of the text:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point() +
* theme_bw(base_size = 40)
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-theme-base-size-1.png)
]

---
### **7. Visual Themes: `theme_*()`**

Use a built-in theme of **`{ggplot2}`**:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
* theme_gray()
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-theme-builtin1-1.png)
]

.pull-right[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
* theme_classic()
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-theme-builtin2-1.png)
]

---
### **7. Visual Themes: `theme_*()`**

... or extension packages such as **`{ggthemes}`**:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
* ggthemes::theme_gdocs() +
* ggthemes::scale_color_gdocs()
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-theme-ggthemes-gdocs-1.png)
]

.pull-right[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(color = "black") +
* ggthemes::theme_tufte()
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-theme-ggthemes-tufte-1.png)
]

---
### **7. Visual Themes: `theme_*()`**

... or extension packages such as **`{hrbrthemes}`**  and **`{tvthemes}`**:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
* hrbrthemes::theme_ft_rc(grid = "Y") +
* hrbrthemes::scale_color_ft()
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-theme-hrbrthemes-ft-1.png)
]

.pull-right[

```r
ggplot(chic, aes(date, temp)) +
  geom_point(aes(color = season)) +
* tvthemes::theme_simpsons() +
* tvthemes::scale_color_simpsons()
```

![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/structure-theme-tvthemes-simpsons-1.png)
]

---
### **7. Visual Themes: `theme_set()` and `theme_update()`**

**`theme_set()`** and **`theme_update()`** override settings completely or partly:

.pull-left[

```r
(g <- ggplot(chic, aes(date, temp)) +
 geom_point())
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-theme-override1-1.png)
]

---
### **7. Visual Themes: `theme_set()` and `theme_update()`**

**`theme_set()`** and **`theme_update()`** override settings completely or partly:

.pull-left[

```r
(g <- ggplot(chic, aes(date, temp)) +
 geom_point())

*old <- theme_set(theme_grey())
*g 
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-theme-override2-1.png)
]

---
### **7. Visual Themes: `theme_set()` and `theme_update()`**

**`theme_set()`** and **`theme_update()`** override settings completely or partly:

.pull-left[

```r
(g <- ggplot(chic, aes(date, temp)) +
 geom_point())

old <- theme_set(theme_grey())
g

*theme_update(
* panel.background = element_rect(
*   fill = "tan",
*   color = "black"
* )
*) 
*g 
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-theme-override3-1.png)
]

---
### **7. Visual Themes: `theme_set()` and `theme_update()`**

**`theme_set()`** and **`theme_update()`** override settings completely or partly:

.pull-left[

```r
(g <- ggplot(chic, aes(date, temp)) +
 geom_point())

old <- theme_set(theme_grey())
g

theme_update(
  panel.background = element_rect(
    fill = "tan",
    color = "black"
  )
)
g

*theme_set(old)
*g 
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-structure-theme-override4-1.png)
]

---
class: center, middle

# **Some more?**

---
class: inverse, center, middle

# **Working with Text**

---
### **Working with Text: Title, Subtitle, Caption, and Tag**

To quickly add a title, use **`ggtitle()`**:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point() +
* ggtitle("Temperatures in Chicago")
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-text-title-1.png)
]

---
### **Working with Text: Title, Subtitle, Caption, and Tag**

**`{ggplot2}`** has a built-in structure for title, subtitle, caption and tags via **`labs()`**:

.pull-left[

```r
(g <- ggplot(chic, aes(date, temp)) +
 geom_point() +
* labs(
* x = "Year",
* y = "Temperature (°F)",
* title = "Temperatures in Chicago",
* subtitle = "Seasonal pattern of daily temperatures\nfrom 1997 to 2001",
* caption = "Data: NMMAPS",
* tag = "(a)"
* )
)
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-text-labs-1.png)
]

---
### **Working with Text: Title, Subtitle, Caption, and Tag**

**`{ggplot2}`** has a built-in structure for title, subtitle, caption and tags:

.pull-left[

```r
g +
* theme(
*   plot.title = element_text(
*     size = 20,
*     face = "bold",
*     hjust = 1,
*     margin = margin(15, 0, 15, 0)
*   ),
*   plot.caption = element_text(
*     face = "italic"
*   ),
*   plot.tag.position = c(0.15, 0.75)
* )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-text-labs-mod-1.png)
]

---
### **Working with Text: `annotate("text")`**

**`annotate()`** allows to add text inside the plot area:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point() +
* annotate(
*   "text",
*   x = as.Date("1998-07-01"),
*   y = 10,
*   label = "The Summer\nof 1998",
*   size = 3,
*   fontface = "bold",
*   color = "firebrick"
* )
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-text-annotate-1.png)
]

---
### **Working with Text: `annotate("text")`**

The **`annotate()`** function comes from ggplot2 and is designed to use a so-called **grob** as input:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
  geom_point() +
  annotate(
    "text",
    x = as.Date("1998-07-01"),
    y = 10,
    label = "The Summer\nof 1998",
    size = 3,
    fontface = "bold",
    color = "firebrick"
  ) +
* facet_wrap(~ year, scales = "free_x")
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-text-annotate-facet-1.png)
]

---
### **Working with Text: `annotation_custom(grob)`**

**`annotation_custom()`** allows to add text by relative positions which solves the problem:

.pull-left[

```r
*text <- grid::grobTree(
* grid::textGrob(
* "Summertime!",
* x = 0.5, y = 0.1, hjust = 0.5,
* gp = grid::gpar(
* fontsize = 12,
* fontface = "bold",
* col = "firebrick"
* )
* )
*)

ggplot(chic, aes(date, temp)) +
  geom_point() +
* annotation_custom(text) +
  facet_wrap(~ year, scales = "free_x")
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-text-annotate-custom-1.png)
]

---
### **Working with Text: `element_markdown()` via `{ggtext}`**

With the new **`{ggtext}`** package, it is possible to use Markdown and basic HTML within strings:

.pull-left[

```r
*library(ggtext)

ggplot(chic, aes(date, temp)) +
 geom_point(aes(color = season)) +
 scale_color_manual(
 name = NULL,
 values = c("#0072B2", "#009E73", "#FF8423", "#B22222"),
* labels = c(
* "Spring: Apr to Jun ",
* "Summer: Jul to Sep ",
* "Autumn: Oct to Dec ",
* "Winter: Jan to Mar "
* )
 ) +
* theme(legend.text = element_markdown())
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-text-ggtext-1.png)
]

---
class: inverse, center, middle

# **Working with Images**

---
### **Working with Images: `annotation_custom(grob)`**

The **`annotation_custom()`** function comes with ggplot2 and is designed to use a so-called **grob** as input:

.pull-left[

```r
*img <- png::readPNG("./img/logo.png")
*logo <- grid::rasterGrob(img, interpolate = T)

ggplot(chic, aes(date, temp)) +
* annotation_custom(
*   logo,
*   xmin = as.Date("1997-06-01"),
*   xmax = as.Date("2000-06-01"),
*   ymin = 0,
*   ymax = 80
* ) +
  geom_point()
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-text-annotate-img-1.png)
]

---
class: inverse, center, middle

# **Working with Geometric Forms**

---
### **Working with Geometric Forms: `annotate("rect")`**

Let's add some background indicating the years:

.pull-left[

```r
ggplot(chic, aes(date, temp)) +
* annotate(
*   "rect",
*   xmin = as.Date("1997-01-01"),
*   xmax = as.Date("1997-12-31"),
*   ymin = -Inf, ymax = Inf,
*   color = NA, alpha = 0.2
* ) +
*   annotate(
*   "rect",
*   xmin = as.Date("1999-01-01"),
*   xmax = as.Date("1999-12-31"),
*   ymin = -Inf, ymax = Inf,
*   color = NA, alpha = 0.2
* ) +
  geom_point()
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-forms-annotate-rect-1.png)
]

---
### **Working with Geometric Forms: `annotate("rect")` or `geom_rect()`?**

If you plot many elements, use **`geom_rect()`**:

.pull-left[

```r
*rect <- tibble(
* xmin = as.Date(c("1997-01-01", "1999-01-01")),
* xmax = as.Date(c("1997-12-31", "1999-12-31")),
* ymin = rep(-Inf, 2),
* ymax = rep(Inf, 2)
*)

ggplot(chic) +
* geom_rect(
*   data = rect,
*   aes(xmin = xmin, xmax = xmax,
*       ymin = ymin, ymax = ymax),
*   color = NA,
*   alpha = 0.2
* ) +
  geom_point(aes(date, temp))
```
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-forms-geom-rect-1.png)
]

---
### **Working with Geometric Forms: `geom_abline()`, `geom_vline()` and `geom_hline()`**

We could also indicate new years by a vertical line:

.pull-left[

```r
*lines <- tibble(
* xintercept = seq(
* as.Date("1997-01-01"),
* as.Date("2001-01-01"),
* length.out = 5)
*)

ggplot(chic, aes(date, temp)) +
  geom_point() +
* geom_vline(
*   data = lines,
*   aes(xintercept = xintercept))
```

There are also **`geom_abline()`** and **`geom_hline()`**.
]

.pull-right[
![](2019-08-28-intro-ggplot-statsIZW_files/figure-html/plot-forms-vline-1.png)
]

---
class: inverse, center, middle

# **You want even more?**

---

* **Free Online Books:**

+ "ggplot2: Elegant Graphics for Data Analysis" by Hadley Wickham  →  [ggplot2-book.org](https://ggplot2-book.org/)
  + "R for Data Science" by Hadley Wickham  →  [r4ds.had.co.nz](https://r4ds.had.co.nz/)
  + "Data Visualization: A Practical Introduction" by Kieran Healy  →  [socviz.co](https://socviz.co/)
  + "Fundamentals of Data Visualization" by Claus Wilke  →  [serialmentor.com/dataviz](https://serialmentor.com/dataviz/)
  + R Graph Gallery  →  [r-graph-gallery.com](https://www.r-graph-gallery.com/)
  + ggplot2 Extension Gallery  →  [ggplot2-exts.org/gallery](http://www.ggplot2-exts.org/gallery/)
  + Color Palettes in R  →  [github.com/EmilHvitfeldt/r-color-palettes](https://github.com/EmilHvitfeldt/r-color-palettes/)
  + &#35;TidyTuesday Collection  →  [tidytuesday.rocks](http://tidytuesday.rocks/)

* **Get Help:**

+ RStudio Community  →  [community.rstudio.com](wwww.community.rstudio.com)
  + R4DS Learning Community  →  [rfordatasci.com](https://www.rfordatasci.com)
  + &#35;TidyTuesday Community  →  [#TidyTuesday on Twitter](https://twitter.com/hashtag/tidytuesday?lang=en)

* **Useful Packages:**

+ **`{ggtext}`** — rich-text rendering  →  [github.com/clauswilke/ggtext](https://github.com/clauswilke/ggtext)
  + **`{ggforce}`** — several interesting add-on features   →  [ggforce.data-imaginist.com](https://ggforce.data-imaginist.com/)
  + **`{ggridges}`** — geoms for joy plots  →  [github.com/clauswilke/ggridges](https://github.com/clauswilke/ggridges)
  + **`{ggrepel}`** — prevent overlapping text labels  →  [github.com/slowkow/ggrepel](https://github.com/slowkow/ggrepel)
  + **`{gganimate}`** — create animations  →  [gganimate.com](https://gganimate.com/)
  + **`{ggplotly}`** — create interactive plots  →  [plot.ly/ggplot2](https://plot.ly/ggplot2/)
  + **`{ggraph}`** — networks, graphs & trees  →  [github.com/thomasp85/ggraph](https://github.com/thomasp85/ggraph)
  + **`{ggalt}`** — alternative coords, geoms, stats & scales  →  [github.com/hrbrmstr/ggalt](https://github.com/hrbrmstr/ggalt)
  + **`{ggpubr}`** — publication-ready plot in one line  →  [github.com/kassambara/ggpubr](https://github.com/kassambara/ggpubr)
  + **`{ggmaps}`** — access to Google & Stamen maps  →  [github.com/dkahle/ggmap](https://github.com/dkahle/ggmap)
  + **`{ggthemes}`** —  additional themes, sclaes & geoms →  [github.com/jrnold/ggthemes](https://github.com/jrnold/ggthemes)
  + **`{hrbrthemes}`** — typography-centric themes  →  [github.com/hrbrmstr/hrbrthemes](https://github.com/hrbrmstr/hrbrthemes)
  + **`{lemon}`** — axis & legend add-ons  →  [github.com/stefanedwards/lemon](https://github.com/stefanedwards/lemon)
  + **`{cowplot}`** — plot arrangements, themes & annotations  →  [wilkelab.org/cowplot](https://wilkelab.org/cowplot/articles/introduction.html)
  + **`{patchwork}`** — combine ggplots  →  [github.com/thomasp85/patchwork](https://github.com/thomasp85/patchwork)
  + **`{showtext}`** — use custom fonts  →  [github.com/yixuan/showtext](https://github.com/yixuan/showtext)
  + **`{showtext}`** — hillshaded maps in 2D & 3D  →  [github.com/tylermorganwall/rayshader](https://github.com/tylermorganwall/rayshader)

.logo[
<img src="./img/logo_ressources.png" width="1328" />
]---

* **Free Online Books:**

+ **Galleries:**

+ R Graph Gallery  →  [r-graph-gallery.com](https://www.r-graph-gallery.com/)
  + ggplot2 Extension Gallery  →  [ggplot2-exts.org/gallery](http://www.ggplot2-exts.org/gallery/)
  + Color Palettes in R  →  [github.com/EmilHvitfeldt/r-color-palettes](https://github.com/EmilHvitfeldt/r-color-palettes/)
  + &#35;TidyTuesday Collection  →  [tidytuesday.rocks](http://tidytuesday.rocks/)

* **Get Help:**

* **Useful packages:**

+ **`{ggtext}`** — rich-text rendering  →  [github.com/clauswilke/ggtext](https://github.com/clauswilke/ggtext)
  + **`{ggforce}`** — several interesting add-on features   →  [ggforce.data-imaginist.com](https://ggforce.data-imaginist.com/)
  + **`{ggridges}`** — geoms for joy plots  →  [github.com/clauswilke/ggridges](https://github.com/clauswilke/ggridges)
  + **`{ggrepel}`** — prevent overlapping text labels  →  [github.com/slowkow/ggrepel](https://github.com/slowkow/ggrepel)
  + **`{gganimate}`** — create animations  →  [gganimate.com](https://gganimate.com/)
  + **`{ggplotly}`** — create interactive plots  →  [plot.ly/ggplot2](https://plot.ly/ggplot2/)
  + **`{ggraph}`** — networks, graphs & trees  →  [github.com/thomasp85/ggraph](https://github.com/thomasp85/ggraph)
  + **`{ggalt}`** — alternative coords, geoms, stats & scales  →  [github.com/hrbrmstr/ggalt](https://github.com/hrbrmstr/ggalt)
  + **`{ggpubr}`** — publication-ready plot in one line  →  [github.com/kassambara/ggpubr](https://github.com/kassambara/ggpubr)
  + **`{ggmaps}`** — access to Google & Stamen maps  →  [github.com/dkahle/ggmap](https://github.com/dkahle/ggmap)
  + **`{ggthemes}`** —  additional themes, sclaes & geoms →  [github.com/jrnold/ggthemes](https://github.com/jrnold/ggthemes)
  + **`{hrbrthemes}`** — typography-centric themes  →  [github.com/hrbrmstr/hrbrthemes](https://github.com/hrbrmstr/hrbrthemes)
  + **`{tvthemes}`** — TV-inspired themes  →  [github.com/Ryo-N7/tvthemes](https://github.com/Ryo-N7/tvthemes)
  + **`{lemon}`** — axis & legend add-ons  →  [github.com/stefanedwards/lemon](https://github.com/stefanedwards/lemon)
  + **`{cowplot}`** — plot arrangements, themes & annotations  →  [wilkelab.org/cowplot](https://wilkelab.org/cowplot/articles/introduction.html)
  + **`{patchwork}`** — combine ggplots  →  [github.com/thomasp85/patchwork](https://github.com/thomasp85/patchwork)
  + **`{showtext}`** — use custom fonts  →  [github.com/yixuan/showtext](https://github.com/yixuan/showtext)
  + **`{rayshader}`** — hillshaded maps in 2D & 3D  →  [github.com/tylermorganwall/rayshader](https://github.com/tylermorganwall/rayshader)

.logo[
<img src="./img/logo_ressources.png" width="1328" />
]