vis01_ggplot.qmd

# ggplot2 basics


> Es gibt nur eine Breitbandleitung ins Hirn und das sind die Augen. [David Kriesel](https://www.youtube.com/watch?v=QqVFEP3_BtY)


In the course, we used a variety of datasets. All of them have different features and difficulties that we need to keep in mind when we want to visualize parts of the data to point out some interesting facts!

The **German Climate Data** data has a temporal component and "continuous" measurements. The **arthoropode species on crop fields** follow the classic plot based design and allows for comparisons between different groups. The **trees and districts of Muenster** potentially have a spatial component to it but also allow for comparisons of the different species and districts.

Fortunately, there is a common language to visualize most datasets and their specific attributes: the grammar of graphics.


## The grammar of graphics in R

Scientific figures and advanced visualization in R are usually done with the `ggplot2` package.


* [https://ggplot2.tidyverse.org/](https://ggplot2.tidyverse.org/)
* tidyverse package: synergies and integration with e.g. dplyr
* layer based language that are combined with `+`
* differentiated by "static" and "dynamic" parameters
* many extensions: [https://exts.ggplot2.tidyverse.org/gallery/](https://exts.ggplot2.tidyverse.org/gallery/)
* Gallery for inspiration: [https://r-graph-gallery.com/index.html](https://r-graph-gallery.com/index.html)


```{r}
library(ggplot2)
library(viridis) # for better color scales

species = read.csv("data/crop_species.csv")
```


### Initialze the plot

Tell ggplot which `data.frame` is the basis for the plot.

```{r}
ggplot(data = species)
```


The `mapping` argument of the `ggplot()` function handles which columns of the data should be used for which purpose.
To specify this, we use the `aes()` function (aesthetics). Common things that we specify:

* x
* y
* group
* color
* fill


Here we specify that on the x-axis in our plot represent the `AraInd` column. As a result we get the proper y-axis, but still no data is shown. For this, we have to specify in which form the data should be depicted.

```{r}
ggplot(data = species, mapping = aes(x = AraInd))
```


### Geometry types

There are many different geometry types build into `ggplot2` and even more from additional packages. The functions to create the geometry layers usually start with `geom_`. The geom functions also have their own arguments you can specify.

* geom_point
* geom_line
* geom_boxplot
* geom_histogram
* ...


```{r}
ggplot(species, aes(x = AraInd))+
    geom_histogram()
```



```{r}
ggplot(species, aes(x = AraInd))+
    geom_histogram(binwidth = 10, fill = "darkgreen", color = "black")
```

Different geometry types require more than one aesthetic. E.g. `geom_point` needs the x and y aesthetics.

```{r}
ggplot(species, aes(x = Croptype, y = AraInd))+
    geom_point()
```



### Axis Formats

The Axis format depends on the type of variable you want to plot. We have to be aware of what scale type we have.

* discrete scale for categorical data
* continuous scale for numeric data
* limits, ticks, labels ...


```{r}
ggplot(species, aes(x = Croptype, y = AraInd))+
    geom_point(size = 4, alpha = 0.8, shape = 18)+
    scale_x_discrete(labels = c("KE" = "Grain Pea",
                                "SM" = "Silage Maize",
                                "WR" = "Winter Oilseed Rape",
                                "WW" = "Winter Wheat",
                                "ZR" = "Sugar Beet"))+
    scale_y_continuous(name = "Aranae [n]", limits = c(0,200))
```


We can also save the plot to a object to reuse and modify it later.


```{r}
araind_plot = ggplot(species, aes(x = Croptype, y = AraInd))+
    geom_point(size = 4, alpha = 0.8, shape = 18)+
    scale_x_discrete(labels = c("KE" = "Grain Pea",
                                "SM" = "Silage Maize",
                                "WR" = "Winter Oilseed Rape",
                                "WW" = "Winter Wheat",
                                "ZR" = "Sugar Beet"))+
    scale_y_continuous(name = "Aranae [n]", limits = c(0,200))



araind_plot + ggtitle("Aranae [n] per crop type")
```


### Color Scales

With the `color` and `fill` arguments in `aes()` we specify which columns of the data.frame should be represented as a color scale in the plot.


```{r}
ggplot(species, aes(x = Croptype, y = AraInd, color = AraSpec))+
    geom_point(size = 4, alpha = 0.8, shape = 18)+
    scale_x_discrete(name = "Crop Type")+
    scale_y_continuous(name = "Aranae [n]", limits = c(0,200))
```

We can specify the colors by adding a `scale_color_` layer. Just like with the axis, there are discrete and continuous color scales available!

```{r}
ggplot(species, aes(x = Croptype, y = AraInd, color = AraSpec))+
    geom_point(size = 4, alpha = 0.8, shape = 18)+
    scale_color_gradient(name = "Aranae\nSpecies [n]", low = "yellow", high = "red")+
    scale_x_discrete(name = "Crop Type")+
    scale_y_continuous(name = "Aranae [n]", limits = c(0,200))
```




### Themes

There are many prebuild themes...

```{r}
ggplot(species, aes(x = Croptype, y = AraInd, color = AraSpec))+
    geom_point(size = 4, alpha = 0.8, shape = 18)+
    scale_color_gradientn(name = "Aranae\nSpecies [n]",colors = mako(50))+
    scale_x_discrete(name = "Crop Type")+
    scale_y_continuous(name = "Aranae [n]", limits = c(0,200))+
    theme_minimal()
```


... or you can specify your own:

```{r}
ggplot(species, aes(x = Croptype, y = AraInd, color = AraSpec))+
    geom_point(size = 4, alpha = 0.8, shape = 18)+
    scale_color_gradientn(name = "Aranae\nSpecies [n]",colors = mako(50))+
    scale_x_discrete(name = "Crop Type")+
    scale_y_continuous(name = "Aranae [n]", limits = c(0,200), expand = c(0,0))+
    theme(panel.background = element_blank(),
          panel.grid.major.y = element_line(color = "black", linetype = "dotted"),
          axis.line = element_line(color = "black"))

```