ggplot_tuto.Rmd

---
title: "ggplot2_tutorial"
output: 
  html_document:
    fig_caption: yes
  md_document:
    variant: markdown_github
---
```{r setup, cache = F}
knitr::opts_chunk$set(error = TRUE)
```

## What is ggplot?

Based on "The Grammar of Graphics", book by Leland Wilkinson. Just like grammar in language, a way to organize different elements of a plot so that it makes sense.

## Actually doing things -- first dive into ggplot2

### Setup

First step: install and import `ggplot2` (surprising, right)

Note about tidyverse: WHY TIDYVERSE TODO

```{r install_lib}
# library(ggplot2)
library(tidyverse)
```

Second step: get data (and here you thought nothing could surprise you anymore)

If you ever want to try something out, you should know that there are built-in datasets for you to test things on.

```{r datasets}
## see all datasets available
# data()

## load one of them
## Since we're the flowers lab, let's go with iris (great computer)
data(iris)

## take a look at what it looks like
head(iris)

```

About data: familiarize yourself with the data, make sure it is in the best format possible (eg CDI: bad). Explore wide format (bad) versus long format (better). Know where there are NAs that could raise errors when plotting, that kind of basic checks. Here two types of variables: numeric-continuous (all except Species) and categorical (Species). Notice how this dataset is half good (should be four columns: Type (Sepal or Petal), Measure (Length or Width), Value (numeric-continuous), Species) but we will not go into more details, and sometimes you just have to make do with what you have -- in the present case, laziness.

```{r check_data}
summary(iris)
```

### Layers

And now, for the actual plotting...
```{r test0}
ggplot()
```

"no offense, but this is blank"

Here we initialized the plot object. Basically, this tells R: hey, this is going to be a plot. Now we would like this plot to have something in it, and to add layers to a plot, we use "+", so let's try to add points from the dataset we chose:

```{r test2}
ggplot()+
  geom_point(data=iris)
```

Oh no, an error! Fortunately, R tells you what is wrong: it requires an aesthetics object containing necessary information (here, x and y). [learning how to read errors thrown by R: very useful!]

So here we said "Hey R, display points!" and R answered "Ok, where?"

### Aesthetics - part 1

What are aesthetics? They are a link between what ggplot should display based on the values of the data; it is a mapping between data and visual features. The most basic ones are the coordinates of things to plot!

Let's indicate to R in which positions we want points and plot the lengths of petals depending on the lengths of sepals:

```{r second_plot}
ggplot()+
  geom_point(aes(x=Sepal.Length, y=Petal.Length), data=iris)
```
 
### Default values
 
What if we also want, say, a smoothed curve for this data?


```{r third_plot}
ggplot()+
  geom_point(data=iris, aes(x=Sepal.Length, y=Petal.Length))+
  geom_smooth(data=iris, aes(x=Sepal.Length, y=Petal.Length))
```

Well this is a bit repetitive, isn't it? Does this mean that we will have to repeat the data, x and y values anytime we want to do something new with them?

Well no! We can put everything only once, as an argument of the `ggplot()` function: anything that is written there will be put in common with all the other layers. This can be considered as a default value for every new layer that is created:

```{r common_plot}
ggplot(data=iris, aes(x=Sepal.Length, y=Petal.Length))+
  geom_point()+
  geom_smooth()

## adding new layers witht the same default
ggplot(data=iris, aes(x=Sepal.Length, y=Petal.Length))+
  geom_point()+
  geom_smooth()+
  geom_rug()+
  geom_density2d()
```

### Aesthetics - part 2

Other information can be specified about the plot: color, size, shape, fill, transparency, ...

```{r color_plot}
ggplot(data=iris, aes(x=Sepal.Length, y=Petal.Length))+
  geom_point(color="violetred4")+
  geom_smooth(color="lightcoral", fill="midnightblue")

```


See any R color cheatsheet, for example https://www.nceas.ucsb.edu/~frazier/RSpatialGuides/colorPaletteCheatsheet.pdf

If this information depends on the data, it should be specified in the aes object:

```{r color2_plot}
ggplot(data=iris, aes(x=Sepal.Length, y=Petal.Length, color=Species))+
  geom_point()+
  geom_smooth()

```

What?! No! I just wanted the points to be colored based on the species they belong to, not to have a different curve for each species! No worries, we can do that too, we just have to specify that information in a layer-specific aes instead of having is as default:

```{r color3_plot}
ggplot(data=iris, aes(x=Sepal.Length, y=Petal.Length))+
  geom_point(aes(color=Species))+
  geom_smooth(color="black", fill="yellow")

```

### stats

Building a new variable to plot based on existing data (usually descriptive statistics, such as mean, se...):

```{r stat_plot}
ggplot(data=iris, aes(x=Sepal.Length, y=Petal.Length))+
  geom_point(aes(color=Species))+
  stat_smooth(method=lm)+
  stat_summary(fun.data = "mean_cl_boot")
  
```


Hm ok... what is "mean_cl_boot" exactly?

Learn how to use all the tools at your disposal! Either type `?mean_cl_boot` int the RStudio console or go to the Help tab on the bottom right panel of RStudio and search "mean_cl_boot", all the information should be there!

### Coordinates

"But I don't care about petal length under 4, that's way to too tiny. And those sepals over 7, ugh." Alright alright, let's fix it:

```{r another_ex_plot}
ggplot(data=iris, aes(x=Sepal.Length, y=Petal.Length))+
  geom_point(aes(color=Species))+
  coord_cartesian(xlim=c(4, 7), ylim=c(0,4))
```

Another example with our favorite plot:
```{r another_example_plot}
ggplot(data=iris, aes(x=Species, y=Petal.Length))+
  geom_violin(aes(color=Species))
```

It's fine and all, but what we really wanted was to have the species on the y axis. Well why didn't you say so before? That's how you do it:

```{r coord_flip}
ggplot(data=iris, aes(x=Species, y=Petal.Length))+
  coord_flip()+
  geom_violin(aes(color=Species, fill=Species, alpha=0.5))
  
```

You got it, `coord` will... change the coordinate system!

### Facets

You would like to split up your data by one or more variable and plot the subsets of data together, use facets!

```{r another_plot}
ggplot(data=iris, aes(x=Sepal.Length, y=Petal.Length))+
  geom_point(aes(color=Species, size=Petal.Width, alpha=0.8))+
  geom_smooth(color="black", fill="yellow")+
  facet_grid(~Species)
```

This floral example not being the best there is, let's look at another example (with more than one categorical value -- basic ggplot example):

```{r other_df}
summary(mpg)
?mpg
ggplot(mpg, aes(cty, hwy, color=manufacturer)) + geom_point() +
  facet_grid(fl ~ year)
```

### Scales

Back to our flowers!

```{r yetanother_plot}
ggplot(data=iris, aes(x=Sepal.Length, y=Petal.Length))+
  geom_point(aes(color=Species, size=Petal.Width, alpha=0.8))+
  geom_smooth(color="black", fill="yellow")+
  scale_x_reverse()

```

### Themes

Those are the non-data components of the graph: fonts, background and so on.

```{r yet2another_plot}
ggplot(data=iris, aes(x=Sepal.Length, y=Petal.Length))+
  geom_point(aes(color=Species, size=Petal.Width, alpha=0.8))+
  geom_smooth(color="black", fill="yellow")+
  theme_light()

```

Play with it a little bit -- try `theme_dark`, `theme_void`

### Other comments

You can put a plot in a variable and add a layer later on:

```{r add_layer}
p <- ggplot(data=iris, aes(x=Sepal.Length, y=Petal.Length))+
  geom_point(aes(color=Species, size=Petal.Width, alpha=0.8))+
  geom_smooth(color="black", fill="yellow")
p
p + facet_grid(~Species)

```

You can retrieve the last plot you made and save it:

```{r save}
# will show the last plot again
last_plot()

# will save the last plot
ggsave("here.png", width=10, height=10)
```


# In the end, lots of trials and errors (see https://twitter.com/accidental__aRt)

# All the help you need: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf and Google! :)

## Your turn!



```{r first_plot}

ggplot()+
  geom_histogram(aes(x=Sepal.Length), fill="green", alpha = 0.5, data=iris)+
  geom_histogram(aes(x=Petal.Length), fill="red", alpha=0.5, data=iris)+
  facet_wrap(~Species)+
  theme_dark()+
  labs(x="Length of sepals (green), length of petals (red)", y="Number of flowers with these features")
```

YES the order matters:

```{r actual_plot}
ggplot(iris, aes(x=Species, y=Petal.Length, color=Species, fill=Species)) +
  geom_boxplot(width = 0.6, alpha=0.5)+
  geom_jitter(width=0.1, height=0)+
  geom_violin(alpha = 0.5, fill="grey") 

ggplot(iris, aes(x=Species, y=Petal.Length, color=Species, fill=Species)) +
  geom_jitter(width=0.1, height=0)+
  geom_violin(alpha = 0.5, fill="grey")+
  geom_boxplot(width = 0.6, alpha=0.5)


ggplot(iris, aes(x=Species, y=Petal.Length, color=Species, fill=Species)) +
  geom_jitter(width=0.1, alpha=0.5)+
  geom_violin(alpha = 0.5, fill="grey", position = position_nudge(x=0.6))+
  geom_boxplot(width = 0.3, alpha=0.5)+
  coord_flip()+
  theme_classic()

```

```{r blop}
ggplot(iris, aes(Sepal.Length, Petal.Length))+
 geom_point(aes(color=Sepal.Width))+
 geom_smooth(method="lm")+
 theme_bw()
```