slides_tidy-viz.qmd

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# Visualization with _ggplot2_

(A few remarks and tips before the practical session)

# R is the best technology for doing computational science

<p align="right">(Subjectively.)</p>

# _ggplot2_ is the most powerful visualization framework

<p align="right">(Objectively. 🙂)</p>

## _ggplot2_ is a core _tidyverse_ package

<center>![](files/r-bootcamp/packages_tidyverse.png){width="50%"}</center>

## "Grammar of Graphics"

A formal syntax and grammar for describing visualizations

<center>![](files/tidy-viz/gog_cover.jpg){width="30%"}</center>

<center>**What does this mean?**</center>

## Let's consider base R plotting

<br>

```{r}
#| eval: false
library(palmerpenguins)

head(penguins, 7)
```

<br>

```{r}
#| echo: false
library(dplyr)
library(palmerpenguins)
library(kableExtra)
penguins %>%
  head(7) %>%
  kable %>%
  kable_styling(font_size = 20)
```

## A base R histogram

```{r}
#| fig-align: center
hist(penguins$body_mass_g)
```

## A base R histogram

```{r}
#| fig-align: center
hist(penguins$body_mass_g, xlim = c(2000, 8000))
```


## A base R histogram

```{r}
#| fig-align: center
hist(penguins$body_mass_g, xlim = c(2000, 8000),
     main = "Histogram of body mass of penguins")
```



## A base R histogram

```{r}
#| fig-align: center
hist(penguins$body_mass_g, xlim = c(2000, 8000),
     main = "Histogram of body mass of penguins",
     xlab = "Body mass [grams]")
```



## A base R histogram

```{r}
#| fig-align: center
hist(penguins$body_mass_g, xlim = c(2000, 8000),
     main = "Histogram of body mass of penguins",
     xlab = "Body mass [grams]",
     col = "black", border = "white")
```




## A base R histogram

```{r}
#| code-fold: true
#| fig-align: center
par(mfrow = c(1, 3))

species_1 <- filter(penguins, species ==  "Adelie")
hist(species_1$body_mass_g, xlim = c(2000, 8000),
     main = "Species 'Adelie'",
     xlab = "Body mass [grams]",
     col = "darkgreen", border = "white")

species_2 <- filter(penguins, species ==  "Chinstrap")
hist(species_2$body_mass_g, xlim = c(2000, 8000),
     main = "Species 'Chinstrap'",
     xlab = "Body mass [grams]",
     col = "darkblue", border = "white")

species_3 <- filter(penguins, species ==  "Gentoo")
hist(species_3$body_mass_g, xlim = c(2000, 8000),
     main = "Species 'Gentoo'",
     xlab = "Body mass [grams]",
     col = "darkorange", border = "white")
```

# 

<center>Base R plots use a single function and specify a
(potentially large) number of parameters which change their
"aesthetic" properties and visual elements.</center>

. . .

<br>
<center>**Creating a new figure (even from the same data) usually requires
a completely different command.**</center>

# _tidyverse_ provides "grammar for data manipulation"...

A "mini-language" with verbs like `select()`, `filter()`, etc.

(Helps us avoid "R bootcamp"-level nightmares.)


# _ggplot2_ provides "grammar for visualizations"...

(To make visualizations easier in a similar sense.)

## Layers in "Grammar of Graphics"

<center>![](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*t4hhk-oxZkFEo5bGmbZ92A.png){width="65%"}
</center>

::: {.aside}
<small>Illustration of Layers in Grammar of Graphics from <a href="https://r.qcbs.ca/">
Quebec Centre for Biodiversity Science</a></small>
:::


## Layers in "Grammar of Graphics"

::: {.columns}

::: {.column}

::: {.fragment}

- `data` --- our data frame

:::

::: {.fragment}

- `aesthetics` --- "mapping" of columns to visual properties of
a figure (`x`, or `y` axes, `color`, `shape`, etc.)

:::

::: {.fragment}

- `geoms` --- graphical elements to be plotted (histograms, points, lines, etc.). 

:::

::: {.fragment}

- many other customizations

:::

:::


::: {.column}

<br>
<br>
<center>![](https://miro.medium.com/v2/resize:fit:1400/format:webp/1*t4hhk-oxZkFEo5bGmbZ92A.png)
</center>

:::

::::

# A tiny example

A quick advertisement for the "layering" concept of _ggplot2_

```{r}
#| echo: false
library(ggplot2)
```


## What data to visualize?

```{r}
#| fig-align: center
#| code-line-numbers: "1"
ggplot(penguins)
```

## How to visualize it? (`x` "aesthetic")

```{r}
#| fig-align: center
#| code-line-numbers: "1"
ggplot(penguins, aes(x = body_mass_g))
```

## How to visualize it? (`x` "aesthetic")

```{r}
#| fig-align: center
#| eval: false
#| code-line-numbers: "1"
ggplot(penguins, aes(x = body_mass_g)) +
```

```{r}
#| fig-align: center
#| echo: false
#| code-line-numbers: "1"
ggplot(penguins, aes(x = body_mass_g))
```


## On what dimensions?

```{r}
#| fig-align: center
#| code-line-numbers: "2"
ggplot(penguins, aes(x = body_mass_g)) + # the + is a bit like %>%
  xlim(2000, 8000) 
```

## What to visualize? ("geom")

```{r}
#| fig-align: center
#| code-line-numbers: "3"
ggplot(penguins, aes(x = body_mass_g)) +
  xlim(2000, 8000) +
  geom_histogram()
```


## How to visualize it? (`fill` "aesthetic")

```{r}
#| fig-align: center
#| code-line-numbers: "1,"
ggplot(penguins, aes(x = body_mass_g, fill = species)) +
  xlim(2000, 8000) +
  geom_histogram()
```

## How to visualize it? (adding "facets")

```{r}
#| fig-align: center
#| code-line-numbers: "4"
ggplot(penguins, aes(x = body_mass_g, fill = species)) +
  xlim(2000, 8000) +
  geom_histogram() +
  facet_wrap(~ species)
```


## What to visualize? ("geom" again)

```{r}
#| fig-align: center
#| code-line-numbers: "3"
ggplot(penguins, aes(x = body_mass_g, fill = species)) +
  xlim(2000, 8000) +
  geom_density() +
  facet_wrap(~ species)
```

## More "layers"...

```{r}
#| fig-align: center
#| code-line-numbers: "5"
ggplot(penguins, aes(x = body_mass_g, fill = species)) +
  xlim(2000, 8000) +
  geom_density() +
  facet_wrap(~ species) +
  theme_minimal()
```

# Today we're going to learn _ggplot2_ on our metadata and IBD data

# A bit of catch-up work on IBDs from yesterday

We need to make a copy of an IBD processing pipeline.

# Let's get started!

1. Go to [www.bodkan.net/simgen](https://bodkan.net/simgen)
2. Click on _"Visualization with _ggplot2_"_ in the left panel
3. _"Cheatsheets and handouts"_ section in the left panel has
a single-page version of these slides and the _dplyr_ cheatsheet
and _ggplot2_ cheatsheets
4. Open your RStudio and start working!


<br><br><br>
<small>_ggplot2_ version 4.0.0 was released just a few days ago. 🤞😬</small>