4.3-Dynamic_plot_creation.qmd

---
title: "Dynamic Plot Creation"
author:
  - Elizabeth King
  - Kevin Middleton
format:
  revealjs:
    theme: [default, custom.scss]
    standalone: true
    self-contained: true
    logo: QMLS_Logo.png
    slide-number: true
    show-slide-number: all
    link-external-newwindow: true
---

## What do we mean "dynamic"?

```{r}
#| label: setup
#| echo: false
#| warning: false
#| message: false

library(tidyverse)
library(cowplot)
ggplot2::theme_set(theme_cowplot())
library(patchwork)

```

Use a template of code to do analysis and make a plot (and potentially other things)

- Reusable analysis
- Reusable plotting
- Edit once, use many times


## Mammals data {.smaller}

- Life history data for 5,400 species of mammals (PanTHERIA data)^[Kate E. Jones, et al.. 2009. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90:2648.]
- Read data from tab-delimited file
- Replace -999 with NA
- Rename and select some traits to work with

```{r}
#| echo: true
#| output-location: slide
M <- read_delim("Data/mammals.txt", show_col_types = FALSE) |> 
  mutate(across(.cols = where(is.double), 
                .fns = ~ ifelse(.x == -999, NA, .x))) |> 
  rename(Order = MSW05_Order,
         Species = MSW05_Binomial,
         Body_mass = `5-1_AdultBodyMass_g`,
         Forearm = `8-1_AdultForearmLen_mm`,
         Body_length = `13-1_AdultHeadBodyLen_mm`,
         Eye_opening = `2-1_AgeatEyeOpening_d`,
         Repro_age = `3-1_AgeatFirstBirth_d`,
         BMR_1 = `18-1_BasalMetRate_mLO2hr`,
         BMR_2 = `5-2_BasalMetRateMass_g`,
         Neonate_mass = `5-3_NeonateBodyMass_g`) |> 
  select(Order, Species, Body_mass, Forearm, Body_length, Eye_opening,
         Repro_age, BMR_1, BMR_2, Neonate_mass)
M
```


## Working with lists

- Lists can hold any kind of R object
- Easy to create empty lists and fill 

```{r}
#| echo: true
ll <- list()

ll[[1]] <- 1
ll[[2]] <- 3
ll
```

Create a counter and iterate through the traits.

## Functions that generate plots

```{r}
#| echo: true
trait_plot <- function(trait) {
  D <- tibble(trait) |> drop_na()
  p <- ggplot(D, aes(trait)) +
    geom_histogram(bins = 30)
  return(p)
}
```

## Testing the function

```{r}
#| echo: true
trait_plot(M$Forearm)
```


## Collecting plots in lists 1

- Setup the vectors of traits

```{r}
#| echo: true
traits <- names(M)[3:10]
traits
```

## Collecting plots in lists 2

- Make an empty list of plots
- `select()` the *ii*th trait by string (`all_of()`)
- Convert to vector (`pull()`)

```{r}
#| echo: true
plots <- list()
for (ii in 1:length(traits)) {
  trait <- M |> 
    select(all_of(traits[ii])) |> 
    pull(1)
  plots[[ii]] <- trait_plot(trait)
}
```


## Checking plots

```{r}
#| echo: true
plots[[1]]
```


## View all plots

```{r}
#| echo: true
wrap_plots(plots)
```


## Rewriting `trait_plot`

- `trait` is a string
- Drop rows with `NA` using `drop_na(all_of(trait))`
- Set `aes()` to `trait` with `.data[[trait]]`

```{r}
#| echo: true
trait_plot_map <- function(trait, M) {
  M_no_NA <- M |> 
    drop_na(all_of(trait))
  p <- ggplot(M_no_NA, aes(.data[[trait]])) +
    geom_histogram(bins = 30) +
    labs(x = str_replace(trait, "_", " "))
  return(p)
}
```


## `purrr::map_` functions

- Efficiently pass a set of values to a function and collect results
- Functions can return:
  - `numeric`, `data.frame`/`tibble`, etc.
- `map()` collects returns to a list


## Using `purrr::map()`

- `.x` is the iterable vector to pass to the function `.f` (note no `()`)
- `M` is passed as an additional parameter

```{r}
#| echo: true
plots_map <- map(.x = names(M)[3:10],
                 .f = trait_plot_map,
                 M = M)
```


## Checking plots

```{r}
#| echo: true
wrap_plots(plots_map)
```


## I could have just used `pivot_longer()` and `facet_wrap()`

Yes. But...