18-Programming-with-ggplot2.Rmd

# (PART\*) Advanced Topics {-}

# Programming with ggplot2

**Learning objectives:**

- Programming single and multiple components
- Use components, annotation, and additional arguments in a plot
- Functional programming

---

```{r message=FALSE, warning=FALSE, include=FALSE, paged.print=FALSE}
library(tidyverse)
```


**What are the components of a plot?**

- data.frame
- aes()
- Scales
- Coords systems
- Theme components

## Programming single and multiple components

In ggplot2 it is possible to build up plot components easily. This is a good practice to reduce duplicated code. 

Generalising code allows you with more flexibility when making customised plots.

### Components

One example of a component of a plot is this one below:
```{r}
bestfit <- geom_smooth(
  method = "lm", 
  se = FALSE, 
  colour = alpha("steelblue", 0.5), 
  size = 2)
```

This single component can be placed inside the syntax of the grammar of graphics and used as a plot layer.
```{r}
ggplot(mpg, aes(cty, hwy)) + 
  geom_point() + 
  bestfit
```

Another way is to bulid a layer passing through build a function:
```{r}
geom_lm <- function(formula = y ~ x, colour = alpha("steelblue", 0.5), 
                    size = 2, ...)  {
  geom_smooth(formula = formula, se = FALSE, method = "lm", colour = colour,
    size = size, ...)
}
```

And the apply the function layer to the plot
```{r}
ggplot(mpg, aes(displ, 1 / hwy)) + 
  geom_point() + 
  geom_lm(y ~ poly(x, 2), size = 1, colour = "red")
```

The book points out attention to the "open" parameter **...**.
A suggestion is to use it inside the function instead of in the function parameters definition.

Instead of only one component, we can build a plot made of more components.
```{r}
geom_mean <- function() {
  list(
    stat_summary(fun = "mean", geom = "bar", fill = "grey70"),
    stat_summary(fun.data = "mean_cl_normal", geom = "errorbar", width = 0.4)
  )
}
```

Whit this result:
```{r message=FALSE, warning=FALSE, paged.print=FALSE}
ggplot(mpg, aes(class, cty)) + geom_mean()
```


## Use components, annotation, and additional arguments in a plot

We have just seen some examples on how to make new components, what if we want to know more about existing components?

As an example the `borders()` option function, provided by {ggplot2}	to create a layer of map borders.

> "A quick and dirty way to get map data (from the maps package) on to your plot."

```{r}
borders <- function(database = "world", regions = ".", fill = NA, 
                    colour = "grey50", ...) {
  df <- map_data(database, regions)
  geom_polygon(
    aes_(~long, ~lat, group = ~group), 
    data = df, fill = fill, colour = colour, ..., 
    inherit.aes = FALSE, show.legend = FALSE
  )
}
```

```{r message=FALSE, warning=FALSE, paged.print=FALSE}
library(maps)
data(us.cities)
capitals <- subset(us.cities, capital == 2)

ggplot(capitals, aes(long, lat)) +
  borders("world", xlim = c(-130, -60), ylim = c(20, 50)) +
  geom_point(aes(size = pop)) +
  scale_size_area() +
  coord_quickmap()
```

We can even add addtional arguments, such as those ones to modify and add things:

      modifyList()
      do.call()

```{r}
geom_mean <- function(..., bar.params = list(), errorbar.params = list()) {
  params <- list(...)
  bar.params <- modifyList(params, bar.params)
  errorbar.params  <- modifyList(params, errorbar.params)
  
  bar <- do.call("stat_summary", modifyList(
    list(fun = "mean", geom = "bar", fill = "grey70"),
    bar.params)
  )
  errorbar <- do.call("stat_summary", modifyList(
    list(fun.data = "mean_cl_normal", geom = "errorbar", width = 0.4),
    errorbar.params)
  )

  list(bar, errorbar)
}
```
      
And here is the result:
```{r}
ggplot(mpg, aes(class, cty)) + 
  geom_mean(
    colour = "steelblue",
    errorbar.params = list(width = 0.5, size = 1)
  )
```

## Functional programming

An example is to make a geom. For this we can have a look at the **"Corporate Reputation"** data from #TidyTuesday 2022 week22. 

```{r}
poll <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-05-31/poll.csv')
reputation <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-05-31/reputation.csv')


rep2<-reputation%>%
  group_by(company,industry)%>%
  summarize(score,rank)%>%
  ungroup()%>%
  mutate(year=2022)


full <- poll%>%
  filter(!is.na(year))%>%
  full_join(rep2,by=c("2022_rank"="rank","2022_rq"="score","company","industry","year")) %>%
  count(year,company,industry,"rank"=`2022_rank`,"score"=`2022_rq`,sort=T) %>%
  arrange(-year)

##################

# mapping = aes(x = fct_reorder(x,-y), y = y, fill = y, color = y, label = y)

rank_plot <- function(data,mapping) {
  data %>%
    ggplot(mapping)+                   # aes(x=fct_reorder(x,-y),y=y)
    geom_col(width =0.3,               # aes(fill=rank)
             show.legend = F)+
    geom_text(hjust=0,fontface="bold", # aes(label=rank,color=rank),
              show.legend = F)+
    scale_y_discrete(expand = c(0, 0, .5, 0))+
    coord_flip()+
    ggthemes::scale_fill_continuous_tableau(palette = "Green-Gold")+
    ggthemes::scale_color_continuous_tableau(palette = "Green-Gold")+
    labs(title="",
         x="",y="")+
    theme(axis.text.x = element_blank(),
          axis.text.y = element_text(face="bold"),
          axis.ticks.x = element_blank(),
          axis.ticks.y = element_line(size=2),
          panel.grid.major.x = element_blank(),
          panel.grid.minor.x = element_blank(),
          panel.grid.major.y = element_line(size=2),
          plot.background = element_rect(color="grey95",fill="grey95"),
          panel.background = element_rect(color="grey92",fill="grey92"))
}

df<-full%>%
  filter(year==2017,
         industry=="Retail")

rank_plot(data = df, 
          mapping = aes(x=fct_reorder(company,-rank),y=rank,
                                   fill = rank, color = rank, label = rank))
```

## References

- [extending ggplot2](https://ggplot2.tidyverse.org/articles/extending-ggplot2.html)
- [functions](https://adv-r.hadley.nz/functions.html)
- [expressions](http://adv-r.had.co.nz/Expressions.html)
- [functional programming](http://adv-r.had.co.nz/Functional-programming.html)
- [advanced R - functionals](https://adv-r.hadley.nz/fp.html)



---

## Meeting Videos

### Cohort 1

`r knitr::include_url("https://www.youtube.com/embed/jf-Qn4iFqHY")`

<details>
  <summary> Meeting chat log </summary>
  
```
00:41:31	Priyanka Gagneja:	There’s a lot of disturbance :(
01:00:48	Priyanka Gagneja:	https://plotly.com/ggplot2/setting-graph-size/
```
</details>