README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
suppressPackageStartupMessages({
  library(ggplot2)
})

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%",
  fig.height = 2
)
```

# ggreverse

<!-- badges: start -->
![](https://img.shields.io/badge/Status-alpha-orange.svg)
![](https://img.shields.io/badge/Version-0.1.1-blue.svg)
<!-- badges: end -->

`ggreverse` takes a ggplot object and returns the code to create that plot.

This package is written as a learning exercise to help me figure out 
the internal structure of a ggplot object.



## Releases

* `0.1.0` - initial release
* `0.1.1` - improved theme handling

## Installation

You can install from [GitHub](https://github.com/coolbutuseless/) with:

``` r
# install.packages("remotes")
remotes::install_github("coolbutuseless/ggreverse")
```



## Example `ggreverse::convert_to_code()`

1. Create a ggplot
2. Convert the ggplot back into code using `ggreverse`
3. Convert the code back into a plot

```{r}
library(ggreverse)

plot_df <- mtcars

# Create a ggplot2 plot object
p <- ggplot(plot_df) +
  geom_point(aes(mpg, wt, colour = cyl), size = 3) +
  labs(title = "hello") +
  theme_bw() + 
  theme(legend.position = 'none') + 
  coord_equal()
```

```{r echo = FALSE}
p
```


```{r echo=TRUE, eval=FALSE}
# Convert the plot object back into code
plot_code <- ggreverse::convert_to_code(p)
print(plot_code)
```


```{r echo=FALSE, eval=TRUE}
plot_code <- convert_to_code(p)
styler::style_text(
  gsub("[+]", "+\n", plot_code)
)
```


```{r}
# Parse the plot code back into a plot - which should match the original plot
eval(parse(text = plot_code))
```




## Technical Notes

* the `data` arguments to `ggplot()` and `geom()` are evaluated at call time. There is
  no easy way to recover the name of the data argument.
    * `ggreverse` tries to match the actual data in the ggplot object against a named
      object in the plotting environment. Otherwise it uses a generic data name
* aesthethic mappings are evaluated at call time, so tidyeval and `aes_string()` mappings
  are supported, but `ggreverse` will only include the final variable name mapping.
* Layers are currently extracted as `geom_x(stat = 'y')` rather than `stat_y(geom='x')`.  
  I'm not sure if there are any cases where these aren't equivalent.

## ToDo

* Extracting `facet` and `scales` information.
* Complete themes which are customisations of built-in themes could be 
  more compact if nested diffs where done between themes, rather than 
  just a `shallow_diff()`
* Lots of other stuff :)


## SessionInfo

Developed against:

* R 3.5.3
* ggplot2 v3.1.1