---
title: "An Introduction to ggdag"
author: "Malcolm Barrett"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{An Introduction to ggdag}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
	fig.align = "center",
	fig.dpi = 320,
	fig.height = 5,
	fig.width = 5,
	message = FALSE,
	warning = FALSE,
	collapse = TRUE,
	comment = "#>"
)
set.seed(2939)
```

# Overview

`ggdag` extends the powerful `dagitty` package to work in the context of the tidyverse. It uses `dagitty`'s algorithms for analyzing structural causal graphs to produce tidy results, which can then be used in `ggplot2` and `ggraph` and manipulated with other tools from the tidyverse, like `dplyr`.  

# Creating Directed Acyclic Graphs

If you already use `dagitty`, `ggdag` can tidy your DAG directly.

```{r dagitty}
library(dagitty)
library(ggdag)

dag <- dagitty("dag{y <- z -> x}")
tidy_dagitty(dag)
```

Note that, while `dagitty` supports a number of graph types, `ggdag` currently only supports DAGs. 

`dagitty` uses a syntax similar to the [dot language of graphviz](https://graphviz.gitlab.io/_pages/doc/info/lang.html). This syntax has the advantage of being compact, but `ggdag` also provides the ability to create a `dagitty` object using a more R-like formula syntax through the `dagify()` function. `dagify()` accepts any number of formulas to create a DAG. It also has options for declaring which variables are exposures, outcomes, or latent, as well as coordinates and labels for each node.

```{r dagify}
dagified <- dagify(x ~ z,
                   y ~ z,
                   exposure = "x",
                   outcome = "y")
tidy_dagitty(dagified)
```

Currently, `ggdag` supports directed (`x ~ y`) and bi-directed (`a ~~ b`) relationships

`tidy_dagitty()` uses layout functions from `ggraph` and `igraph` for coordinates if none are provided, which can be specified with the `layout` argument. Objects of class `tidy_dagitty` or `dagitty` can be plotted quickly with `ggdag()`. If the DAG is not yet tidied, `ggdag()` and most other quick plotting functions in `ggdag` do so internally.

```{r ggdag_layout}
ggdag(dag, layout = "circle")
```


A `tidy_dagitty` object is just a list with a `tbl_df`, called `data`, and the `dagitty` object, called `dag`:

```{r dag_str}
tidy_dag <- tidy_dagitty(dagified)
str(tidy_dag)
```


#  Working with DAGs

Most of the analytic functions in `dagitty` have extensions in `ggdag` and are named `dag_*()` or `node_*()`, depending on if they are working with specific nodes or the entire DAG. A simple example is `node_parents()`, which adds a column to the to the `tidy_dagitty` object about the parents of a given variable:

```{r parents}
node_parents(tidy_dag, "x")
```

Or working with the entire DAG to produce a `tidy_dagitty` that has all pathways between two variables:

```{r pathways}
bigger_dag <- dagify(y ~ x + a + b,
                     x ~ a + b,
                     exposure = "x",
                     outcome = "y")
#  automatically searches the paths between the variables labelled exposure and 
#  outcome
dag_paths(bigger_dag)  
```

`ggdag` also supports [piping](http://r4ds.had.co.nz/pipes.html) of functions and includes the pipe internally (so you don't need to load `dplyr` or `magrittr`). Basic `dplyr` verbs are also supported (and anything more complex can be done directly on the `data` object).

```{r}
library(dplyr)
#  find how many variables are in between x and y in each path
bigger_dag %>% 
  dag_paths() %>%
  group_by(set) %>%
  filter(!is.na(path) & !is.na(name)) %>% 
  summarize(n_vars_between = n() - 1L)
```


#  Plotting DAGs

Most `dag_*()` and `node_*()` functions have corresponding `ggdag_*()` for quickly plotting the results. They call the corresponding `dag_*()` or `node_*()` function internally and plot the results in `ggplot2`.

```{r ggdag_path, fig.width=6.5}
ggdag_paths(bigger_dag)
```



```{r ggdag_parents}
ggdag_parents(bigger_dag, "x")
```

```{r ggdag_adjustment_}
#  quickly get the miniminally sufficient adjustment sets to adjust for when 
#  analyzing the effect of x on y
ggdag_adjustment_set(bigger_dag)
```


#  Plotting directly in `ggplot2`

`ggdag()` and friends are, by and large, fairly thin wrappers around included `ggplot2` geoms for plotting nodes, text, and edges to and from variables. For example, `ggdag_parents()` can be made directly in `ggplot2` like this:

```{r}
bigger_dag %>% 
    node_parents("x") %>%
    ggplot(aes(x = x, y = y, xend = xend, yend = yend, color = parent)) +
      geom_dag_point() +
      geom_dag_edges() +
      geom_dag_text(col = "white") +
      theme_dag() +
      scale_color_hue(breaks  = c("parent", "child")) #  ignores NA in legend
```

The heavy lifters in `ggdag` are `geom_dag_node()`/`geom_dag_point()`, `geom_dag_edges()`, `geom_dag_text()`, `theme_dag()`, and `scale_adjusted()`. `geom_dag_node()` and `geom_dag_text()` plot the nodes and text, respectively, and are only modifications of `geom_point()` and `geom_text()`. `geom_dag_node()` is slightly stylized (it has an internal white circle), while `geom_dag_point()` looks more like `geom_point()` with a larger size. `theme_dag()` removes all axes and ticks, since those have little meaning in a causal model, and also makes a few other changes. `expand_plot()` is a convenience function that makes modifications to the scale of the plot to make them more amenable to nodes with large points and text `scale_adjusted()` provides defaults that are common in analyses of DAGs, e.g. setting the shape of adjusted variables to a square.

`geom_dag_edges()` is also a convenience function that plots directed and bi-directed edges with different geoms and arrows. Directed edges are straight lines with a single arrow head, while bi-directed lines, which are a shorthand for a latent parent variable between the two bi-directed variables (e.g. a <- L -> b), are plotted as an arc with arrow heads on either side. 

You can also call edge functions directly, particularly if you only have directed edges. Much of `ggdag`'s edge functionality comes from `ggraph`, with defaults (e.g. arrow heads, truncated lines) set with DAGs in mind. Currently, `ggdag` has four type of edge geoms: `geom_dag_edges_link()`, which plots straight lines, `geom_dag_edges_arc()`, `geom_dag_edges_diagonal()`, and `geom_dag_edges_fan()`.

```{r}
dagify(y ~ x,
       m ~ x + y) %>% 
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
    geom_dag_point() +
    geom_dag_edges_arc() +
    geom_dag_text() +
    theme_dag()
```


If you have bi-directed edges but would like to plot them as directed, `node_canonical()` will automatically insert the latent variable for you.

```{r canonical}
dagify(y ~ x + z,
       x ~~ z) %>% 
    node_canonical() %>% 
    ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
      geom_dag_point() +
      geom_dag_edges_diagonal() +
      geom_dag_text() +
      theme_dag()
```

There are also geoms based on those in `ggrepel` for inserting text and labels, and a special geom called `geom_dag_collider_edges()` that highlights any biasing pathways opened by adjusting for collider nodes. See the [vignette introducing DAGs](intro-to-dags.html) for more info.

