A Quick Introduction to ggdag

# A Quick Introduction to <code><font color="white">ggdag</font></code>
### Sakeef M. Karim <br><span style="font-size: 50%;">McGill University<br>Consortium on Analytics for Data-Driven Decision-Making</span>
### December 1, 2021<br><br><svg aria-hidden="true" role="img" viewBox="0 0 496 512" style="height:1em;width:0.97em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M165.9 397.4c0 2-2.3 3.6-5.2 3.6-3.3.3-5.6-1.3-5.6-3.6 0-2 2.3-3.6 5.2-3.6 3-.3 5.6 1.3 5.6 3.6zm-31.1-4.5c-.7 2 1.3 4.3 4.3 4.9 2.6 1 5.6 0 6.2-2s-1.3-4.3-4.3-5.2c-2.6-.7-5.5.3-6.2 2.3zm44.2-1.7c-2.9.7-4.9 2.6-4.6 4.9.3 2 2.9 3.3 5.9 2.6 2.9-.7 4.9-2.6 4.6-4.6-.3-1.9-3-3.2-5.9-2.9zM244.8 8C106.1 8 0 113.3 0 252c0 110.9 69.8 205.8 169.5 239.2 12.8 2.3 17.3-5.6 17.3-12.1 0-6.2-.3-40.4-.3-61.4 0 0-70 15-84.7-29.8 0 0-11.4-29.1-27.8-36.6 0 0-22.9-15.7 1.6-15.4 0 0 24.9 2 38.6 25.8 21.9 38.6 58.6 27.5 72.9 20.9 2.3-16 8.8-27.1 16-33.7-55.9-6.2-112.3-14.3-112.3-110.5 0-27.5 7.6-41.3 23.6-58.9-2.6-6.5-11.1-33.3 2.6-67.9 20.9-6.5 69 27 69 27 20-5.6 41.5-8.5 62.8-8.5s42.8 2.9 62.8 8.5c0 0 48.1-33.6 69-27 13.7 34.7 5.2 61.4 2.6 67.9 16 17.7 25.8 31.5 25.8 58.9 0 96.5-58.9 104.2-114.8 110.5 9.2 7.9 17 22.9 17 46.4 0 33.7-.3 75.4-.3 83.6 0 6.5 4.6 14.4 17.3 12.1C428.2 457.8 496 362.9 496 252 496 113.3 383.5 8 244.8 8zM97.2 352.9c-1.3 1-1 3.3.7 5.2 1.6 1.6 3.9 2.3 5.2 1 1.3-1 1-3.3-.7-5.2-1.6-1.6-3.9-2.3-5.2-1zm-10.8-8.1c-.7 1.3.3 2.9 2.3 3.9 1.6 1 3.6.7 4.3-.7.7-1.3-.3-2.9-2.3-3.9-2-.6-3.6-.3-4.3.7zm32.4 35.6c-1.6 1.3-1 4.3 1.3 6.2 2.3 2.3 5.2 2.6 6.5 1 1.3-1.3.7-4.3-1.3-6.2-2.2-2.3-5.2-2.6-6.5-1zm-11.4-14.7c-1.6 1-1.6 3.6 0 5.9 1.6 2.3 4.3 3.3 5.6 2.3 1.6-1.3 1.6-3.9 0-6.2-1.4-2.3-4-3.3-5.6-2z"/></svg> <a href="https://github.com/sakeefkarim/intro_ggdag_script">code</a><br><svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:currentColor;overflow:visible;position:relative;"><path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"/></svg> <a href="https://twitter.com/sakeefkarim">sakeefkarim</a>

---

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# What We’ll Do Today

+ Load the packages you'll need to generate causal diagrams in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#659BDF;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>

```r
library(dagitty)
library(tidyverse)
*library(ggdag)
library(ggthemes)
```

]

+ Revisit an example from last week's [asynchronous module](https://canvas.ubc.ca/courses/75578/assignments/1079082) on directed acyclic graphs

+ Learn how to use `dagitty` and `ggdag` in-tandem

+ Use `ggdag`'s inbuilt functions as _tools_ to identify sources of bias

+ Customize our DAGs using `ggplot2` in conjunction with `ggdag`

---

# A DAG Example

---

# Pre-Natal Vitamins and Birth Defects

> [DAGitty.net](http://www.dagitty.net/dags.html)

]

> [dagitty in <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#659BDF;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>](http://dagitty.net/primer/)

]

> [ggdag](https://ggdag.malco.io/index.html) (alongside `ggplot2`)

]

---

# Using <code><font color="white">dagitty</font></code> and <code><font color="white">ggdag</font></code>

---

# Combining `dagitty` and `ggdag`

.footnote[
**Step 1**: Create a DAG using [DAGitty.net](http://www.dagitty.net/dags.html). Copy the model code.
]

]

.footnote[
**Step 2**: Use `dagitty::dagitty()` and the pasted model code to generate a DAG object within <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#659BDF;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg>. 
]

```r
base_dagitty <- dagitty('dag {
                              A [pos="-2.200,-1.520"]
                              B [pos="1.400,-1.460"]
                              D [outcome,pos="1.400,1.621"]
                              E [exposure,pos="-2.200,1.597"]
                              Z [pos="-0.300,-0.082"]
                              A -> E
                              A -> Z [pos="-0.791,-1.045"]
                              B -> D
                              B -> Z [pos="0.680,-0.496"]
                              E -> D
                              }')
```
]
]

]

---

# Combining `dagitty` and `ggdag`

```r
dag_cand3 <- dagitty('dag {
                          Birth_Defects [outcome,pos="0.109,0.631"]
                          Difficulty_Conceiving [pos="0.117,-1.517"]
                          Genetics [pos="0.850,-0.411"]
                          PNC [pos="-0.837,-0.433"]
                          SES [pos="-1.839,-1.468"]
                          Vitamins [exposure,pos="-1.844,0.645"] 
                          Difficulty_Conceiving -> PNC
                          Genetics -> Birth_Defects
                          Genetics -> Difficulty_Conceiving
                          PNC -> Birth_Defects
                          PNC -> Vitamins
                          SES -> PNC
                          SES -> Vitamins
                          Vitamins -> Birth_Defects
                          }')

*dag_cand3 %>% ggdag()
```

]

<br>

]

---

# Using <code><font color="white">ggdag</font></code> as a Tool

---

# Identifying Covariate Adjustment Sets

```r
*dag_cand3 %>% ggdag_adjustment_set()
```

---

# Identifying Colliders

```r
*dag_cand3 %>% ggdag_collider()
```

---

# Identifying A Variable's Ancestors

```r
#In this case, the ancestors of "pre-natal care!"

*dag_cand3 %>% ggdag_ancestors("PNC")
```

---

# Identifying Open Paths

```r
#Non-faceted version:

*dag_cand3 %>% ggdag_paths_fan(from = "Vitamins", to = "Birth_Defects")
```

---

# Identifying Open Paths

```r
#Faceted version:

*dag_cand3 %>% ggdag_paths(from = "Vitamins", to = "Birth_Defects")
```

---

# Identifying Open Paths

What if we conditioned on a variable — say, "PNC?"

```r
dag_cand3 %>% ggdag_paths(from = "Vitamins", to = "Birth_Defects",
*                         adjust_for = "PNC", shadow = TRUE)
```

.footnote[
**Note**: [Script file](https://github.com/sakeefkarim/intro_ggdag_script/tree/main/code) includes an example of how to identify paths activated by conditioning on a collider — via `ggdag::ggdag_adjust()`.
]

---

# Identifying Open Paths

What if we also adjusted for _genetics_?

```r
dag_cand3 %>% ggdag_paths_fan(from = "Vitamins", to = "Birth_Defects",
*                             adjust_for = c("PNC", "Genetics"))
```

---

# Customization via <code><font color="white">ggdag</font></code> and <code><font color="white">ggplot2</font></code>

---

# Using  <svg aria-hidden="true" role="img" viewBox="0 0 581 512" style="height:1em;width:1.13em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#659BDF;overflow:visible;position:relative;"><path d="M581 226.6C581 119.1 450.9 32 290.5 32S0 119.1 0 226.6C0 322.4 103.3 402 239.4 418.1V480h99.1v-61.5c24.3-2.7 47.6-7.4 69.4-13.9L448 480h112l-67.4-113.7c54.5-35.4 88.4-84.9 88.4-139.7zm-466.8 14.5c0-73.5 98.9-133 220.8-133s211.9 40.7 211.9 133c0 50.1-26.5 85-70.3 106.4-2.4-1.6-4.7-2.9-6.4-3.7-10.2-5.2-27.8-10.5-27.8-10.5s86.6-6.4 86.6-92.7-90.6-87.9-90.6-87.9h-199V361c-74.1-21.5-125.2-67.1-125.2-119.9zm225.1 38.3v-55.6c57.8 0 87.8-6.8 87.8 27.3 0 36.5-38.2 28.3-87.8 28.3zm-.9 72.5H365c10.8 0 18.9 11.7 24 19.2-16.1 1.9-33 2.8-50.6 2.9v-22.1z"/></svg> Syntax to Generate a DAG

```r
dag_cand3 <- dagify(#Here, we see conventional R syntax (e.g., outcome ~ predictors)
                    birth_defects ~ vitamins + pnc + genetics,
                    vitamins ~ ses + pnc,
                    pnc ~ ses + diff_conceiving,
                    diff_conceiving ~ genetics,
                    #These labels will be useful for plotting purposes down the line!
                    labels = c(#\n signals a line break
                               birth_defects = "Birth Defects\n (Outcome)",
                               vitamins = "Vitamins\n (Exposure)",
                               pnc = "Pre-Natal Care",
                               diff_conceiving = "Difficulty\n Conceiving",
                               ses = "SES",
                               genetics = "Genetics"),
                    exposure = "vitamins",
                    outcome = "birth_defects")
```

---

# Identifying Covariate Adjustment Sets Pt II

```r
set.seed(905)

*dag_cand3 %>% ggdag_adjustment_set()
```

---

# Using Labels

```r
set.seed(905)

*dag_cand3 %>% ggdag(text = FALSE,
*                   use_labels = "label")
```

---

# Using Labels (and Identifying Open Paths)

```r
set.seed(905)

*ggdag_paths(dag_cand3, text = FALSE, use_labels = "label")
```

---

# Identifying Covariate Adjustment Sets Pt III

```r
set.seed(905)

dag_cand3 %>% ggdag_adjustment_set(text = FALSE, use_labels = "label") + 
*             theme_dag() + theme(legend.position = "none")
```

<img src="index_files/figure-html/labels_adjustment-1.png" width="80%" height="80%" />
]

---

# Leveraging `ggplot2`

First, tidy your DAG and specify your covariate adjustment set.

```r
set.seed(905)

dag_cand3_gg <- dag_cand3 %>% tidy_dagitty(layout = "nicely") %>% 
*                             node_dconnected(controlling_for = c("pnc", "ses"))

dag_cand3_gg %>% slice(1:3)
```

```
## # A DAG with 2 nodes and 3 edges
## #
## # Exposure: vitamins
## # Outcome: birth_defects
## # Paths opened by conditioning on a collider: 
## #
## # A tibble: 3 × 12
##   name       x     y direction to      xend  yend circular label   collider_line
##   <chr>  <dbl> <dbl> <fct>     <chr>  <dbl> <dbl> <lgl>    <chr>   <lgl>        
## 1 diff_…  10.5  4.23 ->        pnc     9.55  5.25 FALSE    "Diffi… FALSE        
## 2 genet…  11.5  4.90 ->        birth… 10.6   5.83 FALSE    "Genet… FALSE        
## 3 genet…  11.5  4.90 ->        diff_… 10.5   4.23 FALSE    "Genet… FALSE        
## # … with 2 more variables: adjusted <fct>, d_relationship <fct>
```
]

---

# Leveraging `ggplot2`

Then, use `ggdag` and `ggplot2` functions to produce a nicer DAG!

```r
dag_cand3_gg %>% mutate(adjusted = #Simple way to capitalize a string:
                                    str_to_title(adjusted),
                        arrow = #Allows us to modify transparency of arrows as a function of whether or not a variable is adjusted:
                                    ifelse(adjusted == "Adjusted", 0.15, 0.85)) %>% 
                 ggplot(aes(#Coordinates (i.e., where the nodes will be located)
                             x = x, y = y, xend = xend, yend = yend, 
                             #Mapping aesthetics — will vary as a function of whether a variable is adjusted or unadjusted:
                             colour = adjusted, fill = adjusted, shape = adjusted)) +
                  #Adds nodes to plotting area:
                  geom_dag_point() +
                  #Adds arrows connecting the nodes (as specified in your DAG syntax)::
                  geom_dag_edges(aes(#Adjusts transparency of arrows:
                                     edge_alpha = arrow), edge_width = 0.5) +
                  #Changes the shapes corresponding to adjusted/unadjusted.
                  scale_shape_manual(values = c(22, 21)) +
                  #The two lines that follow adjust the colour/fill of the nodes based on ggtheme's Economist theme:
                  scale_fill_economist() + scale_colour_economist() +
                  #The following line uses the logic of geom_label_repel to generate/modify your labels. 
                  geom_dag_label_repel(aes(label = label), colour = "white", show.legend = FALSE) +
                  theme_dag() +
                  #Removes legend title:
                  theme(legend.title = element_blank()) 
```
]

---

# Leveraging `ggplot2`

.footnote[
**Note**: [Script file](https://github.com/sakeefkarim/intro_ggdag_script/tree/main/code) includes examples of how to use custom coordinates (to generate a DAG via `ggplot2`) and how to shorten DAG arrows.
]

---

# The End

---

# Thank you!

Sakeef M. Karim <br>