Directed Acyclic Graphs

Authors

[Editor] Hu Zheng;

[Contributors]

Visualizing directed acyclic graphs.

Setup

System Requirements: Cross-platform (Linux/MacOS/Windows)
Programming language: R
Dependent packages: ggdag

# Install packages
if (!requireNamespace("ggdag", quietly = TRUE)) {
  install.packages("ggdag")
}

# Load packages
library(ggdag)

Data Preparation

# Load data
tidy_ggdag <- dagify(
  y ~ x + z2 + w2 + w1,
  x ~ z1 + w1 + w2,
  z1 ~ w1 + v,
  z2 ~ w2 + v,
  w1 ~ ~w2, # bidirected path
  exposure = "x",
  outcome = "y") %>%
  tidy_dagitty()

# View data
head(tidy_ggdag)

$data
# A tibble: 13 × 8
   name       x      y direction to      xend   yend circular
   <chr>  <dbl>  <dbl> <fct>     <chr>  <dbl>  <dbl> <lgl>   
 1 v      0.294 -1.35  ->        z1     1.15  -0.223 FALSE   
 2 v      0.294 -1.35  ->        z2    -0.845 -0.515 FALSE   
 3 w1     0.529  1.15  ->        x      0.496  0.520 FALSE   
 4 w1     0.529  1.15  ->        y     -0.422  0.384 FALSE   
 5 w1     0.529  1.15  ->        z1     1.15  -0.223 FALSE   
 6 w1     0.529  1.15  <->       w2    -0.643  0.972 FALSE   
 7 w2    -0.643  0.972 ->        x      0.496  0.520 FALSE   
 8 w2    -0.643  0.972 ->        y     -0.422  0.384 FALSE   
 9 w2    -0.643  0.972 ->        z2    -0.845 -0.515 FALSE   
10 x      0.496  0.520 ->        y     -0.422  0.384 FALSE   
11 y     -0.422  0.384 <NA>      <NA>  NA     NA     FALSE   
12 z1     1.15  -0.223 ->        x      0.496  0.520 FALSE   
13 z2    -0.845 -0.515 ->        y     -0.422  0.384 FALSE   

$dag
dag {
v
w1
w2
x [exposure]
y [outcome]
z1
z2
v -> z1
v -> z2
w1 -> x
w1 -> y
w1 -> z1
w1 <-> w2
w2 -> x
w2 -> y
w2 -> z2
x -> y
z1 -> x
z2 -> y
}

Visualization

# Directed Acyclic Graphs
p <- ggdag(tidy_ggdag) +
  theme_dag() 

p