Tidy, analyze, and plot causal directed acyclic graphs (DAGs). ggdag
uses the powerful dagitty package to create and analyze structural
causal models and plot them using ggplot2 and ggraph in a consistent
and easy manner.
You can install ggdag with:
install.packages("ggdag")Or you can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("r-causal/ggdag")ggdag makes it easy to use dagitty in the context of the tidyverse.
You can directly tidy dagitty objects or use convenience functions to
create DAGs using a more R-like syntax:
library(ggdag)
library(ggplot2)
# example from the dagitty package
dag <- dagitty::dagitty("dag {
y <- x <- z1 <- v -> z2 -> y
z1 <- w1 <-> w2 -> z2
x <- w1 -> y
x <- w2 -> y
x [exposure]
y [outcome]
}")
tidy_dag <- tidy_dagitty(dag)
tidy_dag
#> # DAG:
#> # A `dagitty` DAG with: 7 nodes and 12 edges
#> # Exposure: x
#> # Outcome: y
#> #
#> # Data:
#> # A tibble: 13 × 7
#> name x y direction to xend yend
#> <chr> <dbl> <dbl> <fct> <chr> <dbl> <dbl>
#> 1 v -0.180 0.946 -> z1 1.20 0.608
#> 2 v -0.180 0.946 -> z2 0.177 2.32
#> 3 w1 1.64 1.49 -> x 2.23 1.70
#> 4 w1 1.64 1.49 -> y 1.63 2.71
#> 5 w1 1.64 1.49 -> z1 1.20 0.608
#> 6 w1 1.64 1.49 <-> w2 1.16 2.30
#> 7 w2 1.16 2.30 -> x 2.23 1.70
#> 8 w2 1.16 2.30 -> y 1.63 2.71
#> 9 w2 1.16 2.30 -> z2 0.177 2.32
#> 10 x 2.23 1.70 -> y 1.63 2.71
#> 11 y 1.63 2.71 <NA> <NA> NA NA
#> 12 z1 1.20 0.608 -> x 2.23 1.70
#> 13 z2 0.177 2.32 -> y 1.63 2.71
#> #
#> # ℹ Use `pull_dag() (`?pull_dag`)` to retrieve the DAG object and `pull_dag_data() (`?pull_dag_data`)` for the data frame
# using more R-like syntax to create the same DAG
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()
tidy_ggdag
#> # DAG:
#> # A `dagitty` DAG with: 7 nodes and 12 edges
#> # Exposure: x
#> # Outcome: y
#> #
#> # Data:
#> # A tibble: 13 × 7
#> name x y direction to xend yend
#> <chr> <dbl> <dbl> <fct> <chr> <dbl> <dbl>
#> 1 v 0.696 4.02 -> z1 1.07 2.66
#> 2 v 0.696 4.02 -> z2 -0.669 3.67
#> 3 w1 0.175 2.21 -> x -0.000636 1.61
#> 4 w1 0.175 2.21 -> y -1.05 2.22
#> 5 w1 0.175 2.21 -> z1 1.07 2.66
#> 6 w1 0.175 2.21 <-> w2 -0.613 2.67
#> 7 w2 -0.613 2.67 -> x -0.000636 1.61
#> 8 w2 -0.613 2.67 -> y -1.05 2.22
#> 9 w2 -0.613 2.67 -> z2 -0.669 3.67
#> 10 x -0.000636 1.61 -> y -1.05 2.22
#> 11 y -1.05 2.22 <NA> <NA> NA NA
#> 12 z1 1.07 2.66 -> x -0.000636 1.61
#> 13 z2 -0.669 3.67 -> y -1.05 2.22
#> #
#> # ℹ Use `pull_dag() (`?pull_dag`)` to retrieve the DAG object and `pull_dag_data() (`?pull_dag_data`)` for the data frameggdag also provides functionality for analyzing DAGs and plotting them
in ggplot2:
ggdag(tidy_ggdag) +
theme_dag()
ggdag_adjustment_set(tidy_ggdag, node_size = 14) +
theme(legend.position = "bottom")
As well as geoms and other functions for plotting them directly in
ggplot2:
dagify(m ~ x + y) |>
tidy_dagitty() |>
node_dconnected("x", "y", controlling_for = "m") |>
ggplot(aes(
x = x,
y = y,
xend = xend,
yend = yend,
shape = adjusted,
col = d_relationship
)) +
geom_dag_edges(end_cap = ggraph::circle(10, "mm")) +
geom_dag_collider_edges() +
geom_dag_point() +
geom_dag_text(col = "white") +
theme_dag() +
scale_adjusted(include_color = FALSE) +
expand_plot(expand_y = expansion(c(0.2, 0.2))) +
scale_color_viridis_d(
name = "d-relationship",
na.value = "grey85",
begin = .35
)
And common structures of bias:
ggdag_equivalent_dags(confounder_triangle())
ggdag_butterfly_bias(edge_type = "diagonal")