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("malcolmbarrett/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)
# 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
#> # A DAG with 7 nodes and 12 edges
#> #
#> # Exposure: x
#> # Outcome: y
#> #
#> # A tibble: 13 x 8
#> name x y direction to xend yend circular
#> <chr> <dbl> <dbl> <fct> <chr> <dbl> <dbl> <lgl>
#> 1 v 11.8 8.03 -> z1 10.4 7.77 FALSE
#> 2 v 11.8 8.03 -> z2 12.1 6.66 FALSE
#> 3 w1 10.2 6.85 -> x 9.95 6.28 FALSE
#> 4 w1 10.2 6.85 -> y 11.1 6.39 FALSE
#> 5 w1 10.2 6.85 -> z1 10.4 7.77 FALSE
#> 6 w1 10.2 6.85 <-> w2 10.9 5.75 FALSE
#> 7 w2 10.9 5.75 -> x 9.95 6.28 FALSE
#> 8 w2 10.9 5.75 -> y 11.1 6.39 FALSE
#> 9 w2 10.9 5.75 -> z2 12.1 6.66 FALSE
#> 10 x 9.95 6.28 -> y 11.1 6.39 FALSE
#> 11 z1 10.4 7.77 -> x 9.95 6.28 FALSE
#> 12 z2 12.1 6.66 -> y 11.1 6.39 FALSE
#> 13 y 11.1 6.39 <NA> <NA> NA NA FALSE
# 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
#> # A DAG with 7 nodes and 12 edges
#> #
#> # Exposure: x
#> # Outcome: y
#> #
#> # A tibble: 13 x 8
#> name x y direction to xend yend circular
#> <chr> <dbl> <dbl> <fct> <chr> <dbl> <dbl> <lgl>
#> 1 v 9.30 13.4 -> z1 9.74 12.1 FALSE
#> 2 v 9.30 13.4 -> z2 7.96 13.0 FALSE
#> 3 w1 8.74 11.0 -> x 8.86 11.6 FALSE
#> 4 w1 8.74 11.0 -> y 7.68 11.5 FALSE
#> 5 w1 8.74 11.0 -> z1 9.74 12.1 FALSE
#> 6 w1 8.74 11.0 <-> w2 8.00 12.0 FALSE
#> 7 w2 8.00 12.0 -> x 8.86 11.6 FALSE
#> 8 w2 8.00 12.0 -> y 7.68 11.5 FALSE
#> 9 w2 8.00 12.0 -> z2 7.96 13.0 FALSE
#> 10 x 8.86 11.6 -> y 7.68 11.5 FALSE
#> 11 z1 9.74 12.1 -> x 8.86 11.6 FALSE
#> 12 z2 7.96 13.0 -> y 7.68 11.5 FALSE
#> 13 y 7.68 11.5 <NA> <NA> NA NA FALSEggdag 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(aes(end_cap = ggraph::circle(10, "mm"))) +
geom_dag_collider_edges() +
geom_dag_point() +
geom_dag_text(col = "white") +
theme_dag() +
scale_adjusted() +
expand_plot(expand_y = expand_scale(c(0.2, 0.2))) +
scale_color_hue(name = "d-relationship", na.value = "grey75") 
And common structures of bias:
ggdag_equivalent_dags(confounder_triangle())
ggdag_butterfly_bias(edge_type = "diagonal")
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