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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:
Or you can install the development version from GitHub with:
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
#> # A DAG with 7 nodes and 12 edges
#> #
#> # Exposure: x
#> # Outcome: y
#> #
#> # A tibble: 13 × 8
#> name x y direction to xend yend circular
#> <chr> <dbl> <dbl> <fct> <chr> <dbl> <dbl> <lgl>
#> 1 v 0.496 -3.40 -> z1 1.83 -2.92 FALSE
#> 2 v 0.496 -3.40 -> z2 0.0188 -2.08 FALSE
#> 3 w1 1.73 -1.94 -> x 2.07 -1.42 FALSE
#> 4 w1 1.73 -1.94 -> y 1.00 -0.944 FALSE
#> 5 w1 1.73 -1.94 -> z1 1.83 -2.92 FALSE
#> 6 w1 1.73 -1.94 <-> w2 0.873 -1.56 FALSE
#> 7 w2 0.873 -1.56 -> x 2.07 -1.42 FALSE
#> 8 w2 0.873 -1.56 -> y 1.00 -0.944 FALSE
#> 9 w2 0.873 -1.56 -> z2 0.0188 -2.08 FALSE
#> 10 x 2.07 -1.42 -> y 1.00 -0.944 FALSE
#> 11 y 1.00 -0.944 <NA> <NA> NA NA FALSE
#> 12 z1 1.83 -2.92 -> x 2.07 -1.42 FALSE
#> 13 z2 0.0188 -2.08 -> y 1.00 -0.944 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 × 8
#> name x y direction to xend yend circular
#> <chr> <dbl> <dbl> <fct> <chr> <dbl> <dbl> <lgl>
#> 1 v -3.58 3.30 -> z1 -4.05 4.63 FALSE
#> 2 v -3.58 3.30 -> z2 -2.23 3.74 FALSE
#> 3 w1 -3.03 5.74 -> x -3.20 5.14 FALSE
#> 4 w1 -3.03 5.74 -> y -1.98 5.22 FALSE
#> 5 w1 -3.03 5.74 -> z1 -4.05 4.63 FALSE
#> 6 w1 -3.03 5.74 <-> w2 -2.35 4.72 FALSE
#> 7 w2 -2.35 4.72 -> x -3.20 5.14 FALSE
#> 8 w2 -2.35 4.72 -> y -1.98 5.22 FALSE
#> 9 w2 -2.35 4.72 -> z2 -2.23 3.74 FALSE
#> 10 x -3.20 5.14 -> y -1.98 5.22 FALSE
#> 11 y -1.98 5.22 <NA> <NA> NA NA FALSE
#> 12 z1 -4.05 4.63 -> x -3.20 5.14 FALSE
#> 13 z2 -2.23 3.74 -> y -1.98 5.22 FALSE
ggdag
also provides functionality for analyzing DAGs and plotting them in ggplot2
:
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() +
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:
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.