The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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")
::install_github("r-causal/ggdag") devtools
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
<- dagitty::dagitty("dag {
dag y <- x <- z1 <- v -> z2 -> y
z1 <- w1 <-> w2 -> z2
x <- w1 -> y
x <- w2 -> y
x [exposure]
y [outcome]
}")
<- tidy_dagitty(dag)
tidy_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
<- dagify(
tidy_ggdag ~ x + z2 + w2 + w1,
y ~ z1 + w1 + w2,
x ~ w1 + v,
z1 ~ w2 + v,
z2 ~ ~w2, # bidirected path
w1 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
:
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() +
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")
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.