Built on top of dagitty, ggplot2, and ggraph, with influences from
ggnetwork. Still in early development.
You can install ggdag from GitHub with:
# install.packages("devtools")
devtools::install_github("malcolmbarrett/ggdag")ggdag makes it easy to use the powerful dagitty package 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)
#>
#> Attaching package: 'ggdag'
#> The following object is masked from 'package:ggplot2':
#>
#> expand_scale
#> The following object is masked from 'package:stats':
#>
#> filter
# 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 tibble: 13 x 12
#> name from x y direction type to xend yend
#> <chr> <chr> <dbl> <dbl> <fct> <fct> <chr> <dbl> <dbl>
#> 1 v v 11.8 8.03 -> directed z1 10.4 7.77
#> 2 v v 11.8 8.03 -> directed z2 12.1 6.66
#> 3 w1 w1 10.2 6.85 -> directed x 9.95 6.28
#> 4 w1 w1 10.2 6.85 -> directed y 11.1 6.39
#> 5 w1 w1 10.2 6.85 -> directed z1 10.4 7.77
#> 6 w1 w1 10.2 6.85 <-> bidirected w2 10.9 5.75
#> 7 w2 w2 10.9 5.75 -> directed x 9.95 6.28
#> 8 w2 w2 10.9 5.75 -> directed y 11.1 6.39
#> 9 w2 w2 10.9 5.75 -> directed z2 12.1 6.66
#> 10 x x 9.95 6.28 -> directed y 11.1 6.39
#> 11 z1 z1 10.4 7.77 -> directed x 9.95 6.28
#> 12 z2 z2 12.1 6.66 -> directed y 11.1 6.39
#> 13 y y 11.1 6.39 <NA> <NA> <NA> NA NA
#> # ... with 3 more variables: .ggraph.orig_index <int>, circular <lgl>,
#> # .ggraph.index <int>
# using more R-like syntax to create the same DAG
tidy_ggdag <- dagify(y ~ x + z2 + w2 + w1,
x ~ z1 + w1,
z1 ~ w1 + v,
z2 ~ w2 + v,
w1 ~~ w2, # bidirected path
exposure = "x",
outcome = "y") %>% tidy_dagitty()
tidy_ggdag
#> # A tibble: 12 x 12
#> name from x y direction type to xend yend
#> <chr> <chr> <dbl> <dbl> <fct> <fct> <chr> <dbl> <dbl>
#> 1 v v 7.25 17.2 -> directed z1 7.59 18.5
#> 2 v v 7.25 17.2 -> directed z2 8.57 16.8
#> 3 w1 w1 9.03 18.7 -> directed x 8.35 19.3
#> 4 w1 w1 9.03 18.7 -> directed y 8.93 18.0
#> 5 w1 w1 9.03 18.7 -> directed z1 7.59 18.5
#> 6 w1 w1 9.03 18.7 <-> bidirected w2 9.68 17.5
#> 7 w2 w2 9.68 17.5 -> directed y 8.93 18.0
#> 8 w2 w2 9.68 17.5 -> directed z2 8.57 16.8
#> 9 x x 8.35 19.3 -> directed y 8.93 18.0
#> 10 z1 z1 7.59 18.5 -> directed x 8.35 19.3
#> 11 z2 z2 8.57 16.8 -> directed y 8.93 18.0
#> 12 y y 8.93 18.0 <NA> <NA> <NA> NA NA
#> # ... with 3 more variables: .ggraph.orig_index <int>, circular <lgl>,
#> # .ggraph.index <int>ggdag also provides functionality for analyzing DAGs and plotting them
in ggplot2:
ggdag(tidy_ggdag)
ggdag_adjustment_set(tidy_ggdag)
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_node() +
geom_dag_text(col = "white") +
theme_dag() +
scale_dag(expand_y = expand_scale(c(0.2, 0.2)))
And common structures of bias:
ggdag_equivalent_dags(confounder_triangle())
ggdag_butterfly_bias(edge_type = "diagonal")