ggdag
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r-causal
:arrow_lower_left: :arrow_lower_right: An R package for working with causal directed acyclic graphs (DAGs)
Most often than not, my DAGs are rather big and complex. Visualizing them becomes hard if not impossible. It is also true that some variables in the DAG might belong...
I believe this is because the geom is filtering unique values of the label. A better approach, if possible, would be to filter the unique nodes. The point is to...
See #152 for details I believe there is a more robust way to handle this problem
#148 has got me thinking about how I think back support is particularly important for ggdag. I believe I should test this formally on CI with `usethis::use_github_action("check-full")`
This is an interesting problem, and I bumped into [this paper](https://arxiv.org/abs/2111.04513) on [this twitter thread](https://twitter.com/alexpghayes/status/1505944287221063687)
``` r library(ggdag, warn.conflicts = FALSE) coffee_cancer_dag ``` r dagitty::dconnected(coffee_cancer_dag, "coffee", "cancer") # [1] TRUE ``` Created on 2024-08-13 with [reprex v2.1.0](https://reprex.tidyverse.org)
Per an accidental finding from CRAN tests, DAGs without edges fail. Kicking the can down the road for the CRAN version but will do this before the next release. ```...