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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/",
out.width = "100%"
)
library(ggplot2)
set.seed(1542)
```
<!-- badges: start -->
[](https://github.com/r-causal/ggdag/actions)
[](https://cran.r-project.org/package=ggdag)
[](https://lifecycle.r-lib.org/articles/stages.html)
[](https://app.codecov.io/gh/malcolmbarrett/ggdag?branch=main)
[](https://cran.r-project.org/package=ggdag)
<!-- badges: end -->
# ggdag: An R Package for visualizing and analyzing causal directed acyclic graphs <a href="https://r-causal.github.io/ggdag/"><img src="man/figures/logo.png" align="right" height="138" /></a>
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.
## Installation
You can install `ggdag` with:
```{r cran-installation, eval = FALSE}
install.packages("ggdag")
```
Or you can install the development version from GitHub with:
```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("r-causal/ggdag")
```
## Example
`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:
```{r tidydag, dpi=300, message=FALSE}
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
# 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
```
`ggdag` also provides functionality for analyzing DAGs and plotting them in `ggplot2`:
```{r ggdag, dpi=300}
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`:
```{r ggdag_geoms, dpi=300}
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:
```{r ggdag_common, dpi=300}
ggdag_equivalent_dags(confounder_triangle())
ggdag_butterfly_bias(edge_type = "diagonal")
```