inst/doc/intro-to-ggdag.R

In ggdag: Analyze and Create Elegant Directed Acyclic Graphs

## ----setup, include = FALSE---------------------------------------------------
if (identical(Sys.getenv("IN_PKGDOWN"), "true")) {
  dpi <- 320
} else {
  dpi <- 72
}
knitr::opts_chunk$set(
  fig.align = "center",
  fig.dpi = dpi,
  fig.height = 5,
  fig.width = 5,
  message = FALSE,
  warning = FALSE,
  collapse = TRUE,
  comment = "#>"
)
set.seed(2939)

## ----dagitty------------------------------------------------------------------
library(dagitty)
library(ggdag)
library(ggplot2)

dag <- dagitty("dag{y <- z -> x}")
tidy_dagitty(dag)

## ----dagify-------------------------------------------------------------------
dagified <- dagify(x ~ z,
  y ~ z,
  exposure = "x",
  outcome = "y"
)
tidy_dagitty(dagified)

## ----ggdag_layout-------------------------------------------------------------
ggdag(dag, layout = "circle")

## ----dag_str------------------------------------------------------------------
tidy_dag <- tidy_dagitty(dagified)
str(tidy_dag)

## ----parents------------------------------------------------------------------
node_parents(tidy_dag, "x")

## ----pathways-----------------------------------------------------------------
bigger_dag <- dagify(y ~ x + a + b,
  x ~ a + b,
  exposure = "x",
  outcome = "y"
)
#  automatically searches the paths between the variables labelled exposure and
#  outcome
dag_paths(bigger_dag)

## -----------------------------------------------------------------------------
library(dplyr)
#  find how many variables are in between x and y in each path
bigger_dag %>%
  dag_paths() %>%
  group_by(set) %>%
  filter(!is.na(path) & !is.na(name)) %>%
  summarize(n_vars_between = n() - 1L)

## ----ggdag_path, fig.width=6.5------------------------------------------------
ggdag_paths(bigger_dag)

## ----ggdag_parents------------------------------------------------------------
ggdag_parents(bigger_dag, "x")

## ----ggdag_adjustment_--------------------------------------------------------
#  quickly get the miniminally sufficient adjustment sets to adjust for when
#  analyzing the effect of x on y
ggdag_adjustment_set(bigger_dag)

## -----------------------------------------------------------------------------
bigger_dag %>%
  node_parents("x") %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend, color = parent)) +
  geom_dag_point() +
  geom_dag_edges() +
  geom_dag_text(col = "white") +
  theme_dag() +
  scale_color_hue(breaks = c("parent", "child")) #  ignores NA in legend

## -----------------------------------------------------------------------------
dagify(
  y ~ x,
  m ~ x + y
) %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_point() +
  geom_dag_edges_arc() +
  geom_dag_text() +
  theme_dag()

## ----canonical----------------------------------------------------------------
dagify(
  y ~ x + z,
  x ~ ~z
) %>%
  node_canonical() %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_point() +
  geom_dag_edges_diagonal() +
  geom_dag_text() +
  theme_dag()