Package 'ggdag'

Description

Stratifying on colliders can open biasing pathways between variables. activate_collider_paths activates any such pathways given a variable or set of variables to adjust for and adds them to the tidy_dagitty.

Usage

activate_collider_paths(.tdy_dag, adjust_for, ...)

Arguments

Value

a tidy_dagitty with additional rows for collider-activated pathways

See Also

control_for(), ggdag_adjust(), geom_dag_collider_edges()

Examples

dag <- dagify(m ~ x + y, x ~ y)

collided_dag <- activate_collider_paths(dag, adjust_for = "m")
collided_dag

Description

Adjust for variables and activate any biasing paths that result

Usage

control_for(.tdy_dag, var, as_factor = TRUE, activate_colliders = TRUE, ...)

adjust_for(.tdy_dag, var, as_factor = TRUE, activate_colliders = TRUE, ...)

ggdag_adjust(
  .tdy_dag,
  var = NULL,
  ...,
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL,
  collider_lines = TRUE
)

Arguments

Value

a tidy_dagitty with a adjusted column for adjusted variables, as well as any biasing paths that arise, or a ggplot

Examples

dag <- dagify(m ~ a + b, x ~ a, y ~ b)

control_for(dag, var = "m")
ggdag_adjust(dag, var = "m")

Description

A thin wrapper to convert tidy_dagitty and dagitty objects to tbl_graph, which can then be used to work in tidygraph and ggraph directly. See tidygraph::as_tbl_graph().

Usage

## S3 method for class 'tidy_dagitty'
as_tbl_graph(x, directed = TRUE, ...)

## S3 method for class 'dagitty'
as_tbl_graph(x, directed = TRUE, ...)

Arguments

Value

a tbl_graph

Examples

library(ggraph)
library(tidygraph)
butterfly_bias() %>%
  as_tbl_graph() %>%
  ggraph() +
  geom_edge_diagonal() +
  geom_node_point()

Description

An alternative API and specification to tidy_dagitty(), as_tidy_dagitty() allows you to create tidy_dagitty objects from data frames. There is also a method for dagitty objects, which is a thin wrapper for tidy_dagitty(). To create a DAG from a data frame, it must contain name and to columns, representing the nodes and any edges leading from the nodes. If there are x, y, xend, and yend columns, they will be used as coordinates. Otherwise, layout will be used. See tidy_dagitty for more information about layouts. Additionally, you can specify status (one of exposure, outcome, or latent) by including a status column. Any other columns in the data set will also be joined to the tidy_dagitty data.

Usage

as_tidy_dagitty(x, ...)

## S3 method for class 'dagitty'
as_tidy_dagitty(x, seed = NULL, layout = "nicely", ...)

## S3 method for class 'data.frame'
as_tidy_dagitty(x, seed = NULL, layout = "nicely", ...)

Arguments

Value

a tidy_dagitty object

See Also

tidy_dagitty(), pull_dag()

Examples

data.frame(name = c("c", "c", "x"), to = c("x", "y", "y")) %>%
  as_tidy_dagitty()

Description

Convert a tidy_dagitty object to data.frame

Usage

## S3 method for class 'tidy_dagitty'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

Description

Convert a tidy_dagitty object to tbl

Usage

## S3 method for class 'tidy_daggity'
as.tbl(x, row.names = NULL, optional = FALSE, ...)

## S3 method for class 'tidy_daggity'
as_tibble(x, row.names = NULL, optional = FALSE, ...)

Arguments

Description

D-separation is a key concept in causal structural models. Variables are d-separated if there are no open paths between them. The ⁠node_d*()⁠ functions label variables as d-connected or d-separated. The ⁠ggdag_d*()⁠ functions plot the results. The ⁠*_dconnected()⁠, ⁠*_dseparated()⁠, and ⁠*_drelationship()⁠ functions essentially produce the same output and are just different ways of thinking about the relationship. See dagitty::dseparated() for details.

Usage

node_dconnected(
  .tdy_dag,
  from = NULL,
  to = NULL,
  controlling_for = NULL,
  as_factor = TRUE,
  ...
)

node_dseparated(
  .tdy_dag,
  from = NULL,
  to = NULL,
  controlling_for = NULL,
  as_factor = TRUE
)

node_drelationship(
  .tdy_dag,
  from = NULL,
  to = NULL,
  controlling_for = NULL,
  as_factor = TRUE
)

ggdag_drelationship(
  .tdy_dag,
  from = NULL,
  to = NULL,
  controlling_for = NULL,
  ...,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL,
  collider_lines = TRUE
)

ggdag_dseparated(
  .tdy_dag,
  from = NULL,
  to = NULL,
  controlling_for = NULL,
  ...,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL,
  collider_lines = TRUE
)

ggdag_dconnected(
  .tdy_dag,
  from = NULL,
  to = NULL,
  controlling_for = NULL,
  ...,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL,
  collider_lines = TRUE
)

Arguments

Value

a tidy_dagitty with a d_relationship column for variable D relationship or a ggplot

Examples

library(ggplot2)
dag <- dagify(m ~ x + y)
dag %>% ggdag_drelationship("x", "y")
dag %>% ggdag_drelationship("x", "y", controlling_for = "m")

dag %>%
  node_dseparated("x", "y") %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend, shape = adjusted, col = d_relationship)) +
  geom_dag_edges() +
  geom_dag_collider_edges() +
  geom_dag_node() +
  geom_dag_text(col = "white") +
  theme_dag() +
  scale_adjusted()

dag %>%
  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() +
  geom_dag_collider_edges() +
  geom_dag_node() +
  geom_dag_text(col = "white") +
  theme_dag() +
  scale_adjusted()

dagify(m ~ x + y, m_jr ~ m) %>%
  tidy_dagitty(layout = "nicely") %>%
  node_dconnected("x", "y", controlling_for = "m_jr") %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend, shape = adjusted, col = d_relationship)) +
  geom_dag_edges() +
  geom_dag_collider_edges() +
  geom_dag_node() +
  geom_dag_text(col = "white") +
  theme_dag() +
  scale_adjusted()

Description

Parents and children are those nodes that either directly cause or are caused by the variable, respectively. Ancestors and descendants are those nodes that are on the path to or descend from the variable. The ⁠node_*()⁠ functions label variables depending on their relationship. The ⁠ggdag_*()⁠ functions plot the results. See dagitty::children for details.

Usage

node_children(.tdy_dag, .var, as_factor = TRUE)

node_parents(.tdy_dag, .var, as_factor = TRUE)

node_ancestors(.tdy_dag, .var, as_factor = TRUE)

node_descendants(.tdy_dag, .var, as_factor = TRUE)

node_markov_blanket(.tdy_dag, .var, as_factor = TRUE)

node_adjacent(.tdy_dag, .var, as_factor = TRUE)

ggdag_children(
  .tdy_dag,
  .var,
  ...,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

ggdag_parents(
  .tdy_dag,
  .var,
  ...,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

ggdag_ancestors(
  .tdy_dag,
  .var,
  ...,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

ggdag_descendants(
  .tdy_dag,
  .var,
  ...,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

ggdag_markov_blanket(
  .tdy_dag,
  .var,
  ...,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

ggdag_adjacent(
  .tdy_dag,
  .var,
  ...,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

Arguments

Value

a tidy_dagitty with an column related to the given relationship for variable D relationship or a ggplot

Examples

library(ggplot2)
dag <- dagify(
  y ~ x + z2 + w2 + w1,
  x ~ z1 + w1,
  z1 ~ w1 + v,
  z2 ~ w2 + v,
  w1 ~ ~w2
)

ggdag_children(dag, "w1")

dag %>%
  node_children("w1") %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend, color = children)) +
  geom_dag_edges() +
  geom_dag_node() +
  geom_dag_text(col = "white") +
  geom_dag_label_repel(aes(label = children, fill = children), col = "white", show.legend = FALSE) +
  theme_dag() +
  scale_adjusted() +
  scale_color_hue(breaks = c("parent", "child"))

ggdag_parents(dag, "y")

ggdag_ancestors(dag, "x")

ggdag_descendants(dag, "w1")

dag %>%
  node_parents("y") %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend, color = parent)) +
  geom_dag_edges() +
  geom_dag_point() +
  geom_dag_text(col = "white") +
  geom_dag_label_repel(aes(label = parent, fill = parent), col = "white", show.legend = FALSE) +
  theme_dag() +
  scale_adjusted() +
  scale_color_hue(breaks = c("parent", "child"))

Description

Takes an input graph with bidirected edges and replaces every bidirected edge x <-> y with a substructure x <- L -> y, where L is a latent variable. See dagitty::canonicalize() for details. Undirected edges are not currently supported in ggdag.

Usage

node_canonical(.dag, ...)

ggdag_canonical(
  .tdy_dag,
  ...,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

Arguments

Value

a tidy_dagitty that includes L or a ggplot

Examples

dag <- dagify(y ~ x + z, x ~ ~z)

ggdag(dag)

node_canonical(dag)
ggdag_canonical(dag)

Description

Detects any colliders given a DAG. node_collider tags colliders and ggdag_collider plots all exogenous variables.

Usage

node_collider(.dag, as_factor = TRUE, ...)

ggdag_collider(
  .tdy_dag,
  ...,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

Arguments

Value

a tidy_dagitty with a collider column for colliders or a ggplot

Examples

dag <- dagify(m ~ x + y, y ~ x)

node_collider(dag)
ggdag_collider(dag)

Description

Manipulate DAG coordinates

Usage

coords2df(coord_list)

coords2list(coord_df)

Arguments

Value

either a list or a data.frame with DAG node coordinates

Examples

library(dagitty)
coords <- list(
  x = c(A = 1, B = 2, D = 3, C = 3, F = 3, E = 4, G = 5, H = 5, I = 5),
  y = c(A = 0, B = 0, D = 1, C = 0, F = -1, E = 0, G = 1, H = 0, I = -1)
)
coord_df <- coords2df(coords)
coords2list(coord_df)

x <- dagitty("dag{
             G <-> H <-> I <-> G
             D <- B -> C -> I <- F <- B <- A
             H <- E <- C -> G <- D
             }")
coordinates(x) <- coords2list(coord_df)

Description

See dagitty::adjustmentSets() for details.

Usage

dag_adjustment_sets(.tdy_dag, exposure = NULL, outcome = NULL, ...)

ggdag_adjustment_set(
  .tdy_dag,
  exposure = NULL,
  outcome = NULL,
  ...,
  shadow = FALSE,
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL,
  expand_x = expansion(c(0.25, 0.25)),
  expand_y = expansion(c(0.2, 0.2))
)

Arguments

Value

a tidy_dagitty with an adjusted column and set column, indicating adjustment status and DAG ID, respectively, for the adjustment sets or a ggplot

Examples

dag <- dagify(y ~ x + z2 + w2 + w1,
  x ~ z1 + w1,
  z1 ~ w1 + v,
  z2 ~ w2 + v,
  w1 ~ ~w2,
  exposure = "x",
  outcome = "y"
)

tidy_dagitty(dag) %>% dag_adjustment_sets()

ggdag_adjustment_set(dag)

ggdag_adjustment_set(dagitty::randomDAG(10, .5),
  exposure = "x3",
  outcome = "x5"
)

Description

A convenience wrapper for dagitty::dagitty().

Usage

dag(...)

Arguments

Value

a dagitty

Examples

dag("{x m} -> y")

Description

Directed DAG edges

Usage

geom_dag_edges_link(
  mapping = NULL,
  data = NULL,
  arrow = grid::arrow(length = grid::unit(5, "pt"), type = "closed"),
  position = "identity",
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  ...
)

geom_dag_edges_arc(
  mapping = NULL,
  data = NULL,
  curvature = 0.5,
  arrow = grid::arrow(length = grid::unit(5, "pt"), type = "closed"),
  position = "identity",
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  fold = FALSE,
  n = 100,
  lineend = "butt",
  linejoin = "round",
  linemitre = 1,
  label_colour = "black",
  label_alpha = 1,
  label_parse = FALSE,
  check_overlap = FALSE,
  angle_calc = "rot",
  force_flip = TRUE,
  label_dodge = NULL,
  label_push = NULL,
  ...
)

geom_dag_edges_diagonal(
  mapping = NULL,
  data = NULL,
  position = "identity",
  arrow = grid::arrow(length = grid::unit(5, "pt"), type = "closed"),
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  curvature = 1,
  n = 100,
  lineend = "butt",
  linejoin = "round",
  linemitre = 1,
  label_colour = "black",
  label_alpha = 1,
  label_parse = FALSE,
  check_overlap = FALSE,
  angle_calc = "rot",
  force_flip = TRUE,
  label_dodge = NULL,
  label_push = NULL,
  ...
)

geom_dag_edges_fan(
  mapping = NULL,
  data = NULL,
  position = "identity",
  arrow = grid::arrow(length = grid::unit(5, "pt"), type = "closed"),
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  spread = 0.7,
  n = 100,
  lineend = "butt",
  linejoin = "round",
  linemitre = 1,
  label_colour = "black",
  label_alpha = 1,
  label_parse = FALSE,
  check_overlap = FALSE,
  angle_calc = "rot",
  force_flip = TRUE,
  label_dodge = NULL,
  label_push = NULL,
  ...
)

Arguments

Aesthetics

geom_dag_edges_link, geom_dag_edges_arc, geom_dag_edges_diagonal, and geom_dag_edges_fan understand the following aesthetics. Bold aesthetics are required.

• x

• y

• xend

• yend

• edge_colour

• edge_width

• edge_linetype

• edge_alpha

• start_cap

• end_cap

• label

• label_pos

• label_size

• angle

• hjust

• vjust

• family

• fontface

• lineheight

geom_dag_edges_arc and geom_dag_edges_diagonal also require circular, but this is automatically set.

geom_dag_edges_fan requires to and from, but these are also automatically set.

Examples

library(ggplot2)
p <- dagify(
  y ~ x + z2 + w2 + w1,
  x ~ z1 + w1,
  z1 ~ w1 + v,
  z2 ~ w2 + v,
  L ~ w1 + w2
) %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_point() +
  geom_dag_text() +
  theme_dag()

p + geom_dag_edges_link()
p + geom_dag_edges_arc()
p + geom_dag_edges_diagonal()
p + geom_dag_edges_fan()

Description

Label or otherwise retrieve labels from objects of either class tidy_dagitty or dagitty

Usage

label(x) <- value

## S3 replacement method for class 'dagitty'
label(x) <- value

## S3 replacement method for class 'tidy_dagitty'
label(x) <- value

dag_label(.tdy_dag, labels = NULL)

label(.tdy_dag)

has_labels(.tdy_dag)

Arguments

Value

label returns the label attribute of x

Examples

labelled_dag <- dagify(y ~ z, x ~ z) %>%
  tidy_dagitty() %>%
  dag_label(labels = c("x" = "exposure", "y" = "outcome", "z" = "confounder"))

has_labels(labelled_dag)

Description

dagify() creates dagitty DAGs using a more R-like syntax. It currently accepts formulas in the usual R style, e.g. y ~ x + z, which gets translated to ⁠y <- {x z}⁠, as well as using a double tilde (⁠~~⁠) to graph bidirected variables, e.g. x1 ~~ x2 is translated to ⁠x1 <-> x2⁠.

Usage

dagify(
  ...,
  exposure = NULL,
  outcome = NULL,
  latent = NULL,
  labels = NULL,
  coords = NULL
)

Arguments

Value

a dagitty DAG

See Also

dag(), coords2df(), coords2list()

Examples

dagify(y ~ x + z, x ~ z)

coords <- list(
  x = c(A = 1, B = 2, D = 3, C = 3, F = 3, E = 4, G = 5, H = 5, I = 5),
  y = c(A = 0, B = 0, D = 1, C = 0, F = -1, E = 0, G = 1, H = 0, I = -1)
)

dag <- dagify(G ~ ~H,
  G ~ ~I,
  I ~ ~G,
  H ~ ~I,
  D ~ B,
  C ~ B,
  I ~ C + F,
  F ~ B,
  B ~ A,
  H ~ E,
  C ~ E + G,
  G ~ D,
  coords = coords
)

dagitty::is.dagitty(dag)

ggdag(dag)

dag2 <- dagify(y ~ x + z2 + w2 + w1,
  x ~ z1 + w1,
  z1 ~ w1 + v,
  z2 ~ w2 + v,
  w1 ~ ~w2,
  exposure = "x",
  outcome = "y"
)

ggdag(dag2)

Description

Dplyr verb methods for tidy_dagitty objects.

Usage

## S3 method for class 'tidy_dagitty'
select(.data, ...)

## S3 method for class 'tidy_dagitty'
filter(.data, ...)

## S3 method for class 'tidy_dagitty'
mutate(.data, ...)

## S3 method for class 'tidy_dagitty'
summarise(.data, ...)

## S3 method for class 'tidy_dagitty'
distinct(.data, ..., .keep_all = FALSE)

## S3 method for class 'tidy_dagitty'
arrange(.data, ...)

## S3 method for class 'tidy_dagitty'
group_by(.data, ...)

## S3 method for class 'tidy_dagitty'
ungroup(x, ...)

## S3 method for class 'tidy_dagitty'
transmute(.data, ...)

## S3 method for class 'tidy_dagitty'
distinct(.data, ..., .keep_all = FALSE)

## S3 method for class 'tidy_dagitty'
full_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

## S3 method for class 'tidy_dagitty'
inner_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

## S3 method for class 'tidy_dagitty'
left_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

## S3 method for class 'tidy_dagitty'
right_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)

## S3 method for class 'tidy_dagitty'
anti_join(x, y, by = NULL, copy = FALSE, ...)

## S3 method for class 'tidy_dagitty'
semi_join(x, y, by = NULL, copy = FALSE, ...)

## S3 method for class 'tidy_dagitty'
slice(.data, ..., .dots = list())

## S3 method for class 'tidy_dagitty'
select_(.data, ..., .dots = list())

## S3 method for class 'tidy_dagitty'
filter_(.data, ..., .dots = list())

## S3 method for class 'tidy_dagitty'
mutate_(.data, ..., .dots = list())

## S3 method for class 'tidy_dagitty'
summarise_(.data, ..., .dots = list())

## S3 method for class 'tidy_dagitty'
arrange_(.data, ..., .dots = list())

## S3 method for class 'tidy_dagitty'
slice_(.data, ..., .dots = list())

Arguments

Examples

library(dplyr)
tidy_dagitty(m_bias()) %>%
  group_by(name) %>%
  summarize(n = n())

Description

Returns a set of complete partially directed acyclic graphs (CPDAGs) given an input DAG. CPDAGs are Markov equivalent to the input graph. See dagitty::equivalentDAGs() for details. node_equivalent_dags() returns a set of DAGs, while node_equivalent_class() tags reversable edges. ggdag_equivalent_dags() plots all equivalent DAGs, while ggdag_equivalent_class() plots all reversable edges as undirected.

Usage

node_equivalent_dags(.dag, n = 100, layout = "auto", ...)

ggdag_equivalent_dags(
  .tdy_dag,
  ...,
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = "black",
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

node_equivalent_class(.dag, layout = "auto")

ggdag_equivalent_class(
  .tdy_dag,
  expand_x = expansion(c(0.1, 0.1)),
  expand_y = expansion(c(0.1, 0.1)),
  breaks = ggplot2::waiver(),
  ...,
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

Arguments

Value

a tidy_dagitty with at least one DAG, including a dag column to identify graph set for equivalent DAGs or a reversable column for equivalent classes, or a ggplot

Examples

g_ex <- dagify(y ~ x + z, x ~ z)

g_ex %>% node_equivalent_class()

g_ex %>% ggdag_equivalent_dags()

Description

node_exogenous tags exogenous variables given an exposure and outcome. ggdag_exogenous plots all exogenous variables. See dagitty::exogenousVariables() for details.

Usage

node_exogenous(.dag, ...)

ggdag_exogenous(
  .tdy_dag,
  ...,
  node_size = 16,
  text_size = 3.88,
  edge_type = "link_arc",
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

Arguments

Value

a tidy_dagitty with an exogenous column for exogenous variables or a ggplot

Examples

dag <- dagify(y ~ x1 + x2 + x3, b ~ x1 + x2)
ggdag_exogenous(dag)
node_exogenous(dag)

Description

expand_plot() is a convenience function that expands the scales of a ggplot, as the large node sizes in a DAG will often get clipped in themes that don't have DAGs in mind.

Usage

expand_plot(
  expand_x = expansion(c(0.1, 0.1)),
  expand_y = expansion(c(0.1, 0.1))
)

Arguments

Description

Fortify a tidy_dagitty object for ggplot2

Usage

## S3 method for class 'tidy_dagitty'
fortify(model, data = NULL, ...)

## S3 method for class 'dagitty'
fortify(model, data = NULL, ...)

Arguments

Description

Adjusting for a collider activates pathways between the parent of the collider. This geom adds a curved edge between any such parent nodes.

Usage

geom_dag_collider_edges(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  linewidth = 0.6,
  size = NULL,
  curvature = 0.5,
  angle = 90,
  ncp = 5,
  arrow = NULL,
  lineend = "butt",
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Examples

library(dagitty)
library(ggplot2)
dagify(m ~ a + b, x ~ a, y ~ b) %>%
  tidy_dagitty() %>%
  control_for("m") %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend, shape = adjusted)) +
  geom_dag_edges() +
  geom_dag_collider_edges() +
  geom_dag_point() +
  geom_dag_text() +
  theme_dag() +
  scale_adjusted()

Description

Directed and bidirected DAG edges

Usage

geom_dag_edges(
  mapping = NULL,
  data_directed = filter_direction("->"),
  data_bidirected = filter_direction("<->"),
  curvature = 0.3,
  arrow_directed = grid::arrow(length = grid::unit(5, "pt"), type = "closed"),
  arrow_bidirected = grid::arrow(length = grid::unit(5, "pt"), ends = "both", type =
    "closed"),
  position = "identity",
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  fold = FALSE,
  ...
)

Arguments

Aesthetics

geom_dag_edges understand the following aesthetics. Bold aesthetics are required.

• x

• y

• xend

• yend

• edge_colour

• edge_width

• edge_linetype

• edge_alpha

• start_cap

• end_cap

• label

• label_pos

• label_size

• angle

• hjust

• vjust

• family

• fontface

• lineheight

geom_dag_edges also uses geom_dag_edges_arc, which requires the circular aesthetic, but this is automatically set.

Examples

library(ggplot2)
dagify(
  y ~ x + z2 + w2 + w1,
  x ~ z1 + w1,
  z1 ~ w1 + v,
  z2 ~ w2 + v,
  w1 ~ ~w2
) %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_edges() +
  geom_dag_point() +
  geom_dag_text() +
  theme_dag()

Description

Node text labels

Usage

geom_dag_label(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  parse = FALSE,
  nudge_x = 0,
  nudge_y = 0,
  check_overlap = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Aesthetics

geom_dag_label understand the following aesthetics (required aesthetics are in bold):

• x

• y

• label

• alpha

• angle

• colour

• family

• fontface

• group

• hjust

• lineheight

• size

• vjust

Examples

library(ggplot2)
library(ggraph)
g <- dagify(m ~ x + y, y ~ x)

ggdag(g, text = FALSE) + geom_dag_label()

g %>%
  tidy_dagitty() %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_edges(aes(
    start_cap = label_rect(name, padding = margin(2.5, 2.5, 2.5, 2.5, "mm")),
    end_cap = label_rect(name, padding = margin(2.5, 2.5, 2.5, 2.5, "mm"))
  )) +
  geom_dag_label(size = 5, fill = "black", color = "white") +
  theme_dag()

Description

Node text

Usage

geom_dag_text(
  mapping = NULL,
  data = NULL,
  stat = "identity",
  position = "identity",
  ...,
  parse = FALSE,
  nudge_x = 0,
  nudge_y = 0,
  check_overlap = FALSE,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Aesthetics

geom_dag_text understand the following aesthetics (required aesthetics are in bold):

• x

• y

• label

• alpha

• angle

• colour

• family

• fontface

• group

• hjust

• lineheight

• size

• vjust

Examples

library(ggplot2)
g <- dagify(m ~ x + y, y ~ x)
g %>%
  tidy_dagitty() %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_point() +
  geom_dag_edges() +
  geom_dag_text() +
  theme_dag()

Description

ggdag() is a wrapper to quickly plot DAGs.

Usage

ggdag(
  .tdy_dag,
  ...,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = "black",
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

Arguments

Value

a ggplot

See Also

ggdag_classic()

Examples

dag <- dagify(
  y ~ x + z2 + w2 + w1,
  x ~ z1 + w1,
  z1 ~ w1 + v,
  z2 ~ w2 + v,
  w1 ~ ~w2
)

ggdag(dag)
ggdag(dag) + theme_dag_blank()

ggdag(dagitty::randomDAG(5, .5))

Description

ggdag_classic() is a wrapper to quickly plot DAGs in a more traditional style.

Usage

ggdag_classic(
  .tdy_dag,
  ...,
  size = 8,
  label_rect_size = NULL,
  text_label = "name",
  text_col = "black"
)

Arguments

Value

a ggplot

See Also

ggdag()

Examples

dag <- dagify(
  y ~ x + z2 + w2 + w1,
  x ~ z1 + w1,
  z1 ~ w1 + v,
  z2 ~ w2 + v,
  w1 ~ ~w2
)

ggdag_classic(dag)
ggdag_classic(dag) + theme_dag_blank()

ggdag_classic(dagitty::randomDAG(5, .5))

Description

Create a new ggplot

Usage

## S3 method for class 'tidy_dagitty'
ggplot(data = NULL, mapping = aes(), ...)

## S3 method for class 'dagitty'
ggplot(data = NULL, mapping = aes(), ...)

Arguments

Description

These functions are minor modifications of those in the ggrepel package. geom_dag_text_repel adds text directly to the plot. geom_dag_label_repel draws a rectangle underneath the text, making it easier to read. The text labels repel away from each other and away from the data points.

Usage

geom_dag_text_repel(
  mapping = NULL,
  data = NULL,
  parse = FALSE,
  ...,
  box.padding = 0.35,
  point.padding = 1.5,
  segment.color = "#666666",
  fontface = "bold",
  segment.size = 0.5,
  arrow = NULL,
  force = 1,
  max.iter = 2000,
  nudge_x = 0,
  nudge_y = 0,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_dag_label_repel(
  mapping = NULL,
  data = NULL,
  parse = FALSE,
  ...,
  box.padding = grid::unit(0.35, "lines"),
  label.padding = grid::unit(0.25, "lines"),
  point.padding = grid::unit(1.5, "lines"),
  label.r = grid::unit(0.15, "lines"),
  label.size = 0.25,
  segment.color = "grey50",
  segment.size = 0.5,
  arrow = NULL,
  force = 1,
  max.iter = 2000,
  nudge_x = 0,
  nudge_y = 0,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Examples

library(ggplot2)
g <- dagify(m ~ x + y,
  y ~ x,
  exposure = "x",
  outcome = "y",
  latent = "m",
  labels = c("x" = "Exposure", "y" = "Outcome", "m" = "Collider")
)

g %>%
  tidy_dagitty() %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_edges() +
  geom_dag_point() +
  geom_dag_text_repel(aes(label = name), show.legend = FALSE) +
  theme_dag()

g %>%
  tidy_dagitty() %>%
  dag_label(labels = c(
    "x" = "This is the exposure",
    "y" = "Here's the outcome",
    "m" = "Here is where they collide"
  )) %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_edges() +
  geom_dag_point() +
  geom_dag_text() +
  geom_dag_label_repel(aes(label = label, fill = label),
    col = "white", show.legend = FALSE
  ) +
  theme_dag()

Description

node_instrumental tags instrumental variables given an exposure and outcome. ggdag_instrumental plots all instrumental variables. See dagitty::instrumentalVariables() for details.

Usage

node_instrumental(.dag, exposure = NULL, outcome = NULL, ...)

ggdag_instrumental(
  .tdy_dag,
  exposure = NULL,
  outcome = NULL,
  ...,
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

Arguments

Value

a tidy_dagitty with an instrumental column for instrumental variables or a ggplot

Examples

library(dagitty)

node_instrumental(dagitty("dag{ i->x->y; x<->y }"), "x", "y")
ggdag_instrumental(dagitty("dag{ i->x->y; i2->x->y; x<->y }"), "x", "y")

Description

Assess if a variable confounds a relationship

Usage

is_confounder(.tdy_dag, z, x, y, direct = FALSE)

Arguments

Value

Logical. Is the variable a confounder?

Examples

dag <- dagify(y ~ z, x ~ z)

is_confounder(dag, "z", "x", "y")
is_confounder(dag, "x", "z", "y")

Description

Test for object class for tidy_dagitty

Usage

is.tidy_dagitty(x)

Arguments

Description

geom_dag_node and geom_dag_point are very similar to ggplot2::geom_point but with a few defaults changed. geom_dag_node is slightly stylized and includes an internal white circle, while geom_dag_point plots a single point.

Usage

geom_dag_node(
  mapping = NULL,
  data = NULL,
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

geom_dag_point(
  mapping = NULL,
  data = NULL,
  position = "identity",
  ...,
  na.rm = FALSE,
  show.legend = NA,
  inherit.aes = TRUE
)

Arguments

Aesthetics

geom_dag_node and geom_dag_point understand the following aesthetics (required aesthetics are in bold):

• x

• y

• alpha

• colour

• fill

• shape

• size

• stroke

• filter

geom_dag_node also accepts:

• internal_colour

Examples

library(ggplot2)
g <- dagify(m ~ x + y, y ~ x)
p <- g %>%
  tidy_dagitty() %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_edges() +
  theme_dag()

p +
  geom_dag_node() +
  geom_dag_text()

p +
  geom_dag_point() +
  geom_dag_text()

Description

dag_paths finds open paths between a given exposure and outcome. ggdag_paths and ggdag_paths_fan plot all open paths. See dagitty::paths() for details.

Usage

dag_paths(
  .dag,
  from = NULL,
  to = NULL,
  adjust_for = NULL,
  limit = 100,
  directed = FALSE,
  paths_only = FALSE,
  ...
)

ggdag_paths(
  .tdy_dag,
  from = NULL,
  to = NULL,
  adjust_for = NULL,
  limit = 100,
  directed = FALSE,
  shadow = FALSE,
  ...,
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

ggdag_paths_fan(
  .tdy_dag,
  from = NULL,
  to = NULL,
  adjust_for = NULL,
  limit = 100,
  directed = FALSE,
  ...,
  shadow = FALSE,
  spread = 0.7,
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

Arguments

Value

a tidy_dagitty with a path column for path variables and a set grouping column or a ggplot.

Examples

confounder_triangle(x_y_associated = TRUE) %>%
  dag_paths(from = "x", to = "y")

confounder_triangle(x_y_associated = TRUE) %>%
  ggdag_paths(from = "x", to = "y")

butterfly_bias(x_y_associated = TRUE) %>%
  ggdag_paths_fan(shadow = TRUE)

Description

Print a tidy_dagitty

Usage

## S3 method for class 'tidy_dagitty'
print(x, ...)

Arguments

Description

pull_dag() and pull_dag_data() are generic methods to pull components of DAG objects, e.g. tidy_dagitty, such as the dagitty object or the data frame associated with it. These methods are recommended over extracting components manually, e.g. my_dag$data, because the internal structure of these objects may change over time. Similarly, use update_dag() if you want to sync the data back to the DAG object or override it with another DAG; use update_dag_data() to do update the data frame. This is useful with pull_dag_data().

Usage

pull_dag(x, ...)

## S3 method for class 'tidy_dagitty'
pull_dag(x, ...)

## S3 method for class 'dagitty'
pull_dag(x, ...)

pull_dag_data(x, ...)

## S3 method for class 'tidy_dagitty'
pull_dag_data(x, ...)

## S3 method for class 'dagitty'
pull_dag_data(x, ...)

update_dag_data(x) <- value

## S3 replacement method for class 'tidy_dagitty'
update_dag_data(x) <- value

update_dag(x, ...)

update_dag(x) <- value

## S3 method for class 'tidy_dagitty'
update_dag(x, ...)

## S3 replacement method for class 'tidy_dagitty'
update_dag(x) <- value

Arguments

Value

a DAG object, e.g. dagitty, or data frame

Examples

tidy_dagitty_obj <- dagify(y ~ x + z, x ~ z) %>%
  tidy_dagitty()
dag <- pull_dag(tidy_dagitty_obj)
dag_data <- pull_dag_data(tidy_dagitty_obj)

tidy_dagitty_obj %>%
  dplyr::mutate(name = toupper(name)) %>%
  # recreate the DAG component
  update_dag()

dag_data$label <- paste0(dag_data$name, "(observed)")
update_dag_data(tidy_dagitty_obj) <- dag_data

Description

base functions create an object of class dagitty; ⁠ggdag_* ⁠ functions are wrappers that also call ggdag() on the dagitty object.

Usage

m_bias(
  x = NULL,
  y = NULL,
  a = NULL,
  b = NULL,
  m = NULL,
  x_y_associated = FALSE
)

butterfly_bias(
  x = NULL,
  y = NULL,
  a = NULL,
  b = NULL,
  m = NULL,
  x_y_associated = FALSE
)

confounder_triangle(x = NULL, y = NULL, z = NULL, x_y_associated = FALSE)

collider_triangle(x = NULL, y = NULL, m = NULL, x_y_associated = FALSE)

mediation_triangle(x = NULL, y = NULL, m = NULL, x_y_associated = FALSE)

ggdag_m_bias(
  x = NULL,
  y = NULL,
  a = NULL,
  b = NULL,
  m = NULL,
  x_y_associated = FALSE,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

ggdag_butterfly_bias(
  x = NULL,
  y = NULL,
  a = NULL,
  b = NULL,
  m = NULL,
  x_y_associated = FALSE,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

ggdag_confounder_triangle(
  x = NULL,
  y = NULL,
  z = NULL,
  x_y_associated = FALSE,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

ggdag_collider_triangle(
  x = NULL,
  y = NULL,
  m = NULL,
  x_y_associated = FALSE,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

ggdag_mediation_triangle(
  x = NULL,
  y = NULL,
  m = NULL,
  x_y_associated = FALSE,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

Arguments

Value

a DAG of class dagitty or a ggplot

Examples

m_bias() %>% ggdag_adjust("m")
ggdag_confounder_triangle()

Description

remove_axes() and remove_grid() are convenience functions that removes the axes and grids from a ggplot, respectively. This is useful when you want to use an existing theme, e.g. those included in ggplot2, for a DAG.

Usage

remove_axes()

remove_grid()

Examples

library(ggplot2)
ggdag(confounder_triangle()) +
  theme_bw() +
  remove_axes()

Description

scale_adjusted() is a convenience function that implements ways of visualizing adjustment for a variable. By convention, a square shape is used to indicate adjustment and a circle when not adjusted. Arrows out of adjusted variables are often eliminated or de-emphasized, and scale_adjusted() uses a lower alpha for these arrows. When adjusting a collider, a dashed line is sometimes used to demarcate opened pathways, and scale_adjusted() does this whenever geom_dag_collider_edges() is used. scale_dag() is deprecated in favor of scale_adjusted().

Usage

scale_adjusted()

scale_dag(breaks = ggplot2::waiver())

Arguments

Description

This is a thin wrapper for the simulateSEM()function in dagitty that works with tidied dagitty objects. It treats the input DAG as a structural equation model, generating random path coefficients and simulating corresponding data. See dagitty::simulateSEM() for details.

Usage

simulate_data(
  .tdy_dag,
  b.default = NULL,
  b.lower = -0.6,
  b.upper = 0.6,
  eps = 1,
  N = 500,
  standardized = TRUE
)

Arguments

Value

a tblwith N values for each variable in .tdy_dag

Examples

dagify(y ~ z, x ~ z) %>%
  tidy_dagitty() %>%
  simulate_data()

Description

Convert a tidy_dagitty object to tbl_df

Usage

tbl_df.tidy_daggity(.tdy_dag)

Arguments

Description

Detecting colliders in DAGs

Usage

is_collider(.dag, .var, downstream = TRUE)

is_downstream_collider(.dag, .var)

Arguments

Value

Logical. Is the variable a collider or downstream collider?

Examples

dag <- dagify(m ~ x + y, m_jr ~ m)
is_collider(dag, "m")
is_downstream_collider(dag, "m_jr")

#  a downstream collider is also treated as a collider
is_collider(dag, "m_jr")

#  but a direct collider is not treated as a downstream collider
is_downstream_collider(dag, "m")

Description

Minimalist DAG themes

Usage

theme_dag_blank(base_size = 12, base_family = "", ...)

theme_dag(base_size = 12, base_family = "", ...)

theme_dag_grid(base_size = 12, base_family = "", ...)

Arguments

Examples

ggdag(m_bias()) + theme_dag_blank() # the default

Description

Simple grey themes for DAGs

Usage

theme_dag_grey(base_size = 12, base_family = "", ...)

theme_dag_gray(base_size = 12, base_family = "", ...)

theme_dag_grey_grid(base_size = 12, base_family = "", ...)

theme_dag_gray_grid(base_size = 12, base_family = "", ...)

Arguments

Examples

ggdag(m_bias()) + theme_dag_grey()

Description

Tidy a dagitty object

Usage

tidy_dagitty(.dagitty, seed = NULL, layout = "nicely", ...)

Arguments

Value

a tidy_dagitty object

Examples

library(dagitty)
library(ggplot2)

dag <- dagitty("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_dagitty(dag, layout = "fr") %>%
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_node() +
  geom_dag_text() +
  geom_dag_edges() +
  theme_dag()

Description

time_ordered_coords() is a helper function to create time-ordered DAGs. Pass the results to the coords argument of dagify(). If .vars if not specified, these coordinates will be determined automatically. If you want to be specific, you can also use a list or data frame. The default is to assume you want variables to go from left to right in order by time. Variables are spread along the y-axis using a simple algorithm to stack them. You can also work along the y-axis by setting direction = "y".

Usage

time_ordered_coords(
  .vars = NULL,
  time_points = NULL,
  direction = c("x", "y"),
  auto_sort_direction = c("right", "left")
)

Arguments

Value

A tibble with three columns: name, x, and y.

See Also

dagify(), coords2df(), coords2list()

Examples

dagify(
  d ~ c1 + c2 + c3,
  c1 ~ b1 + b2,
  c3 ~ a,
  b1 ~ a,
  coords = time_ordered_coords()
) %>% ggdag()

coords <- time_ordered_coords(list(
  # time point 1
  "a",
  # time point 2
  c("b1", "b2"),
  # time point 3
  c("c1", "c2", "c3"),
  # time point 4
  "d"
))

dagify(
  d ~ c1 + c2 + c3,
  c1 ~ b1 + b2,
  c3 ~ a,
  b1 ~ a,
  coords = coords
) %>% ggdag()

# or use a data frame
x <- data.frame(
  name = c("x1", "x2", "y", "z1", "z2", "z3", "a"),
  time = c(1, 1, 2, 3, 3, 3, 4)
)
dagify(
  z3 ~ y,
  y ~ x1 + x2,
  a ~ z1 + z2 + z3,
  coords = time_ordered_coords(x)
) %>%
  ggdag()

Description

Detects variable status given a DAG (exposure, outcome, latent). See dagitty::VariableStatus() for details.

Usage

node_status(.dag, as_factor = TRUE, ...)

ggdag_status(
  .tdy_dag,
  ...,
  edge_type = "link_arc",
  node_size = 16,
  text_size = 3.88,
  label_size = text_size,
  text_col = "white",
  label_col = text_col,
  node = TRUE,
  stylized = FALSE,
  text = TRUE,
  use_labels = NULL
)

Arguments

Details

node_collider tags variable status and ggdag_collider plots all variable statuses.

Value

a tidy_dagitty with a status column for variable status or a ggplot

Examples

dag <- dagify(l ~ x + y,
  y ~ x,
  exposure = "x",
  outcome = "y",
  latent = "l"
)

node_status(dag)
ggdag_status(dag)