mc_heatmap {metaconfoundr}

Plot a heatmap or traffic light plot of metaconfoundr() summaries

Description

mc_heatmap() and mc_trafficlight() visualize the results of metaconfoundr(), summarizing the quality of confounder control in each study.

Usage

mc_heatmap(
  .df,
  legend_title = "control quality",
  sort = FALSE,
  by_group = FALSE,
  score = c("adequate", "sum", "controlled"),
  non_confounders = FALSE
)

mc_trafficlight(
  .df,
  size = 8,
  legend_title = "control quality",
  sort = FALSE,
  by_group = FALSE,
  score = c("adequate", "sum", "controlled"),
  non_confounders = FALSE
)

Arguments

.df

A data frame, usually the result of metaconfoundr()

legend_title

The legend title

sort

Logical. Sort by confounder score? Calculated by score_control()

by_group

Logical. If sorted, sort within domain?

score

The approach used to calculate the score. adequate tests if the study controlled at a strictly adequate level. sum treats control_quality as an ordinal integer, summing it's values such that a higher score has better control overall. controlled tests if any control, including ⁠some concerns⁠ control, is present.

non_confounders

Logical. Include non-confounders? Default is FALSE.

size

The size of the points in the traffic light plot

Value

a ggplot

See Also

Other plots: facet_constructs(), geom_cochrane(), scale_fill_cochrane(), theme_mc()

Examples

ipi %>%
  metaconfoundr() %>%
  dplyr::mutate(variable = stringr::str_wrap(variable, 10)) %>%
  mc_heatmap() +
  theme_mc() +
  facet_constructs() +
  ggplot2::guides(x = ggplot2::guide_axis(n.dodge = 2))

ipi %>%
  metaconfoundr() %>%
  mc_trafficlight() +
  geom_cochrane() +
  facet_constructs() +
  scale_fill_cochrane() +
  theme_mc() +
  ggplot2::guides(x = ggplot2::guide_axis(n.dodge = 2))