Create a ggplot from the output of summariseCharacteristics.

Usage

plotCharacteristics(
  result,
  plotType = "barplot",
  facet = NULL,
  colour = NULL,
  style = "default",
  plotStyle = lifecycle::deprecated()
)

Arguments

result

A summarised_result object.

plotType

Either barplot, scatterplot or boxplot. If barplot or scatterplot subset to just one estimate.

facet

Columns to facet by. See options with availablePlotColumns(result). Formula is also allowed to specify rows and columns.

colour

Columns to color by. See options with availablePlotColumns(result).

style

Named list that specifies how to style the different parts of the table generated. It can either be a pre-defined style ("default" or "darwin" - the latter just for gt and flextable), NULL to get the table default style, or custom. Keep in mind that styling code is different for all table styles. To see the different styles see visOmopResults::tableStyle().

plotStyle

deprecated.

Value

A ggplot.

Examples

# \donttest{
library(CohortCharacteristics)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockCohortCharacteristics()

results <- summariseCharacteristics(
  cohort = cdm$cohort1,
  ageGroup = list(c(0, 19), c(20, 39), c(40, 59), c(60, 79), c(80, 150)),
  tableIntersectCount = list(
    tableName = "visit_occurrence", window = c(-365, -1)
  ),
  cohortIntersectFlag = list(
    targetCohortTable = "cohort2", window = c(-365, -1)
  )
)
#> ℹ adding demographics columns
#> ℹ adding tableIntersectCount 1/1
#> window names casted to snake_case:
#> • `-365 to -1` -> `365_to_1`
#> ℹ adding cohortIntersectFlag 1/1
#> window names casted to snake_case:
#> • `-365 to -1` -> `365_to_1`
#> ℹ summarising data
#> ℹ summarising cohort cohort_1
#> ℹ summarising cohort cohort_2
#> ℹ summarising cohort cohort_3
#> ✔ summariseCharacteristics finished!

results |>
  filter(
    variable_name == "Cohort2 flag -365 to -1", estimate_name == "percentage"
  ) |>
  plotCharacteristics(
    plotType = "barplot",
    colour = "variable_level",
    facet = c("cdm_name", "cohort_name")
  )
#> Warning: Ignoring empty aesthetic: `width`.


results |>
  filter(variable_name == "Age", estimate_name == "mean") |>
  plotCharacteristics(
    plotType = "scatterplot",
    facet = "cdm_name"
  )


results |>
  filter(variable_name == "Age", group_level == "cohort_1") |>
  plotCharacteristics(
    plotType = "boxplot",
    facet = "cdm_name",
    colour = "cohort_name"
  )
#> Ignoring unknown labels:
#> • fill : "Cohort name"
#> Warning: `label` cannot be a <ggplot2::element_blank> object.


# }