Create a ggplot from the output of summariseCharacteristics.

Usage

plotCharacteristics(result, plotStyle = "barplot", facet = NULL, colour = NULL)

Arguments

result

A summariseCharacteristics result.

plotStyle

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

facet

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

colour

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

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
#> ℹ adding cohortIntersectFlag 1/1
#> ℹ summarising data
#> ✔ summariseCharacteristics finished!

results |>
  filter(
    variable_name == "Cohort2 flag -365 to -1", estimate_name == "percentage"
  ) |>
  plotCharacteristics(
    plotStyle = "barplot",
    colour = "variable_level",
    facet = c("cdm_name", "cohort_name")
  )
#> [1] "variable_level"


results |>
  filter(variable_name == "Age", estimate_name == "mean") |>
  plotCharacteristics(
    plotStyle = "scatterplot",
    facet = "cdm_name"
  )
#> [1] "cohort_name"


results |>
  filter(variable_name == "Age") |>
  plotCharacteristics(
    plotStyle = "boxplot",
    facet = "cdm_name",
    colour = "cohort_name"
  )


mockDisconnect(cdm)
# }