Plots"

In visOmopResults: Graphs and Tables for OMOP Results

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  warning = FALSE,
  message = FALSE,
  fig.width=7.2, 
  fig.height=5
)
options(rmarkdown.html_vignette.check_title = FALSE)

visOmopResults provides plotting tools that simplify visualising data in <summarised_result> format while also being compatible with other formats.

library(visOmopResults)

Plotting with a <summarised_result>

For this vignette, we will use the penguins dataset from the palmerpenguins package. This dataset will be summarised using the PatientProfiles::summariseResult() function, which aggregates the data into the <summarised_result> format:

library(PatientProfiles)
library(palmerpenguins)
library(dplyr)

summariseIsland <- function(island) {
  penguins |>
    filter(.data$island == .env$island) |>
    summariseResult(
      group = "species",
      includeOverallGroup = TRUE,
      strata = list("year", "sex", c("year", "sex")),
      variables = c(
        "bill_length_mm", "bill_depth_mm", "flipper_length_mm", "body_mass_g", 
        "sex"),
      estimates = c(
        "median", "q25", "q75", "min", "max", "count_missing", "count", 
        "percentage", "density")
    ) |>
    suppressMessages() |>
    mutate(cdm_name = island)
}

penguinsSummary <- bind(
  summariseIsland("Torgersen"), 
  summariseIsland("Biscoe"), 
  summariseIsland("Dream")
)

penguinsSummary |> glimpse()

Plotting principles for <summarised_result> objects

1) Tidy Format
When working with <summarised_result> objects, the data is internally converted into the tidy format before plotting. This is an important distinction because columns such as strata_name and strata_level from the original <summarised_result> cannot be used directly with the plotting functions. Instead, tidy columns should be referenced.

For more information about the tidy format, refer to the omopgenerics package vignette on <summarised_result> here.

To identify the available tidy columns, use the tidyColumns() function:

tidyColumns(penguinsSummary)

2) Subsetting Variables
Before calling the plotting functions, always subset the <summarised_result> object to the variable of interest. Avoid combining results from unrelated variables, as this may lead to NA values in the tidy format, which can affect your plots.

Scatter plot

We can create simple scatter plots using the plotScatter() let's see some examples:

penguinsSummary |>
  filter(variable_name == "bill_depth_mm") |>
  filterStrata(year != "overall", sex == "overall") |>
  scatterPlot(
    x = "year", 
    y = "median",
    line = TRUE, 
    point = TRUE,
    ribbon = FALSE,
    facet = "cdm_name",
    colour = "species"
  )

Additionally, we can use the function themeVisOmop() to change the default ggplot2 style to our default style. Not only that, but we can use standard ggplot2 functionalities to the returned plot:

penguinsSummary |>
  filter(variable_name %in% c("bill_length_mm", "bill_depth_mm"))|>
  filterStrata(year == "overall", sex == "overall") |>
  filterGroup(species != "overall") |>
  scatterPlot(
    x = "density_x", 
    y = "density_y",
    line = TRUE, 
    point = FALSE,
    ribbon = FALSE,
    facet = cdm_name ~ variable_name,
    colour = "species"
  ) +
  themeVisOmop() +
  ggplot2::facet_grid(cdm_name ~ variable_name, scales = "free_x") 

penguinsSummary |>
  filter(variable_name == "flipper_length_mm") |>
  filterStrata(year != "overall", sex %in% c("female", "male")) |>
  scatterPlot(
    x = c("year", "sex"), 
    y = "median",
    ymin = "q25",
    ymax = "q75",
    line = FALSE, 
    point = TRUE,
    ribbon = FALSE,
    facet = cdm_name ~ species,
    colour = "sex",
    group = c("year", "sex")
  )  +
  themeVisOmop() +
  ggplot2::coord_flip() +
  ggplot2::labs(y = "Flipper length (mm)") + 
  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust=1))

penguinsSummary |>
  filter(
    variable_name %in% c("flipper_length_mm", "bill_length_mm", "bill_depth_mm")
  ) |>
  filterStrata(sex == "overall") |>
  scatterPlot(
    x = "year", 
    y = "median",
    ymin = "min",
    ymax = "max",
    line = FALSE, 
    point = TRUE,
    ribbon = TRUE,
    facet = cdm_name ~ species,
    colour = "variable_name",
    group = c("variable_name")
  ) +
  themeVisOmop(fontsizeRef = 12) + 
  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust=1))

Bar plot

Let's create a bar plots:

penguinsSummary |>
  filter(variable_name == "number records") |>
  filterGroup(species != "overall") |>
  filterStrata(sex != "overall", year != "overall") |>
  barPlot(
    x = "year",
    y = "count",
    colour = "sex",
    facet = cdm_name ~ species
  ) +
  themeVisOmop(fontsizeRef = 12)

Box plot

Let's create some box plots of their body mass:

penguinsSummary |>
  filter(variable_name == "body_mass_g") |>
  boxPlot(x = "year", facet = species ~ cdm_name, colour = "sex") +
  themeVisOmop()

penguinsSummary |>
  filter(variable_name == "body_mass_g") |>
  filterGroup(species != "overall") |>
  filterStrata(sex %in% c("female", "male"), year != "overall") |>
  boxPlot(x = "cdm_name", facet = c("sex", "species"), colour = "year") +
  themeVisOmop(fontsizeRef = 11)

Note that as we didnt specify x there is no levels in the x axis, but box plots are produced anyway.

Plotting with a <data.frame>

Plotting functions can also be used with the usual <data.frame>. In this case we will use the tidy format of penguinsSummary.

penguinsTidy <- penguinsSummary |>
  filter(!estimate_name %in% c("density_x", "density_y")) |> # remove density for simplicity
  tidy()
penguinsTidy |> glimpse()

Using this tidy format, we can replicate plots. For instance, we recreate the previous example:

penguinsTidy |>
  filter(
    variable_name == "body_mass_g",
    species != "overall",
    sex %in% c("female", "male"),
    year != "overall"
  ) |>
  boxPlot(x = "cdm_name", facet = sex ~ species, colour = "year") +
  themeVisOmop()

Custom plotting

The tidy format is very useful to apply any other custom ggplot2 function that we may be interested on:

library(ggplot2)
penguinsSummary |>
  filter(variable_name == "number records") |>
  tidy() |>
  ggplot(aes(x = year, y = sex, fill = count, label = count)) +
  themeVisOmop() +
  geom_tile() +
  scale_fill_viridis_c(trans = "log") + 
  geom_text() +
  facet_grid(cdm_name ~ species) + 
  ggplot2::theme(axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5, hjust=1))

Combine with ggplot2

The plotting functions are a wrapper around the ggplot2 package, outputs of the plotting functions can be later customised with ggplot2 and similar tools. For example we can use ggplot2::labs() to change the labels and ggplot2::theme() to move the location of the legend.

penguinsSummary |>
  filter(
    group_level != "overall",
    strata_name == "year &&& sex",
    !grepl("NA", strata_level),
    variable_name == "body_mass_g") |>
  boxPlot(x = "species", facet = cdm_name ~ sex, colour = "year") +
  themeVisOmop(fontsizeRef = 12) +
  ylim(c(0, 6500)) +
  labs(x = "My custom x label")

You can also use ggplot2::ggsave() to later save one of this plots into '.png' file.

ggsave(
  "figure8.png", plot = last_plot(), device = "png", width = 15, height = 12, 
  units = "cm", dpi = 300)

Combine with plotly

Although the package currently does not provide any plotly functionality ggplots can be easily converted to <plotly> ones using the function plotly::ggplotly(). This can make the interactivity of some plots better.

visOmopResults documentation built on April 4, 2025, 2:33 a.m.