# Quarto reports

visOmopResults supports table and plot creation functions that can be used to generate publication-ready figures and tables. As an example, in this vignette we show how to create tables and figures for a DARWIN-EU study report.

# Quarto document settings

In the header of the Quarto document, we can set a title to identify the study and the document. Additionally, we can point to a Word document containing the template styles for DARWIN reports. The DOCX template can be found here, together with the Quarto script used to generate the report.

A Quarto header for producing a Word report might look like this:

    ---
    title: "DARWIN-EU PX-CY-Z: Tables and Figures"
    format:
      docx:
        reference-doc: inst/darwinReportRef.docx
        fig-cap-location: top
    execute:
      echo: false
      message: false
      warning: false
    lof: true
    ---

Next, we use the first R chunk to load packages, load data, and set options/variables used across the script. The results we use are obtained from mock data created with the `IncidencePrevalence` and `CohortCharacteristics` packages, which we have stored in the [inst](https://github.com/darwin-eu/visOmopResults/tree/main/inst) folder of the packge.

    # Load necessary packages ----
    library(visOmopResults)
    library(IncidencePrevalence)
    library(CohortCharacteristics)
    library(dplyr)
    library(tidyr)
    library(ggplot2)

    # Load mock results stored in the package ----
    data <- visOmopResults::data

    # Global options ----
    knitr::opts_chunk$set(
      out.width  = "95%",  # figures occupy ~95% of document width
      out.height = "auto",
      dpi        = 320,    # ensure figure quality
      fig.width  = 6,      # default aspect ratio (can be overridden per-figure)
      fig.height = 3,
      results    = "asis"  # enable Markdown produced via cat() inside chunks
    )

    # DARWIN style for visOmopResults plots and tables.
    style <- "darwin"
    tableType <- "flextable"
    plotType  <- "ggplot"
    setGlobalPlotOptions(style = style, type = plotType)
    setGlobalTableOptions(style = style, type = tableType)

    # Calibri font in ggplot figures (requires the extrafont package to be available)
    # Read vignette on styles to learn more
    requireExtrafont()

For tables we choose the `"flextable"` type because it transfers best to Word documents.

The function `requireExtrafont()` installs (if needed) the extrafont package and loads the Calibri font for use with ggplot2 when using the DARWIN style.

# Characterisation results

The report starts with a table showing baseline characteristics of the denominator cohort in our mock study.

    data$summarised_characteristics |>
      dplyr::filter(variable_name != "Sex") |>
      tableCharacteristics(
        header = c("sex"),
        hide   = c("cdm_name", "cohort_name", "table_name"),
        type   = tableType,
        .options = list(style = style)
      )

Variable name | Variable level | Estimate name | Sex
--- | --- | --- | ---
overall | Female | Male
Number records | – | N | 2,564 | 1,302 | 1,262
Number subjects | – | N | 2,564 | 1,302 | 1,262
Cohort start date | – | Median [Q25 - Q75] | 2008-01-01 [2008-01-01 - 2008-01-01] | 2008-01-01 [2008-01-01 - 2008-01-01] | 2008-01-01 [2008-01-01 - 2008-01-01]
|  | Range | 2008-01-01 to 2008-01-01 | 2008-01-01 to 2008-01-01 | 2008-01-01 to 2008-01-01
Cohort end date | – | Median [Q25 - Q75] | 2018-01-01 [2018-01-01 - 2018-01-01] | 2018-01-01 [2018-01-01 - 2018-01-01] | 2018-01-01 [2018-01-01 - 2018-01-01]
|  | Range | 2008-01-17 to 2018-01-01 | 2008-01-17 to 2018-01-01 | 2009-03-24 to 2018-01-01
Age | – | Median [Q25 - Q75] | 46 [36 - 56] | 45 [36 - 55] | 46 [37 - 56]
|  | Mean (SD) | 47.54 (14.81) | 47.52 (15.21) | 47.57 (14.40)
|  | Range | 21 to 99 | 21 to 99 | 21 to 98
Prior observation | – | Median [Q25 - Q75] | 16,834 [13,477 - 20,588] | 16,734 [13,320 - 20,410] | 16,932 [13,636 - 20,634]
|  | Mean (SD) | 17,545.28 (5,406.72) | 17,535.78 (5,551.58) | 17,555.07 (5,255.26)
|  | Range | 7,729 to 36,260 | 7,929 to 36,260 | 7,729 to 36,115
Future observation | – | Median [Q25 - Q75] | 4,010 [3,891 - 4,117] | 4,018 [3,900 - 4,118] | 4,005 [3,878 - 4,116]
|  | Mean (SD) | 3,924.53 (418.47) | 3,937.65 (405.74) | 3,910.98 (430.96)
|  | Range | 16 to 4,201 | 16 to 4,199 | 448 to 4,201
Days in cohort | – | Median [Q25 - Q75] | 3,654 [3,654 - 3,654] | 3,654 [3,654 - 3,654] | 3,654 [3,654 - 3,654]
|  | Mean (SD) | 3,594.61 (332.90) | 3,601.79 (324.60) | 3,587.20 (341.22)
|  | Range | 17 to 3,654 | 17 to 3,654 | 449 to 3,654
Comorbidities flag -inf to 0 | Hiv | N (%) | 475 (18.53%) | 245 (18.82%) | 230 (18.23%)
| Depression | N (%) | 2,531 (98.71%) | 1,285 (98.69%) | 1,246 (98.73%)
| Asthma | N (%) | 1,234 (48.13%) | 623 (47.85%) | 611 (48.42%)
Comedications flag -180 to 0 | Antidiabetes | N (%) | 26 (1.01%) | 11 (0.84%) | 15 (1.19%)
| Opioids | N (%) | 49 (1.91%) | 27 (2.07%) | 22 (1.74%)

If the default order of variable names provided by `tableCharacteristics()` does not match the study requirements, we can create a customised table with this package.

    data$summarised_characteristics |>
      dplyr::filter(variable_name != "Sex") |>
      dplyr::mutate(
        variable_name = customiseText(
          variable_name,
          custom = c(
            "Comorbidities"  = "Comorbidities flag -inf to 0",
            "Comedications"  = "Comedications flag -180 to 0"
          )
        ),
        variable_level = customiseText(
          variable_level,
          custom = c("HIV" = "Hiv")
        )
      ) |>
      visOmopTable(
        header = c("sex"),
        estimateName = c(
          "N (%)"               = "<count> (<percentage>%)",
          "N"                   = "<count>",
          "Median [Q25 - Q75]"  = "<median> [<q25> - <q75>]",
          "Mean (SD)"           = "<mean> (<sd>)",
          "Range"               = "<min> to <max>"
        ),
        factor = list(
          "sex" = c("overall", "Male", "Female"),
          "variable_name" = c(
            "Number records", "Number subjects", "Age", "Days in cohort", "Prior observation",
            "Future observation", "Cohort start date", "Cohort end date",
            "Comedications", "Comorbidities"
          ),
          "variable_level" = c(NA, "Asthma", "Depression", "HIV", "Opioids", "Antidiabetes")
        ),
        hide = c("cdm_name", "cohort_name")
      )

Variable name | Variable level | Estimate name | Sex
--- | --- | --- | ---
overall | Male | Female
Number records | – | N | 2,564 | 1,262 | 1,302
Number subjects | – | N | 2,564 | 1,262 | 1,302
Age | – | Median [Q25 - Q75] | 46 [36 - 56] | 46 [37 - 56] | 45 [36 - 55]
|  | Mean (SD) | 47.54 (14.81) | 47.57 (14.40) | 47.52 (15.21)
|  | Range | 21 to 99 | 21 to 98 | 21 to 99
Days in cohort | – | Median [Q25 - Q75] | 3,654 [3,654 - 3,654] | 3,654 [3,654 - 3,654] | 3,654 [3,654 - 3,654]
|  | Mean (SD) | 3,594.61 (332.90) | 3,587.20 (341.22) | 3,601.79 (324.60)
|  | Range | 17 to 3,654 | 449 to 3,654 | 17 to 3,654
Prior observation | – | Median [Q25 - Q75] | 16,834 [13,477 - 20,588] | 16,932 [13,636 - 20,634] | 16,734 [13,320 - 20,410]
|  | Mean (SD) | 17,545.28 (5,406.72) | 17,555.07 (5,255.26) | 17,535.78 (5,551.58)
|  | Range | 7,729 to 36,260 | 7,729 to 36,115 | 7,929 to 36,260
Future observation | – | Median [Q25 - Q75] | 4,010 [3,891 - 4,117] | 4,005 [3,878 - 4,116] | 4,018 [3,900 - 4,118]
|  | Mean (SD) | 3,924.53 (418.47) | 3,910.98 (430.96) | 3,937.65 (405.74)
|  | Range | 16 to 4,201 | 448 to 4,201 | 16 to 4,199
Cohort start date | – | Median [Q25 - Q75] | 2008-01-01 [2008-01-01 - 2008-01-01] | 2008-01-01 [2008-01-01 - 2008-01-01] | 2008-01-01 [2008-01-01 - 2008-01-01]
|  | Range | 2008-01-01 to 2008-01-01 | 2008-01-01 to 2008-01-01 | 2008-01-01 to 2008-01-01
Cohort end date | – | Median [Q25 - Q75] | 2018-01-01 [2018-01-01 - 2018-01-01] | 2018-01-01 [2018-01-01 - 2018-01-01] | 2018-01-01 [2018-01-01 - 2018-01-01]
|  | Range | 2008-01-17 to 2018-01-01 | 2009-03-24 to 2018-01-01 | 2008-01-17 to 2018-01-01
Comedications | Opioids | N (%) | 49 (1.91%) | 22 (1.74%) | 27 (2.07%)
| Antidiabetes | N (%) | 26 (1.01%) | 15 (1.19%) | 11 (0.84%)
Comorbidities | Asthma | N (%) | 1,234 (48.13%) | 611 (48.42%) | 623 (47.85%)
| Depression | N (%) | 2,531 (98.71%) | 1,246 (98.73%) | 1,285 (98.69%)
| HIV | N (%) | 475 (18.53%) | 230 (18.23%) | 245 (18.82%)

Additionally, we can show the number of people in the overall cohort and stratified by sex. We use the `CohortCharacteristics` plotting function and then apply the DARWIN style and rotate the axis labels.

    data$summarised_characteristics |>
      dplyr::filter(variable_name %in% c("Number records")) |>
      plotCharacteristics(colour = "sex") +
      themeVisOmop(style = style) +
      coord_flip()

# Incidence results

For the incidence results, we want to plot incidence over time by sex group. We can do that with the plotting functions in IncidencePrevalence. We add the `"darwin"` style and rotate x-axis labels for readability.

    data$incidence |>
      dplyr::filter(strata_name == "sex") |>
      plotIncidence(colour = "sex", facet = "sex", ribbon = TRUE) +
      themeVisOmop(style = style) +
      theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

# Additional results

Sometimes a study includes an analysis not yet supported by the OMOP Tidy R packages. In that case, we might or might not convert our result to a `summarised_result`, either way, there will be no package-specific table or plot function.

For these cases, we can use visOmopResults to create styled tables and plots that match the rest of the report.

As an example, we have mock results for a measurement change after an intervention. The tibble looks like this:

    data$measurement_change
    #> # A tibble: 9 × 8
    #>   cohort_name sex     variable_name   median   min   max   q25   q75
    #>   <chr>       <chr>   <chr>            <dbl> <dbl> <dbl> <dbl> <dbl>
    #> 1 denominator overall value_before        60    24   117    55 77.2
    #> 2 denominator overall value_after         58    22   108    55 72
    #> 3 denominator overall change_in_value     -2    -2    -9     0 -5.25
    #> 4 denominator male    value_before        65    30   120    61 80
    #> 5 denominator male    value_after         66    33   117    62 79
    #> 6 denominator male    change_in_value      1     3    -3     1 -1
    #> 7 denominator female  value_before        53    20   100    48 59
    #> 8 denominator female  value_after         50    19   100    47 60
    #> 9 denominator female  change_in_value     -3    -1     0    -1  1

First, we display results in a table. We pivot to the standard columns `estimate_name`, `estimate_type`, and `estimate_value`; this lets us format estimates and use the `header` argument in the `visTable()` function.

    data$measurement_change |>
      tidyr::pivot_longer(
        cols      = c("median", "min", "max", "q25", "q75"),
        names_to  = "estimate_name",
        values_to = "estimate_value"
      ) |>
      dplyr::mutate(
        estimate_type = "numeric",
        estimate_value = as.character(estimate_value),
        variable_name  = customiseText(variable_name),
        sex            = customiseText(sex)
      ) |>
      visTable(
        header = "sex",
        estimateName = c(
          "Median [Q25 - Q75]" = "<median> [<q25> - <q75>]",
          "Range"              = "<min> to <max>"
        ),
        hide   = c("cohort_name", "estimate_type"),
        rename = c("Estimate" = "estimate_name", "Variable" = "variable_name")
      )

Variable | Estimate | Sex
--- | --- | ---
Overall | Male | Female
Value before | Median [Q25 - Q75] | 60.00 [55.00 - 77.25] | 65.00 [61.00 - 80.00] | 53.00 [48.00 - 59.00]
| Range | 24.00 to 117.00 | 30.00 to 120.00 | 20.00 to 100.00
Value after | Median [Q25 - Q75] | 58.00 [55.00 - 72.00] | 66.00 [62.00 - 79.00] | 50.00 [47.00 - 60.00]
| Range | 22.00 to 108.00 | 33.00 to 117.00 | 19.00 to 100.00
Change in value | Median [Q25 - Q75] | -2.00 [0.00 - -5.25] | 1.00 [1.00 - -1.00] | -3.00 [-1.00 - 1.00]
| Range | -2.00 to -9.00 | 3.00 to -3.00 | -1.00 to 0.00

Additionally, we can visualise values before and after in a boxplot:

    data$measurement_change |>
      dplyr::filter(variable_name %in% c("value_before", "value_after")) |>
      dplyr::mutate(
        variable_name = customiseText(variable_name),
        sex           = customiseText(sex)
      ) |>
      boxPlot(x = "variable_name", facet = "sex", colour = "variable_name") +
      theme(axis.text.x = element_blank(), axis.ticks.x = element_blank()) +
      xlab("")

Note that we did not specify `type` or `style` in the visOmopResults functions above because we set these globally at the beginning of the document.

# Captions and footers

## In Markdown

In the reference Word document, styles are defined for figure legends (`CaptionDarwin`) and footers (`FooterDarwin`). These can be used in Quarto as follows:

    :::{custom-style="CaptionDarwin"}
    **Table 1:** Baseline population characteristics.
    :::

## In an R code chunk

We might want to create captions and footers inside an R code chunk (e.g., when generating multiple tables/figures in a loop, or when programmatically numbering them). In those cases, we can use `cat()` within a chunk:

    num_table <- 1
    cat(paste0(
      ':::{custom-style="CaptionDarwin"}\n',
      '**Table ', num_table, ':** Baseline population characteristics.\n',
      ':::\n'
    ))
    num_table <- num_table + 1

It’s important that we use the chunk option `results = 'asis'` so that the content produced by `cat()` is correctly interpreted by Quarto/Pandoc as Markdown.

# Report example

You can access an example Quarto script and the reference DOCX document here.