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Expected Variables Present – Output Generation

Source: R/evp_output.R
evp_output.Rd

Using the tabular output generated by evp_process, this function will build a graph to visualize the results. Each function configuration will output a bespoke ggplot. Theming can be adjusted by the user after the graph has been output using + theme(). Most graphs can also be made interactive using make_interactive_squba()

Usage

evp_output(
  process_output,
  output_level = "patient",
  filter_variable = NULL,
  large_n = FALSE,
  large_n_sites = NULL
)

Arguments

process_output

tabular input || required

The tabular output produced by evp_process

output_level

string || defaults to patient

A string indicating the analysis level that should be reflected in the plot. All checks utilize this parameter EXCEPT Single Site, Anomaly Detection, Cross-Sectional, which executes a Jaccard index using only patient counts

filter_variable

string or vector || defaults to NULL

A string or vector with variable names that should be the focus of the analysis. Only a single value is accepted for the following checks:

  • Multi Site, Exploratory, Longitudinal (non-year time period)

  • Multi Site, Anomaly Detection, Longitudinal

  • Single Site, Anomaly Detection, Longitudinal

Multiple values (up to 3) are accepted for:

  • Multi Site, Exploratory, Longitudinal (year time period)

large_n

boolean || defaults to FALSE

For Multi-Site analyses, a boolean indicating whether the large N visualization, intended for a high volume of sites, should be used. This visualization will produce high level summaries across all sites, with an option to add specific site comparators via the large_n_sites parameter.

large_n_sites

vector || defaults to NULL

When large_n = TRUE, a vector of site names that can add site-level information to the plot for comparison across the high level summary information.

Value

This function will produce a graph to visualize the results from evp_process based on the parameters provided. The default output is typically a static ggplot or gt object, but interactive elements can be activated by passing the plot through make_interactive_squba. For a more detailed description of output specific to each check type, see the PEDSpace metadata repository

Examples


#' Source setup file
source(system.file('setup.R', package = 'expectedvariablespresent'))

#' Create in-memory RSQLite database using data in extdata directory
conn <- mk_testdb_omop()

#' Establish connection to database and generate internal configurations
initialize_dq_session(session_name = 'evp_process_test',
                      working_directory = my_directory,
                      db_conn = conn,
                      is_json = FALSE,
                      file_subdirectory = my_file_folder,
                      cdm_schema = NA)
#> Connected to: :memory:@NA

#' Build mock study cohort
cohort <- cdm_tbl('person') %>% dplyr::distinct(person_id) %>%
  dplyr::mutate(start_date = as.Date(-5000), # RSQLite does not store date objects,
                                      # hence the numerics
                end_date = as.Date(15000),
                site = ifelse(person_id %in% c(1:6), 'synth1', 'synth2'))

#' Execute `evp_process` function
#' This example will use the single site, exploratory, cross sectional
#' configuration
evp_process_example <- evp_process(cohort = cohort,
                                   multi_or_single_site = 'single',
                                   anomaly_or_exploratory = 'exploratory',
                                   time = FALSE,
                                   omop_or_pcornet = 'omop',
                                   evp_variable_file = evp_variable_file_omop) %>%
  suppressMessages()
#> ┌ Output Function Details ──────────────────────────────────────┐
#> │ You can optionally use this dataframe in the accompanying     │
#> │ `evp_output` function. Here are the parameters you will need: │
#> │                                                               │
#> │ Always Required: process_output                               │
#> │ Required for Check: output_level                              │
#> │                                                               │
#> │ See ?evp_output for more details.                             │
#> └───────────────────────────────────────────────────────────────┘

evp_process_example
#> # A tibble: 1 × 9
#>   site  total_pt_ct total_row_ct variable_pt_ct variable_row_ct prop_pt_variable
#>   <chr>       <int>        <int>          <int>           <int>            <dbl>
#> 1 comb…           7           70              5               5            0.714
#> # ℹ 3 more variables: prop_row_variable <dbl>, variable <chr>,
#> #   output_function <chr>

#' Execute `evp_output` function
evp_output_example <- evp_output(process_output = evp_process_example,
                                 output_level = 'patient')

evp_output_example


#' Easily convert the graph into an interactive ggiraph or plotly object with
#' `make_interactive_squba()`

make_interactive_squba(evp_output_example)