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Unmapped Concepts – Output Generation

Source: R/uc_output.R
uc_output.Rd

Using the tabular output generated by uc_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

uc_output(
  process_output,
  output_col,
  filter_variable = NULL,
  large_n = FALSE,
  large_n_sites = NULL
)

Arguments

process_output

tabular input || required

The tabular output produced by uc_process

output_col

string || defaults to NULL

The name of the numerical variable from process_output that should be used to generate the plot. This input is required for all checks EXCEPT Multi Site, Anomaly Detection, Longitudinal

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, Anomaly Detection, Longitudinal

  • Single Site, Anomaly Detection, Longitudinal

Multiple values are accepted for:

  • Multi Site, Exploratory, Cross-Sectional

  • Multi Site, Exploratory, Longitudinal

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 uc_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 = 'unmappedconcepts'))

#' 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 = 'uc_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(-15000), # RSQLite does not store date objects,
                                      # hence the numerics
                end_date = as.Date(20000),
                site = ifelse(person_id %in% c(1:6), 'synth1', 'synth2'))

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

uc_process_example
#> # A tibble: 2 × 13
#>   site  variable total_rows total_pt unmapped_rows unmapped_pt median_all_with0s
#>   <chr> <chr>         <int>    <int>         <int>       <int>             <dbl>
#> 1 comb… visit t…       1627       12             0           0                 0
#> 2 comb… hyperte…         58       12            13           1                 0
#> # ℹ 6 more variables: median_all_without0s <int>, median_site_with0s <dbl>,
#> #   median_site_without0s <int>, unmapped_row_prop <dbl>,
#> #   unmapped_pt_prop <dbl>, output_function <chr>

#' Execute `uc_output` function
uc_output_example <- uc_output(process_output = uc_process_example,
                               output_col = 'unmapped_row_prop')

uc_output_example


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

make_interactive_squba(uc_output_example)