Using the CGMmissingDataR Shiny App

Overview

CGMmissingDataR includes an optional Shiny app for interactive missing glucose imputation. The app is a point-and-click interface around the main package function:

run_missing_glucose_imputation()

The app is useful when users want to:

upload a CSV file without writing R code;
choose the target glucose, subject ID, timestamp, and feature columns from a user interface;
load built-in example data sets for demonstration;
inspect observed missingness before running imputation;
run the imputation workflow;
preview rows where glucose was missing and then imputed;
download the completed data as a CSV file.

The Shiny app does not implement a separate imputation algorithm. It calls run_missing_glucose_imputation() internally and returns the same type of completed data frame as the command-line workflow.

The imputation workflow handles both explicit missing glucose values coded as NA and missing readings implied by timestamp gaps. During imputation, each subject is regularized to the expected interval_minutes timestamp grid, so the returned data can contain more rows than the uploaded data when timestamps are missing.

Installation

Install CGMmissingDataR from CRAN with:

install.packages("CGMissingDataR")

The app requires the optional R package shiny. If Shiny is not already installed, install it with:

install.packages("shiny")

Then load the package:

library(CGMissingDataR)

Launching the app

Launch the app with:

run_app()

During package development, after running devtools::load_all(), the same launcher can be used:

devtools::load_all()
run_app()

The app is bundled inside the installed package, typically under:

system.file(
  "shiny",
  "cgm_imputation_app",
  package = "CGMissingDataR"
)

Users normally do not need to access this directory directly. The run_app() launcher finds it automatically.

Input options

The app provides two ways to load data.

Upload a CSV file

Use the Browse button to upload a CSV file containing CGM data. The file should contain, at minimum, columns corresponding to:

Role	Example column	App selector
Subject identifier	`USUBJID`	Subject ID column
Glucose value	`LBORRES`	Target glucose column
Timestamp	`Time`	Timestamp column
Additional predictors	`AGE`, `hba1c`	Feature columns

After the file is uploaded, the app displays a preview of the uploaded data and populates the column-selection controls.

Load built-in example data

The app can also load built-in example data sets for demonstration. These are useful for quickly showing how the workflow behaves without requiring users to upload their own data.

The example data sets are intended to include:

Example data	Description
`CGMExmplDat5Pct`	Example CGM data with about 5% explicit missing glucose values.
`CGMExmplDat10Pct`	Example CGM data with about 10% explicit missing glucose values.

After selecting an example data set and clicking Load example data, the app uses that data set exactly as if it had been uploaded by the user.

Selecting columns

Once data are loaded, select the columns that map to the imputation function.

Target glucose column

Choose the glucose column with missing values to impute. In the included example data, this is usually:

LBORRES

The original target column is preserved in the returned data. Values that were originally missing, or created from timestamp gaps during regularization, remain NA in this original column. Completed glucose values are written to a new column named:

imputed_glucose_value

Subject ID column

Choose the column identifying each subject or participant. In the example data, this is usually:

USUBJID

The subject ID is used for sorting, timestamp regularization, lag feature creation, rolling-mean feature creation, and subject-level time handling.

Timestamp column

Choose the raw timestamp column. In the example data, this is usually:

Time

The imputation function uses this timestamp column to regularize each subject to an equal interval_minutes CGM grid before imputation. Common timestamp formats are supported, including colon-separated, hyphen-separated, slash-separated, ISO-style, and POSIXct values.

Feature columns

Choose additional predictor columns. In the example data, these commonly include:

SEX
AGE
hba1c

Feature columns should be numeric or coercible to numeric. If a SEX column is present, the underlying function can encode it internally.

Missingness summary card

The app includes a missingness summary card beside the uploaded data preview. After a target glucose column is selected, this card shows the observed missingness in the loaded data before imputation:

the percentage of explicit missing values in the selected target column;
the number of explicit missing rows;
the total number of uploaded rows;
a warning style when missingness is greater than the chosen threshold, such as 20%.

This card is intended as a quick data-quality check before running the imputation workflow. Timestamp gaps are handled during imputation, so the final number of rows imputed can be larger than the explicit missing count shown in this pre-imputation summary.

Timestamp-gap handling

When imputation runs, the underlying function regularizes each subject to the expected interval_minutes grid. For example, if readings jump from 00:05 to 00:30, the function internally creates the missing rows at 00:10, 00:15, 00:20, and 00:25, sets the target glucose value to NA, and then imputes those values.

This means the downloaded data can contain more rows than the uploaded data when there are timestamp gaps.

Backend selection

The app supports the same backends as run_missing_glucose_imputation().

Backend	Description	Recommended use
`mice`	R-native backend using the R package `mice`.	Default, CRAN-safe workflow.
`sklearn`	Optional Python-compatible backend through `reticulate`.	Closest agreement with the Python reference workflow.

MICE backend

The default backend is:

imputer_backend = "mice"

This backend does not require Python and is the safest choice for most users. It is also the backend used in CRAN-safe examples and tests.

Method selection

The Final imputation method control mirrors the models argument in run_missing_glucose_imputation(). The default Automatic by missing rate option uses MICE+ARIMA when missingness is at or below the selected threshold and MICE+XGBoost otherwise.

Users can also force exactly one final method:

MICE+ARIMA;
MICE+XGBoost;
MICE+Random Forest;
MICE+kNN;
MICE+LightGBM.

The app shows only the tuning controls relevant to the selected method. For example, Random Forest shows the tree count, kNN shows the neighbor count, and LightGBM shows boosting rounds.

The Model threads control maps to n_threads. It defaults to 1 for CRAN-friendly and shared-system-friendly CPU use. Increase it for faster local XGBoost, Random Forest, or LightGBM runs.

Optional sklearn backend

The optional Python-compatible backend is:

imputer_backend = "sklearn"

This path sends the data frame to Python through reticulate. Python then uses:

pandas for data-frame operations;
scikit-learn for IterativeImputer;
statsmodels for ARIMA;
Python xgboost for XGBoost regression;
Python lightgbm when forcing LightGBM.

To use the Python backend, install reticulate and declare the Python requirements before launching or running the app:

install.packages("reticulate")

reticulate::py_require(c(
  "numpy",
  "pandas",
  "scikit-learn",
  "statsmodels",
  "xgboost"
))

# Optional, only needed for models = "lightgbm"
reticulate::py_install("lightgbm", pip = TRUE)

The Python backend is optional. It is not required for installing or loading the package.

Running imputation

After loading data and selecting columns, click Run imputation.

Internally, the app calls code equivalent to:

out <- run_missing_glucose_imputation(
  data = uploaded_data,
  target_col = selected_target_col,
  feature_cols = selected_feature_cols,
  id_col = selected_id_col,
  time_col = selected_time_col,
  imputer_backend = selected_backend,
  models = selected_method,
  use_arima_if_missing_leq = selected_threshold,
  xgb_nrounds = selected_xgb_rounds,
  rf_n_estimators = selected_rf_trees,
  knn_k = selected_knn_neighbors,
  lgb_nrounds = selected_lightgbm_rounds,
  n_threads = selected_threads,
  seed = selected_seed,
  export = FALSE
)

The returned object is a data frame containing the original input columns plus:

Column	Meaning
`imputed_glucose_value`	Completed glucose values after imputation.

The original target glucose column is left unchanged. Internal time features, lag features, rolling means, model labels, and missingness-tracking flags are used during imputation but are not included in the returned or downloaded data.

Previewing results

After imputation, the app displays a preview of rows where the target glucose value is missing in the returned data. This includes explicit missing glucose values and, when timestamp gaps exist, rows inserted during timestamp regularization.

For example, the preview is based on logic like:

imputed_rows <- out[is.na(out[[target_col]]), , drop = FALSE]
head(imputed_rows, 15)

The full completed data frame remains available for download.

Downloading results

Use the Download imputed CSV button to save the completed data set. The CSV is intentionally minimal and contains:

the original uploaded columns;
any rows inserted from timestamp gaps;
imputed_glucose_value.

imputed_glucose_value is returned as a continuous numeric model estimate. If whole-number glucose values are needed for reporting, users can round the column after download.

Troubleshooting

The app does not launch

If you see an error saying that Shiny is not installed, run:

install.packages("shiny")

Then restart R and try:

run_app()

No column choices appear

Column choices appear only after data are loaded. Upload a CSV file or load one of the built-in example data sets.

Imputation fails because a timestamp cannot be parsed

Check the timestamp column selected in the app. The values should be parseable dates or datetimes, for example:

"2020:01:16:00:00"
"2020-01-16 00:00:00"
"2020/01/16 00:00:00"
"2020-01-16T00:00:00"

If the wrong column was selected as the timestamp column, select the correct column and rerun imputation.

Downloaded data have more rows than the uploaded file

This can be expected. The imputation workflow creates missing expected CGM rows from timestamp gaps before imputing glucose values.

Python backend fails because a Python module is missing

If imputer_backend = "sklearn" fails because Python packages are missing, run:

reticulate::py_require(c(
  "numpy",
  "pandas",
  "scikit-learn",
  "statsmodels",
  "xgboost"
))

# Optional, only needed for models = "lightgbm"
reticulate::py_install("lightgbm", pip = TRUE)

Then restart R and launch the app again.

Downloaded data contain `NA` in the original glucose column

This is expected. The original target column is intentionally preserved. The completed values are stored in:

imputed_glucose_value

Developer notes

The recommended package structure for the app is:

inst/
└── shiny/
    └── cgm_imputation_app/
        └── app.R

The launcher should live in an exported R function, for example:

run_app <- function() {
  app_dir <- system.file(
    "shiny",
    "cgm_imputation_app",
    package = "CGMissingDataR"
  )
  shiny::runApp(app_dir, display.mode = "normal")
}

Because the app is optional, shiny should usually be listed in Suggests, not Imports, unless the package requires Shiny for normal operation.