Type:	Package
Title:	Data Quality Reporting for Temporal Datasets
Version:	1.2.0
Description:	Generate reports that enable quick visual review of temporal shifts in record-level data. Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single "event", one column contains the "event date", and other columns contain any associated values for the event).
URL:	https://github.com/ropensci/daiquiri, https://ropensci.github.io/daiquiri/
BugReports:	https://github.com/ropensci/daiquiri/issues
License:	GPL (≥ 3)
Encoding:	UTF-8
Imports:	data.table (≥ 1.12.8), readr (≥ 2.0.0), ggplot2 (≥ 3.1.0), scales (≥ 1.1.0), cowplot (≥ 0.9.3), rmarkdown, reactable (≥ 0.2.3), utils, stats, xfun (≥ 0.15)
RoxygenNote:	7.3.2
Suggests:	covr, knitr, testthat (≥ 3.0.0), codemetar
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-06-24 13:55:19 UTC; pquan
Author:	T. Phuong Quan [aut, cre], Jack Cregan [ctb], University of Oxford [cph], National Institute for Health Research (NIHR) [fnd], Brad Cannell [rev]
Maintainer:	T. Phuong Quan <phuong.quan@ndm.ox.ac.uk>
Repository:	CRAN
Date/Publication:	2025-06-24 14:40:02 UTC

daiquiri: Data Quality Reporting for Temporal Datasets

Description

Generate reports that enable quick visual review of temporal shifts in record-level data. Time series plots showing aggregated values are automatically created for each data field (column) depending on its contents (e.g. min/max/mean values for numeric data, no. of distinct values for categorical data), as well as overviews for missing values, non-conformant values, and duplicated rows. The resulting reports are shareable and can contribute to forming a transparent record of the entire analysis process. It is designed with Electronic Health Records in mind, but can be used for any type of record-level temporal data (i.e. tabular data where each row represents a single "event", one column contains the "event date", and other columns contain any associated values for the event).

Author(s)

Maintainer: T. Phuong Quan phuong.quan@ndm.ox.ac.uk (ORCID)

Other contributors:

• Jack Cregan [contributor]

• University of Oxford [copyright holder]

• National Institute for Health Research (NIHR) [funder]

• Brad Cannell [reviewer]

See Also

Useful links:

• https://github.com/ropensci/daiquiri

• https://ropensci.github.io/daiquiri/

• Report bugs at https://github.com/ropensci/daiquiri/issues

Aggregate source data

Description

Aggregates a daiquiri_source_data object based on the field_types() specified at load time. Default time period for aggregation is a calendar day

Usage

aggregate_data(source_data, aggregation_timeunit = "day", show_progress = TRUE)

Arguments

Value

A daiquiri_aggregated_data object

See Also

prepare_data(), report_data()

Examples

# load example data into a data.frame
raw_data <- read_data(
  system.file("extdata", "example_prescriptions.csv", package = "daiquiri"),
  delim = ",",
  col_names = TRUE
)

# validate and prepare the data for aggregation
source_data <- prepare_data(
  raw_data,
  field_types = field_types(
    PrescriptionID = ft_uniqueidentifier(),
    PrescriptionDate = ft_timepoint(),
    AdmissionDate = ft_datetime(includes_time = FALSE),
    Drug = ft_freetext(),
    Dose = ft_numeric(),
    DoseUnit = ft_categorical(),
    PatientID = ft_ignore(),
    Location = ft_categorical(aggregate_by_each_category = TRUE)
  ),
  override_column_names = FALSE,
  na = c("", "NULL")
)

# aggregate the data
aggregated_data <- aggregate_data(
  source_data,
  aggregation_timeunit = "day"
)

aggregated_data

Close any active log file

Description

Close any active log file

Usage

close_log()

Value

If a log file was found, the path to the log file that was closed, otherwise an empty string

Examples

close_log()

Create a data quality report from a data frame

Description

Accepts record-level data from a data frame, validates it against the expected type of content of each column, generates a collection of time series plots for visual inspection, and saves a report to disk.

Usage

daiquiri_report(
  df,
  field_types,
  override_column_names = FALSE,
  na = c("", "NA", "NULL"),
  dataset_description = NULL,
  aggregation_timeunit = "day",
  report_title = "daiquiri data quality report",
  save_directory = ".",
  save_filename = NULL,
  show_progress = TRUE,
  log_directory = NULL
)

Arguments

Value

A list containing information relating to the supplied parameters as well as the resulting daiquiri_source_data and daiquiri_aggregated_data objects.

Details

In order for the package to detect any non-conformant values in numeric or datetime fields, these should be present in the data frame in their raw character format. Rectangular data from a text file will automatically be read in as character type if you use the read_data() function. Data frame columns that are not of class character will still be processed according to the field_types specified.

See Also

read_data(), field_types(), field_types_available()

Examples

# load example data into a data.frame
raw_data <- read_data(
  system.file("extdata", "example_prescriptions.csv", package = "daiquiri"),
  delim = ",",
  col_names = TRUE
)

# create a report in the current directory
daiq_obj <- daiquiri_report(
  raw_data,
  field_types = field_types(
    PrescriptionID = ft_uniqueidentifier(),
    PrescriptionDate = ft_timepoint(),
    AdmissionDate = ft_datetime(includes_time = FALSE, na = "1800-01-01"),
    Drug = ft_freetext(),
    Dose = ft_numeric(),
    DoseUnit = ft_categorical(),
    PatientID = ft_ignore(),
    Location = ft_categorical(aggregate_by_each_category = TRUE)
  ),
  override_column_names = FALSE,
  na = c("", "NULL"),
  dataset_description = "Example data provided with package",
  aggregation_timeunit = "day",
  report_title = "daiquiri data quality report",
  save_directory = ".",
  save_filename = "example_data_report",
  show_progress = TRUE,
  log_directory = NULL
)

Export aggregated data

Description

Export aggregated data to disk. Creates a separate file for each aggregated field in dataset.

Usage

export_aggregated_data(
  aggregated_data,
  save_directory,
  save_file_prefix = "",
  save_file_type = "csv"
)

Arguments

Value

(invisibly) The daiquiri_aggregated_data object that was passed in

Examples

raw_data <- read_data(
  system.file("extdata", "example_prescriptions.csv", package = "daiquiri"),
  delim = ",",
  col_names = TRUE
)

source_data <- prepare_data(
  raw_data,
  field_types = field_types(
    PrescriptionID = ft_uniqueidentifier(),
    PrescriptionDate = ft_timepoint(),
    AdmissionDate = ft_datetime(includes_time = FALSE),
    Drug = ft_freetext(),
    Dose = ft_numeric(),
    DoseUnit = ft_categorical(),
    PatientID = ft_ignore(),
    Location = ft_categorical(aggregate_by_each_category = TRUE)
  ),
  override_column_names = FALSE,
  na = c("", "NULL")
)

aggregated_data <- aggregate_data(
  source_data,
  aggregation_timeunit = "day"
)

export_aggregated_data(
  aggregated_data,
  save_directory = ".",
  save_file_prefix = "ex_"
)

Create field_types specification

Description

Specify the names and types of fields in the source data frame. This is important because the data in each field will be aggregated in different ways, depending on its field_type. See field_types_available

Usage

field_types(...)

Arguments

Value

A field_types object

See Also

field_types_available(), template_field_types()

Examples

fts <- field_types(
  PatientID = ft_uniqueidentifier(),
  TestID = ft_ignore(),
  TestDate = ft_timepoint(),
  TestName = ft_categorical(aggregate_by_each_category = FALSE),
  TestResult = ft_numeric(),
  ResultDate = ft_datetime(),
  ResultComment = ft_freetext(),
  Location = ft_categorical()
)

fts

Create field_types_advanced specification

Description

Specify only a subset of the names and types of fields in the source data frame. The remaining fields will be given the same 'default' type.

Usage

field_types_advanced(..., .default_field_type = ft_simple())

Arguments

Value

A field_types object

See Also

field_types(), field_types_available(), template_field_types()

Examples

fts <- field_types_advanced(
  PrescriptionDate = ft_timepoint(),
  PatientID = ft_ignore(),
  .default_field_type = ft_simple()
)

fts

Types of data fields available for specification

Description

Each column in the source dataset must be assigned to a particular ft_xx depending on the type of data that it contains. This is done through a field_types() specification.

Usage

ft_timepoint(includes_time = TRUE, format = "", na = NULL)

ft_uniqueidentifier(na = NULL)

ft_categorical(aggregate_by_each_category = FALSE, na = NULL)

ft_numeric(na = NULL)

ft_datetime(includes_time = TRUE, format = "", na = NULL)

ft_freetext(na = NULL)

ft_simple(na = NULL)

ft_strata(na = NULL)

ft_ignore()

Arguments

Value

A field_type object denoting the type of data in the column

Details

ft_timepoint() - identifies the data field which should be used as the independent time variable. There should be one and only one of these specified.

ft_uniqueidentifier() - identifies data fields which contain a (usually computer-generated) identifier for an entity, e.g. a patient. It does not need to be unique within the dataset.

ft_categorical() - identifies data fields which should be treated as categorical.

ft_numeric() - identifies data fields which contain numeric values that should be treated as continuous. Any values which contain non-numeric characters (including grouping marks) will be classed as non-conformant

ft_datetime() - identifies data fields which contain date values that should be treated as continuous.

ft_freetext() - identifies data fields which contain free text values. Only presence/missingness will be evaluated.

ft_simple() - identifies data fields where you only want presence/missingness to be evaluated (but which are not necessarily free text).

ft_strata() - identifies a categorical data field which should be used to stratify the rest of the data.

ft_ignore() - identifies data fields which should be ignored. These will not be loaded.

See Also

field_types(), template_field_types()

Examples

fts <- field_types(
  PatientID = ft_uniqueidentifier(),
  TestID = ft_ignore(),
  TestDate = ft_timepoint(),
  TestName = ft_categorical(aggregate_by_each_category = FALSE),
  TestResult = ft_numeric(),
  ResultDate = ft_datetime(),
  ResultComment = ft_freetext(),
  Location = ft_categorical()
)

ft_simple()

Initialise a log file

Description

Choose a directory in which to save the log file. If this is not called, no log file is created.

Usage

initialise_log(log_directory)

Arguments

Value

Character string containing the full path to the newly-created log file

Examples

log_name <- initialise_log(".")

log_name

Prepare source data

Description

Validate a data frame against a field_types() specification, and prepare for aggregation.

Usage

prepare_data(
  df,
  field_types,
  override_column_names = FALSE,
  na = c("", "NA", "NULL"),
  dataset_description = NULL,
  show_progress = TRUE
)

Arguments

Value

A daiquiri_source_data object

See Also

field_types(), field_types_available(), aggregate_data(), report_data(), daiquiri_report()

Examples

# load example data into a data.frame
raw_data <- read_data(
  system.file("extdata", "example_prescriptions.csv", package = "daiquiri"),
  delim = ",",
  col_names = TRUE
)

# validate and prepare the data for aggregation
source_data <- prepare_data(
  raw_data,
  field_types = field_types(
    PrescriptionID = ft_uniqueidentifier(),
    PrescriptionDate = ft_timepoint(),
    AdmissionDate = ft_datetime(includes_time = FALSE),
    Drug = ft_freetext(),
    Dose = ft_numeric(),
    DoseUnit = ft_categorical(),
    PatientID = ft_ignore(),
    Location = ft_categorical(aggregate_by_each_category = TRUE)
  ),
  override_column_names = FALSE,
  na = c("", "NULL"),
  dataset_description = "Example data provided with package"
)

source_data

Read delimited data for optimal use with daiquiri

Description

Popular file readers such as readr::read_delim() perform datatype conversion by default, which can interfere with daiquiri's ability to detect non-conformant values. Use this function instead to ensure optimal compatibility with daiquiri's features.

Usage

read_data(
  file,
  delim = NULL,
  col_names = TRUE,
  quote = "\"",
  trim_ws = TRUE,
  comment = "",
  skip = 0,
  n_max = Inf,
  show_progress = TRUE
)

Arguments

Details

This function is aimed at non-expert users of R, and operates as a restricted implementation of readr::read_delim(). If you prefer to use read_delim() directly, ensure you set the following parameters: col_types = readr::cols(.default = "c") and na = character()

Value

A data frame

See Also

field_types(), field_types_available(), aggregate_data(), report_data(), daiquiri_report()

Examples

raw_data <- read_data(
  system.file("extdata", "example_prescriptions.csv", package = "daiquiri"),
  delim = ",",
  col_names = TRUE
)

head(raw_data)

Generate report from existing objects

Description

Generate report from previously-created daiquiri_source_data and daiquiri_aggregated_data objects

Usage

report_data(
  source_data,
  aggregated_data,
  report_title = "daiquiri data quality report",
  save_directory = ".",
  save_filename = NULL,
  format = "html",
  show_progress = TRUE,
  ...
)

Arguments

Value

A string containing the name and path of the saved report

See Also

prepare_data(), aggregate_data(), daiquiri_report()

Examples

# load example data into a data.frame
raw_data <- read_data(
  system.file("extdata", "example_prescriptions.csv", package = "daiquiri"),
  delim = ",",
  col_names = TRUE
)

# validate and prepare the data for aggregation
source_data <- prepare_data(
  raw_data,
  field_types = field_types(
    PrescriptionID = ft_uniqueidentifier(),
    PrescriptionDate = ft_timepoint(),
    AdmissionDate = ft_datetime(includes_time = FALSE),
    Drug = ft_freetext(),
    Dose = ft_numeric(),
    DoseUnit = ft_categorical(),
    PatientID = ft_ignore(),
    Location = ft_categorical(aggregate_by_each_category = TRUE)
  ),
  override_column_names = FALSE,
  na = c("", "NULL"),
  dataset_description = "Example data provided with package",
  show_progress = TRUE
)

# aggregate the data
aggregated_data <- aggregate_data(
  source_data,
  aggregation_timeunit = "day",
  show_progress = TRUE
)

# save a report in the current directory using the previously-created objects
report_data(
  source_data,
  aggregated_data,
  report_title = "daiquiri data quality report",
  save_directory = ".",
  save_filename = "example_data_report",
  show_progress = TRUE
)

Print a template field_types() specification to console

Description

Helper function to generate template code for a field_types() specification, based on the supplied data frame. All fields (columns) in the specification will be defined using the default_field_type, and the console output can be copied and edited before being used as input to daiquiri_report() or prepare_data().

Usage

template_field_types(df, default_field_type = ft_ignore())

Arguments

Value

(invisibly) Character string containing the template code

See Also

field_types()

Examples

df <- data.frame(
  col1 = rep("2022-01-01", 5),
  col2 = rep(1, 5),
  col3 = 1:5,
  col4 = rnorm(5)
)

template_field_types(df, default_field_type = ft_numeric())