Nothing
R/000_globs.R
In dataquieR: Data Quality in Epidemiological Research
Defines functions
.onLoad
.onAttach
#' All available probability distributions for
#' [acc_shape_or_scale]
#'
#' - `uniform` For uniform distribution
#' - `normal` For Gaussian distribution
#' - `GAMMA` For a gamma distribution
#'
#' @export
DISTRIBUTIONS <- list(
UNIFORM = "uniform",
NORMAL = "normal",
GAMMA = "gamma"
)
#' @title Names of DQ dimensions
#' @name dimensions
#' @description
#'
#' a vector of data quality dimensions. The supported
#' dimensions are Completeness, Consistency and Accuracy.
#'
#' @seealso [Data Quality Concept](
#' https://dataquality.qihs.uni-greifswald.de/DQconceptNew.html)
#'
#' @return Only a definition, not a function, so no return value
#'
dimensions = c("Completeness", "Consistency", "Accuracy")
#' Data Types
#'
#' ## Data Types of Study Data
#' In the metadata, the following entries are allowed for the
#' [variable attribute] [DATA_TYPE]:
#'
#' - `integer` for integer numbers
#' - `string` for text/string/character data
#' - `float` for decimal/floating point numbers
#' - `datetime` for timepoints
#'
#' ## Data Types of Function Arguments
#' As function arguments, [dataquieR] uses additional type specifications:
#'
#' - `numeric` is a numerical value ([float] or [integer]), but it is not an
#' allowed `DATA_TYPE` in the metadata. However, some functions may accept
#' `float` or `integer` for specific function arguments. This is, where we
#' use the term `numeric`.
#' - `enum` allows one element out of a set of allowed options similar to
#' [match.arg]
#' - `set` allows a subset out of a set of allowed options similar to
#' [match.arg] with `several.ok = TRUE`.
#' - `variable` Function arguments of this type expect a character scalar that
#' specifies one variable using the variable identifier given in
#' the metadata attribute `VAR_NAMES` or, if `label_col` is set,
#' given in the metadata attribute given in that argument.
#' Labels can easily be translated using [prep_map_labels]
#' - `variable list` Function arguments of this type expect a character vector
#' that specifies variables using the variable identifiers
#' given in the metadata attribute `VAR_NAMES` or,
#' if `label_col` is set, given in the metadata attribute
#' given in that argument. Labels can easily be translated
#' using [prep_map_labels]
#'
#' @seealso [integer] [string]
#' @aliases integer string float datetime numeric enum
#' FLOAT INTEGER STRING DATETIME variable set
#' @rawRd \alias{variable list}
#'
#' @export
DATA_TYPES <- list(
INTEGER = "integer",
STRING = "string",
FLOAT = "float",
DATETIME = "datetime"
)
#' All available data types, mapped from their respective
#' R types
#' @seealso [`prep_dq_data_type_of`]
#' @export
DATA_TYPES_OF_R_TYPE <- list(
# order important for prep_dq_data_type_of --
# start with the most restrictive types
logical = DATA_TYPES$INTEGER,
integer = DATA_TYPES$INTEGER,
ordered = DATA_TYPES$STRING,
factor = DATA_TYPES$STRING,
POSIXct = DATA_TYPES$DATETIME,
POSIXlt = DATA_TYPES$DATETIME,
POSIXt = DATA_TYPES$DATETIME,
Date = DATA_TYPES$DATETIME,
dates = DATA_TYPES$DATETIME,
times = DATA_TYPES$DATETIME,
chron = DATA_TYPES$DATETIME,
double = DATA_TYPES$FLOAT,
numeric = DATA_TYPES$FLOAT,
character = DATA_TYPES$STRING
)
#' Variable roles can be one of the following:
#'
#' - `intro` a variable holding consent-data
#' - `primary` a primary outcome variable
#' - `secondary` a secondary outcome variable
#' - `process` a variable describing the measurement process
#' - `suppress` a variable added on the fly computing sub-reports, i.e., by
#' [dq_report_by] to have all referred variables available,
#' even if they are not part of the currently processed segment.
#' But they will only be fully assessed in their real segment's
#' report.
#'
#' @rawRd \alias{variable roles}
#' @export
VARIABLE_ROLES <- list(
INTRO = "intro",
PRIMARY = "primary",
SECONDARY = "secondary",
PROCESS = "process",
SUPPRESS = "suppress"
)
#' Character used by default as a separator in metadata such as
#' missing codes
#'
#' This 1 character is according to our metadata concept `r dQuote(SPLIT_CHAR)`.
#' @export
SPLIT_CHAR <- "|"
# constants for the names of defined metadata attributes / meta reference
# attributes
REQUIREMENT_ATT <- "var_att_required"
#' Requirement levels of certain metadata columns
#'
#' These levels are cumulatively used by the function [prep_create_meta] and
#' related in the argument `level` therein.
#'
#' currently available:
#'
#' ```{r echo=FALSE,results='asis'}
#' cat(
#' paste(" -", sQuote(unlist(names(VARATT_REQUIRE_LEVELS))), "=",
#' dQuote(unlist(VARATT_REQUIRE_LEVELS)), collapse = "\n")
#' )
#' ```
#'
#' @export
#' @aliases COMPATIBILITY REQUIRED RECOMMENDED OPTIONAL TECHNICAL UNKNOWN
VARATT_REQUIRE_LEVELS <- list(
COMPATIBILITY = "compatibility",
REQUIRED = "required",
RECOMMENDED = "recommended",
OPTIONAL = "optional",
TECHNICAL = "technical"
)
VARATT_REQUIRE_LEVELS_ORDER <- c(
# for cumulative access from util_get_var_att_names_of_level
VARATT_REQUIRE_LEVELS$REQUIRED,
VARATT_REQUIRE_LEVELS$RECOMMENDED,
VARATT_REQUIRE_LEVELS$OPTIONAL,
VARATT_REQUIRE_LEVELS$COMPATIBILITY,
VARATT_REQUIRE_LEVELS$TECHNICAL
)
#' Well-known metadata column names, names of metadata columns
#'
#' names of the variable attributes in the metadata frame holding
#' the names of the respective observers, devices, lower limits for plausible
#' values, upper limits for plausible values, lower limits for allowed values,
#' upper limits for allowed values, the variable name (column name, e.g.
#' v0020349) used in the study data, the variable name used for processing
#' (readable name, e.g. RR_DIAST_1) and in parameters of the QA-Functions, the
#' variable label, variable long label, variable short label, variable data
#' type (see also [DATA_TYPES]), re-code for definition of lists of event
#' categories, missing lists and jump lists as CSV strings.
#'
#' all entries of this list will be mapped to the package's exported NAMESPACE
#' environment directly, i.e. they are available directly by their names too:
#'
#' `r paste0(' - [', names(WELL_KNOWN_META_VARIABLE_NAMES), ']')`
#'
#' @rawRd \alias{variable attribute}
#'
#' @eval c("@aliases", paste(names(WELL_KNOWN_META_VARIABLE_NAMES)))
#'
#' @seealso [meta_data_segment] for `STUDY_SEGMENT`
#'
#' @examples
#' print(WELL_KNOWN_META_VARIABLE_NAMES$VAR_NAMES)
#' # print(VAR_NAMES) # should usually also work
#' @export
WELL_KNOWN_META_VARIABLE_NAMES <- list(
VAR_NAMES = structure("VAR_NAMES", var_att_required =
VARATT_REQUIRE_LEVELS$REQUIRED),
LABEL = structure("LABEL", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
DATA_TYPE = structure("DATA_TYPE", var_att_required =
VARATT_REQUIRE_LEVELS$REQUIRED),
VALUE_LABELS = structure("VALUE_LABELS", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
MISSING_LIST = structure("MISSING_LIST", var_att_required =
VARATT_REQUIRE_LEVELS$COMPATIBILITY),
JUMP_LIST = structure("JUMP_LIST", var_att_required =
VARATT_REQUIRE_LEVELS$COMPATIBILITY),
MISSING_LIST_TABLE = structure("MISSING_LIST_TABLE", var_att_required =
VARATT_REQUIRE_LEVELS$REQUIRED),
HARD_LIMITS = structure("HARD_LIMITS", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
DETECTION_LIMITS = structure("DETECTION_LIMITS", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
SOFT_LIMITS = structure("SOFT_LIMITS", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
CONTRADICTIONS = structure("CONTRADICTIONS", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
DISTRIBUTION = structure("DISTRIBUTION", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
DECIMALS = structure("DECIMALS", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
DATA_ENTRY_TYPE = structure("DATA_ENTRY_TYPE", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
END_DIGIT_CHECK = structure("END_DIGIT_CHECK", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
CO_VARS = structure("CO_VARS", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
GROUP_VAR_OBSERVER = structure("GROUP_VAR_OBSERVER", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
GROUP_VAR_DEVICE = structure("GROUP_VAR_DEVICE", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
KEY_OBSERVER = structure("KEY_OBSERVER", var_att_required =
VARATT_REQUIRE_LEVELS$COMPATIBILITY),
KEY_DEVICE = structure("KEY_DEVICE", var_att_required =
VARATT_REQUIRE_LEVELS$COMPATIBILITY),
TIME_VAR = structure("TIME_VAR", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
KEY_DATETIME = structure("KEY_DATETIME", var_att_required =
VARATT_REQUIRE_LEVELS$COMPATIBILITY),
PART_VAR = structure("PART_VAR", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
STUDY_SEGMENT = structure("STUDY_SEGMENT", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
KEY_STUDY_SEGMENT = structure("KEY_STUDY_SEGMENT", var_att_required =
VARATT_REQUIRE_LEVELS$COMPATIBILITY),
VARIABLE_ROLE = structure("VARIABLE_ROLE", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
VARIABLE_ORDER = structure("VARIABLE_ORDER", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
LONG_LABEL = structure("LONG_LABEL", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
SOFT_LIMIT_LOW = structure("SOFT_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
SOFT_LIMIT_UP = structure("SOFT_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
HARD_LIMIT_LOW = structure("HARD_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
HARD_LIMIT_UP = structure("HARD_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
DETECTION_LIMIT_LOW = structure("DETECTION_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
DETECTION_LIMIT_UP = structure("DETECTION_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_SOFT_LIMIT_LOW = structure("INCL_SOFT_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_SOFT_LIMIT_UP = structure("INCL_SOFT_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_HARD_LIMIT_LOW = structure("INCL_HARD_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_HARD_LIMIT_UP = structure("INCL_HARD_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
LOCATION_RANGE = structure("LOCATION_RANGE", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
LOCATION_METRIC = structure("LOCATION_METRIC", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
PROPORTION_RANGE = structure("PROPORTION_RANGE", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
LOCATION_LIMIT_LOW = structure("LOCATION_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
LOCATION_LIMIT_UP = structure("LOCATION_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_LOCATION_LIMIT_LOW = structure("INCL_LOCATION_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_LOCATION_LIMIT_UP = structure("INCL_LOCATION_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
PROPORTION_LIMIT_LOW = structure("PROPORTION_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
PROPORTION_LIMIT_UP = structure("PROPORTION_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_PROPORTION_LIMIT_LOW = structure("INCL_PROPORTION_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_PROPORTION_LIMIT_UP = structure("INCL_PROPORTION_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
# VARSHORTLABEL = structure("varshortlabel", var_att_required =
# VARATT_REQUIRE_LEVELS$OPTIONAL),
RECODE = structure("recode", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL)
)
.onAttach <- function(...) { # nocov start
if (length(user_hints$l) > 0) {
packageStartupMessage(paste(user_hints$l, collapse = "\n"))
}
}
# nocov end
.onLoad <- function(...) { # nocov start
if (getRversion() >= "2.15.1") {
utils::globalVariables(
c(
names(WELL_KNOWN_META_VARIABLE_NAMES),
"APP_SCORE",
"FITTED_VALUE",
"GRADING",
"IMPLEMENTATION",
"INTERVALS",
"LCL",
"LOWER_CL",
"PROB",
"TIME",
"UCL",
"UPPER_CL",
"VARIABLES",
"PCT_con_con",
"Variables",
"category",
"lwl",
"margins",
"percent",
"upl",
"x",
"y",
"z2",
"ds1",
"meta_data",
"variable",
"value",
"Rules",
"progress",
"progress_msg",
"REQUIRED", # a var att requirement level, using NSE
"label_col", # generated by prep_prepare_dataframes like ds1
"tr", # htmltools withTags
"td", # htmltools withTags
"th", # htmltools withTags
"table" # htmltools withTags
)
)
}
if (requireNamespace("pkgload", quietly = TRUE)) {
is_dev_package <- pkgload::is_dev_package
} else {
is_dev_package <- function(...) { return(FALSE) }
}
for (name in names(WELL_KNOWN_META_VARIABLE_NAMES)) {
if (exists(name, asNamespace("dataquieR")) &&
!is_dev_package("dataquieR")) {
util_warning("Variable %s is in dataquieR too!", name,
applicability_problem = FALSE)
}
assign(name, WELL_KNOWN_META_VARIABLE_NAMES[[name]],
asNamespace("dataquieR"))
}
if (!is_dev_package("dataquieR")) {
namespaceExport(asNamespace("dataquieR"),
names(WELL_KNOWN_META_VARIABLE_NAMES))
}
makeActiveBinding(".manual", function() {
if (!length(ls(..manual))) {
util_load_manual()
}
return(..manual)
}, environment(util_load_manual))
makeActiveBinding(".called_in_pipeline", function() {
.dq2_globs$.called_in_pipeline
}, environment(.onLoad))
util_fix_rstudio_bugs()
if (!l10n_info()[["UTF-8"]]) {
update_recommended <-
(.Platform$OS.type == "windows" &&
(R.version$major < 4 || (R.version$major >= 4 && R.version$minor < 2)))
util_user_hint(sprintf(
paste("Your R session is not using a UTF-8 character set,",
"be prepared to encoding problems. Also use %s. %s"),
"Sys.setlocale() to select a UTF-8 locale in your .Rprofile",
ifelse(update_recommended,
paste(
"\nOn Windows, you should consider updating R to a version >= 4.2.0,",
"which supports UTF-8."),
"")
))
}
}
# nocov end
# name of the additional system missingness column in com_item_missingness
.SM_LAB <- "ADDED: SysMiss"
# TODO JM: Update the following block to also comprise the new columns for qualified missingness and add a link to the documentation of ? cause_label_df (CAVE: In the new version, we would not need it, but we should (do already?) support linking other names than VAR_NAMES, e.g., LABEL).
#' Data frame with labels for missing- and jump-codes
#' @name cause_label_df
#' @aliases missing_matchtable
#' @description
#' [data.frame] with the following columns:
#' - `CODE_VALUE`: [numeric] Missing code (the number)
#' - `CODE_LABEL`: [character] a label for the missing code
#' - `CODE_CLASS`: [enum] JUMP | MISSING. Class of the missing code.
#' - `resp_vars`: [character] optional, if a missing code is specific for some
#' variables, it is listed for each such variable
#' with one entry in `resp_vars`, If `NA`, the
#' code is assumed shared among all variables.
#' For v1.0 metadata, you need to refer to
#' `VAR_NAMES` here.
NULL
#' Data frame with contradiction rules
#' @name check_table
#' @seealso [meta_data_cross]
#' @description
#' Two versions exist, the newer one is used by [con_contradictions_redcap] and
#' is described [here](https://dataquality.qihs.uni-greifswald.de/VIN_con_impl_contradictions_redcap.html#Example_output).,
#' the older one used by [con_contradictions] is described
#' [here](https://dataquality.qihs.uni-greifswald.de/VIN_con_impl_contradictions.html#Example_output).
NULL
#' Data frame with metadata about the study data on variable level
#'
#' @name meta_data
#' @description
#'
#' Variable level metadata.
#'
#' @seealso [further details on variable level metadata.](https://dataquality.qihs.uni-greifswald.de/Annotation_of_Metadata.html)
#' @seealso [meta_data_segment]
#' @seealso [meta_data_dataframe]
#'
NULL
#' Data frame with the study data whose quality is being assessed
#' @name study_data
#' @description
#' Study data is expected in wide format. If should contain all variables
#' for all segments in one large table, even, if some variables are not
#' measured for all observational utils (study participants).
NULL
#' Well known columns on the `meta_data_segment` sheet
#' @name meta_data_segment
#' @description
#' Metadata describing study segments, e.g., a full questionnaire, not its
#' items.
NULL
# this has already been defined
# @inherit meta_data_segment
# @export
# STUDY_SEGMENT <- "STUDY_SEGMENT"
#' Segment level metadata attribute name
#'
#' Number of expected data records in each segment.
#' [numeric]. Check only conducted if number entered
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_RECORD_COUNT <- "SEGMENT_RECORD_COUNT"
#' Segment level metadata attribute name
#'
#' The name of the data frame containing the reference IDs to be compared
#' with the IDs in the targeted segment.
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_ID_TABLE <- "SEGMENT_ID_TABLE"
#' Segment level metadata attribute name
#'
#' The type of check to be conducted when comparing the reference ID
#' table with the IDs in a segment.
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_RECORD_CHECK <- "SEGMENT_RECORD_CHECK"
#' Segment level metadata attribute name
#'
#' All variables that are to be used as one single ID
#' variable (combined key) in a segment.
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_ID_VARS <- "SEGMENT_ID_VARS"
#' Segment level metadata attribute name
#'
#' Specifies whether identical data is permitted across rows in a
#' segment (excluding ID variables)
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_UNIQUE_ROWS <- "SEGMENT_UNIQUE_ROWS"
#' Segment level metadata attribute name
#'
#' The name of the segment participation status variable
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_PART_VARS <- "SEGMENT_PART_VARS"
#' Segment level metadata attribute name
#'
#' `true` or `false` to suppress crude segment missingness output
#' (`Completeness/Misg. Segments` in the report). Defaults to compute
#' the output, if more than one segment is available in the item-level
#' metadata.
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_MISS <- "SEGMENT_MISS"
#' Well known columns on the `meta_data_dataframe` sheet
#' @name meta_data_dataframe
#' @description
#' Metadata describing data delivered on one data frame/table sheet,
#' e.g., a full questionnaire, not its items.
NULL
#' Data frame level metadata attribute name
#'
#' Name of the data frame
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_NAME <- "DF_NAME"
#' Data frame level metadata attribute name
#'
#' Number of expected data elements in a data frame.
#' [numeric]. Check only conducted if number entered
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_ELEMENT_COUNT <- "DF_ELEMENT_COUNT"
#' Data frame level metadata attribute name
#'
#' Number of expected data records in a data frame.
#' [numeric]. Check only conducted if number entered
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_RECORD_COUNT <- "DF_RECORD_COUNT"
#' Data frame level metadata attribute name
#'
#' The name of the data frame containing the reference IDs to be
#' compared with the IDs in the study data set.
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_ID_REF_TABLE <- "DF_ID_REF_TABLE"
#' Data frame level metadata attribute name
#'
#' The type of check to be conducted when comparing the reference
#' ID table with the IDs delivered in the study data files.
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_RECORD_CHECK <- "DF_RECORD_CHECK"
#' Data frame level metadata attribute name
#'
#' Defines expectancies on the uniqueness of the IDs
#' across the rows of a data frame, or the number of times some ID can be repeated.
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_UNIQUE_ID <- "DF_UNIQUE_ID"
#' Data frame level metadata attribute name
#'
#' All variables that are to be used as one single ID
#' variable (combined key) in a data frame.
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_ID_VARS <- "DF_ID_VARS"
#' Data frame level metadata attribute name
#'
#' Specifies whether identical data is permitted across rows in a
#' data frame (excluding ID variables)
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_UNIQUE_ROWS <- "DF_UNIQUE_ROWS"
#' Well known columns on the `meta_data_cross-item` sheet
#' @name meta_data_cross
#' @seealso check_table
#' @description
#' Metadata describing groups of variables, e.g., for their multivariate
#' distribution or for defining contradiction rules.
NULL
#' Cross-item level metadata attribute name
#'
#' Specifies the unique labels for cross-item level metadata records
#'
#' if missing, `dataquieR` will create such labels
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
CHECK_LABEL <- "CHECK_LABEL"
#' Cross-item level metadata attribute name
#'
#' Specifies the unique IDs for cross-item level metadata records
#'
#' if missing, `dataquieR` will create such IDs
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
CHECK_ID <- "CHECK_ID"
#' Cross-item level metadata attribute name
#'
#' Specifies a group of variables for multivariate analyses. Separated
#' by `r SPLIT_CHAR`, please use variable names from [VAR_NAMES] or
#' a label as specified in `label_col`, usually [LABEL] or [LONG_LABEL].
#'
#' if missing, `dataquieR` will create such IDs from [CONTRADICTION_TERM],
#' if specified.
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
VARIABLE_LIST <- "VARIABLE_LIST" # TODO: STS Add to 000_globs
#' Cross-item level metadata attribute name
#'
#' Specifies a contradiction rule. Use `REDCap` like syntax, see
#' [online vignette](https://dataquality.qihs.uni-greifswald.de/VIN_con_impl_contradictions_redcap.html)
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
CONTRADICTION_TERM <- "CONTRADICTION_TERM"
#' Cross-item level metadata attribute name
#'
#' Specifies the type of a contradiction. According to the data quality
#' concept, there are logical and empirical contradictions, see
#' [online vignette](https://dataquality.qihs.uni-greifswald.de/VIN_con_impl_contradictions_redcap.html)
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
CONTRADICTION_TYPE <- "CONTRADICTION_TYPE"
#' Cross-item and item level metadata attribute name
#'
#' Select, how many violated outlier criteria make an observation an outlier,
#' see [acc_multivariate_outlier].
#'
#' You can leave the cell empty, then, all applied checks must deem an
#' observation an outlier to have it flagged. See
#' [UNIVARIATE_OUTLIER_CHECKTYPE] and
#' [MULTIVARIATE_OUTLIER_CHECKTYPE] for the selected outlier criteria.
#'
#' @seealso [meta_data_cross]
#' @seealso [meta_data]
#' @family meta_data_cross
#'
#' @export
N_RULES <- "N_RULES" # TODO: STS add to WELL_KNOWN_META_VARIABLE_NAMES
#' Cross-item level metadata attribute name
#'
#' Select, which outlier criteria to compute, see [acc_multivariate_outlier].
#'
#' You can leave the cell empty, then, all checks will apply. If you enter
#' a set of methods, the maximum for [N_RULES] changes. See also
#' [`UNIVARIATE_OUTLIER_CHECKTYPE`].
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
MULTIVARIATE_OUTLIER_CHECKTYPE <- "MULTIVARIATE_OUTLIER_CHECKTYPE"
#' Item level metadata attribute name
#'
#' Select, which outlier criteria to compute, see [acc_univariate_outlier].
#'
#' You can leave the cell empty, then, all checks will apply. If you enter
#' a set of methods, the maximum for [N_RULES] changes. See also
#' [`MULTIVARIATE_OUTLIER_CHECKTYPE`].
#'
#' @seealso [WELL_KNOWN_META_VARIABLE_NAMES]
#'
#' @export
UNIVARIATE_OUTLIER_CHECKTYPE <- "UNIVARIATE_OUTLIER_CHECKTYPE" # TODO: STS add to WELL_KNOWN_META_VARIABLE_NAMES
#' Cross-item level metadata attribute name
#'
#' TODO JM
#'
#' @family meta_data_cross
#' @seealso [meta_data_cross]
#'
#' @export
ASSOCIATION_RANGE <- "ASSOCIATION_RANGE"
#' Cross-item level metadata attribute name
#'
#' TODO JM
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
ASSOCIATION_METRIC <- "ASSOCIATION_METRIC"
#' Cross-item level metadata attribute name
#'
#' TODO
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
ASSOCIATION_DIRECTION <- "ASSOCIATION_DIRECTION"
#' Cross-item level metadata attribute name
#'
#' TODO JM
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
ASSOCIATION_FORM <- "ASSOCIATION_FORM"
#' Cross-item level metadata attribute name
#'
#' TODO JM
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
REL_VAL <- "REL_VAL"
#' Cross-item level metadata attribute name
#'
#' TODO JM
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
GOLDSTANDARD <- "GOLDSTANDARD"
#' Cross-item level metadata attribute name
#'
#' TODO JM: MISSING_LABEL will not work for non-factor variables
#'
#' LABEL MISSING LIMITS MISSING_LABEL MISSING_INTERPRET
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
DATA_PREPARATION <- "DATA_PREPARATION"
#' Dimension Titles for Prefixes
#'
#' order does matter, because it defines the order in the `dq_report2`.
#'
#' @seealso [util_html_for_var()]
#' @seealso [util_html_for_dims()]
dims <- c(
int = "Integrity",
com = "Completeness",
con = "Consistency",
acc = "Accuracy"
)
# Use cli, if available.
rlang::local_use_cli()
# Maximum length for labels according to
# file names https://stackoverflow.com/a/265782/4242747
# and label handling in plot.ly
MAX_LABEL_LEN <- min(200L, 30L)
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Defines functions .onLoad .onAttach
#' All available probability distributions for
#' [acc_shape_or_scale]
#'
#' - `uniform` For uniform distribution
#' - `normal` For Gaussian distribution
#' - `GAMMA` For a gamma distribution
#'
#' @export
DISTRIBUTIONS <- list(
UNIFORM = "uniform",
NORMAL = "normal",
GAMMA = "gamma"
)
#' @title Names of DQ dimensions
#' @name dimensions
#' @description
#'
#' a vector of data quality dimensions. The supported
#' dimensions are Completeness, Consistency and Accuracy.
#'
#' @seealso [Data Quality Concept](
#' https://dataquality.qihs.uni-greifswald.de/DQconceptNew.html)
#'
#' @return Only a definition, not a function, so no return value
#'
dimensions = c("Completeness", "Consistency", "Accuracy")
#' Data Types
#'
#' ## Data Types of Study Data
#' In the metadata, the following entries are allowed for the
#' [variable attribute] [DATA_TYPE]:
#'
#' - `integer` for integer numbers
#' - `string` for text/string/character data
#' - `float` for decimal/floating point numbers
#' - `datetime` for timepoints
#'
#' ## Data Types of Function Arguments
#' As function arguments, [dataquieR] uses additional type specifications:
#'
#' - `numeric` is a numerical value ([float] or [integer]), but it is not an
#' allowed `DATA_TYPE` in the metadata. However, some functions may accept
#' `float` or `integer` for specific function arguments. This is, where we
#' use the term `numeric`.
#' - `enum` allows one element out of a set of allowed options similar to
#' [match.arg]
#' - `set` allows a subset out of a set of allowed options similar to
#' [match.arg] with `several.ok = TRUE`.
#' - `variable` Function arguments of this type expect a character scalar that
#' specifies one variable using the variable identifier given in
#' the metadata attribute `VAR_NAMES` or, if `label_col` is set,
#' given in the metadata attribute given in that argument.
#' Labels can easily be translated using [prep_map_labels]
#' - `variable list` Function arguments of this type expect a character vector
#' that specifies variables using the variable identifiers
#' given in the metadata attribute `VAR_NAMES` or,
#' if `label_col` is set, given in the metadata attribute
#' given in that argument. Labels can easily be translated
#' using [prep_map_labels]
#'
#' @seealso [integer] [string]
#' @aliases integer string float datetime numeric enum
#' FLOAT INTEGER STRING DATETIME variable set
#' @rawRd \alias{variable list}
#'
#' @export
DATA_TYPES <- list(
INTEGER = "integer",
STRING = "string",
FLOAT = "float",
DATETIME = "datetime"
)
#' All available data types, mapped from their respective
#' R types
#' @seealso [`prep_dq_data_type_of`]
#' @export
DATA_TYPES_OF_R_TYPE <- list(
# order important for prep_dq_data_type_of --
# start with the most restrictive types
logical = DATA_TYPES$INTEGER,
integer = DATA_TYPES$INTEGER,
ordered = DATA_TYPES$STRING,
factor = DATA_TYPES$STRING,
POSIXct = DATA_TYPES$DATETIME,
POSIXlt = DATA_TYPES$DATETIME,
POSIXt = DATA_TYPES$DATETIME,
Date = DATA_TYPES$DATETIME,
dates = DATA_TYPES$DATETIME,
times = DATA_TYPES$DATETIME,
chron = DATA_TYPES$DATETIME,
double = DATA_TYPES$FLOAT,
numeric = DATA_TYPES$FLOAT,
character = DATA_TYPES$STRING
)
#' Variable roles can be one of the following:
#'
#' - `intro` a variable holding consent-data
#' - `primary` a primary outcome variable
#' - `secondary` a secondary outcome variable
#' - `process` a variable describing the measurement process
#' - `suppress` a variable added on the fly computing sub-reports, i.e., by
#' [dq_report_by] to have all referred variables available,
#' even if they are not part of the currently processed segment.
#' But they will only be fully assessed in their real segment's
#' report.
#'
#' @rawRd \alias{variable roles}
#' @export
VARIABLE_ROLES <- list(
INTRO = "intro",
PRIMARY = "primary",
SECONDARY = "secondary",
PROCESS = "process",
SUPPRESS = "suppress"
)
#' Character used by default as a separator in metadata such as
#' missing codes
#'
#' This 1 character is according to our metadata concept `r dQuote(SPLIT_CHAR)`.
#' @export
SPLIT_CHAR <- "|"
# constants for the names of defined metadata attributes / meta reference
# attributes
REQUIREMENT_ATT <- "var_att_required"
#' Requirement levels of certain metadata columns
#'
#' These levels are cumulatively used by the function [prep_create_meta] and
#' related in the argument `level` therein.
#'
#' currently available:
#'
#' ```{r echo=FALSE,results='asis'}
#' cat(
#' paste(" -", sQuote(unlist(names(VARATT_REQUIRE_LEVELS))), "=",
#' dQuote(unlist(VARATT_REQUIRE_LEVELS)), collapse = "\n")
#' )
#' ```
#'
#' @export
#' @aliases COMPATIBILITY REQUIRED RECOMMENDED OPTIONAL TECHNICAL UNKNOWN
VARATT_REQUIRE_LEVELS <- list(
COMPATIBILITY = "compatibility",
REQUIRED = "required",
RECOMMENDED = "recommended",
OPTIONAL = "optional",
TECHNICAL = "technical"
)
VARATT_REQUIRE_LEVELS_ORDER <- c(
# for cumulative access from util_get_var_att_names_of_level
VARATT_REQUIRE_LEVELS$REQUIRED,
VARATT_REQUIRE_LEVELS$RECOMMENDED,
VARATT_REQUIRE_LEVELS$OPTIONAL,
VARATT_REQUIRE_LEVELS$COMPATIBILITY,
VARATT_REQUIRE_LEVELS$TECHNICAL
)
#' Well-known metadata column names, names of metadata columns
#'
#' names of the variable attributes in the metadata frame holding
#' the names of the respective observers, devices, lower limits for plausible
#' values, upper limits for plausible values, lower limits for allowed values,
#' upper limits for allowed values, the variable name (column name, e.g.
#' v0020349) used in the study data, the variable name used for processing
#' (readable name, e.g. RR_DIAST_1) and in parameters of the QA-Functions, the
#' variable label, variable long label, variable short label, variable data
#' type (see also [DATA_TYPES]), re-code for definition of lists of event
#' categories, missing lists and jump lists as CSV strings.
#'
#' all entries of this list will be mapped to the package's exported NAMESPACE
#' environment directly, i.e. they are available directly by their names too:
#'
#' `r paste0(' - [', names(WELL_KNOWN_META_VARIABLE_NAMES), ']')`
#'
#' @rawRd \alias{variable attribute}
#'
#' @eval c("@aliases", paste(names(WELL_KNOWN_META_VARIABLE_NAMES)))
#'
#' @seealso [meta_data_segment] for `STUDY_SEGMENT`
#'
#' @examples
#' print(WELL_KNOWN_META_VARIABLE_NAMES$VAR_NAMES)
#' # print(VAR_NAMES) # should usually also work
#' @export
WELL_KNOWN_META_VARIABLE_NAMES <- list(
VAR_NAMES = structure("VAR_NAMES", var_att_required =
VARATT_REQUIRE_LEVELS$REQUIRED),
LABEL = structure("LABEL", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
DATA_TYPE = structure("DATA_TYPE", var_att_required =
VARATT_REQUIRE_LEVELS$REQUIRED),
VALUE_LABELS = structure("VALUE_LABELS", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
MISSING_LIST = structure("MISSING_LIST", var_att_required =
VARATT_REQUIRE_LEVELS$COMPATIBILITY),
JUMP_LIST = structure("JUMP_LIST", var_att_required =
VARATT_REQUIRE_LEVELS$COMPATIBILITY),
MISSING_LIST_TABLE = structure("MISSING_LIST_TABLE", var_att_required =
VARATT_REQUIRE_LEVELS$REQUIRED),
HARD_LIMITS = structure("HARD_LIMITS", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
DETECTION_LIMITS = structure("DETECTION_LIMITS", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
SOFT_LIMITS = structure("SOFT_LIMITS", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
CONTRADICTIONS = structure("CONTRADICTIONS", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
DISTRIBUTION = structure("DISTRIBUTION", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
DECIMALS = structure("DECIMALS", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
DATA_ENTRY_TYPE = structure("DATA_ENTRY_TYPE", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
END_DIGIT_CHECK = structure("END_DIGIT_CHECK", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
CO_VARS = structure("CO_VARS", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
GROUP_VAR_OBSERVER = structure("GROUP_VAR_OBSERVER", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
GROUP_VAR_DEVICE = structure("GROUP_VAR_DEVICE", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
KEY_OBSERVER = structure("KEY_OBSERVER", var_att_required =
VARATT_REQUIRE_LEVELS$COMPATIBILITY),
KEY_DEVICE = structure("KEY_DEVICE", var_att_required =
VARATT_REQUIRE_LEVELS$COMPATIBILITY),
TIME_VAR = structure("TIME_VAR", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
KEY_DATETIME = structure("KEY_DATETIME", var_att_required =
VARATT_REQUIRE_LEVELS$COMPATIBILITY),
PART_VAR = structure("PART_VAR", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
STUDY_SEGMENT = structure("STUDY_SEGMENT", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
KEY_STUDY_SEGMENT = structure("KEY_STUDY_SEGMENT", var_att_required =
VARATT_REQUIRE_LEVELS$COMPATIBILITY),
VARIABLE_ROLE = structure("VARIABLE_ROLE", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
VARIABLE_ORDER = structure("VARIABLE_ORDER", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL),
LONG_LABEL = structure("LONG_LABEL", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
SOFT_LIMIT_LOW = structure("SOFT_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
SOFT_LIMIT_UP = structure("SOFT_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
HARD_LIMIT_LOW = structure("HARD_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
HARD_LIMIT_UP = structure("HARD_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
DETECTION_LIMIT_LOW = structure("DETECTION_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
DETECTION_LIMIT_UP = structure("DETECTION_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_SOFT_LIMIT_LOW = structure("INCL_SOFT_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_SOFT_LIMIT_UP = structure("INCL_SOFT_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_HARD_LIMIT_LOW = structure("INCL_HARD_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_HARD_LIMIT_UP = structure("INCL_HARD_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
LOCATION_RANGE = structure("LOCATION_RANGE", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
LOCATION_METRIC = structure("LOCATION_METRIC", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
PROPORTION_RANGE = structure("PROPORTION_RANGE", var_att_required =
VARATT_REQUIRE_LEVELS$RECOMMENDED),
LOCATION_LIMIT_LOW = structure("LOCATION_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
LOCATION_LIMIT_UP = structure("LOCATION_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_LOCATION_LIMIT_LOW = structure("INCL_LOCATION_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_LOCATION_LIMIT_UP = structure("INCL_LOCATION_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
PROPORTION_LIMIT_LOW = structure("PROPORTION_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
PROPORTION_LIMIT_UP = structure("PROPORTION_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_PROPORTION_LIMIT_LOW = structure("INCL_PROPORTION_LIMIT_LOW", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
INCL_PROPORTION_LIMIT_UP = structure("INCL_PROPORTION_LIMIT_UP", var_att_required =
VARATT_REQUIRE_LEVELS$TECHNICAL),
# VARSHORTLABEL = structure("varshortlabel", var_att_required =
# VARATT_REQUIRE_LEVELS$OPTIONAL),
RECODE = structure("recode", var_att_required =
VARATT_REQUIRE_LEVELS$OPTIONAL)
)
.onAttach <- function(...) { # nocov start
if (length(user_hints$l) > 0) {
packageStartupMessage(paste(user_hints$l, collapse = "\n"))
}
}
# nocov end
.onLoad <- function(...) { # nocov start
if (getRversion() >= "2.15.1") {
utils::globalVariables(
c(
names(WELL_KNOWN_META_VARIABLE_NAMES),
"APP_SCORE",
"FITTED_VALUE",
"GRADING",
"IMPLEMENTATION",
"INTERVALS",
"LCL",
"LOWER_CL",
"PROB",
"TIME",
"UCL",
"UPPER_CL",
"VARIABLES",
"PCT_con_con",
"Variables",
"category",
"lwl",
"margins",
"percent",
"upl",
"x",
"y",
"z2",
"ds1",
"meta_data",
"variable",
"value",
"Rules",
"progress",
"progress_msg",
"REQUIRED", # a var att requirement level, using NSE
"label_col", # generated by prep_prepare_dataframes like ds1
"tr", # htmltools withTags
"td", # htmltools withTags
"th", # htmltools withTags
"table" # htmltools withTags
)
)
}
if (requireNamespace("pkgload", quietly = TRUE)) {
is_dev_package <- pkgload::is_dev_package
} else {
is_dev_package <- function(...) { return(FALSE) }
}
for (name in names(WELL_KNOWN_META_VARIABLE_NAMES)) {
if (exists(name, asNamespace("dataquieR")) &&
!is_dev_package("dataquieR")) {
util_warning("Variable %s is in dataquieR too!", name,
applicability_problem = FALSE)
}
assign(name, WELL_KNOWN_META_VARIABLE_NAMES[[name]],
asNamespace("dataquieR"))
}
if (!is_dev_package("dataquieR")) {
namespaceExport(asNamespace("dataquieR"),
names(WELL_KNOWN_META_VARIABLE_NAMES))
}
makeActiveBinding(".manual", function() {
if (!length(ls(..manual))) {
util_load_manual()
}
return(..manual)
}, environment(util_load_manual))
makeActiveBinding(".called_in_pipeline", function() {
.dq2_globs$.called_in_pipeline
}, environment(.onLoad))
util_fix_rstudio_bugs()
if (!l10n_info()[["UTF-8"]]) {
update_recommended <-
(.Platform$OS.type == "windows" &&
(R.version$major < 4 || (R.version$major >= 4 && R.version$minor < 2)))
util_user_hint(sprintf(
paste("Your R session is not using a UTF-8 character set,",
"be prepared to encoding problems. Also use %s. %s"),
"Sys.setlocale() to select a UTF-8 locale in your .Rprofile",
ifelse(update_recommended,
paste(
"\nOn Windows, you should consider updating R to a version >= 4.2.0,",
"which supports UTF-8."),
"")
))
}
}
# nocov end
# name of the additional system missingness column in com_item_missingness
.SM_LAB <- "ADDED: SysMiss"
# TODO JM: Update the following block to also comprise the new columns for qualified missingness and add a link to the documentation of ? cause_label_df (CAVE: In the new version, we would not need it, but we should (do already?) support linking other names than VAR_NAMES, e.g., LABEL).
#' Data frame with labels for missing- and jump-codes
#' @name cause_label_df
#' @aliases missing_matchtable
#' @description
#' [data.frame] with the following columns:
#' - `CODE_VALUE`: [numeric] Missing code (the number)
#' - `CODE_LABEL`: [character] a label for the missing code
#' - `CODE_CLASS`: [enum] JUMP | MISSING. Class of the missing code.
#' - `resp_vars`: [character] optional, if a missing code is specific for some
#' variables, it is listed for each such variable
#' with one entry in `resp_vars`, If `NA`, the
#' code is assumed shared among all variables.
#' For v1.0 metadata, you need to refer to
#' `VAR_NAMES` here.
NULL
#' Data frame with contradiction rules
#' @name check_table
#' @seealso [meta_data_cross]
#' @description
#' Two versions exist, the newer one is used by [con_contradictions_redcap] and
#' is described [here](https://dataquality.qihs.uni-greifswald.de/VIN_con_impl_contradictions_redcap.html#Example_output).,
#' the older one used by [con_contradictions] is described
#' [here](https://dataquality.qihs.uni-greifswald.de/VIN_con_impl_contradictions.html#Example_output).
NULL
#' Data frame with metadata about the study data on variable level
#'
#' @name meta_data
#' @description
#'
#' Variable level metadata.
#'
#' @seealso [further details on variable level metadata.](https://dataquality.qihs.uni-greifswald.de/Annotation_of_Metadata.html)
#' @seealso [meta_data_segment]
#' @seealso [meta_data_dataframe]
#'
NULL
#' Data frame with the study data whose quality is being assessed
#' @name study_data
#' @description
#' Study data is expected in wide format. If should contain all variables
#' for all segments in one large table, even, if some variables are not
#' measured for all observational utils (study participants).
NULL
#' Well known columns on the `meta_data_segment` sheet
#' @name meta_data_segment
#' @description
#' Metadata describing study segments, e.g., a full questionnaire, not its
#' items.
NULL
# this has already been defined
# @inherit meta_data_segment
# @export
# STUDY_SEGMENT <- "STUDY_SEGMENT"
#' Segment level metadata attribute name
#'
#' Number of expected data records in each segment.
#' [numeric]. Check only conducted if number entered
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_RECORD_COUNT <- "SEGMENT_RECORD_COUNT"
#' Segment level metadata attribute name
#'
#' The name of the data frame containing the reference IDs to be compared
#' with the IDs in the targeted segment.
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_ID_TABLE <- "SEGMENT_ID_TABLE"
#' Segment level metadata attribute name
#'
#' The type of check to be conducted when comparing the reference ID
#' table with the IDs in a segment.
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_RECORD_CHECK <- "SEGMENT_RECORD_CHECK"
#' Segment level metadata attribute name
#'
#' All variables that are to be used as one single ID
#' variable (combined key) in a segment.
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_ID_VARS <- "SEGMENT_ID_VARS"
#' Segment level metadata attribute name
#'
#' Specifies whether identical data is permitted across rows in a
#' segment (excluding ID variables)
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_UNIQUE_ROWS <- "SEGMENT_UNIQUE_ROWS"
#' Segment level metadata attribute name
#'
#' The name of the segment participation status variable
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_PART_VARS <- "SEGMENT_PART_VARS"
#' Segment level metadata attribute name
#'
#' `true` or `false` to suppress crude segment missingness output
#' (`Completeness/Misg. Segments` in the report). Defaults to compute
#' the output, if more than one segment is available in the item-level
#' metadata.
#'
#' @seealso [meta_data_segment]
#'
#' @export
SEGMENT_MISS <- "SEGMENT_MISS"
#' Well known columns on the `meta_data_dataframe` sheet
#' @name meta_data_dataframe
#' @description
#' Metadata describing data delivered on one data frame/table sheet,
#' e.g., a full questionnaire, not its items.
NULL
#' Data frame level metadata attribute name
#'
#' Name of the data frame
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_NAME <- "DF_NAME"
#' Data frame level metadata attribute name
#'
#' Number of expected data elements in a data frame.
#' [numeric]. Check only conducted if number entered
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_ELEMENT_COUNT <- "DF_ELEMENT_COUNT"
#' Data frame level metadata attribute name
#'
#' Number of expected data records in a data frame.
#' [numeric]. Check only conducted if number entered
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_RECORD_COUNT <- "DF_RECORD_COUNT"
#' Data frame level metadata attribute name
#'
#' The name of the data frame containing the reference IDs to be
#' compared with the IDs in the study data set.
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_ID_REF_TABLE <- "DF_ID_REF_TABLE"
#' Data frame level metadata attribute name
#'
#' The type of check to be conducted when comparing the reference
#' ID table with the IDs delivered in the study data files.
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_RECORD_CHECK <- "DF_RECORD_CHECK"
#' Data frame level metadata attribute name
#'
#' Defines expectancies on the uniqueness of the IDs
#' across the rows of a data frame, or the number of times some ID can be repeated.
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_UNIQUE_ID <- "DF_UNIQUE_ID"
#' Data frame level metadata attribute name
#'
#' All variables that are to be used as one single ID
#' variable (combined key) in a data frame.
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_ID_VARS <- "DF_ID_VARS"
#' Data frame level metadata attribute name
#'
#' Specifies whether identical data is permitted across rows in a
#' data frame (excluding ID variables)
#'
#' @seealso [meta_data_dataframe]
#'
#' @export
DF_UNIQUE_ROWS <- "DF_UNIQUE_ROWS"
#' Well known columns on the `meta_data_cross-item` sheet
#' @name meta_data_cross
#' @seealso check_table
#' @description
#' Metadata describing groups of variables, e.g., for their multivariate
#' distribution or for defining contradiction rules.
NULL
#' Cross-item level metadata attribute name
#'
#' Specifies the unique labels for cross-item level metadata records
#'
#' if missing, `dataquieR` will create such labels
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
CHECK_LABEL <- "CHECK_LABEL"
#' Cross-item level metadata attribute name
#'
#' Specifies the unique IDs for cross-item level metadata records
#'
#' if missing, `dataquieR` will create such IDs
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
CHECK_ID <- "CHECK_ID"
#' Cross-item level metadata attribute name
#'
#' Specifies a group of variables for multivariate analyses. Separated
#' by `r SPLIT_CHAR`, please use variable names from [VAR_NAMES] or
#' a label as specified in `label_col`, usually [LABEL] or [LONG_LABEL].
#'
#' if missing, `dataquieR` will create such IDs from [CONTRADICTION_TERM],
#' if specified.
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
VARIABLE_LIST <- "VARIABLE_LIST" # TODO: STS Add to 000_globs
#' Cross-item level metadata attribute name
#'
#' Specifies a contradiction rule. Use `REDCap` like syntax, see
#' [online vignette](https://dataquality.qihs.uni-greifswald.de/VIN_con_impl_contradictions_redcap.html)
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
CONTRADICTION_TERM <- "CONTRADICTION_TERM"
#' Cross-item level metadata attribute name
#'
#' Specifies the type of a contradiction. According to the data quality
#' concept, there are logical and empirical contradictions, see
#' [online vignette](https://dataquality.qihs.uni-greifswald.de/VIN_con_impl_contradictions_redcap.html)
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
CONTRADICTION_TYPE <- "CONTRADICTION_TYPE"
#' Cross-item and item level metadata attribute name
#'
#' Select, how many violated outlier criteria make an observation an outlier,
#' see [acc_multivariate_outlier].
#'
#' You can leave the cell empty, then, all applied checks must deem an
#' observation an outlier to have it flagged. See
#' [UNIVARIATE_OUTLIER_CHECKTYPE] and
#' [MULTIVARIATE_OUTLIER_CHECKTYPE] for the selected outlier criteria.
#'
#' @seealso [meta_data_cross]
#' @seealso [meta_data]
#' @family meta_data_cross
#'
#' @export
N_RULES <- "N_RULES" # TODO: STS add to WELL_KNOWN_META_VARIABLE_NAMES
#' Cross-item level metadata attribute name
#'
#' Select, which outlier criteria to compute, see [acc_multivariate_outlier].
#'
#' You can leave the cell empty, then, all checks will apply. If you enter
#' a set of methods, the maximum for [N_RULES] changes. See also
#' [`UNIVARIATE_OUTLIER_CHECKTYPE`].
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
MULTIVARIATE_OUTLIER_CHECKTYPE <- "MULTIVARIATE_OUTLIER_CHECKTYPE"
#' Item level metadata attribute name
#'
#' Select, which outlier criteria to compute, see [acc_univariate_outlier].
#'
#' You can leave the cell empty, then, all checks will apply. If you enter
#' a set of methods, the maximum for [N_RULES] changes. See also
#' [`MULTIVARIATE_OUTLIER_CHECKTYPE`].
#'
#' @seealso [WELL_KNOWN_META_VARIABLE_NAMES]
#'
#' @export
UNIVARIATE_OUTLIER_CHECKTYPE <- "UNIVARIATE_OUTLIER_CHECKTYPE" # TODO: STS add to WELL_KNOWN_META_VARIABLE_NAMES
#' Cross-item level metadata attribute name
#'
#' TODO JM
#'
#' @family meta_data_cross
#' @seealso [meta_data_cross]
#'
#' @export
ASSOCIATION_RANGE <- "ASSOCIATION_RANGE"
#' Cross-item level metadata attribute name
#'
#' TODO JM
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
ASSOCIATION_METRIC <- "ASSOCIATION_METRIC"
#' Cross-item level metadata attribute name
#'
#' TODO
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
ASSOCIATION_DIRECTION <- "ASSOCIATION_DIRECTION"
#' Cross-item level metadata attribute name
#'
#' TODO JM
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
ASSOCIATION_FORM <- "ASSOCIATION_FORM"
#' Cross-item level metadata attribute name
#'
#' TODO JM
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
REL_VAL <- "REL_VAL"
#' Cross-item level metadata attribute name
#'
#' TODO JM
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
GOLDSTANDARD <- "GOLDSTANDARD"
#' Cross-item level metadata attribute name
#'
#' TODO JM: MISSING_LABEL will not work for non-factor variables
#'
#' LABEL MISSING LIMITS MISSING_LABEL MISSING_INTERPRET
#'
#' @seealso [meta_data_cross]
#' @family meta_data_cross
#'
#' @export
DATA_PREPARATION <- "DATA_PREPARATION"
#' Dimension Titles for Prefixes
#'
#' order does matter, because it defines the order in the `dq_report2`.
#'
#' @seealso [util_html_for_var()]
#' @seealso [util_html_for_dims()]
dims <- c(
int = "Integrity",
com = "Completeness",
con = "Consistency",
acc = "Accuracy"
)
# Use cli, if available.
rlang::local_use_cli()
# Maximum length for labels according to
# file names https://stackoverflow.com/a/265782/4242747
# and label handling in plot.ly
MAX_LABEL_LEN <- min(200L, 30L)
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