com_item_missingness {dataquieR}R Documentation

Summarize missingness columnwise (in variable)

Description

Item-Missingness (also referred to as item nonresponse (De Leeuw et al. 2003)) describes the missingness of single values, e.g. blanks or empty data cells in a data set. Item-Missingness occurs for example in case a respondent does not provide information for a certain question, a question is overlooked by accident, a programming failure occurs or a provided answer were missed while entering the data.

Indicator

Usage

com_item_missingness(
  resp_vars = NULL,
  study_data,
  label_col,
  item_level = "item_level",
  show_causes = TRUE,
  cause_label_df,
  include_sysmiss = TRUE,
  threshold_value,
  suppressWarnings = FALSE,
  assume_consistent_codes = TRUE,
  expand_codes = assume_consistent_codes,
  drop_levels = FALSE,
  expected_observations = c("HIERARCHY", "ALL", "SEGMENT"),
  pretty_print = lifecycle::deprecated(),
  meta_data = item_level,
  meta_data_v2
)

Arguments

resp_vars

variable list the name of the measurement variables

study_data

data.frame the data frame that contains the measurements

label_col

variable attribute the name of the column in the metadata with labels of variables

item_level

data.frame the data frame that contains metadata attributes of study data

show_causes

logical if TRUE, then the distribution of missing codes is shown

cause_label_df

data.frame missing code table. If missing codes have labels the respective data frame can be specified here or in the metadata as assignments, see cause_label_df

include_sysmiss

logical Optional, if TRUE system missingness (NAs) is evaluated in the summary plot

threshold_value

numeric from=0 to=100. a numerical value ranging from 0-100

suppressWarnings

logical warn about consistency issues with missing and jump lists

assume_consistent_codes

logical if TRUE and no labels are given and the same missing/jump code is used for more than one variable, the labels assigned for this code are treated as being be the same for all variables.

expand_codes

logical if TRUE, code labels are copied from other variables, if the code is the same and the label is set somewhere

drop_levels

logical if TRUE, do not display unused missing codes in the figure legend.

expected_observations

enum HIERARCHY | ALL | SEGMENT. If ALL, all observations are expected to comprise all study segments. If SEGMENT, the PART_VAR is expected to point to a variable with values of 0 and 1, indicating whether the variable was expected to be observed for each data row. If HIERARCHY, this is also checked recursively, so, if a variable points to such a participation variable, and that other variable does has also a PART_VAR entry pointing to a variable, the observation of the initial variable is only expected, if both segment variables are 1.

pretty_print

logical deprecated. If you want to have a human readable output, use SummaryData instead of SummaryTable

meta_data

data.frame old name for item_level

meta_data_v2

character path to workbook like metadata file, see prep_load_workbook_like_file for details. ALL LOADED DATAFRAMES WILL BE PURGED, using prep_purge_data_frame_cache, if you specify meta_data_v2.

Value

a list with:

ALGORITHM OF THIS IMPLEMENTATION:

See Also

Online Documentation


[Package dataquieR version 2.5.1 Index]