R: Summarize missingness columnwise (in variable)

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

Arguments

`study_data`	data.frame the data frame that contains the measurements
`meta_data`	data.frame the data frame that contains metadata attributes of study data
`resp_vars`	variable list the name of the measurement variables
`label_col`	variable attribute the name of the column in the metadata with labels of variables
`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`

Value

a list with:

SummaryTable: data frame about item missingness per response variable
SummaryData: data frame about item missingness per response variable formatted for user
SummaryPlot: ggplot2 heatmap plot, if show_causes was TRUE
ReportSummaryTable: data frame underlying SummaryPlot

ALGORITHM OF THIS IMPLEMENTATION: