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.
com_item_missingness(
study_data,
meta_data,
resp_vars = NULL,
label_col,
show_causes = TRUE,
cause_label_df,
include_sysmiss = NULL,
threshold_value,
suppressWarnings = FALSE
)data.frame the data frame that contains the measurements
data.frame the data frame that contains metadata attributes of study data
variable list the name of the measurement variables
variable attribute the name of the column in the metadata with labels of variables
logical if TRUE, then the distribution of missing codes is shown
data.frame missing code table. If missing codes have labels the respective data frame must be specified here
logical Optional, if TRUE system missingness (NAs) is evaluated in the summary plot
numeric from=0 to=100. a numerical value ranging from 0-100
logical warn about mixed missing and jump code lists
a list with:
SummaryTable: data frame about item missingness per response variable
SummaryPlot: ggplot2 heatmap plot, if show_causes was TRUE
ReportSummaryTable: data frame underlying SummaryPlot
Lists of missing codes and, if applicable, jump codes are selected from the metadata
The no. of system missings (NA) in each variable is calculated
The no. of used missing codes is calculated for each variable
The no. of used jump codes is calculated for each variable
Two result dataframes (1: on the level of observations, 2: a summary for each variable) are generated
OPTIONAL: if show_causes is selected, one summary plot for all
resp_vars is provided