Missing values identifies subjects without any measurements on the provided target variables. The interpretation of findings depends on the scope of the provided variables and data records. In this example, the study data set comprises examined SHIP participants, not the target sample. Accordingly, the check is not about study participation. Rather, it identifies cases for which (unexpectedly) no information has been provided. Any identified case would indicate a data management problem.

Missing values can be assessed with com_unit_missingness in the following way:

# Load dataquieR
library(dataquieR)

# Load data
sd1 <- prep_get_data_frame("ship")

# Load metadata
file_name <- system.file("extdata", "ship_meta_v2.xlsx", package = "dataquieR")
prep_load_workbook_like_file(file_name)
meta_data_item <- prep_get_data_frame("item_level") # item_level is a sheet in ship_meta_v2.xlsx

# Apply indicator function
unit_missings <- com_unit_missingness(
  study_data  = sd1, 
  meta_data   = meta_data_item,
  label_col   = "LABEL",
  id_vars     = "ID"
)
unit_missings$SummaryData
N X.
0 0


In total 0 units have missings in all variables of the study data. Thus, for each participant there is at least one variable with information.