util_dist_selection {dataquieR} | R Documentation |
Utility function to characterize study variables
Description
This function summarizes some properties of measurement variables.
Usage
util_dist_selection(study_data, val_lab = NULL)
Arguments
study_data |
study data, pre-processed with |
val_lab |
matching metadata column containing the |
Value
data frame with one row for each variable in the study data and the
following columns:
Variables
contains the names of the variables
IsInteger
contains a check whether the variable contains integer values
only (variables coded as factor will be converted to integers)
IsMultCat
contains a check for variables with integer or string values
whether there are more than two categories
NCategory
contains the number of distinct values for variables with
values coded as integers or strings (excluding NA
and
empty entries)
AnyNegative
contains a check whether the variable contains any negative
values
NDistinct
contains the number of distinct values
PropZeroes
reports the proportion of zeroes
See Also
Other metadata_management:
CAN_THIS_BE_REMOVED_util_combine_missing_lists()
,
util_find_free_missing_code()
,
util_find_var_by_meta()
,
util_get_var_att_names_of_level()
,
util_get_vars_in_segment()
,
util_looks_like_missing()
,
util_no_value_labels()
,
util_validate_known_meta()
,
util_validate_missing_lists()