check_ped: Check pedigree file
In UW-GAC/dbgaptools: Creates and Checks Standard Files for dbGaP submission

Description Usage Arguments Details Value

View source: R/check_functions.R

Check contents of a pedigree file for dbGaP posting

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check_ped(dsfile, ddfile = NULL, na_vals = c("NA", "N/A", "na", "n/a"),
  subj_exp = NULL, subjectID_col = "SUBJECT_ID", check_incons = TRUE,
  male = 1, female = 2)

`dsfile`	Path to the data file on disk
`ddfile`	Path to the data dictionary file on disk
`na_vals`	Vector of strings that should be read in as NA/missing in data file (see details of `read_ds_file`)
`subj_exp`	Vector of expected subject IDs
`subjectID_col`	Column name for subject-level ID
`check_incons`	Logical whether to report pedigree inconsistencies, using `GWASTools pedigreeCheck`
`male`	Encoded value for male in SEX column
`female`	Encoded value for female in SEX column

If an MZ twin column is detected, returns issues including column name other than 'MZ_TWIN_ID' and a data frame of all twin pairs with logical flags to indicate > 1 family ID per pair (chk_family=TRUE); non-unique subject ID (chk_subjectID=TRUE); > 1 sex, which could indicate dizygotic twins are included (chk_sex=TRUE).

If a data dictionary is provided (ddfile != NULL), additionally checks correspondence between column names in data file and entries in data dictionary. Data dictionary files can be Excel (.xls, .xlsx) or tab-delimited .txt.

ped_report, a list of the following issues (when present):

`lowercase`	Logical flag indicating non-upper case variable names
`missing_vars`	Missing and required variables
`dd_errors`	Differences in fields between data file and data dictionary
`dup_subjects`	List of duplicated subject IDs
`extra_subjects`	Subjects in data file missing from `ssm_exp`
`missing_subjects`	Subjects in `ssm_exp` missing from data file
`extra_sexvals`	Additional values in SEX column beyond what's specified by `male` and `female` function arguments
`mztwin_errors`	List of potential errors with MZ twins