1 Overview
I want to make the data sing, but it is torturing me instead. Will the real data please stand up?
The goal of dbGaPCheckup is to make your National Library of Medicine database of Genotypes and Phenotypes (dbGaP) data set submission a tiny bit easier. Specifically, our package implements several check, awareness, utility, and reporting functions designed to help you ensure that your Subject Phenotype data set and data dictionary meet a variety of dbGaP specific formatting requirements. A list of the functions available can be found below.
The software announcement for our package has been published in BMC Bioinformatics and is available at https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-023-05200-8.
Heinsberg, L.W., Weeks, D.E. dbGaPCheckup: pre-submission checks of dbGaP-formatted subject phenotype files. BMC Bioinformatics 24, 77 (2023). https://doi.org/10.1186/s12859-023-05200-8
| Function_Name | Function_Type | Function_Description |
|---|---|---|
| field_check | check | Checks for dbGaP required fields: variable name (VARNAME), variable description (VARDESC), units (UNITS), and variable value and meaning (VALUES). |
| pkg_field_check | check | Checks for package-level required fields: variable type (TYPE), minimum value (MIN), and maximum value (MAX). |
| dimension_check | check | Checks that the number of variables match between the data set and data dictionary. |
| name_check | check | Checks that variable names match between the data set and data dictionary. |
| id_check | check | Checks that the first column of the data set is the primary ID for each participant labeled as SUBJECT_ID, that values contain no illegal characters or padded zeros, and that each participant has an ID. |
| row_check | check | Checks for empty or duplicate rows in the data set and data dictionary. |
| NA_check | check | Checks for NA values in the data set and, if NA values are present, also checks for an encoded NA value=meaning description. |
| type_check | check | If a TYPE field exists, this function checks for any TYPE entries that aren’t allowable per dbGaP instructions. |
| values_check | check | Checks for potential errors in the VALUES columns by ensuring (1) required format of VALUE=MEANING (e.g., 0=No or 1=Yes); (2) no leading/trailing spaces near the equals sign (e.g., 0=No vs. 0 = No); (3) all variables of TYPE encoded have VALUES entries; and (4) all variables with VALUES entries are listed as TYPE encoded. |
| integer_check | check | Checks for variables that appear to be incorrectly listed as TYPE integer. |
| decimal_check | check | Checks for variables that appear to be incorrectly listed as TYPE decimal. |
| misc_format_check | check | Checks miscellaneous dbGaP formatting requirements to ensure (1) no empty variable names; (2) no duplicate variable names; (3) variable names do not contain “dbgap”; (4) there are no duplicate column names in the dictionary; and (5) column names falling after VALUES column are unnamed. |
| description_check | check | Checks for unique and non-missing descriptions (VARDESC) for every variable in the data dictionary. |
| minmax_check | check | Checks for variables that have values exceeding the listed MIN or MAX. |
| missing_value_check | check | Checks for variables that have non-encoded missing value codes. |
| complete_check | bulk check | Runs the entire workflow (field_check, pkg_field_check, dimension_check, name_check, id_check, row_check, NA_check, type_check, values_check, integer_check, decimal_check, misc_format_check, description_check, minmax_check, and missing_value_check). |
| add_missing_fields | utility | Adds additional fields required by this package including variable type (‘TYPE’), minimum value (‘MIN’), and maximum value (‘MAX’). |
| name_correct | utility | Updates the data set so variable names match those listed in the data dictionary. |
| reorder_dictionary | utility | Reorders the data dictionary to match the data set. |
| reorder_data | utility | Reorders the data set to match the data dictionary. |
| id_first_data | utility | Reorders the data set so that SUBJECT_ID comes first. |
| id_first_dict | utility | Reorders the data dictionary so that SUBJECT_ID comes first. |
| label_data | utility, awareness | Adds non-missing information from the data dictionary as attributes to the data. |
| value_meaning_table | utility, awareness | Generates a value-meaning table by parsing the VALUES fields. |
| missingness_summary | awareness | Summarizes the amount of missingness in the data set. |
| value_missing_table | awareness | Checks for consistent usage of encoded values and missing value codes between the data dictionary and the data set. |
| dictionary_search | awareness | Facilitates searches of the data dictionary. |
| check_report | bulk check, reporting | Generates a user-readable report of the checks run by the complete_check function. |
| create_report | reporting, awareness | Generates a textual and graphical report of the selected variables in HTML format. |
| create_awareness_report | reporting, awareness | Generates an awareness report, calling missingness_summary and value_missing_table functions. |
List of function names and types.
2 Copyright information
Copyright 2022, University of Pittsburgh. All Rights Reserved. License: GPL-2
3 Installation
You can install dbGaPCheckup from CRAN using:
You can install the development version of dbGaPCheckup from GitHub using:
# install.packages("devtools")
devtools::install_github("lwheinsberg/dbGaPCheckup")4 Example
An introductory example is provided below. For more details see https://lwheinsberg.github.io/dbGaPCheckup/ or the dbGaPCheckup_vignette (expanded instructions) or dbGaPCheckup (Quick Start) vignette documents.
A special note: As you will see below, this package requires several fields beyond those required by the dbGaP formatting requirements. Specifically, while dbGaP requires that only the VARNAME, VARDESC, UNITS, and VALUES columns be present in the data dictionary, this package requires that MIN, MAX, and TYPE fields are also included. If your data dictionary does not include these fields already, you can use the add_missing_fields function to auto fill them (see below).
Read in your Subject Phenotype data into DS.data.
DS.path <- system.file("extdata", "DS_Example.txt",
package = "dbGaPCheckup", mustWork=TRUE)
DS.data <- read.table(DS.path, header=TRUE, sep="\t", quote="", as.is = TRUE)Read in your Subject Phenotype data dictionary into DD.dict.
DD.path <- system.file("extdata", "DD_Example2f.xlsx",
package = "dbGaPCheckup", mustWork=TRUE)
DD.dict <- readxl::read_xlsx(DD.path)
#> New names:
#> • `` -> `...15`
#> • `` -> `...16`
#> • `` -> `...17`
#> • `` -> `...18`
#> • `` -> `...19`Run the function check_report. Note that, for many functions, specification of missing value codes are important for accurate results.
report <- check_report(DD.dict = DD.dict, DS.data = DS.data, non.NA.missing.codes=c(-4444, -9999))
#> # A tibble: 15 × 3
#> Function Status Message
#> <chr> <chr> <chr>
#> 1 field_check Passed Passed: required fields VARNAME, VARDESC, …
#> 2 pkg_field_check Failed ERROR: not all package-level required fiel…
#> 3 dimension_check Passed Passed: the variable count matches between…
#> 4 name_check Passed Passed: the variable names match between t…
#> 5 id_check Passed Passed: All ID variable checks passed.
#> 6 row_check Passed Passed: no blank or duplicate rows detecte…
#> 7 NA_check Not attempted ERROR: Required pre-check pkg_field_check …
#> 8 type_check Failed ERROR: TYPE column not found. Consider usi…
#> 9 values_check Not attempted ERROR: Required pre-check type_check faile…
#> 10 integer_check Not attempted ERROR: Required pre-check pkg_field_check …
#> 11 decimal_check Not attempted ERROR: Required pre-check pkg_field_check …
#> 12 misc_format_check Passed Passed: no check-specific formatting issue…
#> 13 description_check Failed ERROR: missing and duplicate descriptions …
#> 14 minmax_check Not attempted ERROR: Required pre-check pkg_field_check …
#> 15 missing_value_check Not attempted ERROR: Required pre-check pkg_field_check …
#> --------------------
#> pkg_field_check: Failed
#> ERROR: not all package-level required fields are present in the data dictionary. Consider using the add_missing_fields function to auto fill these fields.
#> $pkg_field_check.Info
#> TYPE MIN MAX
#> FALSE FALSE FALSE
#>
#> --------------------
#> type_check: Failed
#> ERROR: TYPE column not found. Consider using the add_missing_fields function to autofill TYPE.
#> $type_check.Info
#> [1] "ERROR: TYPE column not found."
#>
#> --------------------
#> description_check: Failed
#> ERROR: missing and duplicate descriptions found in data dictionary.
#> $description_check.Info
#> # A tibble: 4 × 2
#> VARNAME VARDESC
#> <chr> <chr>
#> 1 PREGNANT <NA>
#> 2 REACT <NA>
#> 3 HEIGHT Height of participant
#> 4 WEIGHT Height of participant
#>
#> --------------------As described in more detail in the dbGaPCheckup_vignette vignette, some checks contain embedded “pre-checks” that must be passed before the check can be run. For example, as mentioned above, this package requires MIN, MAX, and TYPE fields in the data dictionary. We can see above that several of the checks (e.g., NA_check, integer_check, decimal_check) were not run because these additional required fields were missing. Never fear though! We have created a function to auto fill these fields that can be used to get further along in the checks!
DD.dict.updated <- add_missing_fields(DD.dict, DS.data)
#> $Message
#> [1] "CORRECTED ERROR: not all package-level required fields were present in the data dictionary. The missing fields have now been added! TYPE was inferred from the data, and MIN/MAX have been added as empty fields."
#>
#> $Missing
#> [1] "TYPE" "MIN" "MAX"Once the fields are added, you can simply return to run your checks. Don’t forget to use the updated dictionary though!
report.v2 <- check_report(DD.dict = DD.dict.updated , DS.data = DS.data, non.NA.missing.codes=c(-4444, -9999))
#> # A tibble: 15 × 3
#> Function Status Message
#> <chr> <chr> <chr>
#> 1 field_check Passed Passed: required fields VARNAME, VARDESC, UNITS, …
#> 2 pkg_field_check Passed Passed: package-level required fields TYPE, MIN, …
#> 3 dimension_check Passed Passed: the variable count matches between the da…
#> 4 name_check Passed Passed: the variable names match between the data…
#> 5 id_check Passed Passed: All ID variable checks passed.
#> 6 row_check Passed Passed: no blank or duplicate rows detected in da…
#> 7 NA_check Passed Passed: no NA values detected in data set.
#> 8 type_check Passed Passed: All TYPE entries found are accepted by db…
#> 9 values_check Passed Passed: all four VALUES checks look good.
#> 10 integer_check Passed Passed: all variables listed as TYPE integer appe…
#> 11 decimal_check Passed Passed: all variables listed as TYPE decimal appe…
#> 12 misc_format_check Passed Passed: no check-specific formatting issues ident…
#> 13 description_check Failed ERROR: missing and duplicate descriptions found i…
#> 14 minmax_check Passed Passed: when provided, all variables are within t…
#> 15 missing_value_check Failed ERROR: some variables have non-encoded missing va…
#> --------------------
#> description_check: Failed
#> ERROR: missing and duplicate descriptions found in data dictionary.
#> $description_check.Info
#> # A tibble: 4 × 2
#> VARNAME VARDESC
#> <chr> <chr>
#> 1 PREGNANT <NA>
#> 2 REACT <NA>
#> 3 HEIGHT Height of participant
#> 4 WEIGHT Height of participant
#>
#> --------------------
#> missing_value_check: Failed
#> ERROR: some variables have non-encoded missing value codes.
#> $missing_value_check.Info
#> VARNAME VALUE MEANING PASS
#> 16 CUFFSIZE -9999 <NA> FALSE
#>
#> --------------------Now we see that 13 out of 15 checks pass, but the workflow fails at description_check and missing_value_check. Specifically, in description_check we see that variables PREGNANT and REACT were identified as having missing variable descriptions (VARDESC), and variables HEIGHT and WEIGHT incorrectly have identical descriptions. In missing_value_check, we see that the variable CUFFSIZE contains a -9999 encoded value that is not specified in a VALUES column. While we have included functions that support “simple fixes”, the issues identified here would need to be corrected manually in your data dictionary before moving on.
Note that our package also includes some helpful reporting functions (create_report and create_awareness_report) that can help you more fully interrogate your data and catch potential errors prior to dbGaP data submission.
Finally, after your data dictionary is fully consistent with your data, you can use the label_data function to convert your data to labelled data, essentially embedding the data dictionary into the data.
For more information about these reporting functions, and all of the checks available in our package, see the Quick Start (dbGaPCheckup) or expanded (dbGaPCheckup_vignette) vignettes for more information!
5 Contact information
If you have any questions or comments, please feel free to contact us!
Lacey W. Heinsberg, PhD, RN: law145@pitt.edu
Daniel E. Weeks, PhD: weeks@pitt.edu
Bug reports: https://github.com/lwheinsberg/dbGaPCheckup/issues
6 Acknowledgments
This package was developed with partial support from the National Institutes of Health under award numbers R01HL093093, R01HL133040, and K99HD107030. The eval_function and dat_function functions that form the backbone of the awareness reports were inspired by an elegant 2016 homework answer submitted by Tanbin Rahman in our HUGEN 2070 course ‘Bioinformatics for Human Genetics’. We would also like to thank Nick Moshgat for testing and providing feedback on our package during development.