dbGaPCheckup Quick Start
Lacey W. Heinsberg and Daniel E. Weeks
September 27, 2023
Source:vignettes/dbGaPCheckup.Rmd
dbGaPCheckup.Rmd1 Copyright information
Copyright 2022, University of Pittsburgh. All Rights Reserved. License: GPL-2
2 Installation
To install from CRAN use:
install.packages("dbGaPCheckup")To install the development version from GitHub use:
devtools::install_github("lwheinsberg/dbGaPCheckup/pkg")3 Quick start
This document is designed to provide “quick start” guidance for using
the dbGaPCheckUp R package. Please see the table below and
dbGaPCheckup_vignette for more detailed information.
| 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. |
4 Usage
After the dbGaPCheckup package has been installed, you
can load the R package using this command:
Then proceed as follows:
- Read in your data into
DS.data; - Read in your data dictionary into
DD.dict; - Run the function
check_report, optionally defining any missing value codes (e.g., -9999) via thenon.NA.missing.codesargument.
Note, as you will see below, this package requires several fields
beyond those required by the dbGaP formatting
requirements. Specifically, the data dictionary is required to also
have MIN, MAX, and TYPE fields.
If your data dictionary does not include these fields already, you can
use the add_missing_fields function to auto fill them (see
below).
5 Example usage
5.2 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)
5.3 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`
5.4 Run the function
check_report.
With 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
#>
#> --------------------
5.4.1 If needed, run the
function add_missing_fields and repeat
check_report
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 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 return to run your checks.
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.
5.5 Reporting
Note that we have also created reporting functions that generate
graphical and textual descriptions and awareness checks of the data in
HTML format (see dbGaPCheckup_vignette vignette:
create_awareness_report (Appendix A) and
create_report (Appendix B) for more details). These reports
are designed to help you catch other potential errors in your data set.
Note that the create_report generated is quite long
however, so we recommend that you only submit subsets of variables at a
time. Specification of missing value codes are also important for
effective plotting.
# Functions not run here as they work best when initiated interactively
# Awareness Report (See Appendix A of the `dbGaPCheckup` vignette)
create_awareness_report(DD.dict.updated, DS.data, non.NA.missing.codes=c(-9999, -4444),
output.path= tempdir())
# Data Report (See Appendix B of the `dbGaPCheckup` vignette)
create_report(DD.dict.updated, DS.data, sex.split=TRUE, sex.name= "SEX",
start = 3, end = 7, non.NA.missing.codes=c(-9999,-4444),
output.path= tempdir(), open.html=TRUE)More details on execution and interpretation have been provided in
the dbGaPCheckup_vignette vignette.
5.6 Labelled data
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 future use!
DS_labelled_data <- label_data(DD.dict.updated, DS.data, non.NA.missing.codes=c(-9999))
labelled::var_label(DS_labelled_data$SEX)
#> [1] "Sex assigned at birth"
labelled::val_labels(DS_labelled_data$SEX)
#> male female
#> 0 1
attributes(DS_labelled_data$SEX)
#> $labels
#> male female
#> 0 1
#>
#> $label
#> [1] "Sex assigned at birth"
#>
#> $class
#> [1] "haven_labelled" "vctrs_vctr" "integer"
#>
#> $TYPE
#> [1] "integer, encoded value"
labelled::na_values(DS_labelled_data$HX_DEPRESSION)
#> missing value
#> -99996 Contact information
If you have any questions or comments, please feel free to contact us!
Lacey W. Heinsberg: law145@pitt.edu
Daniel E. Weeks: weeks@pitt.edu
Bug reports: https://github.com/lwheinsberg/dbGaPCheckup/issues
7 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.