This function leverages metadata available in a metacore object to apply labels to a data frame.
Arguments
- data
A dataframe or tibble upon which labels will be applied
- metacore
metacore object that contains the specifications for the dataset of interest.
- dataset_name
Optional string to specify the dataset that is being built. This is only needed if the metacore object provided hasn't already been subsetted.
Note: Deprecated in version 0.2.0. Thedataset_name
argument will be removed in a future release. Please usemetacore::select_dataset
to subset themetacore
object to obtain metadata for a single dataset.
Examples
mc <- metacore::spec_to_metacore(
metacore::metacore_example("p21_mock.xlsx"),
quiet=TRUE
)
#> ✔ Metadata successfully imported
#> ℹ Dataset metadata imported with suppressed warnings
#> ℹ To use the Metacore object with metatools package, first subset a dataset
#> using `metacore::select_dataset()`
#>
dm <- haven::read_xpt(metatools_example("dm.xpt"))
set_variable_labels(dm, mc, dataset_name = "DM")
#> ℹ The `dataset_name` argument will be removed in a future release. Please use
#> `metacore::select_dataset()` to subset the metacore object to obtain metadata
#> for a single dataset.
#> Warning: The `dataset_name` argument of `check_unique_keys()` is deprecated as of
#> metatools 0.2.0.
#> Warning: `core` from the `ds_vars` table only contains missing values.
#> Warning: `supp_flag` from the `ds_vars` table only contains missing values.
#> Warning: `format` from the `var_spec` table only contains missing values.
#> Warning: `sig_dig` from the `value_spec` table only contains missing values.
#> Warning: `where` from the `value_spec` table only contains missing values.
#> Warning: `dataset` from the `supp` table only contains missing values.
#> Warning: `variable` from the `supp` table only contains missing values.
#> Warning: `idvar` from the `supp` table only contains missing values.
#> Warning: `qeval` from the `supp` table only contains missing values.
#> ✔ DM dataset successfully selected
#>
#> # A tibble: 306 × 25
#> STUDYID DOMAIN USUBJID SUBJID RFSTDTC RFENDTC RFXSTDTC RFXENDTC RFICDTC
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 CDISCPILOT01 DM 01-701-… 1015 "2014-… "2014-… "2014-0… "2014-0… ""
#> 2 CDISCPILOT01 DM 01-701-… 1023 "2012-… "2012-… "2012-0… "2012-0… ""
#> 3 CDISCPILOT01 DM 01-701-… 1028 "2013-… "2014-… "2013-0… "2014-0… ""
#> 4 CDISCPILOT01 DM 01-701-… 1033 "2014-… "2014-… "2014-0… "2014-0… ""
#> 5 CDISCPILOT01 DM 01-701-… 1034 "2014-… "2014-… "2014-0… "2014-1… ""
#> 6 CDISCPILOT01 DM 01-701-… 1047 "2013-… "2013-… "2013-0… "2013-0… ""
#> 7 CDISCPILOT01 DM 01-701-… 1057 "" "" "" "" ""
#> 8 CDISCPILOT01 DM 01-701-… 1097 "2014-… "2014-… "2014-0… "2014-0… ""
#> 9 CDISCPILOT01 DM 01-701-… 1111 "2012-… "2012-… "2012-0… "2012-0… ""
#> 10 CDISCPILOT01 DM 01-701-… 1115 "2012-… "2013-… "2012-1… "2013-0… ""
#> # ℹ 296 more rows
#> # ℹ 16 more variables: RFPENDTC <chr>, DTHDTC <chr>, DTHFL <chr>, SITEID <chr>,
#> # AGE <dbl>, AGEU <chr>, SEX <chr>, RACE <chr>, ETHNIC <chr>, ARMCD <chr>,
#> # ARM <chr>, ACTARMCD <chr>, ACTARM <chr>, COUNTRY <chr>, DMDTC <chr>,
#> # DMDY <dbl>