# Example

    library(metacore)
    library(xml2)

CDISC standards provide a standard for submission of data set metadata through a document known as define.xml. The define provides a great deal of useful information that is both machine readable and can be viewed through your web browser. While many organizations wait to produce a define until the datasets are finalized, it can still be advantageous to be able to read metadata directly from a define. For this purpose, we developed readers that can go directly from a define.xml to a metacore object.

To do this, we’ve built separate reader function for each of the metacore tables. For more information on the structure of the metacore tables, check out the README.

We start by reading the define from disk using the `xmlTreeParse()` function from the `XML` package.

    doc <- read_xml(metacore_example("SDTM_define.xml"))
    xml_ns_strip(doc)

Next, we use the metacore readers for each of the separate tables necessary for a metacore object.

    ds_spec2 <- xml_to_ds_spec(doc)
    ds_vars <- xml_to_ds_vars(doc)
    var_spec <- xml_to_var_spec(doc)
    value_spec <- xml_to_value_spec(doc)
    code_list <- xml_to_codelist(doc)
    derivations <- xml_to_derivations(doc)

Great! Now we’re ready to create our metacore object.

    test <- metacore(ds_spec2, ds_vars, var_spec, value_spec, derivations, code_list)
    #> Warning: The following word in value_spec$origin is not allowed:
    #> ℹ edt
    #> Warning: `core` from the `ds_vars` table only contains missing values.
    #> Warning: `supp_flag` from the `ds_vars` table only contains missing values.
    #> Warning: `common` from the `var_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.
    #> Warning: The following derivations are never used:
    #> ℹ MT.SUPPAE.QVAL, MT.SUPPDM.QVAL
    #> Warning: The following codelists are never used:
    #> ℹ DRUG DICTIONARY, MEDICAL HISTORY DICTIONARY
    #> ✔ Metadata successfully imported
    #> ℹ To use the Metacore object with metatools package, first subset a dataset
    #>   using `metacore::select_dataset()`

Something to note about a metacore object is that it inherently holds all data from your source of metadata, be it your specification, define.xml, database, etc. So that means you have all the metadata. In your program, it’s likely that you’ll just want to keep metadata relevant to the dataset you’re currently programming. We’ve made process easily, with functions that filter metadata down to information only relevant to a specific dataset.

    # a metacore object with all your dataframes
    subset <- test %>% select_dataset("DM")
    #> 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: `common` 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
    #>
    subset$ds_spec
    #> # A tibble: 1 × 3
    #>   dataset structure              label
    #>   <chr>   <chr>                  <chr>
    #> 1 DM      One record per subject Demographics

    # a simplified dataframe
    subset_t <- test %>% select_dataset("DM", simplify = TRUE)

As can be seen above, the metacore object can be filtered directly, or by using the `simplify = TRUE` argument, a simplified data frame can be returned.

    subset_t
    #> # A tibble: 25 × 21
    #>    dataset variable key_seq order keep  core  supp_flag type     length label
    #>    <chr>   <chr>      <int> <int> <lgl> <chr> <lgl>     <chr>     <int> <chr>
    #>  1 DM      STUDYID        1     1 TRUE  <NA>  NA        text         12 Study I…
    #>  2 DM      DOMAIN        NA     2 TRUE  <NA>  NA        text          2 Domain …
    #>  3 DM      USUBJID        2     3 TRUE  <NA>  NA        text         11 Unique …
    #>  4 DM      SUBJID        NA     4 TRUE  <NA>  NA        text          4 Subject…
    #>  5 DM      RFSTDTC       NA     5 FALSE <NA>  NA        date         10 Subject…
    #>  6 DM      RFENDTC       NA     6 FALSE <NA>  NA        date         10 Subject…
    #>  7 DM      RFXSTDTC      NA     7 FALSE <NA>  NA        datetime     20 Date/Ti…
    #>  8 DM      RFXENDTC      NA     8 FALSE <NA>  NA        datetime     20 Date/Ti…
    #>  9 DM      RFICDTC       NA     9 FALSE <NA>  NA        datetime     20 Date/Ti…
    #> 10 DM      RFPENDTC      NA    10 FALSE <NA>  NA        datetime     20 Date/Ti…
    #> # ℹ 15 more rows
    #> # ℹ 11 more variables: format <chr>, common <lgl>, code_id <chr>,
    #> #   derivation_id <chr>, origin <chr>, where <chr>, sig_dig <int>,
    #> #   derivation <chr>, codes <list>, idvar <chr>, qeval <chr>