# Creating Custom Tplyr Metadata

As covered in `vignette('metadata')`,Tplyr can produce metadata for any result that it calculates. But what about data that Tplyr can’t produce, such as a efficacy results or some sort of custom analysis? You may still want that drill down capability either on your own or paired with an existing Tplyr table.

Take for instance Table 14-3.01 from the CDISC Pilot. Skipping the actual construction of the table, here’s the output data from Tplyr and some manual calculation:

    kable(full_data)

row_id | row_label1 | row_label2 | var1_Placebo | var1_Xanomeline Low Dose | var1_Xanomeline High Dose
--- | --- | --- | --- | --- | ---
d1_1 | Baseline | n | 79 | 81 | 74
d2_1 |  | Mean (SD) | 24.1 (12.19) | 24.4 (12.92) | 21.3 (11.74)
d3_1 |  | Median (Range) | 21.0 ( 5;61) | 21.0 ( 5;57) | 18.0 ( 3;57)
d1_2 | Week 24 | n | 79 | 81 | 74
d2_2 |  | Mean (SD) | 26.7 (13.79) | 26.4 (13.18) | 22.8 (12.48)
d3_2 |  | Median (Range) | 24.0 ( 5;62) | 25.0 ( 6;62) | 20.0 ( 3;62)
d1_3 | Change from Baseline | n | 79 | 81 | 74
d2_3 |  | Mean (SD) | 2.5 ( 5.80) | 2.0 ( 5.55) | 1.5 ( 4.26)
d3_3 |  | Median (Range) | 2.0 (-11;16) | 2.0 (-11;17) | 1.0 ( -7;13)
x4_1 | p-value(Dose Response) [1][2] |  |  |  | 0.245
x4_2 |  |  |  |  |
x4_3 | p-value(Xan - Placebo) [1][3] |  |  | 0.569 | 0.233
x4_4 | Diff of LS Means (SE) |  |  | -0.5 (0.82) | -1.0 (0.84)
x4_5 | 95% CI |  |  | (-2.1;1.1) | (-2.7;0.7)
x4_6 |  |  |  |  |
x4_7 | p-value(Xan High - Xan Low) [1][3] |  |  |  | 0.520
x4_8 | Diff of LS Means (SE) |  |  |  | -0.5 (0.84)
x4_9 | 95% CI |  |  |  | (-2.2;1.1)

This is the primary efficacy table from the trial. The top portion of this table is fairly straightforward with Tplyr and can be done using descriptive statistic layers. Once you hit the p-values on the lower house, this becomes beyond Tplyr’s remit. To produce the table, you can combine Tplyr output with a separate data frame analyzed and formatted yourself (but note you can still use some help from Tplyr tools like `apply_formats()`).

But what about the metadata? How do you get the drill down capabilities for that lower half of the table? We’ve provided a couple additional tools in Tplyr to allow you to construct your own metadata and append existing metadata present in a Tplyr table.

## Build a `tplyr_meta` object

As covered in `vignette('metadata')`, a `tplyr_meta` object consists of two different fields: A list of variable names, and a list of filter conditions. You provide both of these fields as a list of quosures:

    m <- tplyr_meta(
      names = quos(a, b, c),
      filters = quos(a==1, b==2, c==3)
    )
    m
    #> tplyr_meta: 3 names, 3 filters
    #> Names:
    #>      a, b, c
    #> Filters:
    #>      a == 1, b == 2, c == 3

The `tplyr_meta()` function can take these fields immediately upon creation. If you need to dynamically create a `tplyr_meta` object such as how Tplyr constructs the objects internally), the functions `add_variables()` and `add_filters()` are available to extend an existing `tplyr_meta` object:

    m <- m %>%
      add_variables(quos(x)) %>%
      add_filters(quos(x == 'a'))

    m
    #> tplyr_meta: 4 names, 4 filters
    #> Names:
    #>      a, b, c, x
    #> Filters:
    #>      a == 1, b == 2, c == 3, x == "a"

## Building your own metadata table

Now that we can create our own `tplyr_meta` objects, let’s assemble the metadata for the bottom portion of Table 14-3.01:

    # Overall model subset of data
    meta <- tplyr_meta(
      names = quos(TRTP, EFFFL, ITTFL, ANL01FL, SITEGR1, AVISIT, AVISITN, PARAMCD, AVAL, BASE, CHG),
      filters = quos(EFFFL == "Y", ITTFL == "Y", PARAMCD == "ACTOT", ANL01FL == "Y", AVISITN == 24)
    )

    # Xan High / Placebo contrast
    meta_xhp <- meta %>%
      add_filters(quos(TRTP %in% c("Xanomeline High Dose", "Placebo")))

    # Xan Low / Placbo Contrast
    meta_xlp <- meta %>%
      add_filters(quos(TRTP %in% c("Xanomeline Low Dose", "Placebo")))

    # Xan High / Xan Low Contrast
    meta_xlh <- meta %>%
      add_filters(quos(TRTP %in% c("Xanomeline High Dose", "Xanomeline Low Dose")))

    eff_meta <- tibble::tribble(
      ~"row_id",  ~"row_label1",                       ~"var1_Xanomeline Low Dose", ~"var1_Xanomeline High Dose",
      "x4_1",    "p-value(Dose Response) [1][2]",      NULL,                        meta,
      "x4_3",    "p-value(Xan - Placebo) [1][3]",        meta_xlp,                    meta_xhp,
      "x4_4",    "   Diff of LS Means (SE)",           meta_xlp,                    meta_xhp,
      "x4_5",    "   95% CI",                          meta_xlp,                    meta_xhp,
      "x4_7",    "p-value(Xan High - Xan Low) [1][3]", NULL,                        meta_xlh,
      "x4_8",    "   Diff of LS Means (SE)",           NULL,                        meta_xlh,
      "x4_9",    "   95% CI",                          NULL,                        meta_xlh
    )

Let’s break down what happened here:

- First, we assemble the the overarching metadata object for the model. A lot of this metadata is shared across each of the different result cells for all of the efficacy data, so we can start by collecting this information into a `tplyr_meta` object.
- Next, we can use that starting point to build `tplyr_meta` objects for the other result cells. The model data contains contrasts of each of the different treatment group comparisons. By using `add_filters()`, we can create those additional three `tplyr_meta` objects using the starting point and attaching an additional filter condition.
- Lastly, to extend the metadata in the original `tplyr_table` object that created the summary portion of this table, we need a data frame. There’s a lot of ways to do this, but I like the display and explicitness of `tibble::tribble()`.

When building a data frame for use with `tplyr_table` metadata, there are really only two rules:

- You need a column in the data frame called `row_id`
- The `row_id` values cannot be duplicates of any other value within the existing metadata.

The `row_id` values built by Tplyr will always follow the format “n_n”, where the first letter of the layer type will either be “c”, “d”, or “s”. The next number is the layer number (i.e. the order in which the layer was inserted to the Tplyr table), and then finally the row of that layer within the output. For example, the third row of a count layer that was the second layer in the table would have a `row_id` of “c2_3”. In this example, I chose “x4_n” as the format for the “x” to symbolize custom, and these data can be thought of as the fourth layer. That said, these values would typically be masked by the viewer of the table so they really just need to be unique - so you can choose whatever you want.

### Anti-joins

If the custom metadata you’re constructing requires references to data outside your target dataset, this is also possible with a `tplyr_meta` object. If you’re looking for non-overlap with the target dataset, you can use an anti-join. Anti-joins can be added to a `tplyr_meta` object using the `add_anti_join()` function.

    meta %>%
      add_anti_join(
        join_meta = tplyr_meta(
          names = quos(TRT01P, EFFFL, ITTFL, SITEGR1),
          filters = quos(EFFFL == "Y", ITTFL == "Y")
        ),
        on = quos(USUBJID)
      )
    #> tplyr_meta: 11 names, 5 filters
    #> Names:
    #>      TRTP, EFFFL, ITTFL, ANL01FL, SITEGR1, AVISIT, AVISITN, PARAMCD, AVAL, BASE, CHG
    #> Filters:
    #>      EFFFL == "Y", ITTFL == "Y", PARAMCD == "ACTOT", ANL01FL == "Y", AVISITN == 24
    #> Anti-join:
    #>     Join Meta:
    #>         tplyr_meta: 4 names, 2 filters
    #>         Names:
    #>              TRT01P, EFFFL, ITTFL, SITEGR1
    #>         Filters:
    #>              EFFFL == "Y", ITTFL == "Y"
    #>     On:
    #>         USUBJID

## Appending Existing Tplyr Metadata

Now that we’ve created our custom extension of the Tplyr metadata, let’s extend the existing data frame. To do this, Tplyr has the function `append_metadata()`:

    t <- append_metadata(t, eff_meta)

Behind the scenes, this function simply binds the new metadata with the old in the proper section of the `tplyr_table` object. You can view the the `tplyr_table` metadata with the function `get_metadata()`:

    get_metadata(t)
    #> # A tibble: 16 × 6
    #>    row_id row_label1              row_label2 var1_Placebo var1_Xanomeline High…¹
    #>    <chr>  <chr>                   <chr>      <list>       <list>
    #>  1 d1_1   "Baseline"              n          <tplyr_mt>   <tplyr_mt>
    #>  2 d2_1   "Baseline"              Mean (SD)  <tplyr_mt>   <tplyr_mt>
    #>  3 d3_1   "Baseline"              Median (R… <tplyr_mt>   <tplyr_mt>
    #>  4 d1_2   "Week 24"               n          <tplyr_mt>   <tplyr_mt>
    #>  5 d2_2   "Week 24"               Mean (SD)  <tplyr_mt>   <tplyr_mt>
    #>  6 d3_2   "Week 24"               Median (R… <tplyr_mt>   <tplyr_mt>
    #>  7 d1_3   "Change from Baseline"  n          <tplyr_mt>   <tplyr_mt>
    #>  8 d2_3   "Change from Baseline"  Mean (SD)  <tplyr_mt>   <tplyr_mt>
    #>  9 d3_3   "Change from Baseline"  Median (R… <tplyr_mt>   <tplyr_mt>
    #> 10 x4_1   "p-value(Dose Response… <NA>       <NULL>       <tplyr_mt>
    #> 11 x4_3   "p-value(Xan - Placebo… <NA>       <NULL>       <tplyr_mt>
    #> 12 x4_4   "   Diff of LS Means (… <NA>       <NULL>       <tplyr_mt>
    #> 13 x4_5   "   95% CI"             <NA>       <NULL>       <tplyr_mt>
    #> 14 x4_7   "p-value(Xan High - Xa… <NA>       <NULL>       <tplyr_mt>
    #> 15 x4_8   "   Diff of LS Means (… <NA>       <NULL>       <tplyr_mt>
    #> 16 x4_9   "   95% CI"             <NA>       <NULL>       <tplyr_mt>
    #> # ℹ abbreviated name: ¹​`var1_Xanomeline High Dose`
    #> # ℹ 1 more variable: `var1_Xanomeline Low Dose` <list>

Finally, as with the automatically created metadata from Tplyr, we can query these result cells just the same:

    get_meta_subset(t, 'x4_1', "var1_Xanomeline High Dose") %>%
      head() %>%
      kable()

USUBJID | TRTP | EFFFL | ITTFL | ANL01FL | SITEGR1 | AVISIT | AVISITN | PARAMCD | AVAL | BASE | CHG
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
01-701-1015 | Placebo | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 8 | 13 | -5
01-701-1023 | Placebo | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 12 | 13 | -1
01-701-1028 | Xanomeline High Dose | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 3 | 3 | 0
01-701-1033 | Xanomeline Low Dose | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 7 | 7 | 0
01-701-1034 | Xanomeline High Dose | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 11 | 11 | 0
01-701-1047 | Placebo | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 19 | 10 | 9

## Metadata Without Tplyr

You very well may have a scenario where you want to use these metadata functions outside of Tplyr in general. As such, there are S3 methods available to query metadata from a dataframe instead of a Tplyr table, and parameters to provide your own target data frame:

    get_meta_subset(eff_meta, 'x4_1', "var1_Xanomeline High Dose", target=tplyr_adas) %>%
      head() %>%
      kable()

USUBJID | TRTP | EFFFL | ITTFL | ANL01FL | SITEGR1 | AVISIT | AVISITN | PARAMCD | AVAL | BASE | CHG
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
01-701-1015 | Placebo | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 8 | 13 | -5
01-701-1023 | Placebo | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 12 | 13 | -1
01-701-1028 | Xanomeline High Dose | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 3 | 3 | 0
01-701-1033 | Xanomeline Low Dose | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 7 | 7 | 0
01-701-1034 | Xanomeline High Dose | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 11 | 11 | 0
01-701-1047 | Placebo | Y | Y | Y | 701 | Week 24 | 24 | ACTOT | 19 | 10 | 9

As with the Tplyr metadata, the only strict criteria here is that your custom metadata have a `row_id` column.

## Tying it Together

The vignette wouldn’t be complete without the final contextual example - so here we go. Ultimately these pieces an all fit together in the context of a Shiny application and give you the desired click-through experience.

Source code available here