library(dplyr)
library(tidytlg)The tlgsetup helper function was created to support generating different column structures for our outputs. This allows us to specify column types once that can be used across multiple outputs. This metadata can be hardcoded as well (please see the code below), or pulled from another source like a dataframe, file or database.
Here we see an example of the column metadata which defines our columns, how to combine columns, column labels and spanning headers across columns.
column_metadata <-
tibble::tribble(
~tbltype, ~coldef, ~decode, ~span1,
"type1", "0", "Placebo", "",
"type1", "54", "Low Dose", "Xanomeline",
"type1", "81", "High Dose", "Xanomeline",
"type1", "54+81", "Total Xanomeline", ""
)
column_metadata| tbltype | coldef | decode | span1 |
|---|---|---|---|
| type1 | 0 | Placebo | |
| type1 | 54 | Low Dose | Xanomeline |
| type1 | 81 | High Dose | Xanomeline |
| type1 | 54+81 | Total Xanomeline |
In this example, we would summarize Placebo, Xanomeline Low Dose, and
Xanomeline High Dose which are already available in the data. The helper
function will add the observations to the data for the Total Xanomeline
column based off of coldef. So for our adsl data, we will
add a spanning header for Xanomeline and a total Xanomeline column which
is a combination of the Xanomeline Low Dose and Xanomeline High
Dose.
In addition, the helper function will add the factor variable
colnbr which is used as our new column summary variable.
Note that our column summary variable has been converted to a factor.
This is required and allows us to define column order as well as label
in one variable.
Let’s read in adsl and check the dimensions.
data("cdisc_adsl")
adsl <- cdisc_adsl %>%
filter(ITTFL == "Y") %>%
select(USUBJID, TRT01PN, TRT01P, ITTFL, SEX, RACE, AGE)
glimpse(adsl)
#> Rows: 15
#> Columns: 7
#> $ USUBJID <chr> "01-701-1015", "01-701-1023", "01-701-1047", "01-701-1118", "0…
#> $ TRT01PN <dbl> 0, 0, 0, 0, 0, 54, 54, 54, 54, 54, 81, 81, 81, 81, 81
#> $ TRT01P <chr> "Placebo", "Placebo", "Placebo", "Placebo", "Placebo", "Xanome…
#> $ ITTFL <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y…
#> $ SEX <chr> "F", "M", "F", "M", "M", "M", "M", "F", "M", "M", "M", "F", "F…
#> $ RACE <chr> "WHITE", "WHITE", "WHITE", "WHITE", "WHITE", "WHITE", "WHITE",…
#> $ AGE <dbl> 63, 64, 85, 52, 84, 74, 68, 81, 84, 71, 71, 77, 81, 75, 57#> [1] "Dimensions prior to the tlgsetup call are 15 rows and 7 columns."Now let’s pass the adsl data through our tlgsetup function to add
observations to support the type1 column structure and
check out the dimensions.
setup_table <- tlgsetup(adsl,
var = "TRT01PN",
column_metadata = column_metadata)
glimpse(setup_table)
#> Rows: 25
#> Columns: 9
#> $ tbltype <chr> "type1", "type1", "type1", "type1", "type1", "type1", "type1",…
#> $ colnbr <fct> col1, col1, col1, col1, col1, col2, col2, col2, col2, col2, co…
#> $ USUBJID <chr> "01-701-1015", "01-701-1023", "01-701-1047", "01-701-1118", "0…
#> $ TRT01PN <chr> "0", "0", "0", "0", "0", "54", "54", "54", "54", "54", "81", "…
#> $ TRT01P <chr> "Placebo", "Placebo", "Placebo", "Placebo", "Placebo", "Xanome…
#> $ ITTFL <chr> "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y", "Y…
#> $ SEX <chr> "F", "M", "F", "M", "M", "M", "M", "F", "M", "M", "M", "F", "F…
#> $ RACE <chr> "WHITE", "WHITE", "WHITE", "WHITE", "WHITE", "WHITE", "WHITE",…
#> $ AGE <dbl> 63, 64, 85, 52, 84, 74, 68, 81, 84, 71, 71, 77, 81, 75, 57, 74…#> [1] "Dimensions after to the rmtsetup call are 25 rows and 9 columns."Here we see we have added two new variables, colnbr and
tbltype, which will now be used as our treatment variable
when generating our results.
If we take a look at the observation counts for colnbr,
we see the 168 records added to the data support
Total Xanomeline as we expected!
setup_table %>%
group_by(colnbr) %>%
count()
#> # A tibble: 4 × 2
#> # Groups: colnbr [4]
#> colnbr n
#> <fct> <int>
#> 1 col1 5
#> 2 col2 5
#> 3 col3 5
#> 4 col4 10