teal.gallery use with my CDSIC data

[asjohans]

I would like to use this app to look at my own CDISC data. I am a SAS and Python programmer and R is new to me. I see where the data is being imported from the SCDA.2022 library. Could I read in my data as shown below and comment out the other ADSL? I also see one other place where the data seems to be imported. How should I handle that? I mainly need to prove the concept and then have an R programmer take over to program it correctly. Thanks for any help you can provide!

#ADSL <- read_sas('D:\Users\xxx.xxxxx\Documents\r_code\test_teal\Test_data\adsl_dummy.sas7bdat')
ADSL <- synthetic_cdisc_data("latest")$adsl
ADRS <- synthetic_cdisc_data("latest")$adrs
ADLB <- synthetic_cdisc_data("latest")$adlb

adsl <- cdisc_dataset("ADSL", ADSL, code = "ADSL <- synthetic_cdisc_data("latest")$adsl")
adrs <- cdisc_dataset("ADRS", ADRS, code = "ADRS <- synthetic_cdisc_data("latest")$adrs")
adlb <- cdisc_dataset("ADLB", ADLB, code = "ADLB <- synthetic_cdisc_data("latest")$adlb")

[donyunardi]

Hi @asjohans

Yes, you should be able to reuse the app.R code in this package and plug in your own dataset.

Your update should be done before this line, because this is where teal app got initialize:

teal.gallery/inst/apps/early-dev/app.R

Line 405 in 0054787

app <- teal::init(

Your code should look something line this:

# your data code and pre-processing, if applicable
ADSL <- read_sas("your-local-path")
adsl <- cdisc_data("ADSL", ADSL) # make sure to include `code` and `vars` argument if needed

# then init starts here:
app <- teal::init( 
  data = cdisc_data(
    adsl,
    ...

However, I would like to inform you in advance that certain teal modules require the data to be structured in a specific format for the analysis to function properly. As an illustration, one instance could be where a teal module anticipates a variable with factor levels. It is important to be aware of this requirement. If you encounter any issues with a particular column or variable, there should be a message prompting the issue at hand, or another approach would be to compare the data type in your dataset with the one generated by the data processing mentioned in the app.R file.

Hope this helps.