Examining Delayed Data Objects

Overview

Once your delayed data object has been created as described in Delayed Data Objects, teal.data provides a useful set of functions to examine the object outside of a shiny application, i.e. the global environment. Below is an exhaustive list of all such functions:

	`TealDataset`	`TealDatasetConnector`	`TealDataConnector` & `TealData`
Get Reproducible Code (Optionally Deparsed)	`get_code`	`get_code`	`get_code`
Get data.frame	`get_raw_data`	`get_raw_data`	`get_raw_data`
Get Dataset Name	`get_dataname`	`get_dataname`	`get_dataname`
Get Single Dataset Object	`get_dataset`	`get_dataset`	`get_dataset`
Get All Dataset Objects	-	-	`get_datasets`
Load Data	-	`load_dataset`	`load_datasets`
Check if Loaded	-	`is_pulled`	`is_pulled`
Mutate Single Dataset	`mutate_dataset`	`mutate_dataset`	`mutate_dataset`
Mutate All Datasets	-	-	`mutate_data`

The most basic function get_dataname returns the name of the dataset or datasets in your delayed data object:

library(teal.data)
library(magrittr)

adsl_cf <- callable_function(teal.data::example_cdisc_data) %>%
  set_args(list(dataname = "ADSL"))
adsl <- cdisc_dataset_connector(
  dataname = "ADSL",
  pull_callable = adsl_cf,
  keys = get_cdisc_keys("ADSL")
)
get_dataname(adsl) # "ADSL"

## [1] "ADSL"

adae_cf <- callable_function(teal.data::example_cdisc_data) %>%
  set_args(list(dataname = "ADAE"))
adae <- cdisc_dataset_connector(
  dataname = "ADAE",
  pull_callable = adae_cf,
  keys = get_cdisc_keys("ADAE")
)
delayed_data <- cdisc_data(adsl, adae)
get_dataname(delayed_data) # "ADSL" "ADAE"

## [1] "ADSL" "ADAE"

The delayed data objects described above all also contain a launch method which can be used to test the data loading screen:

if (interactive()) {
  delayed_data$launch()
}

There is also a pull method to test that the data can be loaded without launching a shiny app. See Delayed Data Advanced.

Alternatively teal.data provides a load_dataset function for <...>Dataset<...> objects which is used to pull the data without launching the delayed loading screen, and a load_datasetsfunction for <...>Data<...> objects which launches the delayed loading screen used to pull the datasets from the connection.

After loading the data, it can be checked that the data has been successfully pulled using the is_pulled function:

if (interactive()) {
  load_datasets(delayed_data)
}
is_pulled(delayed_data)

## [1] FALSE

Aside: Loading page UI

It is possible to set default values of the boxes on the loading page using the set_ui_input method:

adae$set_ui_input(function(ns) {
  list(numericInput(inputId = ns("n"), label = "Choose number of records", min = 0, value = 5))
})

Testing data loading continued

Once the data are loaded, it’s also possible to access the individual dataset objects using the get_dataset function, or for <...>Data<...> objects, retrieve all dataset objects using the get_datasets function:

lapply(delayed_data$get_items(), function(item) item$pull())

# return a particular dataset by name
get_dataset(delayed_data, dataname = "ADSL")

# or return all datasets
load_datasets(delayed_data)
get_datasets(delayed_data)

Note that when a connector is loaded, the result is a dataset object:

# "CDISCTealDatasetConnector" "TealDatasetConnector" "R6"
class(adsl)

## [1] "CDISCTealDatasetConnector" "TealDatasetConnector"     
## [3] "R6"

# "CDISCTealDataset" "TealDataset" "R6"
class(get_dataset(adsl))

## [1] "CDISCTealDataset" "TealDataset"      "R6"

To view the raw dataframe object, use the get_raw_data function:

# for a single <...>Dataset<..> object
head(get_raw_data(adsl), 2)

##   STUDYID               USUBJID SUBJID SITEID AGE  AGEU SEX
## 1 AB12345  AB12345-CHN-3-id-128 id-128  CHN-3  32 YEARS   M
## 2 AB12345 AB12345-CHN-15-id-262 id-262 CHN-15  35 YEARS   M
##                        RACE                 ETHNIC COUNTRY DTHFL         INVID
## 1                     ASIAN     HISPANIC OR LATINO     CHN     Y  INV ID CHN-3
## 2 BLACK OR AFRICAN AMERICAN NOT HISPANIC OR LATINO     CHN     N INV ID CHN-15
##           INVNAM            ARM ARMCD         ACTARM ACTARMCD         TRT01P
## 1  Dr. CHN-3 Doe      A: Drug X ARM A      A: Drug X    ARM A      A: Drug X
## 2 Dr. CHN-15 Doe C: Combination ARM C C: Combination    ARM C C: Combination
##           TRT01A     TRT02P         TRT02A REGION1 STRATA1 STRATA2    BMRKR1
## 1      A: Drug X B: Placebo      A: Drug X    Asia       C      S2 14.424934
## 2 C: Combination B: Placebo C: Combination    Asia       C      S1  4.055463
##   BMRKR2 ITTFL SAFFL BMEASIFL BEP01FL AEWITHFL     RANDDT             TRTSDTM
## 1 MEDIUM     Y     Y        Y       Y        N 2019-02-22 2019-02-24 11:09:25
## 2    LOW     Y     Y        N       N        Y 2019-02-26 2019-02-26 09:05:10
##               TRTEDTM           TRT01SDTM           TRT01EDTM
## 1 2022-02-12 04:28:08 2019-02-24 11:09:25 2021-02-11 22:28:08
## 2 2022-02-26 03:05:10 2019-02-26 09:05:10 2021-02-25 21:05:10
##             TRT02SDTM           TRT02EDTM            AP01SDTM
## 1 2021-02-11 22:28:08 2022-02-12 04:28:08 2019-02-24 11:09:25
## 2 2021-02-25 21:05:10 2022-02-26 03:05:10 2019-02-26 09:05:10
##              AP01EDTM            AP02SDTM            AP02EDTM       EOSSTT
## 1 2021-02-11 22:28:08 2021-02-11 22:28:08 2022-02-12 04:28:08 DISCONTINUED
## 2 2021-02-25 21:05:10 2021-02-25 21:05:10 2022-02-26 03:05:10    COMPLETED
##         EOTSTT      EOSDT EOSDY DCSREAS      DTHDT       DTHCAUS        DTHCAT
## 1 DISCONTINUED 2022-02-12  1084   DEATH 2022-03-06 ADVERSE EVENT ADVERSE EVENT
## 2    COMPLETED 2022-02-26  1096    <NA>       <NA>          <NA>          <NA>
##   LDDTHELD LDDTHGR1   LSTALVDT DTHADY ADTHAUT
## 1       22     <=30 2022-03-06   1105     Yes
## 2       NA     <NA> 2022-03-17     NA    <NA>

# or for a <...>Data<...> object containing multiple datasets, specify the name of the dataset of interest
raw <- get_raw_data(delayed_data, "ADSL")
head(raw, 2)

##   STUDYID               USUBJID SUBJID SITEID AGE  AGEU SEX
## 1 AB12345  AB12345-CHN-3-id-128 id-128  CHN-3  32 YEARS   M
## 2 AB12345 AB12345-CHN-15-id-262 id-262 CHN-15  35 YEARS   M
##                        RACE                 ETHNIC COUNTRY DTHFL         INVID
## 1                     ASIAN     HISPANIC OR LATINO     CHN     Y  INV ID CHN-3
## 2 BLACK OR AFRICAN AMERICAN NOT HISPANIC OR LATINO     CHN     N INV ID CHN-15
##           INVNAM            ARM ARMCD         ACTARM ACTARMCD         TRT01P
## 1  Dr. CHN-3 Doe      A: Drug X ARM A      A: Drug X    ARM A      A: Drug X
## 2 Dr. CHN-15 Doe C: Combination ARM C C: Combination    ARM C C: Combination
##           TRT01A     TRT02P         TRT02A REGION1 STRATA1 STRATA2    BMRKR1
## 1      A: Drug X B: Placebo      A: Drug X    Asia       C      S2 14.424934
## 2 C: Combination B: Placebo C: Combination    Asia       C      S1  4.055463
##   BMRKR2 ITTFL SAFFL BMEASIFL BEP01FL AEWITHFL     RANDDT             TRTSDTM
## 1 MEDIUM     Y     Y        Y       Y        N 2019-02-22 2019-02-24 11:09:25
## 2    LOW     Y     Y        N       N        Y 2019-02-26 2019-02-26 09:05:10
##               TRTEDTM           TRT01SDTM           TRT01EDTM
## 1 2022-02-12 04:28:08 2019-02-24 11:09:25 2021-02-11 22:28:08
## 2 2022-02-26 03:05:10 2019-02-26 09:05:10 2021-02-25 21:05:10
##             TRT02SDTM           TRT02EDTM            AP01SDTM
## 1 2021-02-11 22:28:08 2022-02-12 04:28:08 2019-02-24 11:09:25
## 2 2021-02-25 21:05:10 2022-02-26 03:05:10 2019-02-26 09:05:10
##              AP01EDTM            AP02SDTM            AP02EDTM       EOSSTT
## 1 2021-02-11 22:28:08 2021-02-11 22:28:08 2022-02-12 04:28:08 DISCONTINUED
## 2 2021-02-25 21:05:10 2021-02-25 21:05:10 2022-02-26 03:05:10    COMPLETED
##         EOTSTT      EOSDT EOSDY DCSREAS      DTHDT       DTHCAUS        DTHCAT
## 1 DISCONTINUED 2022-02-12  1084   DEATH 2022-03-06 ADVERSE EVENT ADVERSE EVENT
## 2    COMPLETED 2022-02-26  1096    <NA>       <NA>          <NA>          <NA>
##   LDDTHELD LDDTHGR1   LSTALVDT DTHADY ADTHAUT
## 1       22     <=30 2022-03-06   1105     Yes
## 2       NA     <NA> 2022-03-17     NA    <NA>

# note the raw data is now just a regular R table
class(raw)

## [1] "tbl_df"     "tbl"        "data.frame"

The get_code function is called to check that the processing code is as expected (and for reproducibility).

get_code(delayed_data)

## [1] "ADSL <- teal.data::example_cdisc_data(dataname = \"ADSL\")\nADAE <- teal.data::example_cdisc_data(dataname = \"ADAE\")"

See the section on pre-processing Delayed Data to specify additional code instructions to transform your delayed data which will also be added to the output of get_code.

Aside: Piping functions

The examples above covered some basic piping, but there is a natural sequence to the loading and inspection of a delayed data object. For this reason, the magrittr pipe %>% works well for many pre-processing tasks.

library(teal.data)
library(magrittr)

adsl_cf <- callable_function(teal.data::example_cdisc_data) %>%
  set_args(list(dataname = "ADSL"))
cdisc_dataset_connector(
  dataname = "ADSL",
  pull_callable = adsl_cf,
  keys = get_cdisc_keys("ADSL")
) %>%
  mutate_dataset("ADSL$TRTDUR <- round(as.numeric(ADSL$TRTEDTM - ADSL$TRTSDTM), 1)") %>%
  load_dataset() %>%
  get_raw_data() %>%
  head(n = 2)

##   STUDYID               USUBJID SUBJID SITEID AGE  AGEU SEX
## 1 AB12345  AB12345-CHN-3-id-128 id-128  CHN-3  32 YEARS   M
## 2 AB12345 AB12345-CHN-15-id-262 id-262 CHN-15  35 YEARS   M
##                        RACE                 ETHNIC COUNTRY DTHFL         INVID
## 1                     ASIAN     HISPANIC OR LATINO     CHN     Y  INV ID CHN-3
## 2 BLACK OR AFRICAN AMERICAN NOT HISPANIC OR LATINO     CHN     N INV ID CHN-15
##           INVNAM            ARM ARMCD         ACTARM ACTARMCD         TRT01P
## 1  Dr. CHN-3 Doe      A: Drug X ARM A      A: Drug X    ARM A      A: Drug X
## 2 Dr. CHN-15 Doe C: Combination ARM C C: Combination    ARM C C: Combination
##           TRT01A     TRT02P         TRT02A REGION1 STRATA1 STRATA2    BMRKR1
## 1      A: Drug X B: Placebo      A: Drug X    Asia       C      S2 14.424934
## 2 C: Combination B: Placebo C: Combination    Asia       C      S1  4.055463
##   BMRKR2 ITTFL SAFFL BMEASIFL BEP01FL AEWITHFL     RANDDT             TRTSDTM
## 1 MEDIUM     Y     Y        Y       Y        N 2019-02-22 2019-02-24 11:09:25
## 2    LOW     Y     Y        N       N        Y 2019-02-26 2019-02-26 09:05:10
##               TRTEDTM           TRT01SDTM           TRT01EDTM
## 1 2022-02-12 04:28:08 2019-02-24 11:09:25 2021-02-11 22:28:08
## 2 2022-02-26 03:05:10 2019-02-26 09:05:10 2021-02-25 21:05:10
##             TRT02SDTM           TRT02EDTM            AP01SDTM
## 1 2021-02-11 22:28:08 2022-02-12 04:28:08 2019-02-24 11:09:25
## 2 2021-02-25 21:05:10 2022-02-26 03:05:10 2019-02-26 09:05:10
##              AP01EDTM            AP02SDTM            AP02EDTM       EOSSTT
## 1 2021-02-11 22:28:08 2021-02-11 22:28:08 2022-02-12 04:28:08 DISCONTINUED
## 2 2021-02-25 21:05:10 2021-02-25 21:05:10 2022-02-26 03:05:10    COMPLETED
##         EOTSTT      EOSDT EOSDY DCSREAS      DTHDT       DTHCAUS        DTHCAT
## 1 DISCONTINUED 2022-02-12  1084   DEATH 2022-03-06 ADVERSE EVENT ADVERSE EVENT
## 2    COMPLETED 2022-02-26  1096    <NA>       <NA>          <NA>          <NA>
##   LDDTHELD LDDTHGR1   LSTALVDT DTHADY ADTHAUT TRTDUR
## 1       22     <=30 2022-03-06   1105     Yes 1083.7
## 2       NA     <NA> 2022-03-17     NA    <NA> 1095.8

Since these functions modify (operate on) the objects that are given to them, there is no need to assign the result.

For an introduction to pipes, refer to the documentation for %>% or other resources on pipes.

Dawid Kałędkowski

15.03.2022

Overview

Aside: Loading page UI

Testing data loading continued

Aside: Piping functions