Get dataset from TealDatasetConnector
get_dataset.Rd![[Stable]](figures/lifecycle-stable.svg)
Get dataset from TealDatasetConnector
Usage
get_dataset(x, dataname)
# S3 method for TealDatasetConnector
get_dataset(x, dataname = NULL)
# S3 method for TealDataset
get_dataset(x, dataname = NULL)
# S3 method for TealDataAbstract
get_dataset(x, dataname = NULL)Arguments
- x
(
TealDatasetConnectororTealDatasetConnectororTealDataAbstract)- dataname
(
character) a name of dataset to be retrieved
Details
See help(TealDataConnector) and help(TealData) for more complex examples.
Examples
# TealDatasetConnector --------
library(magrittr)
pull_fun_adae <- callable_function(teal.data::example_cdisc_data) %>%
set_args(list(dataname = "ADAE"))
ADSL <- teal.data::example_cdisc_data("ADSL")
dc <- dataset_connector(
dataname = "ADAE", pull_callable = pull_fun_adae,
keys = get_cdisc_keys("ADSL")
)
if (FALSE) {
load_dataset(dc)
get_dataset(dc)
}
# TealDataset --------
ADSL <- example_cdisc_data("ADSL")
x <- dataset("ADSL", ADSL)
get_dataset(x)
#> A TealDataset object containing the following data.frame (400 rows and 55 columns):
#> 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
#> 3 AB12345 AB12345-RUS-3-id-378 id-378 RUS-3 30 YEARS F
#> 4 AB12345 AB12345-CHN-11-id-220 id-220 CHN-11 26 YEARS F
#> 5 AB12345 AB12345-CHN-7-id-267 id-267 CHN-7 40 YEARS M
#> 6 AB12345 AB12345-CHN-15-id-201 id-201 CHN-15 49 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
#> 3 ASIAN NOT HISPANIC OR LATINO RUS N INV ID RUS-3
#> 4 ASIAN NOT HISPANIC OR LATINO CHN N INV ID CHN-11
#> 5 ASIAN NOT HISPANIC OR LATINO CHN N INV ID CHN-7
#> 6 ASIAN NOT HISPANIC OR LATINO CHN Y 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
#> 3 Dr. RUS-3 Doe C: Combination ARM C C: Combination ARM C C: Combination
#> 4 Dr. CHN-11 Doe B: Placebo ARM B B: Placebo ARM B B: Placebo
#> 5 Dr. CHN-7 Doe B: Placebo ARM B B: Placebo ARM B B: Placebo
#> 6 Dr. CHN-15 Doe C: Combination ARM C C: Combination ARM C C: Combination
#> TRT01A TRT02P TRT02A REGION1 STRATA1 STRATA2
#> 1 A: Drug X B: Placebo A: Drug X Asia C S2
#> 2 C: Combination B: Placebo C: Combination Asia C S1
#> 3 C: Combination A: Drug X B: Placebo Eurasia A S1
#> 4 B: Placebo B: Placebo B: Placebo Asia B S2
#> 5 B: Placebo C: Combination A: Drug X Asia C S1
#> 6 C: Combination B: Placebo C: Combination Asia C S2
#> BMRKR1 BMRKR2 ITTFL SAFFL BMEASIFL BEP01FL AEWITHFL RANDDT
#> 1 14.424934 MEDIUM Y Y Y Y N 2019-02-22
#> 2 4.055463 LOW Y Y N N Y 2019-02-26
#> 3 2.803240 HIGH Y Y Y N N 2019-02-24
#> 4 10.262734 MEDIUM Y Y Y Y N 2019-02-27
#> 5 6.206763 LOW Y Y N N N 2019-03-01
#> 6 6.906799 MEDIUM Y Y Y N N 2019-03-05
#> TRTSDTM TRTEDTM TRT01SDTM
#> 1 2019-02-24 11:09:25 2022-02-12 04:28:08 2019-02-24 11:09:25
#> 2 2019-02-26 09:05:10 2022-02-26 03:05:10 2019-02-26 09:05:10
#> 3 2019-02-28 03:19:22 2022-02-27 21:19:22 2019-02-28 03:19:22
#> 4 2019-03-01 13:33:19 2022-03-01 07:33:19 2019-03-01 13:33:19
#> 5 2019-03-02 00:09:33 2022-03-01 18:09:33 2019-03-02 00:09:33
#> 6 2019-03-05 15:24:07 2022-02-19 04:06:48 2019-03-05 15:24:07
#> TRT01EDTM TRT02SDTM TRT02EDTM
#> 1 2021-02-11 22:28:08 2021-02-11 22:28:08 2022-02-12 04:28:08
#> 2 2021-02-25 21:05:10 2021-02-25 21:05:10 2022-02-26 03:05:10
#> 3 2021-02-27 15:19:22 2021-02-27 15:19:22 2022-02-27 21:19:22
#> 4 2021-03-01 01:33:19 2021-03-01 01:33:19 2022-03-01 07:33:19
#> 5 2021-03-01 12:09:33 2021-03-01 12:09:33 2022-03-01 18:09:33
#> 6 2021-02-18 22:06:48 2021-02-18 22:06:48 2022-02-19 04:06:48
#> AP01SDTM AP01EDTM AP02SDTM
#> 1 2019-02-24 11:09:25 2021-02-11 22:28:08 2021-02-11 22:28:08
#> 2 2019-02-26 09:05:10 2021-02-25 21:05:10 2021-02-25 21:05:10
#> 3 2019-02-28 03:19:22 2021-02-27 15:19:22 2021-02-27 15:19:22
#> 4 2019-03-01 13:33:19 2021-03-01 01:33:19 2021-03-01 01:33:19
#> 5 2019-03-02 00:09:33 2021-03-01 12:09:33 2021-03-01 12:09:33
#> 6 2019-03-05 15:24:07 2021-02-18 22:06:48 2021-02-18 22:06:48
#> AP02EDTM EOSSTT EOTSTT EOSDT EOSDY DCSREAS
#> 1 2022-02-12 04:28:08 DISCONTINUED DISCONTINUED 2022-02-12 1084 DEATH
#> 2 2022-02-26 03:05:10 COMPLETED COMPLETED 2022-02-26 1096 <NA>
#> 3 2022-02-27 21:19:22 COMPLETED COMPLETED 2022-02-27 1096 <NA>
#> 4 2022-03-01 07:33:19 COMPLETED COMPLETED 2022-03-01 1096 <NA>
#> 5 2022-03-01 18:09:33 COMPLETED COMPLETED 2022-03-01 1096 <NA>
#> 6 2022-02-19 04:06:48 DISCONTINUED DISCONTINUED 2022-02-19 1082 DEATH
#> DTHDT DTHCAUS DTHCAT LDDTHELD LDDTHGR1 LSTALVDT DTHADY
#> 1 2022-03-06 ADVERSE EVENT ADVERSE EVENT 22 <=30 2022-03-06 1105
#> 2 <NA> <NA> <NA> NA <NA> 2022-03-17 NA
#> 3 <NA> <NA> <NA> NA <NA> 2022-03-11 NA
#> 4 <NA> <NA> <NA> NA <NA> 2022-03-26 NA
#> 5 <NA> <NA> <NA> NA <NA> 2022-03-15 NA
#> 6 2022-02-22 ADVERSE EVENT ADVERSE EVENT 3 <=30 2022-02-22 1084
#> ADTHAUT
#> 1 Yes
#> 2 <NA>
#> 3 <NA>
#> 4 <NA>
#> 5 <NA>
#> 6 Yes
#> ...
# TealData (not containing connectors) --------
adsl <- cdisc_dataset(
dataname = "ADSL",
x = example_cdisc_data("ADSL"),
code = "library(teal.data)\nADSL <- example_cdisc_data(\"ADSL\")"
)
adae <- cdisc_dataset(
dataname = "ADAE",
x = example_cdisc_data("ADAE"),
code = "library(teal.data)\nADAE <- example_cdisc_data(\"ADAE\")"
)
rd <- teal.data:::TealData$new(adsl, adae)
get_dataset(rd, "ADSL")
#> A CDISCTealDataset object containing the following data.frame (400 rows and 55 columns):
#> 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
#> 3 AB12345 AB12345-RUS-3-id-378 id-378 RUS-3 30 YEARS F
#> 4 AB12345 AB12345-CHN-11-id-220 id-220 CHN-11 26 YEARS F
#> 5 AB12345 AB12345-CHN-7-id-267 id-267 CHN-7 40 YEARS M
#> 6 AB12345 AB12345-CHN-15-id-201 id-201 CHN-15 49 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
#> 3 ASIAN NOT HISPANIC OR LATINO RUS N INV ID RUS-3
#> 4 ASIAN NOT HISPANIC OR LATINO CHN N INV ID CHN-11
#> 5 ASIAN NOT HISPANIC OR LATINO CHN N INV ID CHN-7
#> 6 ASIAN NOT HISPANIC OR LATINO CHN Y 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
#> 3 Dr. RUS-3 Doe C: Combination ARM C C: Combination ARM C C: Combination
#> 4 Dr. CHN-11 Doe B: Placebo ARM B B: Placebo ARM B B: Placebo
#> 5 Dr. CHN-7 Doe B: Placebo ARM B B: Placebo ARM B B: Placebo
#> 6 Dr. CHN-15 Doe C: Combination ARM C C: Combination ARM C C: Combination
#> TRT01A TRT02P TRT02A REGION1 STRATA1 STRATA2
#> 1 A: Drug X B: Placebo A: Drug X Asia C S2
#> 2 C: Combination B: Placebo C: Combination Asia C S1
#> 3 C: Combination A: Drug X B: Placebo Eurasia A S1
#> 4 B: Placebo B: Placebo B: Placebo Asia B S2
#> 5 B: Placebo C: Combination A: Drug X Asia C S1
#> 6 C: Combination B: Placebo C: Combination Asia C S2
#> BMRKR1 BMRKR2 ITTFL SAFFL BMEASIFL BEP01FL AEWITHFL RANDDT
#> 1 14.424934 MEDIUM Y Y Y Y N 2019-02-22
#> 2 4.055463 LOW Y Y N N Y 2019-02-26
#> 3 2.803240 HIGH Y Y Y N N 2019-02-24
#> 4 10.262734 MEDIUM Y Y Y Y N 2019-02-27
#> 5 6.206763 LOW Y Y N N N 2019-03-01
#> 6 6.906799 MEDIUM Y Y Y N N 2019-03-05
#> TRTSDTM TRTEDTM TRT01SDTM
#> 1 2019-02-24 11:09:25 2022-02-12 04:28:08 2019-02-24 11:09:25
#> 2 2019-02-26 09:05:10 2022-02-26 03:05:10 2019-02-26 09:05:10
#> 3 2019-02-28 03:19:22 2022-02-27 21:19:22 2019-02-28 03:19:22
#> 4 2019-03-01 13:33:19 2022-03-01 07:33:19 2019-03-01 13:33:19
#> 5 2019-03-02 00:09:33 2022-03-01 18:09:33 2019-03-02 00:09:33
#> 6 2019-03-05 15:24:07 2022-02-19 04:06:48 2019-03-05 15:24:07
#> TRT01EDTM TRT02SDTM TRT02EDTM
#> 1 2021-02-11 22:28:08 2021-02-11 22:28:08 2022-02-12 04:28:08
#> 2 2021-02-25 21:05:10 2021-02-25 21:05:10 2022-02-26 03:05:10
#> 3 2021-02-27 15:19:22 2021-02-27 15:19:22 2022-02-27 21:19:22
#> 4 2021-03-01 01:33:19 2021-03-01 01:33:19 2022-03-01 07:33:19
#> 5 2021-03-01 12:09:33 2021-03-01 12:09:33 2022-03-01 18:09:33
#> 6 2021-02-18 22:06:48 2021-02-18 22:06:48 2022-02-19 04:06:48
#> AP01SDTM AP01EDTM AP02SDTM
#> 1 2019-02-24 11:09:25 2021-02-11 22:28:08 2021-02-11 22:28:08
#> 2 2019-02-26 09:05:10 2021-02-25 21:05:10 2021-02-25 21:05:10
#> 3 2019-02-28 03:19:22 2021-02-27 15:19:22 2021-02-27 15:19:22
#> 4 2019-03-01 13:33:19 2021-03-01 01:33:19 2021-03-01 01:33:19
#> 5 2019-03-02 00:09:33 2021-03-01 12:09:33 2021-03-01 12:09:33
#> 6 2019-03-05 15:24:07 2021-02-18 22:06:48 2021-02-18 22:06:48
#> AP02EDTM EOSSTT EOTSTT EOSDT EOSDY DCSREAS
#> 1 2022-02-12 04:28:08 DISCONTINUED DISCONTINUED 2022-02-12 1084 DEATH
#> 2 2022-02-26 03:05:10 COMPLETED COMPLETED 2022-02-26 1096 <NA>
#> 3 2022-02-27 21:19:22 COMPLETED COMPLETED 2022-02-27 1096 <NA>
#> 4 2022-03-01 07:33:19 COMPLETED COMPLETED 2022-03-01 1096 <NA>
#> 5 2022-03-01 18:09:33 COMPLETED COMPLETED 2022-03-01 1096 <NA>
#> 6 2022-02-19 04:06:48 DISCONTINUED DISCONTINUED 2022-02-19 1082 DEATH
#> DTHDT DTHCAUS DTHCAT LDDTHELD LDDTHGR1 LSTALVDT DTHADY
#> 1 2022-03-06 ADVERSE EVENT ADVERSE EVENT 22 <=30 2022-03-06 1105
#> 2 <NA> <NA> <NA> NA <NA> 2022-03-17 NA
#> 3 <NA> <NA> <NA> NA <NA> 2022-03-11 NA
#> 4 <NA> <NA> <NA> NA <NA> 2022-03-26 NA
#> 5 <NA> <NA> <NA> NA <NA> 2022-03-15 NA
#> 6 2022-02-22 ADVERSE EVENT ADVERSE EVENT 3 <=30 2022-02-22 1084
#> ADTHAUT
#> 1 Yes
#> 2 <NA>
#> 3 <NA>
#> 4 <NA>
#> 5 <NA>
#> 6 Yes
#> ...