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chore: #96 style and spelling
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bms63 committed Jan 4, 2024
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Expand Up @@ -3,7 +3,7 @@ title: "admiral 1.0.0"
author:
- name: Ben Straub
description: "1.0.0 brings a commitment to stability, new features, a few bug fixes, argument alignment and onboarding resources!"
date: "2023-01-04"
date: "2024-01-04"
# please do not use any non-default categories.
# You can find the default categories in the repository README.md
categories: [admiral, ADaMs]
Expand Down Expand Up @@ -80,26 +80,25 @@ In this example, we only use `ADSL` as the source dataset, so it is a bit contri

```{r}
derive_vars_extreme_event(
adsl,
by_vars = exprs(STUDYID, USUBJID),
events = list(
event(
dataset_name = "adsl",
condition = !is.na(DTHDT),
set_values_to = exprs(LSTALVDT = DTHDT, DTHFL = "Y")
),
event(
dataset_name = "adsl",
condition = !is.na(TRTEDT),
set_values_to = exprs(LSTALVDT = TRTEDT, DTHFL = "N")
)
adsl,
by_vars = exprs(STUDYID, USUBJID),
events = list(
event(
dataset_name = "adsl",
condition = !is.na(DTHDT),
set_values_to = exprs(LSTALVDT = DTHDT, DTHFL = "Y")
),
source_datasets = list(adsl = adsl),
order = exprs(LSTALVDT),
mode = "last",
new_vars = exprs(LSTALVDT = LSTALVDT, DTHFL = DTHFL)
)
event(
dataset_name = "adsl",
condition = !is.na(TRTEDT),
set_values_to = exprs(LSTALVDT = TRTEDT, DTHFL = "N")
)
),
source_datasets = list(adsl = adsl),
order = exprs(LSTALVDT),
mode = "last",
new_vars = exprs(LSTALVDT = LSTALVDT, DTHFL = DTHFL)
)
```

Okay! We used a very small example to showcase how to find extreme observations and
Expand All @@ -114,48 +113,47 @@ We develop some _simple_ dummy data for `ADSL`, `CM` and `PR`. Our goal is to fl

```{r, message = FALSE, warning = FALSE}
adsl <- tribble(
~USUBJID,
"1",
"2",
"3",
"4",
)
~USUBJID,
"1",
"2",
"3",
"4",
)
cm <- tribble(
~USUBJID, ~CMCAT, ~CMSEQ,
"1", "ANTI-CANCER", 1,
"1", "GENERAL", 2,
"2", "GENERAL", 1,
"3", "ANTI-CANCER", 1
)
pr <- tribble(
~USUBJID, ~PRSEQ,
"2", 1,
"3", 1
)
~USUBJID, ~CMCAT, ~CMSEQ,
"1", "ANTI-CANCER", 1,
"1", "GENERAL", 2,
"2", "GENERAL", 1,
"3", "ANTI-CANCER", 1
)
pr <- tribble(
~USUBJID, ~PRSEQ,
"2", 1,
"3", 1
)
```


Now we have the argument `flag_events` that takes a list of objects where we define the conditions and datasets to check in.

```{r}
derive_var_merged_ef_msrc(
adsl,
flag_events = list(
flag_event(
dataset_name = "cm",
condition = CMCAT == "ANTI-CANCER"
),
flag_event(
dataset_name = "pr"
)
adsl,
flag_events = list(
flag_event(
dataset_name = "cm",
condition = CMCAT == "ANTI-CANCER"
),
source_datasets = list(cm = cm, pr = pr),
by_vars = exprs(USUBJID),
new_var = CANCTRFL
)
flag_event(
dataset_name = "pr"
)
),
source_datasets = list(cm = cm, pr = pr),
by_vars = exprs(USUBJID),
new_var = CANCTRFL
)
```

Let's go! We searched over multiple datasets, `CM` and `PR`, with multiple conditions and appended a new variable `CANCTRFL` to `ADSL` setting to `"Y"` if those conditions were met.
Expand All @@ -167,44 +165,42 @@ This function is very similar to [derive_param_computed()](https://pharmaverse.g
Let's make some dummy data for an `ADSL` and `ADVS`. Our goal is to derive a `BMIBL` variable pulled from `ADVS` and append to `ADSL`.

```{r}
adsl <- tribble(
~STUDYID, ~USUBJID, ~AGE, ~AGEU,
"PILOT01", "01-1302", 61, "YEARS",
"PILOT01", "17-1344", 64, "YEARS"
)
advs <- tribble(
~STUDYID, ~USUBJID, ~PARAMCD, ~PARAM, ~VISIT, ~AVAL, ~AVALU, ~ABLFL,
"PILOT01", "01-1302", "HEIGHT", "Height (cm)", "SCREENING", 177.8, "cm", "Y",
"PILOT01", "01-1302", "WEIGHT", "Weight (kg)", "SCREENING", 81.19, "kg", "N",
"PILOT01", "01-1302", "WEIGHT", "Weight (kg)", "BASELINE", 82.1, "kg", "Y",
"PILOT01", "01-1302", "WEIGHT", "Weight (kg)", "WEEK 2", 81.19, "kg", "N",
"PILOT01", "01-1302", "WEIGHT", "Weight (kg)", "WEEK 4", 82.56, "kg", "N",
"PILOT01", "01-1302", "WEIGHT", "Weight (kg)", "WEEK 6", 80.74, "kg", "N",
"PILOT01", "17-1344", "HEIGHT", "Height (cm)", "SCREENING", 163.5, "cm", "Y",
"PILOT01", "17-1344", "WEIGHT", "Weight (kg)", "SCREENING", 58.06, "kg", "N",
"PILOT01", "17-1344", "WEIGHT", "Weight (kg)", "BASELINE", 58.06, "kg", "Y",
"PILOT01", "17-1344", "WEIGHT", "Weight (kg)", "WEEK 2", 58.97, "kg", "N",
"PILOT01", "17-1344", "WEIGHT", "Weight (kg)", "WEEK 4", 57.97, "kg", "N",
"PILOT01", "17-1344", "WEIGHT", "Weight (kg)", "WEEK 6", 58.97, "kg", "N"
)
adsl <- tribble(
~STUDYID, ~USUBJID, ~AGE, ~AGEU,
"PILOT01", "01-1302", 61, "YEARS",
"PILOT01", "17-1344", 64, "YEARS"
)
advs <- tribble(
~STUDYID, ~USUBJID, ~PARAMCD, ~PARAM, ~VISIT, ~AVAL, ~AVALU, ~ABLFL,
"PILOT01", "01-1302", "HEIGHT", "Height (cm)", "SCREENING", 177.8, "cm", "Y",
"PILOT01", "01-1302", "WEIGHT", "Weight (kg)", "SCREENING", 81.19, "kg", "N",
"PILOT01", "01-1302", "WEIGHT", "Weight (kg)", "BASELINE", 82.1, "kg", "Y",
"PILOT01", "01-1302", "WEIGHT", "Weight (kg)", "WEEK 2", 81.19, "kg", "N",
"PILOT01", "01-1302", "WEIGHT", "Weight (kg)", "WEEK 4", 82.56, "kg", "N",
"PILOT01", "01-1302", "WEIGHT", "Weight (kg)", "WEEK 6", 80.74, "kg", "N",
"PILOT01", "17-1344", "HEIGHT", "Height (cm)", "SCREENING", 163.5, "cm", "Y",
"PILOT01", "17-1344", "WEIGHT", "Weight (kg)", "SCREENING", 58.06, "kg", "N",
"PILOT01", "17-1344", "WEIGHT", "Weight (kg)", "BASELINE", 58.06, "kg", "Y",
"PILOT01", "17-1344", "WEIGHT", "Weight (kg)", "WEEK 2", 58.97, "kg", "N",
"PILOT01", "17-1344", "WEIGHT", "Weight (kg)", "WEEK 4", 57.97, "kg", "N",
"PILOT01", "17-1344", "WEIGHT", "Weight (kg)", "WEEK 6", 58.97, "kg", "N"
)
```

Take a look at how we use `new_vars` and `filter_add`. We use a function inside of `new_vars` to help us calculate the `BMI` while using the `filter_add` argugment to only look at baseline records for the calculation.
Take a look at how we use `new_vars` and `filter_add`. We use a function inside of `new_vars` to help us calculate the `BMI` while using the `filter_add` argument to only look at baseline records for the calculation.

```{r}
derive_vars_computed(
dataset = adsl,
dataset_add = advs,
by_vars = exprs(STUDYID, USUBJID),
parameters = c("WEIGHT"),
constant_by_vars = exprs(STUDYID, USUBJID),
constant_parameters = c("HEIGHT"),
new_vars = exprs(BMIBL = compute_bmi(height = AVAL.HEIGHT, weight = AVAL.WEIGHT)),
filter_add = ABLFL == "Y"
)
dataset = adsl,
dataset_add = advs,
by_vars = exprs(STUDYID, USUBJID),
parameters = c("WEIGHT"),
constant_by_vars = exprs(STUDYID, USUBJID),
constant_parameters = c("HEIGHT"),
new_vars = exprs(BMIBL = compute_bmi(height = AVAL.HEIGHT, weight = AVAL.WEIGHT)),
filter_add = ABLFL == "Y"
)
```

Alright! Simple enough. We just took records from `ADVS`to help us calculate the `BMI` at baseline using this function and appended our new variable to `ADSL`.
Expand All @@ -228,7 +224,7 @@ consolidate_metadata(
)
```

In previous versions of `{admiral}` the `consolidate_metada()` function had the argument `check_keys`, which helps to check uniqueness. Other functions had a similar argument, but were called `check_unique`. Therefore, to better align our common API for `{admiral}` functions we decided to rename the `check_keys` argument to `check_unique`. You can follow the discussion around this renaming effort in this [GitHub Issue](https://github.com/pharmaverse/admiral/issues/2184).
In previous versions of `{admiral}` the `consolidate_metadata()` function had the argument `check_keys`, which helps to check uniqueness. Other functions had a similar argument, but were called `check_unique`. Therefore, to better align our common API for `{admiral}` functions we decided to rename the `check_keys` argument to `check_unique`. You can follow the discussion around this renaming effort in this [GitHub Issue](https://github.com/pharmaverse/admiral/issues/2184).

```{r, fig.align = 'center', echo=FALSE}
knitr::include_graphics("check.png")
Expand Down Expand Up @@ -259,7 +255,7 @@ For example, if you click through the issue for [`derive_extreme_event()`](https

## [admiraldiscovery](https://pharmaverse.github.io/admiraldiscovery/index.html)

This is a dedicated website that lists out in a tabular format standard ADaM datsets and their common variables with corresponding `{admiral}` functions that could be used to create the variables. Very handy when you just want to get some starter code on deriving `EOSDT` or `TRTSDT`!
This is a dedicated website that lists out in a tabular format standard ADaM datasets and their common variables with corresponding `{admiral}` functions that could be used to create the variables. Very handy when you just want to get some starter code on deriving `EOSDT` or `TRTSDT`!


```{r, fig.align = 'center', echo=FALSE}
Expand Down

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