[skip vbump] 90 cran comments (#91)

* Fix Cran comments

* Doubtful fix for CRAN comments.

* Add tidyr, remove memoise and clean pkgdown file

* Fix typo

---------

Co-authored-by: Adam Forys <[email protected]>

DESCRIPTION

@@ -47,11 +47,11 @@ Imports:
     admiraldev (>= 1.1.0),
     dplyr (>= 1.0.0),
     purrr (>= 1.0.1),
+    tidyr (>= 1.2.0),
     rlang (>= 1.0.2),
     tibble (>= 3.2.0),
     vctrs (>= 0.5.0),
     stringr (>= 1.4.0),
-    memoise (>= 2.0.1),
     assertthat,
     pillar,
     cli

NAMESPACE

@@ -8,12 +8,10 @@ export("%.>%")
 export(assign_ct)
 export(assign_datetime)
 export(assign_no_ct)
-export(clear_cache)
 export(condition_add)
 export(create_iso8601)
 export(ct_map)
 export(ct_spec_example)
-export(ct_spec_vars)
 export(derive_blfl)
 export(derive_seq)
 export(derive_study_day)

R/cnd_df.R

@@ -177,6 +177,8 @@ rm_cnd_df <- function(dat) {
 #'
 #' @seealso [ctl_new_rowid_pillar.cnd_df()].
 #'
+#' @return A character vector with header values of the conditioned data frame.
+#'
 #' @examples
 #' df <- data.frame(x = c(1L, NA_integer_, 3L))
 #' (cnd_df <- condition_add(dat = df, x >= 2L))
@@ -200,6 +202,8 @@ lgl_to_chr <- function(x) {
 #' @inheritParams pillar::ctl_new_rowid_pillar
 #' @importFrom pillar ctl_new_rowid_pillar
 #'
+#' @return A character vector to print the tibble which is a conditioned dataframe.
+#'
 #' @seealso [tbl_sum.cnd_df()].
 #'
 #' @export
@@ -340,6 +344,7 @@ condition_add <- function(dat, ..., .na = NA, .dat2 = rlang::env()) {
 #'
 #' @inheritParams dplyr::mutate
 #' @importFrom dplyr mutate
+#' @return A conditioned data frame, meaning a tibble with mutated values.
 #' @export
 mutate.cnd_df <- function(.data,
                           ...,

R/ct.R

@@ -14,7 +14,6 @@
 #'   `"from"` or `"to"`.
 #'
 #' @keywords internal
-#' @export
 ct_spec_vars <- function(set = c("all", "ct_clst", "from", "to")) {
   admiraldev::assert_character_vector(set)
 

R/dtc_create_iso8601.R

@@ -241,6 +241,9 @@ format_iso8601 <- function(m, .cutoff_2000 = 68L) {
 #'   interpretation of the formats.
 #' @param .warn Whether to warn about parsing failures.
 #'
+#' @return A vector of dates, times or date-times in [ISO
+#' 8601](https://en.wikipedia.org/wiki/ISO_8601) format
+#'
 #' @examples
 #' # Converting dates
 #' create_iso8601(c("2020-01-01", "20200102"), .format = "y-m-d")

R/pipe.R

@@ -47,6 +47,8 @@
 #' the dot placeholder, even when piping to the first argument of the
 #' right-hand side (`rhs`).
 #'
+#' @return No Return Value.
+#'
 #' @examples
 #'
 #' # Equivalent to `subset(head(iris), 1:nrow(head(iris)) %% 2 == 0)`

README.md

@@ -9,11 +9,7 @@
 status](https://www.r-pkg.org/badges/version/sdtm.oak)](https://CRAN.R-project.org/package=sdtm.oak)
 <!-- badges: end -->
 
-An EDC and Data Standard agnostic solution that enables the
-pharmaceutical programming community to develop SDTM datasets in R. The
-reusable algorithms concept in `{sdtm.oak}` provides a framework for
-modular programming and also can automate SDTM creation based on the
-standard SDTM spec.
+An EDC (Electronic Data Capture systems) and Data Standard agnostic solution that enables the pharmaceutical programming community to develop CDISC (Clinical Data Interchange Standards Consortium) SDTM (Study Data Tabulation Model) datasets in R. The reusable algorithms concept in 'sdtm.oak' provides a framework for modular programming and also can automate SDTM creation based on the standard SDTM spec.
 
 ## Installation
 
@@ -23,7 +19,7 @@ The package is available from CRAN and can be installed with:
 install.packages("sdtm.oak")
 ```
 
-You can install the development version of `{sdtm.oak}` from
+You can install the development version of 'sdtm.oak' from
 [GitHub](https://github.com/pharmaverse/sdtm.oak/) with:
 
 ``` r
@@ -36,17 +32,15 @@ remotes::install_github("pharmaverse/sdtm.oak")
 - Raw Data Structure: Data from different EDC systems come in varying
   structures, with different variable names, dataset names, etc.
 
-- Varying Data Collection Standards: Despite the availability of CDASH,
-  pharmaceutical companies still create different eCRFs using CDASH
-  standards.
+- Varying Data Collection Standards: Despite the availability of CDASH (Clinical Data Acquisition Standards Harmonization), pharmaceutical companies still create different eCRFs using CDASH standards.
 
 Due to the differences in raw data structures and data collection
 standards, it may seem impossible to develop a common approach for
 programming SDTM datasets.
 
 ## GOAL
 
-`{sdtm.oak}` aims to address this issue by providing an EDC-agnostic,
+'sdtm.oak' aims to address this issue by providing an EDC-agnostic,
 standards-agnostic solution. It is an open-source R package that offers
 a framework for the modular programming of SDTM in R. With future
 releases; it will also strive to automate the creation of SDTM datasets
@@ -55,7 +49,7 @@ specifications.
 
 ## Scope
 
-Our goal is to use `{sdtm.oak}` to program most of the domains specified
+Our goal is to use 'sdtm.oak' to program most of the domains specified
 in SDTMIG (Study Data Tabulation Model Implementation Guide: Human
 Clinical Trials) and SDTMIG-AP (Study Data Tabulation Model
 Implementation Guide: Associated Persons). This R package is based on
@@ -64,19 +58,19 @@ carrying out the SDTM mappings for any domains listed in the CDISC
 SDTMIG and across different versions of SDTM IGs. The design of these
 functions allows users to specify a raw dataset and a variable name(s)
 as parameters, making it EDC (Electronic Data Capture) agnostic. As long
-as the raw dataset and variable name(s) exist, `{sdtm.oak}` will execute
+as the raw dataset and variable name(s) exist, 'sdtm.oak' will execute
 the SDTM mapping using the selected function. It’s important to note
-that `{sdtm.oak}` may not handle sponsor-specific details related to
+that 'sdtm.oak' may not handle sponsor-specific details related to
 managing metadata for LAB tests, unit conversions, and coding
 information, as many companies have unique business processes. With
 subsequent releases, strive to automate SDTM creation using a
 metadata-driven approach based on a standard SDTM specification format.
 
 ## Road Map
 
-This Release: The V0.1.0 release of `{sdtm.oak}` users can create the
+This Release: The V0.1.0 release of 'sdtm.oak' users can create the
 majority of the SDTM domains. Domains that are NOT in scope for the
-V0.1.0 release are DM, Trial Design Domains, SV, SE, RELREC, Associated
+V0.1.0 release are DM (Demographics), Trial Design Domains, SV (Subject Visits), SE (Subject Elements), RELREC (Related Records), Associated
 Person domains, and EPOCH Variable across all domains.
 
 Subsequent Releases: We are planning to develop the below features in
@@ -106,7 +100,7 @@ specification.
 
 ## Feedback
 
-We ask users to follow the mentioned approach and try `{sdtm.oak}` to
+We ask users to follow the mentioned approach and try 'sdtm.oak' to
 map any SDTM domains supported in this release. Users can also utilize
 the test data in the package to become familiar with the concepts before
 attempting on their own data. Please get in touch with us using one of
@@ -118,7 +112,7 @@ the recommended approaches listed below:
 ## Acknowledgments
 
 We thank the contributors and authors of the package. We also thank the
-CDISC COSA for sponsoring the `{sdtm.oak}`. Additionally, we would like
+CDISC COSA for sponsoring the 'sdtm.oak'. Additionally, we would like
 to sincerely thank the volunteers from Roche, Pfizer, GSK, Vertex, and
 Merck for their valuable input as integral members of the CDISC COSA -
 OAK leadership team.

_pkgdown.yml

@@ -54,10 +54,6 @@ reference:
   contents:
   - "%.>%"
 
-- title: Package global state
-  contents:
-  - clear_cache
-
 authors:
   Ramiro Magno:
     href: https://www.pattern.institute/team/rmagno/