Fix minor documention issues

ropensci · Nov 17, 2021 · ee9c495 · ee9c495
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diff --git a/.github/ISSUE_TEMPLATE/issue-template.md → ...ISSUE_TEMPLATE/ropensci-issue-template.md b/.github/ISSUE_TEMPLATE/issue-template.md → ...ISSUE_TEMPLATE/ropensci-issue-template.md
diff --git a/...REQUEST_TEMPLATE/pull_request_template.md → ...EMPLATE/ropensci-pull-request-template.md b/...REQUEST_TEMPLATE/pull_request_template.md → ...EMPLATE/ropensci-pull-request-template.md
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -57,6 +57,7 @@ Suggests:
     knitr (>= 1.36),
     mockr (>= 0.1),
     readr (>= 2.0.2),
+    rlang (>= 0.4.12),
     rmarkdown (>= 2.11),
     spelling (>= 2.2),
     stats (>= 4.0.0),

diff --git a/R/micro_mctq.R b/R/micro_mctq.R
@@ -59,8 +59,7 @@
 #' If you prefer to view `Duration` objects as `hms` objects, run
 #' `pretty_mctq(micro_mctq)`.
 #'
-#' @format A tibble with `r ncol(mctq::micro_mctq)` columns and
-#'  `r nrow(mctq::micro_mctq)` rows:
+#' @format A [`tibble`][dplyr::tibble()] with 17 columns and 50 rows:
 #'
 #' \describe{
 #'   \item{id}{

diff --git a/R/qplot_walk.R b/R/qplot_walk.R
@@ -97,7 +97,7 @@
 #'   must apply a midday change for `hms` variables with values greater than
 #'   `22:00:00` (see Details section to learn more) (default: `TRUE`).
 #'
-#' @return An invisible `NULL`. `qplot_walk()` was made to only display plots.
+#' @return An invisible `NULL`. This function don't aim to return values.
 #'
 #' @family utility functions
 #' @export

diff --git a/R/shift_mctq.R b/R/shift_mctq.R
@@ -59,8 +59,7 @@
 #' If you prefer to view `Duration` objects as `hms` objects, run
 #' `pretty_mctq(shift_mctq)`.
 #'
-#' @format A tibble with `r ncol(mctq::shift_mctq)` columns and
-#'   `r nrow(mctq::shift_mctq)` rows:
+#' @format A [`tibble`][dplyr::tibble()] with 128 columns and 50 rows:
 #'
 #' \describe{
 #'   \item{id}{

diff --git a/R/std_mctq.R b/R/std_mctq.R
@@ -58,8 +58,7 @@
 #' If you prefer to view `Duration` objects as `hms` objects, run
 #' `pretty_mctq(std_mctq)`.
 #'
-#' @format A tibble with `r ncol(mctq::std_mctq)` columns and
-#'   `r nrow(mctq::std_mctq)` rows:
+#' @format A [`tibble`][dplyr::tibble()] with 37 columns and 50 rows:
 #'
 #' \describe{
 #'   \item{id}{

diff --git a/README.Rmd b/README.Rmd
@@ -21,7 +21,7 @@ library(hms)
 
 <!-- badges: start -->
 [![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/434_status.svg)](https://github.com/ropensci/software-review/issues/434)
-[![CRAN status](https://www.r-pkg.org/badges/version/mctq)](https://CRAN.R-project.org/package=mctq)
+[![CRAN status](https://www.r-pkg.org/badges/version/mctq)](https://cran.r-project.org/package=mctq)
 [![CRAN downloads](https://cranlogs.r-pkg.org/badges/grand-total/mctq)](https://cran.r-project.org/package=mctq)
 [![mctq status badge](https://ropensci.r-universe.dev/badges/mctq)](https://ropensci.r-universe.dev)
 [![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
@@ -50,7 +50,7 @@ You need to have some familiarity with the [R programming language](https://www.
 
 In case you don't feel comfortable with R, we strongly recommend checking Hadley Wickham and Garrett Grolemund free and online book [R for Data Science](https://r4ds.had.co.nz/) and the Coursera course from John Hopkins University [Data Science: Foundations using R](https://www.coursera.org/specializations/data-science-foundations-r) (free for audit students).
 
-Please refer to the [lubridate](https://lubridate.tidyverse.org/) and [hms](https://hms.tidyverse.org/) package documentation to learn more about them. These two are essential packages to deal with date/time data in R. We also recommend that you read the [Dates and times](https://r4ds.had.co.nz/dates-and-times.html#time-spans) chapter from Wickham & Grolemund's book [R for Data Science](https://r4ds.had.co.nz/).
+Please refer to the [lubridate](https://lubridate.tidyverse.org/) and [hms](https://hms.tidyverse.org/) package documentation to learn more about them. These two are essential packages to deal with date/time data in R. We also recommend that you read the [Dates and times](https://r4ds.had.co.nz/dates-and-times.html#dates-and-times) chapter from Wickham & Grolemund's book [R for Data Science](https://r4ds.had.co.nz/).
 
 ## Installation
 

diff --git a/README.md b/README.md
@@ -8,7 +8,7 @@
 [![Status at rOpenSci Software Peer
 Review](https://badges.ropensci.org/434_status.svg)](https://github.com/ropensci/software-review/issues/434)
 [![CRAN
-status](https://www.r-pkg.org/badges/version/mctq)](https://CRAN.R-project.org/package=mctq)
+status](https://www.r-pkg.org/badges/version/mctq)](https://cran.r-project.org/package=mctq)
 [![CRAN
 downloads](https://cranlogs.r-pkg.org/badges/grand-total/mctq)](https://cran.r-project.org/package=mctq)
 [![mctq status
@@ -78,8 +78,8 @@ Please refer to the [lubridate](https://lubridate.tidyverse.org/) and
 [hms](https://hms.tidyverse.org/) package documentation to learn more
 about them. These two are essential packages to deal with date/time data
 in R. We also recommend that you read the [Dates and
-times](https://r4ds.had.co.nz/dates-and-times.html#time-spans) chapter
-from Wickham & Grolemund’s book [R for Data
+times](https://r4ds.had.co.nz/dates-and-times.html#dates-and-times)
+chapter from Wickham & Grolemund’s book [R for Data
 Science](https://r4ds.had.co.nz/).
 
 ## Installation

diff --git a/data-raw/micro_mctq.R b/data-raw/micro_mctq.R
@@ -1,5 +1,7 @@
-# Source the file before running the functions
-# Don't forget to uncomment the `library` functions below
+# # Notes
+#
+# * Source the file before running the functions.
+# * Don't forget to uncomment the `library` functions below.
 
 # library(checkmate)
 # library(dplyr)
@@ -36,7 +38,8 @@
 #' \dontrun{
 #' if (requireNamespace("utils", quietly = TRUE)) {
 #'     utils::View(mctq::build_micro_mctq())
-#' }}
+#' }
+#' }
 build_micro_mctq <- function(write = FALSE, random_cases = TRUE) {
     # Check arguments -----
 
@@ -296,16 +299,16 @@ build_micro_mctq <- function(write = FALSE, random_cases = TRUE) {
     invisible(micro_mctq)
 }
 
-#' Tidy [mctq::build_micro_mctq()] output
+#' Tidy `build_micro_mctq()` output
 #'
 #' @description
 #'
-#' `tidy_micro_mctq` tidy the output of [mctq::build_micro_mctq()]. See
-#' [mctq::micro_mctq] to learn more.
+#' `tidy_micro_mctq` tidy the output of `build_micro_mctq()`. See
+#' [micro_mctq][mctq::micro_mctq] to learn more.
 #'
 #' @details
 #'
-#' Here, the process of _tiding_ a dataset is understood as transforming it in
+#' Here the process of _tiding_ a dataset is understood as transforming it in
 #' input data, like described in Loo and Jonge (2018). It's a very similar
 #' process of tiding data described in the workflow proposed by Wickham and
 #' Grolemund (n.d.).
@@ -330,10 +333,10 @@ build_micro_mctq <- function(write = FALSE, random_cases = TRUE) {
 #'
 #' @examples
 #' \dontrun{
-#' \dontrun{
 #' if (requireNamespace("utils", quietly = TRUE)) {
 #'     utils::View(mctq::tidy_micro_mctq())
-#' }}
+#' }
+#' }
 tidy_micro_mctq <- function(write = FALSE) {
     # Check arguments -----
 
@@ -411,7 +414,7 @@ tidy_micro_mctq <- function(write = FALSE) {
 #'
 #' Here, the process of _validating_ a dataset is understood as detecting
 #' invalid data, by checking whether data satisfies certain assumptions from
-#' domain knowledge, to them,  removing or, if possible, fixing them. You can
+#' domain knowledge, to then,  removing or, if possible, fixing them. You can
 #' find more about data validation and error location in Loo and Jonge (2018).
 #'
 #' This process can be considered as part of the process of transforming data,
@@ -430,7 +433,8 @@ tidy_micro_mctq <- function(write = FALSE) {
 #' \dontrun{
 #' if (requireNamespace("utils", quietly = TRUE)) {
 #'     utils::View(mctq::validate_micro_mctq())
-#' }}
+#' }
+#' }
 validate_micro_mctq <- function(write = FALSE) {
     # To do -----
     #
@@ -538,7 +542,8 @@ validate_micro_mctq <- function(write = FALSE) {
 #' \dontrun{
 #' if (requireNamespace("utils", quietly = TRUE)) {
 #'     utils::View(mctq::analyze_micro_mctq())
-#' }}
+#' }
+#' }
 analyze_micro_mctq <- function(write = FALSE, round = TRUE, hms = FALSE) {
     # Check arguments -----
 

diff --git a/data-raw/random_mctq.R b/data-raw/random_mctq.R
@@ -1,5 +1,7 @@
-# Source the file before running the functions
-# Don't forget to uncomment the `library` functions below
+# # Notes
+#
+# * Source the file before running the functions.
+# * Don't forget to uncomment the `library` functions below.
 
 # library(checkmate)
 # library(cli)

diff --git a/data-raw/shift_mctq.R b/data-raw/shift_mctq.R
@@ -1,5 +1,7 @@
-# Source the file before running the functions
-# Don't forget to uncomment the `library` functions below
+# # Notes
+#
+# * Source the file before running the functions.
+# * Don't forget to uncomment the `library` functions below.
 
 # library(checkmate)
 # library(dplyr)

diff --git a/data-raw/std_mctq.R b/data-raw/std_mctq.R
@@ -1,5 +1,7 @@
-# Source the file before running the functions
-# Don't forget to uncomment the `library` functions below
+# # Notes
+#
+# * Source the file before running the functions.
+# * Don't forget to uncomment the `library` functions below.
 
 # library(checkmate)
 # library(dplyr)
@@ -828,10 +830,12 @@ validate_std_mctq <- function(write = FALSE) {
                     TRUE ~ FALSE)) %>%
             dplyr::select(dummy)
 
-        std_mctq <- dplyr::bind_cols(std_mctq, test) %>%
-            dplyr::mutate(
-                dplyr::across(dplyr::ends_with(i),
-                              ~ dplyr::if_else(dummy, mctq:::na_as(.x), .x))) %>%
+        std_mctq <- std_mctq %>% 
+            dplyr::bind_cols(test) %>%
+            dplyr::mutate(dplyr::across(
+                dplyr::ends_with(i),
+                ~ dplyr::if_else(dummy, mctq:::na_as(.x), .x))
+            ) %>%
             dplyr::select(-dummy)
     }
 
@@ -914,13 +918,7 @@ analyze_std_mctq <- function(write = FALSE, round = TRUE, hms = FALSE) {
     checkmate::assert_flag(round)
     checkmate::assert_flag(hms)
 
-    # Set values -----
-
-    std_mctq <- validate_std_mctq()
-
-    # Create computed variables -----
-
-    std_mctq <- std_mctq %>%
+    std_mctq <- validate_std_mctq() %>%
         dplyr::mutate(
             fd = fd(wd),
             so_w = so(sprep_w, slat_w),

diff --git a/data-raw/vignette_mctq.R b/data-raw/vignette_mctq.R
@@ -1,5 +1,7 @@
-# Source the file before running the function
-# Don't forget to uncomment the `library` functions below
+# # Notes
+#
+# * Source the file before running the functions.
+# * Don't forget to uncomment the `library` functions below.
 
 # library(checkmate)
 # library(dplyr)

diff --git a/man/micro_mctq.Rd b/man/micro_mctq.Rd
diff --git a/man/qplot_walk.Rd b/man/qplot_walk.Rd
diff --git a/man/shift_mctq.Rd b/man/shift_mctq.Rd
diff --git a/man/std_mctq.Rd b/man/std_mctq.Rd
diff --git a/tests/testthat/test-utils-dialogs.R b/tests/testthat/test-utils-dialogs.R
@@ -20,19 +20,25 @@ test_that("dialog_line() | general test", {
             is_interactive = function(...) TRUE,
             require_namespace = function(...) TRUE,
             read_line = function(...) TRUE,
-            dialog_line(1, combined_styles = "red", space_above = TRUE,
-                        space_below = TRUE))
+            dialog_line(1, space_above = TRUE, space_below = TRUE))
     }
 
     expect_equal(utils::capture.output(mock()), c("", "", "[1] TRUE"))
 })
 
 test_that("dialog_line() | error test", {
+    # "assert_has_length(list(...))"
     expect_error(dialog_line(), "Assertion on 'list\\(...\\)' failed")
+
+    # "checkmate::assert_flag(space_above)"
     expect_error(dialog_line(1, space_above = ""),
                  "Assertion on 'space_above' failed")
+
+    # "checkmate::assert_flag(space_below)"
     expect_error(dialog_line(1, space_below = ""),
                  "Assertion on 'space_below' failed")
+
+    # "checkmate::assert_flag(abort)"
     expect_error(dialog_line(1, abort = ""),
                  "Assertion on 'abort' failed")
 })