From 99d3cd861854808c8fdef4e130a3b7eef2023a16 Mon Sep 17 00:00:00 2001
From: Martin Chan <martinchan53@gmail.com>
Date: Thu, 17 Aug 2023 23:25:43 +0100
Subject: [PATCH] chore: add donttest clauses

---
 R/IV_by_Period.R           | 3 ++-
 R/create_ITSA.R            | 3 ++-
 R/subject_scan.R           | 3 ++-
 R/tm_freq.R                | 2 ++
 R/workpatterns_hclust.R    | 4 ++--
 man/IV_by_period.Rd        | 3 ++-
 man/create_ITSA.Rd         | 3 ++-
 man/subject_scan.Rd        | 3 ++-
 man/tm_freq.Rd             | 2 ++
 man/workpatterns_hclust.Rd | 4 ++--
 10 files changed, 20 insertions(+), 10 deletions(-)

diff --git a/R/IV_by_Period.R b/R/IV_by_Period.R
index 21e4a229..63fe6f13 100644
--- a/R/IV_by_Period.R
+++ b/R/IV_by_Period.R
@@ -40,6 +40,7 @@
 #' @family Time-series
 #'
 #' @examples
+#' \donttest{
 #' # Returns a data frame
 #' sq_data %>%
 #'   IV_by_period(
@@ -48,7 +49,7 @@
 #'     after_start = "2020-01-05",
 #'     after_end = "2020-01-26"
 #'   )
-#'
+#' }
 #' @export
 
 IV_by_period <-
diff --git a/R/create_ITSA.R b/R/create_ITSA.R
index bb156c7c..0e791675 100644
--- a/R/create_ITSA.R
+++ b/R/create_ITSA.R
@@ -64,8 +64,8 @@
 #'
 #'
 #' @examples
+#' \donttest{
 #' # Returns summary table
-#'
 #' create_ITSA(
 #'   data = sq_data,
 #'   before_start = "12/15/2019",
@@ -89,6 +89,7 @@
 #'
 #' # Extract a plot as an example
 #' plot_list$Workweek_span
+#' }
 #'
 #' @export
 
diff --git a/R/subject_scan.R b/R/subject_scan.R
index c31e7ac0..79b0ff86 100644
--- a/R/subject_scan.R
+++ b/R/subject_scan.R
@@ -50,6 +50,7 @@
 #' @import ggplot2
 #'
 #' @examples
+#' \donttest{
 #' # return a heatmap table for words
 #' mt_data %>% subject_scan(hrvar = "Organizer_Organization")
 #'
@@ -68,7 +69,7 @@
 #'
 #' # grouped by days
 #' mt_data %>% subject_scan(mode = "days")
-#'
+#' }
 #' @export
 subject_scan <- function(data,
                          hrvar,
diff --git a/R/tm_freq.R b/R/tm_freq.R
index f1a2af6f..f3e6aa00 100644
--- a/R/tm_freq.R
+++ b/R/tm_freq.R
@@ -41,8 +41,10 @@
 #'
 #'
 #' @examples
+#' \donttest{
 #' tm_freq(mt_data, token = "words")
 #' tm_freq(mt_data, token = "ngrams")
+#' }
 #'
 #' @family Text-mining
 #'
diff --git a/R/workpatterns_hclust.R b/R/workpatterns_hclust.R
index b9a743b4..b7952fca 100644
--- a/R/workpatterns_hclust.R
+++ b/R/workpatterns_hclust.R
@@ -68,16 +68,16 @@
 #' @importFrom tidyr replace_na
 #'
 #' @examples
+#' \donttest{
 #' # Run clusters, returning plot
 #' workpatterns_hclust(em_data, k = 5, return = "plot")
 #'
 #' # Run clusters, return raw data
 #' workpatterns_hclust(em_data, k = 4, return = "data") %>% head()
 #'
-#'
 #' # Run clusters for instant messages only, return hclust object
 #' workpatterns_hclust(em_data, k = 4, return = "hclust", signals = c("IM"))
-#'
+#' }
 #'
 #' @family Clustering
 #' @family Working Patterns
diff --git a/man/IV_by_period.Rd b/man/IV_by_period.Rd
index 53de3295..7bc6164d 100644
--- a/man/IV_by_period.Rd
+++ b/man/IV_by_period.Rd
@@ -48,6 +48,7 @@ This function uses the Information Value algorithm to predict
 which Workplace Analytics metrics are most explained by the change in dates.
 }
 \examples{
+\donttest{
 # Returns a data frame
 sq_data \%>\%
   IV_by_period(
@@ -56,7 +57,7 @@ sq_data \%>\%
     after_start = "2020-01-05",
     after_end = "2020-01-26"
   )
-
+}
 }
 \seealso{
 Other Variable Association: 
diff --git a/man/create_ITSA.Rd b/man/create_ITSA.Rd
index 2c67de32..d71b636a 100644
--- a/man/create_ITSA.Rd
+++ b/man/create_ITSA.Rd
@@ -72,8 +72,8 @@ has since been removed and dependent functions \code{Ljungbox()} incorporated in
 the \strong{wpa} package.
 }
 \examples{
+\donttest{
 # Returns summary table
-
 create_ITSA(
   data = sq_data,
   before_start = "12/15/2019",
@@ -97,6 +97,7 @@ plot_list <-
 
 # Extract a plot as an example
 plot_list$Workweek_span
+}
 
 }
 \seealso{
diff --git a/man/subject_scan.Rd b/man/subject_scan.Rd
index 1011f0fe..2bad7c8d 100644
--- a/man/subject_scan.Rd
+++ b/man/subject_scan.Rd
@@ -87,6 +87,7 @@ grouped by a specified attribute such as organisational attribute, day of the
 week, or hours of the day.
 }
 \examples{
+\donttest{
 # return a heatmap table for words
 mt_data \%>\% subject_scan(hrvar = "Organizer_Organization")
 
@@ -105,5 +106,5 @@ mt_data \%>\% subject_scan(mode = "hours")
 
 # grouped by days
 mt_data \%>\% subject_scan(mode = "days")
-
+}
 }
diff --git a/man/tm_freq.Rd b/man/tm_freq.Rd
index bc0ffbaf..596ef50e 100644
--- a/man/tm_freq.Rd
+++ b/man/tm_freq.Rd
@@ -45,8 +45,10 @@ There is an option to remove stopwords by passing a data frame into the
 \code{stopwords} argument.
 }
 \examples{
+\donttest{
 tm_freq(mt_data, token = "words")
 tm_freq(mt_data, token = "ngrams")
+}
 
 }
 \seealso{
diff --git a/man/workpatterns_hclust.Rd b/man/workpatterns_hclust.Rd
index c446f97a..10bebd28 100644
--- a/man/workpatterns_hclust.Rd
+++ b/man/workpatterns_hclust.Rd
@@ -80,16 +80,16 @@ In other words, the clustering is applied on a dataset where the collaboration h
 are averaged by person and calculated as \% of total daily collaboration.
 }
 \examples{
+\donttest{
 # Run clusters, returning plot
 workpatterns_hclust(em_data, k = 5, return = "plot")
 
 # Run clusters, return raw data
 workpatterns_hclust(em_data, k = 4, return = "data") \%>\% head()
 
-
 # Run clusters for instant messages only, return hclust object
 workpatterns_hclust(em_data, k = 4, return = "hclust", signals = c("IM"))
-
+}
 
 }
 \seealso{