diff --git a/NAMESPACE b/NAMESPACE
index b2020df..366c797 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,6 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
 export(catch)
+export(create_object_list)
 export(loupe)
 export(release)
 importFrom(lifecycle,deprecated)
diff --git a/R/catch.R b/R/catch.R
index 5afe165..24206f8 100644
--- a/R/catch.R
+++ b/R/catch.R
@@ -5,6 +5,8 @@
 #' which contains only rows that have changed compared to previous data. It will
 #' not return any new rows.
 #'
+#' The underlying functionality is handled by `create_object_list()`.
+#'
 #' @param df_current data.frame, the newest/current version of dataset x.
 #' @param df_previous data.frame, the old version of dataset, for example x - t1.
 #' @param datetime_variable character, which variable to use as unique ID to join `df_current` and `df_previous`. Usually a "datetime" variable.
@@ -13,6 +15,7 @@
 #' also returns a waldo object as in `loupe()`.
 #'
 #' @seealso [loupe()]
+#' @seealso [create_object_list()]
 #'
 #' @examples
 #' df_caught <- butterfly::catch(
@@ -25,92 +28,29 @@
 #'
 #' @export
 catch <- function(df_current, df_previous, datetime_variable) {
-  # Check input is as expected
-  stopifnot("`df_current` must be a data.frame" = is.data.frame(df_current))
-  stopifnot("`df_previous` must be a data.frame" = is.data.frame(df_previous))
-
-  # Check if `datetime_variable` is in both `df_current` and `df_previous`
-  if (!datetime_variable %in% names(df_current) || !datetime_variable %in% names(df_previous)) {
-    stop(
-      "`datetime_variable` must be present in both `df_current` and `df_previous`"
-    )
-  }
-
-  # Using semi_join to extract rows with matching datetime_variables
-  # (ie previously generated data)
-  df_current_without_new_row <- dplyr::semi_join(
+  butterfly_object_list <- create_object_list(
     df_current,
     df_previous,
-    by = datetime_variable
+    datetime_variable
   )
 
-  # Compare the current data with the previous data, without "new" values
-  waldo_object <- waldo::compare(
-    df_current_without_new_row,
-    df_previous
-  )
-
-  # Obtaining the new rows to provide in feedback
-  df_current_new_rows <- dplyr::anti_join(
-    df_current,
-    df_previous,
-    by = datetime_variable
-  )
-
-  if (nrow(df_current_new_rows) == 0) {
-    warning(
-      "There are no new rows. Check '",
-      deparse(substitute(df_current)),
-      "' is your most recent data, and '",
-      deparse(substitute(df_previous)),
-      "' is your previous data."
-    )
-  } else {
-    # Tell the user which rows are new, regardless of previous data changing
-    cli::cat_line(
-      paste0(
-        "The following rows are new in '",
-        deparse(substitute(df_current)),
-        "': "
-      ),
-      col = "green"
-    )
-
-    cli::cat_print(
-      df_current_new_rows
+  # By using an inner join, we drop any row which does not match in
+  # df_previous.
+  df_rows_changed_from_previous <- suppressMessages(
+    dplyr::anti_join(
+      butterfly_object_list$df_current_without_new_row,
+      df_previous
     )
-  }
-
-  # Return a simple message if there are no changes in previous data
-  if (length(waldo_object) == 0) {
-    stop(
-      "There are no differences between current and previous data."
-    )
-  } else {
-    # Return detailed breakdown and warning if previous data have changed.
-    if (length(waldo_object) > 0) {
-      cli::cat_line()
+  )
 
-      cli::cat_bullet(
-        "The following rows have changed from the previous data, and will be returned:",
-        bullet = "info",
-        col = "orange",
-        bullet_col = "orange"
-      )
+  cli::cat_line()
 
-      cli::cat_print(
-        waldo_object
-      )
+  cli::cat_bullet(
+    "Only these rows are returned.",
+    bullet = "info",
+    col = "orange",
+    bullet_col = "orange"
+  )
 
-      # By using an inner join, we drop any row which does not match in
-      # df_previous.
-      df_rows_changed_from_previous <- suppressMessages(
-        dplyr::anti_join(
-          df_current_without_new_row,
-          df_previous
-        )
-      )
-    }
-  }
   return(df_rows_changed_from_previous)
 }
diff --git a/R/create_object_list.R b/R/create_object_list.R
new file mode 100644
index 0000000..b14295d
--- /dev/null
+++ b/R/create_object_list.R
@@ -0,0 +1,139 @@
+#' create_object_list: creates a list of objects used in all butterfly functions
+#'
+#' This function creates a list of objects which is used by all of `loupe()`,
+#' `catch()` and `release()`.
+#'
+#' This function matches two dataframe objects by their unique identifier
+#' (usually "time" or "datetime in a timeseries).
+#'
+#' It informs the user of new (unmatched) rows which have appeared, and then
+#' returns a `waldo::compare()` call to give a detailed breakdown of changes.
+#'
+#' The main assumption is that `df_current` and `df_previous` are a newer and
+#' older versions of the same data, and that the `datetime_variable` variable name always
+#' remains the same. Elsewhere new columns can of appear, and these will be
+#' returned in the report.
+#'
+#' @param df_current data.frame, the newest/current version of dataset x.
+#' @param df_previous data.frame, the old version of dataset, for example x - t1.
+#' @param datetime_variable string, which variable to use as unique ID to join
+#'  `df_current` and `df_previous`. Usually a "datetime" variable.
+#'
+#' @returns A list containing boolean where TRUE indicates no changes to
+#' previous data and FALSE indicates unexpected changes, a dataframe of
+#' the current data without new rows and a dataframe of new rows only
+#'
+#' @examples
+#' butterfly_object_list <- butterfly::create_object_list(
+#'   butterflycount$february,
+#'   butterflycount$january,
+#'   datetime_variable = "time"
+#' )
+#'
+#' butterfly_object_list
+#'
+#' @export
+create_object_list <- function(df_current, df_previous, datetime_variable) {
+  # Check input is as expected
+  stopifnot("`df_current` must be a data.frame" = is.data.frame(df_current))
+  stopifnot("`df_previous` must be a data.frame" = is.data.frame(df_previous))
+
+  # Check if `datetime_variable` is in both `df_current` and `df_previous`
+  if (!datetime_variable %in% names(df_current) || !datetime_variable %in% names(df_previous)) {
+    stop(
+      "`datetime_variable` must be present in both `df_current` and `df_previous`"
+    )
+  }
+
+  # Initialise list to store objects used by `loupe()`, `catch()` and `release()`
+  list_butterfly <- list(
+    "waldo_object" = character(),
+    "df_current_without_new_row" = data.frame(),
+    "df_current_new_rows" = data.frame()
+  )
+
+  # Using semi_join to extract rows with matching datetime_variables
+  # (ie previously generated data)
+  df_current_without_new_row <- dplyr::semi_join(
+    df_current,
+    df_previous,
+    by = datetime_variable
+  )
+
+  # Obtaining the new rows to provide in feedback
+  df_current_new_rows <- dplyr::anti_join(
+    df_current,
+    df_previous,
+    by = datetime_variable
+  )
+
+  # Compare the current data with the previous data, without "new" values
+  waldo_object <- waldo::compare(
+    df_current_without_new_row,
+    df_previous
+  )
+
+  # Creating a feedback message depending on the waldo object's output
+  # First checking if there are new rows at all:
+  if (nrow(df_current_new_rows) == 0) {
+    stop(
+      "There are no new rows. Check '",
+      deparse(substitute(df_current)),
+      "' is your most recent data, and '",
+      deparse(substitute(df_previous)),
+      "' is your previous data. If comparing like for like, try waldo::compare()."
+    )
+  } else {
+    # Tell the user which rows are new, regardless of previous data changing
+    cli::cat_line(
+      "The following rows are new in '",
+      deparse(substitute(df_current)),
+      "': ",
+      col = "green"
+    )
+    cli::cat_print(
+      df_current_new_rows
+    )
+  }
+
+  # Return a simple message if there are no changes in previous data
+  if (length(waldo_object) == 0) {
+    cli::cat_bullet(
+      "And there are no differences with previous data.",
+      bullet = "tick",
+      col = "green",
+      bullet_col = "green"
+    )
+
+    butterfly_status <- TRUE
+
+  } else {
+    # Return detailed breakdown and warning if previous data have changed.
+    if (length(waldo_object) > 0) {
+      cli::cat_line()
+
+      cli::cat_bullet(
+        "The following values have changes from the previous data.",
+        bullet = "info",
+        col = "orange",
+        bullet_col = "orange"
+      )
+
+      cli::cat_print(
+        waldo_object
+      )
+
+      butterfly_status <- FALSE
+
+    }
+  }
+
+  # Populate list with objects
+  list_butterfly <- list(
+    butterfly_status = butterfly_status,
+    df_current_without_new_row = df_current_without_new_row,
+    df_current_new_rows = df_current_new_rows
+  )
+
+  return(list_butterfly)
+}
diff --git a/R/loupe.R b/R/loupe.R
index d0ea25a..5212893 100644
--- a/R/loupe.R
+++ b/R/loupe.R
@@ -18,11 +18,15 @@
 #' remains the same. Elsewhere new columns can of appear, and these will be
 #' returned in the report.
 #'
+#' The underlying functionality is handled by `create_object_list()`.
+#'
 #' @param df_current data.frame, the newest/current version of dataset x.
 #' @param df_previous data.frame, the old version of dataset, for example x - t1.
 #' @param datetime_variable string, which variable to use as unique ID to join `df_current` and `df_previous`. Usually a "datetime" variable.
 #'
-#' @returns A waldo object containing a message on differences or 'And there are no differences with previous data'.
+#' @returns A boolean where TRUE indicates no changes to previous data and FALSE indicates unexpected changes.
+#'
+#' @seealso [create_object_list()]
 #'
 #' @examples
 #' # This example contains no differences with previous data
@@ -41,81 +45,12 @@
 #'
 #' @export
 loupe <- function(df_current, df_previous, datetime_variable) {
-  # Check input is as expected
-  stopifnot("`df_current` must be a data.frame" = is.data.frame(df_current))
-  stopifnot("`df_previous` must be a data.frame" = is.data.frame(df_previous))
-
-  # Check if `datetime_variable` is in both `df_current` and `df_previous`
-  if (!datetime_variable %in% names(df_current) || !datetime_variable %in% names(df_previous)) {
-    stop(
-      "`datetime_variable` must be present in both `df_current` and `df_previous`"
-    )
-  }
-
-  # Using semi_join to extract rows with matching datetime_variables
-  # (ie previously generated data)
-  df_current_without_new_row <- dplyr::semi_join(
+  butterfly_object_list <- create_object_list(
     df_current,
     df_previous,
-    by = datetime_variable
-  )
-
-  # Compare the current data with the previous data, without "new" values
-  waldo_object <- waldo::compare(
-    df_current_without_new_row,
-    df_previous
+    datetime_variable
   )
 
-  # Obtaining the new rows to provide in feedback
-  df_current_new_rows <- dplyr::anti_join(
-    df_current,
-    df_previous,
-    by = datetime_variable
-  )
-
-  # Creating a feedback message depending on the waldo object's output
-  # First checking if there are new rows at all:
-  if (nrow(df_current_new_rows) == 0) {
-    stop(
-      "There are no new rows. Check '",
-      deparse(substitute(df_current)),
-      "' is your most recent data, and '",
-      deparse(substitute(df_previous)),
-      "' is your previous data. If comparing like for like, try waldo::compare()."
-    )
-  } else {
-    # Tell the user which rows are new, regardless of previous data changing
-    cli::cat_line(
-      "The following rows are new in '",
-      deparse(substitute(df_current)),
-      "': ",
-      col = "green"
-    )
-    cli::cat_print(
-      df_current_new_rows
-    )
-  }
-
-  # Return a simple message if there are no changes in previous data
-  if (length(waldo_object) == 0) {
-    cli::cat_bullet(
-      "And there are no differences with previous data.",
-      bullet = "tick",
-      col = "green",
-      bullet_col = "green"
-    )
-  } else {
-    # Return detailed breakdown and warning if previous data have changed.
-    if (length(waldo_object) > 0) {
-      cli::cat_line()
+  return(butterfly_object_list$butterfly_status)
 
-      cli::cat_bullet(
-        "But the following values have changes from the previous data:",
-        bullet = "info",
-        col = "orange",
-        bullet_col = "orange"
-      )
-      return(waldo_object)
-    }
-  }
 }
diff --git a/R/release.R b/R/release.R
index 356cba9..e65ba48 100644
--- a/R/release.R
+++ b/R/release.R
@@ -8,11 +8,13 @@
 #' @param df_current data.frame, the newest/current version of dataset x.
 #' @param df_previous data.frame, the old version of dataset, for example x - t1.
 #' @param datetime_variable string, which variable to use as unique ID to join `df_current` and `df_previous`. Usually a "datetime" variable.
+#' @param include_new boolean, should new rows be included? Default is TRUE.
 #'
 #' @returns A dataframe which contains only rows of `df_current` that have not changed from `df_previous`, and includes new rows.
 #' also returns a waldo object as in `loupe()`.
 #'
 #' @seealso [loupe()]
+#' @seealso [create_object_list()]
 #'
 #' @examples
 #' df_released <- butterfly::release(
@@ -24,102 +26,52 @@
 #' df_released
 #'
 #' @export
-release <- function(df_current, df_previous, datetime_variable) {
-  # Check input is as expected
-  stopifnot("`df_current` must be a data.frame" = is.data.frame(df_current))
-  stopifnot("`df_previous` must be a data.frame" = is.data.frame(df_previous))
-
-  # Check if `datetime_variable` is in both `df_current` and `df_previous`
-  if (!datetime_variable %in% names(df_current) || !datetime_variable %in% names(df_previous)) {
-    stop(
-      "`datetime_variable` must be present in both `df_current` and `df_previous`"
-    )
-  }
-
-  # Using semi_join to extract rows with matching datetime_variables
-  # (ie previously generated data)
-  df_current_without_new_row <- dplyr::semi_join(
+release <- function(df_current, df_previous, datetime_variable, include_new = TRUE) {
+  butterfly_object_list <- create_object_list(
     df_current,
     df_previous,
-    by = datetime_variable
+    datetime_variable
   )
 
-  # Compare the current data with the previous data, without "new" values
-  waldo_object <- waldo::compare(
-    df_current_without_new_row,
-    df_previous
-  )
-
-  # Obtaining the new rows to provide in feedback
-  df_current_new_rows <- dplyr::anti_join(
-    df_current,
-    df_previous,
-    by = datetime_variable
+  # By using an inner join, we drop any row which does not match in
+  # df_previous.
+  df_current_without_changed_rows <- suppressMessages(
+    dplyr::inner_join(
+      butterfly_object_list$df_current_without_new_row,
+      df_previous
+    )
   )
 
-  if (nrow(df_current_new_rows) == 0) {
-    warning(
-      "There are no new rows. Check '",
-      deparse(substitute(df_current)),
-      "' is your most recent data, and '",
-      deparse(substitute(df_previous)),
-      "' is your previous data."
-    )
-  } else {
-    # Tell the user which rows are new, regardless of previous data changing
-    cli::cat_line(
-      paste0(
-        "The following rows are new in '",
-        deparse(substitute(df_current)),
-        "': "
-      ),
-      col = "green"
+  # Returng the dataframe with or without new rows added
+  if (include_new == TRUE) {
+    # Then we add the new rows back in and return the dataframe as such
+    df_release <- dplyr::bind_rows(
+      butterfly_object_list$df_current_new_rows,
+      df_current_without_changed_rows
     )
-    cli::cat_print(
-      df_current_new_rows
-    )
-  }
 
-  # Return a simple message if there are no changes in previous data
-  if (length(waldo_object) == 0) {
-    warning(
-      "There are no differences between current and previous data. Returning object identical to: ",
-      deparse(substitute(df_current))
-    )
+    cli::cat_line()
 
-    df_release <- df_current
-  } else {
-    # Return detailed breakdown and warning if previous data have changed.
-    if (length(waldo_object) > 0) {
-      cli::cat_line()
+    cli::cat_bullet(
+      "These will be dropped, but new rows are included.",
+      bullet = "info",
+      col = "orange",
+      bullet_col = "orange"
+    )
 
-      cli::cat_bullet(
-        "The following rows have changed from the previous data, and will be dropped: ",
-        bullet = "info",
-        col = "orange",
-        bullet_col = "orange"
-      )
+    return(df_release)
 
-      cli::cat_print(
-        waldo_object
-      )
+  } else if (include_new == FALSE) {
+    cli::cat_line()
 
-      # By using an inner join, we drop any row which does not match in
-      # df_previous.
-      df_current_without_changed_rows <- suppressMessages(
-        dplyr::inner_join(
-          df_current_without_new_row,
-          df_previous
-        )
-      )
+    cli::cat_bullet(
+      "These will be dropped, along with new rows.",
+      bullet = "info",
+      col = "orange",
+      bullet_col = "orange"
+    )
 
-      # Using inner_join does mean that the new rows will need to be added
-      # back in.
-      df_release <- dplyr::bind_rows(
-        df_current_new_rows,
-        df_current_without_changed_rows
-      )
-    }
+    # If new rows are not included, simply return the df without changed rows
+    return(df_current_without_changed_rows)
   }
-  return(df_release)
 }
diff --git a/README.Rmd b/README.Rmd
index d027d6f..1981d77 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -46,9 +46,10 @@ devtools::install_github("thomaszwagerman/butterfly")
 ## Overview
 The butterfly package contains the following:
 
-  * `butterfly::loupe()` - examines in detail whether previous values have changed, and reports them using `waldo::compare()`.
+  * `butterfly::loupe()` - examines in detail whether previous values have changed, and returns TRUE/FALSE for no change/change.
   * `butterfly::catch()` - returns rows which contain previously changed values in a dataframe.
   * `butterfly::release()` - drops rows which contain previously changed values, and returns a dataframe containing new and unchanged rows.
+  * `butterfly::create_object_list()` - returns a list of objects required by all of `loupe()`, `catch()` and `release()`. Contains underlying functionality.
   * `butterflycount` - a list of monthly dataframes, which contain fictional butterfly counts for a given date.
 
 ## Examples
diff --git a/README.md b/README.md
index cd35727..ec89c31 100644
--- a/README.md
+++ b/README.md
@@ -46,11 +46,14 @@ devtools::install_github("thomaszwagerman/butterfly")
 The butterfly package contains the following:
 
 - `butterfly::loupe()` - examines in detail whether previous values have
-  changed, and reports them using `waldo::compare()`.
+  changed, and returns TRUE/FALSE for no change/change.
 - `butterfly::catch()` - returns rows which contain previously changed
   values in a dataframe.
 - `butterfly::release()` - drops rows which contain previously changed
   values, and returns a dataframe containing new and unchanged rows.
+- `butterfly::create_object_list()` - returns a list of objects required
+  by all of `loupe()`, `catch()` and `release()`. Contains underlying
+  functionality.
 - `butterflycount` - a list of monthly dataframes, which contain
   fictional butterfly counts for a given date.
 
@@ -96,21 +99,22 @@ butterfly::loupe(
   butterflycount$january,
   datetime_variable = "time"
 )
-#> The following rows are new in 'butterflycount$february': 
+#> The following rows are new in 'df_current': 
 #>         time count
 #> 1 2024-02-01    17
 #> ✔ And there are no differences with previous data.
+#> [1] TRUE
 
 butterfly::loupe(
   butterflycount$march,
   butterflycount$february,
   datetime_variable = "time"
 )
-#> The following rows are new in 'butterflycount$march': 
+#> The following rows are new in 'df_current': 
 #>         time count
 #> 1 2024-03-01    23
 #> 
-#> ℹ But the following values have changes from the previous data:
+#> ℹ The following values have changes from the previous data.
 #> old vs new
 #>            count
 #>   old[1, ]    17
@@ -121,6 +125,7 @@ butterfly::loupe(
 #> 
 #> `old$count`: 17 22 55 18
 #> `new$count`: 17 22 55 11
+#> [1] FALSE
 ```
 
 `butterfly::loupe()` uses `dplyr::semi_join()` to match the new and old
@@ -147,11 +152,11 @@ df_caught <- butterfly::catch(
   butterflycount$february,
   datetime_variable = "time"
 )
-#> The following rows are new in 'butterflycount$march': 
+#> The following rows are new in 'df_current': 
 #>         time count
 #> 1 2024-03-01    23
 #> 
-#> ℹ The following rows have changed from the previous data, and will be returned:
+#> ℹ The following values have changes from the previous data.
 #> old vs new
 #>            count
 #>   old[1, ]    17
@@ -162,6 +167,8 @@ df_caught <- butterfly::catch(
 #> 
 #> `old$count`: 17 22 55 18
 #> `new$count`: 17 22 55 11
+#> 
+#> ℹ Only these rows are returned.
 
 df_caught
 #>         time count
@@ -177,11 +184,11 @@ df_released <- butterfly::release(
   butterflycount$february,
   datetime_variable = "time"
 )
-#> The following rows are new in 'butterflycount$march': 
+#> The following rows are new in 'df_current': 
 #>         time count
 #> 1 2024-03-01    23
 #> 
-#> ℹ The following rows have changed from the previous data, and will be dropped: 
+#> ℹ The following values have changes from the previous data.
 #> old vs new
 #>            count
 #>   old[1, ]    17
@@ -192,6 +199,8 @@ df_released <- butterfly::release(
 #> 
 #> `old$count`: 17 22 55 18
 #> `new$count`: 17 22 55 11
+#> 
+#> ℹ These will be dropped, but new rows are included.
 
 df_released
 #>         time count
diff --git a/codemeta.json b/codemeta.json
index 052e16e..31830e1 100644
--- a/codemeta.json
+++ b/codemeta.json
@@ -128,8 +128,9 @@
     },
     "SystemRequirements": null
   },
-  "fileSize": "330.734KB",
+  "fileSize": "337.771KB",
   "readme": "https://github.com/thomaszwagerman/butterfly/blob/main/README.md",
   "contIntegration": ["https://github.com/thomaszwagerman/butterfly/actions/workflows/R-CMD-check.yaml", "https://app.codecov.io/gh/thomaszwagerman/butterfly?branch=main"],
+  "developmentStatus": "https://lifecycle.r-lib.org/articles/stages.html#experimental",
   "keywords": ["qaqc", "timeseries"]
 }
diff --git a/man/catch.Rd b/man/catch.Rd
index 337ba5b..ee0ea5a 100644
--- a/man/catch.Rd
+++ b/man/catch.Rd
@@ -23,6 +23,9 @@ This function matches two dataframe objects by their unique identifier
 which contains only rows that have changed compared to previous data. It will
 not return any new rows.
 }
+\details{
+The underlying functionality is handled by \code{create_object_list()}.
+}
 \examples{
 df_caught <- butterfly::catch(
   butterflycount$march,
@@ -35,4 +38,6 @@ df_caught
 }
 \seealso{
 \code{\link[=loupe]{loupe()}}
+
+\code{\link[=create_object_list]{create_object_list()}}
 }
diff --git a/man/create_object_list.Rd b/man/create_object_list.Rd
new file mode 100644
index 0000000..d1a6fa0
--- /dev/null
+++ b/man/create_object_list.Rd
@@ -0,0 +1,47 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/create_object_list.R
+\name{create_object_list}
+\alias{create_object_list}
+\title{create_object_list: creates a list of objects used in all butterfly functions}
+\usage{
+create_object_list(df_current, df_previous, datetime_variable)
+}
+\arguments{
+\item{df_current}{data.frame, the newest/current version of dataset x.}
+
+\item{df_previous}{data.frame, the old version of dataset, for example x - t1.}
+
+\item{datetime_variable}{string, which variable to use as unique ID to join
+\code{df_current} and \code{df_previous}. Usually a "datetime" variable.}
+}
+\value{
+A list containing boolean where TRUE indicates no changes to
+previous data and FALSE indicates unexpected changes, a dataframe of
+the current data without new rows and a dataframe of new rows only
+}
+\description{
+This function creates a list of objects which is used by all of \code{loupe()},
+\code{catch()} and \code{release()}.
+}
+\details{
+This function matches two dataframe objects by their unique identifier
+(usually "time" or "datetime in a timeseries).
+
+It informs the user of new (unmatched) rows which have appeared, and then
+returns a \code{waldo::compare()} call to give a detailed breakdown of changes.
+
+The main assumption is that \code{df_current} and \code{df_previous} are a newer and
+older versions of the same data, and that the \code{datetime_variable} variable name always
+remains the same. Elsewhere new columns can of appear, and these will be
+returned in the report.
+}
+\examples{
+butterfly_object_list <- butterfly::create_object_list(
+  butterflycount$february,
+  butterflycount$january,
+  datetime_variable = "time"
+)
+
+butterfly_object_list
+
+}
diff --git a/man/loupe.Rd b/man/loupe.Rd
index 6c7eea2..cb59b2e 100644
--- a/man/loupe.Rd
+++ b/man/loupe.Rd
@@ -14,7 +14,7 @@ loupe(df_current, df_previous, datetime_variable)
 \item{datetime_variable}{string, which variable to use as unique ID to join \code{df_current} and \code{df_previous}. Usually a "datetime" variable.}
 }
 \value{
-A waldo object containing a message on differences or 'And there are no differences with previous data'.
+A boolean where TRUE indicates no changes to previous data and FALSE indicates unexpected changes.
 }
 \description{
 A loupe is a simple, small magnification device used to examine small details
@@ -35,6 +35,8 @@ The main assumption is that \code{df_current} and \code{df_previous} are a newer
 older versions of the same data, and that the \code{datetime_variable} variable name always
 remains the same. Elsewhere new columns can of appear, and these will be
 returned in the report.
+
+The underlying functionality is handled by \code{create_object_list()}.
 }
 \examples{
 # This example contains no differences with previous data
@@ -52,3 +54,6 @@ butterfly::loupe(
 )
 
 }
+\seealso{
+\code{\link[=create_object_list]{create_object_list()}}
+}
diff --git a/man/release.Rd b/man/release.Rd
index ebd91c8..ed62da8 100644
--- a/man/release.Rd
+++ b/man/release.Rd
@@ -4,7 +4,7 @@
 \alias{release}
 \title{Release: return current dataframe without changed old rows}
 \usage{
-release(df_current, df_previous, datetime_variable)
+release(df_current, df_previous, datetime_variable, include_new = TRUE)
 }
 \arguments{
 \item{df_current}{data.frame, the newest/current version of dataset x.}
@@ -12,6 +12,8 @@ release(df_current, df_previous, datetime_variable)
 \item{df_previous}{data.frame, the old version of dataset, for example x - t1.}
 
 \item{datetime_variable}{string, which variable to use as unique ID to join \code{df_current} and \code{df_previous}. Usually a "datetime" variable.}
+
+\item{include_new}{boolean, should new rows be included? Default is TRUE.}
 }
 \value{
 A dataframe which contains only rows of \code{df_current} that have not changed from \code{df_previous}, and includes new rows.
@@ -35,4 +37,6 @@ df_released
 }
 \seealso{
 \code{\link[=loupe]{loupe()}}
+
+\code{\link[=create_object_list]{create_object_list()}}
 }
diff --git a/tests/testthat/test-catch.R b/tests/testthat/test-catch.R
index 12f3054..311dd72 100644
--- a/tests/testthat/test-catch.R
+++ b/tests/testthat/test-catch.R
@@ -1,28 +1,3 @@
-test_that("warning when no new rows", {
-  # And when the previous/current dfs have been swapped.
-  expect_warning(
-    catch(
-      butterflycount$january,
-      butterflycount$february,
-      datetime_variable = "time"
-    )
-  )
-})
-
-test_that("error when rows are identical", {
-  # This should occur when dfs are identical
-  expect_error(
-    # Suppressing warning, as this is also given (see above)
-    suppressWarnings(
-      catch(
-        butterflycount$january,
-        butterflycount$january,
-        datetime_variable = "time"
-      )
-    )
-  )
-})
-
 test_that("correct message is fed back", {
   expect_output(
     catch(
@@ -39,7 +14,7 @@ test_that("correct message is fed back", {
       butterflycount$february,
       datetime_variable = "time"
     ),
-    "The following rows have changed from the previous data, and will be returned:"
+    "Only these rows are returned"
   )
 })
 
diff --git a/tests/testthat/test-create_object_list.R b/tests/testthat/test-create_object_list.R
new file mode 100644
index 0000000..3523b13
--- /dev/null
+++ b/tests/testthat/test-create_object_list.R
@@ -0,0 +1,70 @@
+test_that("error when no new rows", {
+  # This should occur when dfs are identical
+  expect_error(
+    create_object_list(
+      butterflycount$january,
+      butterflycount$january,
+      datetime_variable = "time"
+    )
+  )
+  # And when the previous/current dfs have been swapped.
+  expect_error(
+    create_object_list(
+      butterflycount$january,
+      butterflycount$february,
+      datetime_variable = "time"
+    )
+  )
+})
+
+test_that("correct message is fed back", {
+  expect_output(
+    create_object_list(
+      butterflycount$february,
+      butterflycount$january,
+      datetime_variable = "time"
+    ),
+    "The following rows are new in"
+  )
+  expect_output(
+    create_object_list(
+      butterflycount$february,
+      butterflycount$january,
+      datetime_variable = "time"
+    ),
+    "And there are no differences with previous data."
+  )
+  expect_output(
+    create_object_list(
+      butterflycount$march,
+      butterflycount$february,
+      datetime_variable = "time"
+    ),
+    "The following values have changes from the previous data."
+  )
+})
+
+test_that("a list of three objects is returned", {
+  expect_length(
+    create_object_list(
+      butterflycount$february,
+      butterflycount$january,
+      datetime_variable = "time"
+    ),
+    3
+  )
+})
+
+test_that("comparison object is returned when not equal", {
+  create_object_list_output <- create_object_list(
+    butterflycount$march,
+    butterflycount$february,
+    datetime_variable = "time"
+  )
+  expect_gt(
+    length(
+      create_object_list_output
+    ),
+    0
+  )
+})
diff --git a/tests/testthat/test-loupe.R b/tests/testthat/test-loupe.R
index 7082059..eced681 100644
--- a/tests/testthat/test-loupe.R
+++ b/tests/testthat/test-loupe.R
@@ -1,70 +1,19 @@
-test_that("error when no new rows", {
-  # This should occur when dfs are identical
-  expect_error(
+test_that("TRUE is returned when equal", {
+  expect_true(
     loupe(
-      butterflycount$january,
-      butterflycount$january,
-      datetime_variable = "time"
-    )
-  )
-  # And when the previous/current dfs have been swapped.
-  expect_error(
-    loupe(
-      butterflycount$january,
       butterflycount$february,
+      butterflycount$january,
       datetime_variable = "time"
     )
   )
 })
 
-test_that("correct message is fed back", {
-  expect_output(
-    loupe(
-      butterflycount$february,
-      butterflycount$january,
-      datetime_variable = "time"
-    ),
-    "The following rows are new in"
-  )
-  expect_output(
-    loupe(
-      butterflycount$february,
-      butterflycount$january,
-      datetime_variable = "time"
-    ),
-    "And there are no differences with previous data."
-  )
-  expect_output(
+test_that("FALSE is returned when NOT equal", {
+  expect_false(
     loupe(
       butterflycount$march,
       butterflycount$february,
       datetime_variable = "time"
-    ),
-    "But the following values have changes from the previous data:"
-  )
-})
-
-test_that("comparison object is not returned when equal", {
-  expect_length(
-    loupe(
-      butterflycount$february,
-      butterflycount$january,
-      datetime_variable = "time"
-    ),
-    0
-  )
-})
-
-test_that("comparison object is returned when not equal", {
-  loupe_output <- loupe(
-    butterflycount$march,
-    butterflycount$february,
-    datetime_variable = "time"
-  )
-  expect_gt(
-    length(
-      loupe_output
-    ),
-    0
+    )
   )
 })
diff --git a/tests/testthat/test-release.R b/tests/testthat/test-release.R
index ee60ec4..fab33f0 100644
--- a/tests/testthat/test-release.R
+++ b/tests/testthat/test-release.R
@@ -1,33 +1,22 @@
-test_that("warning when no new rows", {
-  # And when the previous/current dfs have been swapped.
-  expect_warning(
-    release(
-      butterflycount$january,
-      butterflycount$february,
-      datetime_variable = "time"
-    )
-  )
-})
-
-test_that("warning when there are no different rows to drop", {
-  # This should occur when dfs are identical
-  expect_warning(
+test_that("correct message is fed back", {
+  expect_output(
     release(
+      butterflycount$march,
       butterflycount$february,
-      butterflycount$january,
-      datetime_variable = "time"
-    )
+      datetime_variable = "time",
+      include_new = TRUE
+    ),
+    "These will be dropped, but new rows are included"
   )
-})
 
-test_that("correct message is fed back", {
   expect_output(
     release(
       butterflycount$march,
       butterflycount$february,
-      datetime_variable = "time"
+      datetime_variable = "time",
+      include_new = FALSE
     ),
-    "The following rows have changed from the previous data, and will be dropped:"
+    "These will be dropped, along with new rows"
   )
 })
 
diff --git a/vignettes/butterfly.Rmd b/vignettes/butterfly.Rmd
index a1e22f5..a06a16c 100644
--- a/vignettes/butterfly.Rmd
+++ b/vignettes/butterfly.Rmd
@@ -38,9 +38,10 @@ devtools::install_github("thomaszwagerman/butterfly")
 ## Overview
 The butterfly package contains the following:
 
-  * `butterfly::loupe()` - examines in detail whether previous values have changed, and reports them using `waldo::compare()`.
+  * `butterfly::loupe()` - examines in detail whether previous values have changed, and returns TRUE/FALSE for no change/change.
   * `butterfly::catch()` - returns rows which contain previously changed values in a dataframe.
   * `butterfly::release()` - drops rows which contain previously changed values, and returns a dataframe containing new and unchanged rows.
+  * `butterfly::create_object_list()` - returns a list of objects required by all of `loupe()`, `catch()` and `release()`. Contains underlying functionality.
   * `butterflycount` - a list of monthly dataframes, which contain fictional butterfly counts for a given date.
 
 ## How to use butterfly