diff --git a/DESCRIPTION b/DESCRIPTION
index 5562d25..9067d58 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: tidyREDCap
 Title: Helper Functions for Working with 'REDCap' Data
-Version: 1.2.0
+Version: 2.0.0
 Authors@R: 
     c(person(
         given = "Raymond",
@@ -72,6 +72,7 @@ Imports:
     dplyr,
     janitor,
     labelVector,
+    lifecycle,
     magrittr,
     purrr,
     REDCapR,
diff --git a/NAMESPACE b/NAMESPACE
index 478f9ea..f2b4935 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -47,6 +47,7 @@ importFrom(janitor,adorn_pct_formatting)
 importFrom(janitor,tabyl)
 importFrom(labelVector,is_labelled)
 importFrom(labelVector,set_label)
+importFrom(lifecycle,deprecate_soft)
 importFrom(magrittr,"%>%")
 importFrom(purrr,map_chr)
 importFrom(purrr,map_df)
diff --git a/NEWS.md b/NEWS.md
index 69340af..9483ecd 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,17 @@
+# tidyREDCap 2.0.0 (CRAN release)
+
+## Breaking changes
+
+* `drop_label()` now preserves variable class attributes (e.g., `"character"`, `"numeric"`) while removing only the `"labelled"` class and `label` attribute. Previous versions removed all attributes.
+
+## New features
+
+* `drop_label()` now can be used on data frames. When called without arguments on a data frame, it removes labels from all variables.
+
+## Fixes/Changes
+
+* `drop_labels()` is now deprecated in favor of `drop_label()`. Use `drop_label()` without arguments to remove labels from all variables.
+
 # tidyREDCap 1.2.0 (CRAN release)
 
 * Updated `drop_label()` now accepts multiple variables and uses `tidyselect` helpers.
diff --git a/R/drop_label.R b/R/drop_label.R
new file mode 100644
index 0000000..d079af7
--- /dev/null
+++ b/R/drop_label.R
@@ -0,0 +1,84 @@
+
+#' Drop attributes/labels from variables or data frames
+#' 
+#' @description Some functions don't work with labelled variables. As a solution, 
+#' this function can be used to drop labels (and all other attributes) from 
+#' one or more variables within a data frame, from all variables if none are 
+#' specified, or from a vector directly.
+#'
+#' @param x A data frame or a vector/column.
+#' @param ... When `x` is a data frame, select variables using tidyselect 
+#'   helpers (e.g., `contains()`, `starts_with()`) or unquoted names. 
+#'   If empty, removes labels from ALL variables. Ignored when `x` is a vector.
+#'
+#' @return The modified data frame or vector with attributes removed.
+#'
+#' @examples
+#' \dontrun{
+#' # Dataset-level: Remove labels from ALL variables
+#' df |> drop_label()
+#'
+#' # Dataset-level: Remove labels from specific variables
+#' df |> drop_label(employment, starts_with("dem_"))
+#'
+#' # Variable-level: Use inside mutate
+#' df |> mutate(name_first = drop_label(name_first))
+#' 
+#' # Variable-level: Use with across()
+#' df |> mutate(across(c(age, income), drop_label))
+#' }
+#'
+#' @export
+drop_label <- function(x, ...) {
+  # 1. Dataset-level logic
+  if (is.data.frame(x)) {
+    old_class_char <- class(x)
+    
+    # Check if any variables were selected
+    vars_idx <- tidyselect::eval_select(rlang::expr(c(...)), x)
+    
+    # If no variables selected, process ALL columns
+    if (length(vars_idx) == 0) {
+      # seq_along will either capture the variable column number
+      vars_idx <- seq_along(x)
+    }
+    
+    # Remove attributes from selected columns
+    for (col_idx in vars_idx) {
+      # col_idx evaluates to:
+      # 1. variable name(s) if passed with the `...` argument
+      # 2. variable column position number if the whole dataset was passed for dropping
+
+      # Get all current attributes
+      col_attrs <- attributes(x[[col_idx]])
+
+      # Remove the label attribute
+      col_attrs$label <- NULL
+
+      # Remove the "labelled" from class if it exists
+      if (!is.null(col_attrs$class)) {
+        col_attrs$class <- col_attrs$class[col_attrs$class != "labelled"]
+        # If the class now becomes empty, remove it to use R's default
+        if (length(col_attrs$class) == 0) col_attrs$class <- NULL
+      }
+
+      # Reapply the modified attributes
+      attributes(x[[col_idx]]) <- col_attrs
+    }
+    
+    # Preserve original class structure (data.frame vs tibble)
+    if (("data.frame" %in% old_class_char) && !("tbl_df" %in% old_class_char)) {
+      class(x) <- "data.frame"
+    }
+    
+    return(x)
+  }
+  
+  # 2. Variable-level logic
+  if (is.character(substitute(x))) {
+    stop('It looks like you quoted your variable. The variable must be unquoted when used inside mutate().')
+  }
+
+  attributes(x) <- NULL
+  return(x)
+}
diff --git a/R/drop_labels.R b/R/drop_labels.R
index 901a9b1..865978b 100644
--- a/R/drop_labels.R
+++ b/R/drop_labels.R
@@ -5,6 +5,7 @@
 #' @param df The data frame with column labels that you want to drop
 #' 
 #' @importFrom purrr map_df
+#' @importFrom lifecycle deprecate_soft
 #'
 #' @export
 #' 
@@ -17,6 +18,13 @@
 #'   skimr::skim()
 #' }
 drop_labels <- function(df) {
+  # Add deprecate message:
+  lifecycle::deprecate_soft(
+    when = "1.2.0",  # Version when this was deprecated
+    what = "drop_labels()",
+    with = "drop_label()"
+  )
+
   old_class_char <- class(df)
   if (!("data.frame" %in% old_class_char)) {
     stop("df must have class data.frame", call. = FALSE)
diff --git a/R/make_instrument_auto.R b/R/make_instrument_auto.R
index a73e906..2ddf8e4 100644
--- a/R/make_instrument_auto.R
+++ b/R/make_instrument_auto.R
@@ -171,51 +171,3 @@ fix_class_bug <- function(df) {
   return(df)
 }
 "fix_class_bug"
-
-
-#' Drop attributes/labels from variables or data frames
-#' 
-#' @description Some functions don't work with labelled variables. As a solution, 
-#' this function can be used to drop labels (and all other attributes) from 
-#' one or more variables within a data frame, or from a vector directly.
-#'
-#' @param x A data frame or a vector/column.
-#' @param ... When `x` is a data frame, select variables using tidyselect 
-#'   helpers (e.g., `contains()`, `starts_with()`) or unquoted names. 
-#'   Ignored when `x` is a vector.
-#'
-#' @return The modified data frame or vector with attributes removed.
-#'
-#' @examples
-#' \dontrun{
-#' # Dataset-level: Remove labels from specific variables
-#' df |> drop_label(employment, starts_with("dem_"))
-#'
-#' # Variable-level: Use inside mutate
-#' df |> mutate(name_first = drop_label(name_first))
-#' 
-#' # Variable-level: Use with across()
-#' df |> mutate(across(c(age, income), drop_label))
-#' }
-#'
-#' @export
-drop_label <- function(x, ...) {
-  # 1. Dataset-level logic
-  if (is.data.frame(x)) {
-    vars_idx <- tidyselect::eval_select(rlang::expr(c(...)), x)
-    if (length(vars_idx) == 0) return(x)
-    
-    for (col_idx in vars_idx) {
-      attributes(x[[col_idx]]) <- NULL
-    }
-    return(x)
-  }
-  
-  # 2. Variable-level logic
-  if (is.character(substitute(x))) {
-    stop('It looks like you quoted your variable. The variable must be unquoted when used inside mutate().')
-  }
-
-  attributes(x) <- NULL
-  return(x)
-}
diff --git a/README.md b/README.md
index 030ce15..343e391 100644
--- a/README.md
+++ b/README.md
@@ -25,7 +25,7 @@ tidyREDCap is an R package with functions for processing REDCap data.
 
 * After loading data into R using RStudio with the `import_instruments()` function, you can see both the variable name and the text that appears to users of REDCap.  All you need to do is click on the dataset's name in the **Environment** tab or use the `View()` function. The column headings will include both the Variable Name and the Field Label from REDCap. 
 
-* 💥 NEW in Version 1.1 💥  Functions coming from packages outside of `tidyREDCap` may not understand what to do with labeled variables.  So, `tidyREDCap` includes a new `drop_labels()` function that will allow you to strip the labels before using functions that want unlabeled data.
+* 💥 NEW in Version 2.0.0 💥  Functions coming from packages outside of `tidyREDCap` may not understand what to do with labeled variables.  So, `tidyREDCap` includes a new `drop_label()` function that will allow you to strip the labels before using functions that want unlabeled data.
 
 #### Working with <i>Choose One</i> Questions
 
diff --git a/man/drop_label.Rd b/man/drop_label.Rd
index 378525e..bda08ef 100644
--- a/man/drop_label.Rd
+++ b/man/drop_label.Rd
@@ -1,5 +1,5 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/make_instrument_auto.R
+% Please edit documentation in R/drop_label.R
 \name{drop_label}
 \alias{drop_label}
 \title{Drop attributes/labels from variables or data frames}
@@ -11,7 +11,7 @@ drop_label(x, ...)
 
 \item{...}{When \code{x} is a data frame, select variables using tidyselect
 helpers (e.g., \code{contains()}, \code{starts_with()}) or unquoted names.
-Ignored when \code{x} is a vector.}
+If empty, removes labels from ALL variables. Ignored when \code{x} is a vector.}
 }
 \value{
 The modified data frame or vector with attributes removed.
@@ -19,10 +19,14 @@ The modified data frame or vector with attributes removed.
 \description{
 Some functions don't work with labelled variables. As a solution,
 this function can be used to drop labels (and all other attributes) from
-one or more variables within a data frame, or from a vector directly.
+one or more variables within a data frame, from all variables if none are
+specified, or from a vector directly.
 }
 \examples{
 \dontrun{
+# Dataset-level: Remove labels from ALL variables
+df |> drop_label()
+
 # Dataset-level: Remove labels from specific variables
 df |> drop_label(employment, starts_with("dem_"))
 
diff --git a/tests/testthat/test-drop_label.R b/tests/testthat/test-drop_label.R
new file mode 100644
index 0000000..d0f5e3f
--- /dev/null
+++ b/tests/testthat/test-drop_label.R
@@ -0,0 +1,181 @@
+demographics <- structure(
+  list(
+    record_id = c(1, 2, 3, 4, 5),
+    name_first = structure(
+      c("Nutmeg", "Tumtum", "Marcus", "Trudy", "John Lee"),
+      label = "First Name",
+      class = c("labelled", "character")
+    ), 
+    name_last = structure(
+      c("Nutmouse", "Nutmouse", "Wood", "DAG", "Walker"),
+      label = "Last Name",
+      class = c("labelled", "character")
+    ),
+    address = structure(
+      c(
+        "14 Rose Cottage St.\nKenning UK, 323232",
+        "14 Rose Cottage Blvd.\nKenning UK 34243",
+        "243 Hill St.\nGuthrie OK 73402",
+        "342 Elm\nDuncanville TX, 75116",
+        "Hotel Suite\nNew Orleans LA, 70115"
+      ),
+      label = "Street, City, State, ZIP",
+      class = c("labelled", "character")
+    ),
+    telephone = structure(
+      c(
+        "(405) 321-1111",
+        "(405) 321-2222",
+        "(405) 321-3333",
+        "(405) 321-4444",
+        "(405) 321-5555"
+      ),
+      label = "Phone number",
+      class = c("labelled", "character")
+    ), email = structure(
+      c(
+        "nutty@mouse.com",
+        "tummy@mouse.comm",
+        "mw@mwood.net",
+        "peroxide@blonde.com",
+        "left@hippocket.com"
+      ),
+      label = "E-mail",
+      class = c("labelled", "character")
+    ),
+    dob = structure(
+      c(12294, 12121, -13051, -6269, -5375),
+      class = c("labelled", "Date"),
+      label = "Date of birth"
+    ),
+    age = structure(
+      c(11, 11, 80, 61, 59),
+      label = "Age (years)",
+      class = c("labelled", "numeric")
+    ),
+    days = structure(
+      c(1, 2, 3, 4, 5),
+      label = "Days",
+      class = c("labelled", "numeric")
+    ),    
+    sex = structure(
+      c("Female", "Male", "Male", "Female", "Male"),
+      label = "Gender",
+      class = c("labelled", "character")
+    ),
+    demographics_complete = structure(
+      c("Complete", "Complete", "Complete", "Complete", "Complete"),
+      label = "Complete?...10",
+      class = c("labelled", "character")
+    )
+  ),
+  row.names = c(NA, -5L),
+  class = "data.frame"
+)
+
+test_that("drop_label() removes all labels when no variables specified", {
+  result <- drop_label(demographics)
+  
+  # Check that label attribute is removed from all columns
+  expect_null(attr(result$name_first, "label"))
+  expect_null(attr(result$name_last, "label"))
+  
+  # Check that "labelled" class is removed but base class preserved
+  expect_false("labelled" %in% class(result$name_first))
+  expect_true("character" %in% class(result$name_first))
+})
+
+test_that("drop_label() removes label from single variable", {
+  result <- drop_label(demographics, name_first)
+  
+  # name_first should have label removed
+  expect_null(attr(result$name_first, "label"))
+  expect_false("labelled" %in% class(result$name_first))
+  expect_true("character" %in% class(result$name_first))
+  
+  # name_last should still have label
+  expect_equal(attr(result$name_last, "label"), "Last Name")
+  expect_true("labelled" %in% class(result$name_last))
+})
+
+test_that("drop_label() removes labels from multiple variables", {
+  result <- drop_label(demographics, name_first, name_last)
+  
+  # Both should have labels removed
+  expect_null(attr(result$name_first, "label"))
+  expect_null(attr(result$name_last, "label"))
+  expect_false("labelled" %in% class(result$name_first))
+  expect_false("labelled" %in% class(result$name_last))
+  
+  # Other columns should still have labels (if they had them)
+  if ("labelled" %in% class(demographics$email)) {
+    expect_true("labelled" %in% class(result$email))
+  }
+})
+
+test_that("drop_label() works with tidyselect helpers", {
+  result <- drop_label(demographics, contains('name'))
+  
+  # Variables containing 'name' should have labels removed
+  expect_null(attr(result$name_first, "label"))
+  expect_null(attr(result$name_last, "label"))
+  expect_false("labelled" %in% class(result$name_first))
+  expect_false("labelled" %in% class(result$name_last))
+})
+
+test_that("drop_label() works inside mutate() with single variable", {
+  result <- demographics |> 
+    mutate(name_first = drop_label(name_first))
+  
+  # name_first should have label removed
+  expect_null(attr(result$name_first, "label"))
+  expect_false("labelled" %in% class(result$name_first))
+  
+  # name_last should still have label
+  expect_equal(attr(result$name_last, "label"), "Last Name")
+})
+
+test_that("drop_label() works inside mutate() with across()", {
+  result <- demographics |> 
+    mutate(across(contains('name'), drop_label))
+  
+  # Both name variables should have labels removed
+  expect_null(attr(result$name_first, "label"))
+  expect_null(attr(result$name_last, "label"))
+  expect_false("labelled" %in% class(result$name_first))
+  expect_false("labelled" %in% class(result$name_last))
+})
+
+test_that("drop_label() errors when variable is quoted in mutate()", {
+  expect_error(
+    demographics |> mutate(name_first = drop_label('name_first')),
+    "quoted your variable"
+  )
+})
+
+test_that("drop_label() preserves data frame class", {
+  # Test with base data.frame
+  df_base <- as.data.frame(demographics)
+  result_base <- drop_label(df_base)
+  expect_s3_class(result_base, "data.frame")
+  expect_false(inherits(result_base, "tbl_df"))
+  
+  # Test with tibble
+  df_tibble <- tibble::as_tibble(demographics)
+  result_tibble <- drop_label(df_tibble)
+  expect_s3_class(result_tibble, "tbl_df")
+})
+
+test_that("drop_labels() shows deprecation warning", {
+  lifecycle::expect_deprecated(
+    drop_labels(demographics),
+    "drop_labels.*deprecated.*drop_label"
+  )
+})
+
+test_that("drop_label() on quoted variable doesn't work at dataset level", {
+  # This should process the variable 'name_first' (not error)
+  # Quoting is only an error inside mutate()
+  result <- drop_label(demographics, 'name_first')
+  expect_null(attr(result$name_first, "label"))
+})
\ No newline at end of file
diff --git a/vignettes/.gitignore b/vignettes/.gitignore
index 097b241..47018d6 100644
--- a/vignettes/.gitignore
+++ b/vignettes/.gitignore
@@ -1,2 +1,5 @@
 *.html
 *.R
+
+/.quarto/
+**/*.quarto_ipynb
diff --git a/vignettes/dropLabels.Rmd b/vignettes/dropLabels.Rmd
index 3647f73..860e192 100644
--- a/vignettes/dropLabels.Rmd
+++ b/vignettes/dropLabels.Rmd
@@ -183,9 +183,16 @@ demographics_from_mutate <- demographics |>
   mutate(across(starts_with('name'), drop_label))
 ```
 
-You can drop all the labels using the `drop_labels()` function.  For example:
+## Drop all dataset variable labels
 
-```{r}
-demographics_without_labels <- drop_labels(demographics)
+You can drop all the labels using the `drop_label()` function.  For example:
+
+```{r drop-label-dataset}
+demographics_without_labels <- drop_label(demographics)
 ```
 
+> NOTE: tidyREDCap versions prior to 1.2.0 handled dropping all variable labels from a dataset by using `drop_labels()`. This function can still be used, but we added a polite message to use `drop_label()` instead.
+
+```{r, warning=TRUE}
+demographics_without_labels <- drop_labels(demographics)
+```