diff --git a/DESCRIPTION b/DESCRIPTION
index e01f37595..92571a5bf 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Type: Package
 Package: datawizard
 Title: Easy Data Wrangling and Statistical Transformations
-Version: 1.2.0.8
+Version: 1.2.0.9
 Authors@R: c(
     person("Indrajeet", "Patil", , "patilindrajeet.science@gmail.com", role = "aut",
            comment = c(ORCID = "0000-0003-1995-6531")),
diff --git a/NAMESPACE b/NAMESPACE
index dfe242add..f10b5720d 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -8,6 +8,8 @@ S3method(as.double,parameters_smoothness)
 S3method(as.numeric,parameters_kurtosis)
 S3method(as.numeric,parameters_skewness)
 S3method(as.numeric,parameters_smoothness)
+S3method(as.prop.table,datawizard_crosstab)
+S3method(as.prop.table,datawizard_crosstabs)
 S3method(as.table,datawizard_crosstab)
 S3method(as.table,datawizard_crosstabs)
 S3method(as.table,datawizard_table)
@@ -230,6 +232,7 @@ S3method(winsorize,factor)
 S3method(winsorize,logical)
 S3method(winsorize,numeric)
 export(adjust)
+export(as.prop.table)
 export(assign_labels)
 export(categorize)
 export(center)
diff --git a/NEWS.md b/NEWS.md
index 0267ea254..e8417386f 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -10,6 +10,9 @@ BREAKING CHANGES
 
 CHANGES
 
+* `data_tabulate()` now saves the table of proportions for crosstables as
+  attribute, accessible via the new `as.prop.table()` method (#656).
+
 * Due to changes in the package `insight`, `data_tabulate()` no longer prints
   decimals when all values in a column are integers (#641).
 
diff --git a/R/data_tabulate.R b/R/data_tabulate.R
index cb9e865da..0a5c72972 100644
--- a/R/data_tabulate.R
+++ b/R/data_tabulate.R
@@ -32,11 +32,6 @@
 #' used for large numbers. If `NULL` (default), a big mark is added automatically for
 #' large numbers (i.e. numbers with more than 5 digits). If you want to remove
 #' the big mark, set `big_mark = ""`.
-#' @param simplify Logical, if `TRUE`, the returned table is simplified to a
-#' single table object if there is only one frequency or contingency table
-#' input. Else, always for multiple table inputs or when `simplify = FALSE`, a
-#' list of tables is returned. This is only relevant for the `as.table()`
-#' methods. To ensure consistent output, the default is `FALSE`.
 #' @param object An object returned by `data_tabulate()`.
 #' @param format String, indicating the output format. Can be `"markdown"`
 #' `"html"`, or `"tt"`. `format = "html"` create an HTML table using the *gt*
@@ -52,11 +47,12 @@
 #' data frame. The structure of the returned object is a nested data frame,
 #' where the first column contains name of the variable for which frequencies
 #' were calculated, and the second column is a list column that contains the
-#' frequency tables as data frame. See 'Examples'.
+#' frequency tables as data frame. See [as.table.datawizard_table].
 #'
 #' There is also an `as.table()` method, which returns a table object with the
 #' frequencies of the variable. This is useful for further statistical analysis,
-#' e.g. for using `chisq.test()` on the frequency table. See 'Examples'.
+#' e.g. for using `chisq.test()` on the frequency table. See
+#' [as.table.datawizard_table].
 #'
 #' @section Crosstables:
 #' If `by` is supplied, a crosstable is created. The crosstable includes `<NA>`
@@ -77,6 +73,8 @@
 #' @return A data frame, or a list of data frames, with one frequency table
 #' as data frame per variable.
 #'
+#' @seealso [as.prop.table]
+#'
 #' @examplesIf requireNamespace("poorman")
 #' # frequency tables -------
 #' # ------------------------
@@ -139,28 +137,6 @@
 #' # round percentages
 #' out <- data_tabulate(efc, "c172code", by = "e16sex", proportions = "column")
 #' print(out, digits = 0)
-#'
-#' # coerce to data frames
-#' result <- data_tabulate(efc, "c172code", by = "e16sex")
-#' as.data.frame(result)
-#' as.data.frame(result)$table
-#' as.data.frame(result, add_total = TRUE)$table
-#'
-#' # post-processing ------
-#' # ----------------------
-#'
-#' out <- data_tabulate(efc, "c172code", by = "e16sex")
-#' # we need to simplify the output, else we get a list of tables
-#' suppressWarnings(chisq.test(as.table(out, simplify = TRUE)))
-#'
-#' # apply chisq.test to each table
-#' out <- data_tabulate(efc, c("c172code", "e16sex"))
-#' suppressWarnings(lapply(as.table(out), chisq.test))
-#'
-#' # can also handle grouped data frames
-#' d <- data_group(mtcars, "am")
-#' x <- data_tabulate(d, "cyl", by = "gear")
-#' as.table(x)
 #' @export
 data_tabulate <- function(x, ...) {
   UseMethod("data_tabulate")
@@ -464,10 +440,147 @@ insight::print_md
 #' @export
 insight::display
 
+#' Convert a crosstable to a frequency or a propensity table
+#'
+#' @description
+#' `as.prop.table()` is an S3 generic. It can be used on objects of class
+#' `datawizard_crosstab` created by `data_tabulate()` when it was run with the
+#' arguments `by` and `proportions`.
+#'
+#' @param x An object created by `data_tabulate()`. It must be of class
+#' `datawizard_crosstab` for `as.prop.table()`.
+#' @param simplify Logical, if `TRUE`, the returned table is simplified to a
+#' single table object if there is only one frequency or contingency table
+#' input. Else, always for multiple table inputs or when `simplify = FALSE`, a
+#' list of tables is returned. This is only relevant for the `as.table()`
+#' methods. To ensure consistent output, the default is `FALSE`.
+#' @inheritParams data_tabulate
+#'
+#' @export
+#' @seealso [data_tabulate]
+#'
+#' @examples
+#' data(efc)
+#'
+#' # Some cross tabulation
+#' cross <- data_tabulate(efc, select = "e42dep", by = "c172code", proportions = "row")
+#' cross
+#'
+#' # Convert to a propensity table
+#' as.prop.table(cross)
+#'
+#' # Convert to data.frame
+#' result <- data_tabulate(efc, "c172code", by = "e16sex")
+#' as.data.frame(result)
+#' as.data.frame(result)$table
+#' as.data.frame(result, add_total = TRUE)$table
+#'
+#' # Convert to a table that can be passed to chisq.test()
+#'
+#' out <- data_tabulate(efc, "c172code", by = "e16sex")
+#' # we need to simplify the output, else we get a list of tables
+#' tbl <- as.table(out, simplify = TRUE)
+#' tbl
+#' suppressWarnings(chisq.test(tbl))
+#'
+#' # apply chisq.test to each table
+#' out <- data_tabulate(efc, c("c172code", "e16sex"))
+#' suppressWarnings(lapply(as.table(out), chisq.test))
+#'
+#' # can also handle grouped data frames
+#' d <- data_group(mtcars, "am")
+#' x <- data_tabulate(d, "cyl", by = "gear")
+#' as.table(x)
+as.prop.table <- function(x, ...) {
+  UseMethod("as.prop.table")
+}
+
+#' @rdname as.prop.table
+#' @export
+as.prop.table.datawizard_crosstab <- function(
+  x,
+  remove_na = TRUE,
+  simplify = FALSE,
+  verbose = TRUE,
+  ...
+) {
+  # sanity check - the `.data.frame` method  returns a list, but not the
+  # default method
+  if (!is.data.frame(x)) {
+    x <- x[[1]]
+  }
+  prop_table <- attributes(x)$prop_table
+
+  if (is.null(prop_table)) {
+    insight::format_warning("No proportions available.")
+    return(NULL)
+  }
+
+  if (remove_na) {
+    if (
+      verbose &&
+        ("NA" %in% colnames(prop_table) || "NA" %in% rownames(prop_table))
+    ) {
+      insight::format_alert("Removing NA values from frequency table.")
+    }
+    if (!is.null(prop_table[["NA"]])) {
+      prop_table[["NA"]] <- NULL
+    }
+    if ("NA" %in% rownames(prop_table)) {
+      prop_table <- prop_table[rownames(prop_table) != "NA", ]
+    }
+  }
+  # coerce to table
+  result <- as.table(as.matrix(prop_table))
+  # if we don't want to simplify the table, we wrap it into a list
+  if (!simplify) {
+    result <- list(result)
+  }
+
+  result
+}
+
+#' @export
+as.prop.table.datawizard_crosstabs <- function(
+  x,
+  remove_na = TRUE,
+  simplify = FALSE,
+  verbose = TRUE,
+  ...
+) {
+  # only show message once we set `verbose = FALSE` in the lapply()
+  if (remove_na && verbose) {
+    prop_table <- attributes(x[[1]])$prop_table
+    if ("NA" %in% colnames(prop_table) || "NA" %in% rownames(prop_table)) {
+      insight::format_alert("Removing NA values from frequency table.")
+    }
+  }
+
+  out <- insight::compact_list(lapply(
+    x,
+    as.prop.table.datawizard_crosstab,
+    remove_na = remove_na,
+    simplify = TRUE,
+    verbose = FALSE,
+    ...
+  ))
+
+  # if no proportions found, return NULL
+  if (!length(out)) {
+    return(NULL)
+  }
+
+  # if only one table is returned, "unlist"
+  if (length(out) == 1 && simplify) {
+    out <- out[[1]]
+  }
+  out
+}
+
 
 # as.data.frame --------------------
 
-#' @rdname data_tabulate
+#' @rdname as.prop.table
 #' @param add_total For crosstables (i.e. when `by` is not `NULL`), a row and
 #' column with the total N values are added to the data frame. `add_total` has
 #' no effect in `as.data.frame()` for simple frequency tables.
@@ -527,7 +640,7 @@ as.data.frame.datawizard_crosstabs <- as.data.frame.datawizard_tables
 
 # as.table --------------------
 
-#' @rdname data_tabulate
+#' @rdname as.prop.table
 #' @export
 as.table.datawizard_table <- function(
   x,
diff --git a/R/data_xtabulate.R b/R/data_xtabulate.R
index a1b4ac1ec..f29387b36 100644
--- a/R/data_xtabulate.R
+++ b/R/data_xtabulate.R
@@ -90,12 +90,100 @@
   attr(out, "proportions") <- proportions
   attr(out, "varname") <- obj_name
   attr(out, "grouped_df") <- !is.null(group_variable)
+  attr(out, "prop_table") <- .prop_table(out)
 
   class(out) <- c("datawizard_crosstab", "data.frame")
 
   out
 }
 
+# Helper function to calculate a table of proportions from a frequency table
+.prop_table <- function(x) {
+  # Extract the "proportions" attribute, which determines the type of calculation (row, column, or full)
+  props <- attributes(x)$proportions
+  out <- NULL
+
+  # Proceed only if the "proportions" attribute is set
+  if (!is.null(props)) {
+    # Identify numeric columns, as proportions are only calculated for these
+    numeric_columns <- vapply(x, is.numeric, logical(1))
+    # Get the total count from the attributes, used for "full" proportions
+    total_n <- attributes(x)$total_n
+
+    # Use a switch to perform the calculation based on the "props" value
+    out <- switch(
+      props,
+      # Calculate row-wise proportions
+      row = lapply(seq_len(nrow(x)), function(i) {
+        # Sum of the current row's numeric values
+        row_sum <- sum(x[i, numeric_columns], na.rm = TRUE)
+        # Avoid division by zero; if row sum is 0, return a row of zeros
+        if (row_sum == 0) {
+          tmp <- as.data.frame(as.list(rep(0, sum(numeric_columns))))
+          # for later rbind, we need identical column names
+          colnames(tmp) <- colnames(x)[numeric_columns]
+          tmp
+        } else {
+          x[i, numeric_columns] / row_sum
+        }
+      }),
+      # Calculate column-wise proportions
+      column = lapply(seq_len(ncol(x))[numeric_columns], function(i) {
+        # Sum of the current column's values
+        col_sum <- sum(x[, i], na.rm = TRUE)
+        # Avoid division by zero; if column sum is 0, return a vector of zeros
+        if (col_sum == 0) {
+          rep(0, nrow(x))
+        } else {
+          x[, i] / col_sum
+        }
+      }),
+      # Calculate proportions relative to the total count of the entire table
+      full = lapply(seq_len(ncol(x))[numeric_columns], function(i) {
+        # Avoid division by zero; if total is 0, return a vector of zeros
+        if (total_n == 0) {
+          rep(0, nrow(x))
+        } else {
+          x[, i] / total_n
+        }
+      })
+    )
+  }
+
+  # If a proportion table was calculated, format it into a data frame
+  if (!is.null(out)) {
+    # The output of the switch is a list. We need to bind it into a data frame.
+    # For row proportions, we bind rows. For column/full, we bind columns.
+    out <- switch(
+      props,
+      row = as.data.frame(do.call(rbind, out)),
+      as.data.frame(do.call(cbind, out))
+    )
+    # Set the column names of the new proportion table
+    colnames(out) <- colnames(x)[numeric_columns]
+
+    # Check if the dimensions are consistent before setting row names
+    if (nrow(out) == nrow(x)) {
+      # If the first column of the original data is not numeric, it's likely a
+      # label column. Use these labels as row names in the output for better
+      # readability. This is useful for identifying rows, especially when NAs
+      # are present.
+      if (isFALSE(numeric_columns[1])) {
+        r_names <- x[[1]]
+        r_names <- as.character(r_names)
+        # Replace NA in labels with the string "NA", else we cannot set rownames
+        r_names[is.na(r_names)] <- "NA"
+        rownames(out) <- r_names
+      } else {
+        # Otherwise, just use the original row names
+        rownames(out) <- rownames(x)
+      }
+    }
+  }
+
+  out
+}
+
 
 # methods ---------------------
 
@@ -113,7 +201,7 @@ format.datawizard_crosstab <- function(
   x <- as.data.frame(x)
 
   # find numeric columns, only for these we need row/column sums
-  numeric_columns <- vapply(x, is.numeric, logical(1))
+  numeric_columns <- which(vapply(x, is.numeric, logical(1)))
 
   # compute total N for rows and columns
   total_n <- attributes(x)$total_n
@@ -122,55 +210,18 @@ format.datawizard_crosstab <- function(
 
   # proportions?
   props <- attributes(x)$proportions
-
-  if (!is.null(props)) {
-    # we copy x to tmp, because when we create strings with "sprintf()", the
-    # variable is coerced to character, and in subsequent iterations of the loop,
-    # mathemathical operations are not possible anymore
-    tmp <- x
-    if (identical(props, "row")) {
-      for (i in seq_len(nrow(x))) {
-        row_sum <- sum(x[i, numeric_columns], na.rm = TRUE)
-        if (row_sum == 0) {
-          row_sum_string <- "(0%)"
-        } else {
-          row_sum_string <- sprintf(
-            "(%.*f%%)",
-            digits,
-            100 * x[i, numeric_columns] / row_sum
-          )
-        }
-        tmp[i, numeric_columns] <- paste(
-          format(x[i, numeric_columns]),
-          format(row_sum_string, justify = "right")
+  prop_table <- attributes(x)$prop_table
+
+  if (!is.null(props) && !is.null(prop_table)) {
+    for (i in seq_len(ncol(prop_table))) {
+      x[, numeric_columns[i]] <- paste(
+        format(x[, numeric_columns[i]]),
+        format(
+          sprintf("(%.*f%%)", digits, 100 * prop_table[, i]),
+          justify = "right"
         )
-      }
-    } else if (identical(props, "column")) {
-      for (i in seq_len(ncol(x))[numeric_columns]) {
-        col_sum <- sum(x[, i], na.rm = TRUE)
-        if (col_sum == 0) {
-          col_sum_string <- "(0%)"
-        } else {
-          col_sum_string <- sprintf("(%.*f%%)", digits, 100 * x[, i] / col_sum)
-        }
-        tmp[, i] <- paste(
-          format(x[, i]),
-          format(col_sum_string, justify = "right")
-        )
-      }
-    } else if (identical(props, "full")) {
-      for (i in seq_len(ncol(x))[numeric_columns]) {
-        tmp[, i] <- paste(
-          format(x[, i]),
-          format(
-            sprintf("(%.*f%%)", digits, 100 * x[, i] / total_n),
-            justify = "right"
-          )
-        )
-      }
+      )
     }
-    # copy back final result
-    x <- tmp
   }
 
   x[] <- lapply(x, as.character)
diff --git a/R/utils.R b/R/utils.R
index 6083ee562..a42b1a630 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -163,7 +163,7 @@
   for (nm in setdiff(names(custom_attr), names(attributes(data.frame())))) {
     attr(data, which = nm) <- custom_attr[[nm]]
   }
-  return(data)
+  data
 }
 
 
@@ -241,7 +241,7 @@
 }
 
 
-#' Taken from https://github.com/coolbutuseless/gluestick [licence: MIT]
+#' Taken from https://github.com/coolbutuseless/gluestick (licence: MIT)
 #' Same functionality as `{glue}`
 #'
 #' @noRd
@@ -292,15 +292,15 @@
 
   # Evaluate
   if (eval) {
-    args <- lapply(exprs, function(expr) {
+    fun_args <- lapply(exprs, function(expr) {
       eval(parse(text = expr), envir = src)
     })
   } else {
-    args <- unname(mget(exprs, envir = as.environment(src)))
+    fun_args <- unname(mget(exprs, envir = as.environment(src)))
   }
 
   # Create the string(s)
-  do.call(sprintf, c(list(fmt_sprintf), args))
+  do.call(sprintf, c(list(fmt_sprintf), fun_args))
 }
 
 
diff --git a/man/as.prop.table.Rd b/man/as.prop.table.Rd
new file mode 100644
index 000000000..1cf84f1ca
--- /dev/null
+++ b/man/as.prop.table.Rd
@@ -0,0 +1,103 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data_tabulate.R
+\name{as.prop.table}
+\alias{as.prop.table}
+\alias{as.prop.table.datawizard_crosstab}
+\alias{as.data.frame.datawizard_tables}
+\alias{as.table.datawizard_table}
+\title{Convert a crosstable to a frequency or a propensity table}
+\usage{
+as.prop.table(x, ...)
+
+\method{as.prop.table}{datawizard_crosstab}(x, remove_na = TRUE, simplify = FALSE, verbose = TRUE, ...)
+
+\method{as.data.frame}{datawizard_tables}(
+  x,
+  row.names = NULL,
+  optional = FALSE,
+  ...,
+  stringsAsFactors = FALSE,
+  add_total = FALSE
+)
+
+\method{as.table}{datawizard_table}(x, remove_na = TRUE, simplify = FALSE, verbose = TRUE, ...)
+}
+\arguments{
+\item{x}{An object created by \code{data_tabulate()}. It must be of class
+\code{datawizard_crosstab} for \code{as.prop.table()}.}
+
+\item{...}{not used.}
+
+\item{remove_na}{Logical, if \code{FALSE}, missing values are included in the
+frequency or crosstable, else missing values are omitted. Note that the
+default for the \code{as.table()} method is \code{remove_na = TRUE}, so that missing
+values are not included in the returned table, which makes more sense for
+post-processing of the table, e.g. using \code{chisq.test()}.}
+
+\item{simplify}{Logical, if \code{TRUE}, the returned table is simplified to a
+single table object if there is only one frequency or contingency table
+input. Else, always for multiple table inputs or when \code{simplify = FALSE}, a
+list of tables is returned. This is only relevant for the \code{as.table()}
+methods. To ensure consistent output, the default is \code{FALSE}.}
+
+\item{verbose}{Toggle warnings and messages.}
+
+\item{row.names}{\code{NULL} or a character vector giving the row
+    names for the data frame.  Missing values are not allowed.}
+
+\item{optional}{logical. If \code{TRUE}, setting row names and
+    converting column names (to syntactic names: see
+    \code{\link[base]{make.names}}) is optional.  Note that all of \R's
+    \pkg{base} package \code{as.data.frame()} methods use
+    \code{optional} only for column names treatment, basically with the
+    meaning of \code{\link[base]{data.frame}(*, check.names = !optional)}.
+    See also the \code{make.names} argument of the \code{matrix} method.}
+
+\item{stringsAsFactors}{logical: should the character vector be converted
+    to a factor?}
+
+\item{add_total}{For crosstables (i.e. when \code{by} is not \code{NULL}), a row and
+column with the total N values are added to the data frame. \code{add_total} has
+no effect in \code{as.data.frame()} for simple frequency tables.}
+}
+\description{
+\code{as.prop.table()} is an S3 generic. It can be used on objects of class
+\code{datawizard_crosstab} created by \code{data_tabulate()} when it was run with the
+arguments \code{by} and \code{proportions}.
+}
+\examples{
+data(efc)
+
+# Some cross tabulation
+cross <- data_tabulate(efc, select = "e42dep", by = "c172code", proportions = "row")
+cross
+
+# Convert to a propensity table
+as.prop.table(cross)
+
+# Convert to data.frame
+result <- data_tabulate(efc, "c172code", by = "e16sex")
+as.data.frame(result)
+as.data.frame(result)$table
+as.data.frame(result, add_total = TRUE)$table
+
+# Convert to a table that can be passed to chisq.test()
+
+out <- data_tabulate(efc, "c172code", by = "e16sex")
+# we need to simplify the output, else we get a list of tables
+tbl <- as.table(out, simplify = TRUE)
+tbl
+suppressWarnings(chisq.test(tbl))
+
+# apply chisq.test to each table
+out <- data_tabulate(efc, c("c172code", "e16sex"))
+suppressWarnings(lapply(as.table(out), chisq.test))
+
+# can also handle grouped data frames
+d <- data_group(mtcars, "am")
+x <- data_tabulate(d, "cyl", by = "gear")
+as.table(x)
+}
+\seealso{
+\link{data_tabulate}
+}
diff --git a/man/data_tabulate.Rd b/man/data_tabulate.Rd
index 75b3fd1e1..87656e17f 100644
--- a/man/data_tabulate.Rd
+++ b/man/data_tabulate.Rd
@@ -4,8 +4,6 @@
 \alias{data_tabulate}
 \alias{data_tabulate.default}
 \alias{data_tabulate.data.frame}
-\alias{as.data.frame.datawizard_tables}
-\alias{as.table.datawizard_table}
 \alias{print.datawizard_table}
 \alias{display.datawizard_table}
 \title{Create frequency and crosstables of variables}
@@ -40,17 +38,6 @@ data_tabulate(x, ...)
   ...
 )
 
-\method{as.data.frame}{datawizard_tables}(
-  x,
-  row.names = NULL,
-  optional = FALSE,
-  ...,
-  stringsAsFactors = FALSE,
-  add_total = FALSE
-)
-
-\method{as.table}{datawizard_table}(x, remove_na = TRUE, simplify = FALSE, verbose = TRUE, ...)
-
 \method{print}{datawizard_table}(x, big_mark = NULL, ...)
 
 \method{display}{datawizard_table}(object, big_mark = NULL, format = "markdown", ...)
@@ -146,30 +133,6 @@ functions (see 'Details'), this argument may be used as workaround.}
 \item{collapse}{Logical, if \code{TRUE} collapses multiple tables into one larger
 table for printing. This affects only printing, not the returned object.}
 
-\item{row.names}{\code{NULL} or a character vector giving the row
-    names for the data frame.  Missing values are not allowed.}
-
-\item{optional}{logical. If \code{TRUE}, setting row names and
-    converting column names (to syntactic names: see
-    \code{\link[base]{make.names}}) is optional.  Note that all of \R's
-    \pkg{base} package \code{as.data.frame()} methods use
-    \code{optional} only for column names treatment, basically with the
-    meaning of \code{\link[base]{data.frame}(*, check.names = !optional)}.
-    See also the \code{make.names} argument of the \code{matrix} method.}
-
-\item{stringsAsFactors}{logical: should the character vector be converted
-    to a factor?}
-
-\item{add_total}{For crosstables (i.e. when \code{by} is not \code{NULL}), a row and
-column with the total N values are added to the data frame. \code{add_total} has
-no effect in \code{as.data.frame()} for simple frequency tables.}
-
-\item{simplify}{Logical, if \code{TRUE}, the returned table is simplified to a
-single table object if there is only one frequency or contingency table
-input. Else, always for multiple table inputs or when \code{simplify = FALSE}, a
-list of tables is returned. This is only relevant for the \code{as.table()}
-methods. To ensure consistent output, the default is \code{FALSE}.}
-
 \item{big_mark}{Optional character string, indicating the big mark that is
 used for large numbers. If \code{NULL} (default), a big mark is added automatically for
 large numbers (i.e. numbers with more than 5 digits). If you want to remove
@@ -198,11 +161,12 @@ There is an \code{as.data.frame()} method, to return the frequency tables as a
 data frame. The structure of the returned object is a nested data frame,
 where the first column contains name of the variable for which frequencies
 were calculated, and the second column is a list column that contains the
-frequency tables as data frame. See 'Examples'.
+frequency tables as data frame. See \link{as.table.datawizard_table}.
 
 There is also an \code{as.table()} method, which returns a table object with the
 frequencies of the variable. This is useful for further statistical analysis,
-e.g. for using \code{chisq.test()} on the frequency table. See 'Examples'.
+e.g. for using \code{chisq.test()} on the frequency table. See
+\link{as.table.datawizard_table}.
 }
 \note{
 There are \code{print_html()} and \code{print_md()} methods available for printing
@@ -285,27 +249,8 @@ data_tabulate(
 # round percentages
 out <- data_tabulate(efc, "c172code", by = "e16sex", proportions = "column")
 print(out, digits = 0)
-
-# coerce to data frames
-result <- data_tabulate(efc, "c172code", by = "e16sex")
-as.data.frame(result)
-as.data.frame(result)$table
-as.data.frame(result, add_total = TRUE)$table
-
-# post-processing ------
-# ----------------------
-
-out <- data_tabulate(efc, "c172code", by = "e16sex")
-# we need to simplify the output, else we get a list of tables
-suppressWarnings(chisq.test(as.table(out, simplify = TRUE)))
-
-# apply chisq.test to each table
-out <- data_tabulate(efc, c("c172code", "e16sex"))
-suppressWarnings(lapply(as.table(out), chisq.test))
-
-# can also handle grouped data frames
-d <- data_group(mtcars, "am")
-x <- data_tabulate(d, "cyl", by = "gear")
-as.table(x)
 \dontshow{\}) # examplesIf}
 }
+\seealso{
+\link{as.prop.table}
+}
diff --git a/pkgdown/_pkgdown.yaml b/pkgdown/_pkgdown.yaml
index 01b701f03..9d6308271 100644
--- a/pkgdown/_pkgdown.yaml
+++ b/pkgdown/_pkgdown.yaml
@@ -61,6 +61,7 @@ reference:
     desc: |
       Functions to compute statistical summaries of data properties and distributions
     contents:
+      - as.prop.table
       - data_codebook
       - data_summary
       - data_tabulate
diff --git a/tests/testthat/_snaps/data_tabulate.md b/tests/testthat/_snaps/data_tabulate.md
index c83b293a1..872b3764c 100644
--- a/tests/testthat/_snaps/data_tabulate.md
+++ b/tests/testthat/_snaps/data_tabulate.md
@@ -516,17 +516,17 @@
     Code
       print(data_tabulate(efc, c("c172code", "e42dep"), by = "e16sex", proportions = "row"))
     Output
-      Variable | Value |       male |     female |       <NA> | Total
-      ---------+-------+------------+------------+------------+------
-      c172code |     1 |  5 (62.5%) |  2 (25.0%) |  1 (12.5%) |     8
-      c172code |     2 | 31 (47.0%) | 33 (50.0%) |  2  (3.0%) |    66
-      c172code |     3 |  4 (25.0%) | 11 (68.8%) |  1  (6.2%) |    16
-      c172code |  <NA> |  5 (50.0%) |  4 (40.0%) |  1 (10.0%) |    10
-      e42dep   |     1 | 2 (100.0%) | 0   (0.0%) | 0   (0.0%) |     2
-      e42dep   |     2 |  2 (50.0%) |  2 (50.0%) |  0  (0.0%) |     4
-      e42dep   |     3 |  8 (28.6%) | 18 (64.3%) |  2  (7.1%) |    28
-      e42dep   |     4 | 32 (50.8%) | 28 (44.4%) |  3  (4.8%) |    63
-      e42dep   |  <NA> |  1 (33.3%) |  2 (66.7%) |  0  (0.0%) |     3
+      Variable | Value |        male |     female |      <NA> | Total
+      ---------+-------+-------------+------------+-----------+------
+      c172code |     1 |   5 (62.5%) |  2 (25.0%) | 1 (12.5%) |     8
+      c172code |     2 |  31 (47.0%) | 33 (50.0%) | 2  (3.0%) |    66
+      c172code |     3 |   4 (25.0%) | 11 (68.8%) | 1  (6.2%) |    16
+      c172code |  <NA> |   5 (50.0%) |  4 (40.0%) | 1 (10.0%) |    10
+      e42dep   |     1 |  2 (100.0%) |  0  (0.0%) |  0 (0.0%) |     2
+      e42dep   |     2 |  2  (50.0%) |  2 (50.0%) |  0 (0.0%) |     4
+      e42dep   |     3 |  8  (28.6%) | 18 (64.3%) |  2 (7.1%) |    28
+      e42dep   |     4 | 32  (50.8%) | 28 (44.4%) |  3 (4.8%) |    63
+      e42dep   |  <NA> |  1  (33.3%) |  2 (66.7%) |  0 (0.0%) |     3
 
 # data_tabulate, cross tables, tinytable
 
@@ -655,17 +655,17 @@
     Output
       Grouped by e42dep (1)
       
-      Variable | Value |       male | female |       <NA> | Total
-      ---------+-------+------------+--------+------------+------
-      c172code |     2 | 2 (100.0%) |   <NA> | 0   (0.0%) |     2
-               |  <NA> |     0 (0%) |   <NA> |     0 (0%) |     0
+      Variable | Value |       male | female |     <NA> | Total
+      ---------+-------+------------+--------+----------+------
+      c172code |     2 | 2 (100.0%) |   <NA> | 0 (0.0%) |     2
+               |  <NA> | 0   (0.0%) |   <NA> | 0 (0.0%) |     0
       
       Grouped by e42dep (2)
       
-      Variable | Value |      male |    female |      <NA> | Total
-      ---------+-------+-----------+-----------+-----------+------
-      c172code |     2 | 2 (50.0%) | 2 (50.0%) | 0  (0.0%) |     4
-               |  <NA> |    0 (0%) |    0 (0%) |    0 (0%) |     0
+      Variable | Value |      male |    female |     <NA> | Total
+      ---------+-------+-----------+-----------+----------+------
+      c172code |     2 | 2 (50.0%) | 2 (50.0%) | 0 (0.0%) |     4
+               |  <NA> | 0  (0.0%) | 0  (0.0%) | 0 (0.0%) |     0
       
       Grouped by e42dep (3)
       
@@ -687,10 +687,10 @@
       
       Grouped by e42dep (NA)
       
-      Variable | Value |       male |     female |       <NA> | Total
-      ---------+-------+------------+------------+------------+------
-      c172code |     2 | 0   (0.0%) | 2 (100.0%) | 0   (0.0%) |     2
-               |  <NA> | 1 (100.0%) | 0   (0.0%) | 0   (0.0%) |     1
+      Variable | Value |       male |     female |     <NA> | Total
+      ---------+-------+------------+------------+----------+------
+      c172code |     2 | 0   (0.0%) | 2 (100.0%) | 0 (0.0%) |     2
+               |  <NA> | 1 (100.0%) | 0   (0.0%) | 0 (0.0%) |     1
 
 # data_tabulate, cross tables, print/format works
 
@@ -796,17 +796,17 @@
     Output
       
       
-      |Variable | Value|       male|     female|       (NA)| Total|
-      |:--------|-----:|----------:|----------:|----------:|-----:|
-      |c172code |     1|  5 (62.5%)|  2 (25.0%)|  1 (12.5%)|     8|
-      |c172code |     2| 31 (47.0%)| 33 (50.0%)|  2  (3.0%)|    66|
-      |c172code |     3|  4 (25.0%)| 11 (68.8%)|  1  (6.2%)|    16|
-      |c172code |  (NA)|  5 (50.0%)|  4 (40.0%)|  1 (10.0%)|    10|
-      |e42dep   |     1| 2 (100.0%)| 0   (0.0%)| 0   (0.0%)|     2|
-      |e42dep   |     2|  2 (50.0%)|  2 (50.0%)|  0  (0.0%)|     4|
-      |e42dep   |     3|  8 (28.6%)| 18 (64.3%)|  2  (7.1%)|    28|
-      |e42dep   |     4| 32 (50.8%)| 28 (44.4%)|  3  (4.8%)|    63|
-      |e42dep   |  (NA)|  1 (33.3%)|  2 (66.7%)|  0  (0.0%)|     3|
+      |Variable | Value|        male|     female|     (NA) | Total|
+      |:--------|-----:|-----------:|----------:|:---------|-----:|
+      |c172code |     1|   5 (62.5%)|  2 (25.0%)|1 (12.5%) |     8|
+      |c172code |     2|  31 (47.0%)| 33 (50.0%)|2  (3.0%) |    66|
+      |c172code |     3|   4 (25.0%)| 11 (68.8%)|1  (6.2%) |    16|
+      |c172code |  (NA)|   5 (50.0%)|  4 (40.0%)|1 (10.0%) |    10|
+      |e42dep   |     1|  2 (100.0%)|  0  (0.0%)| 0 (0.0%) |     2|
+      |e42dep   |     2|  2  (50.0%)|  2 (50.0%)| 0 (0.0%) |     4|
+      |e42dep   |     3|  8  (28.6%)| 18 (64.3%)| 2 (7.1%) |    28|
+      |e42dep   |     4| 32  (50.8%)| 28 (44.4%)| 3 (4.8%) |    63|
+      |e42dep   |  (NA)|  1  (33.3%)|  2 (66.7%)| 0 (0.0%) |     3|
 
 ---
 
@@ -831,31 +831,31 @@
     Output
       
       
-      |Variable | Value|       male|     female|       (NA)| Total|
-      |:--------|-----:|----------:|----------:|----------:|-----:|
-      |c172code |     1|  5 (62.5%)|  2 (25.0%)|  1 (12.5%)|     8|
-      |c172code |     2| 31 (47.0%)| 33 (50.0%)|  2  (3.0%)|    66|
-      |c172code |     3|  4 (25.0%)| 11 (68.8%)|  1  (6.2%)|    16|
-      |c172code |  (NA)|  5 (50.0%)|  4 (40.0%)|  1 (10.0%)|    10|
-      |e42dep   |     1| 2 (100.0%)| 0   (0.0%)| 0   (0.0%)|     2|
-      |e42dep   |     2|  2 (50.0%)|  2 (50.0%)|  0  (0.0%)|     4|
-      |e42dep   |     3|  8 (28.6%)| 18 (64.3%)|  2  (7.1%)|    28|
-      |e42dep   |     4| 32 (50.8%)| 28 (44.4%)|  3  (4.8%)|    63|
-      |e42dep   |  (NA)|  1 (33.3%)|  2 (66.7%)|  0  (0.0%)|     3|
+      |Variable | Value|        male|     female|     (NA) | Total|
+      |:--------|-----:|-----------:|----------:|:---------|-----:|
+      |c172code |     1|   5 (62.5%)|  2 (25.0%)|1 (12.5%) |     8|
+      |c172code |     2|  31 (47.0%)| 33 (50.0%)|2  (3.0%) |    66|
+      |c172code |     3|   4 (25.0%)| 11 (68.8%)|1  (6.2%) |    16|
+      |c172code |  (NA)|   5 (50.0%)|  4 (40.0%)|1 (10.0%) |    10|
+      |e42dep   |     1|  2 (100.0%)|  0  (0.0%)| 0 (0.0%) |     2|
+      |e42dep   |     2|  2  (50.0%)|  2 (50.0%)| 0 (0.0%) |     4|
+      |e42dep   |     3|  8  (28.6%)| 18 (64.3%)| 2 (7.1%) |    28|
+      |e42dep   |     4| 32  (50.8%)| 28 (44.4%)| 3 (4.8%) |    63|
+      |e42dep   |  (NA)|  1  (33.3%)|  2 (66.7%)| 0 (0.0%) |     3|
 
 # data_tabulate, correct 0% for proportions
 
     Code
       print(out[[1]])
     Output
-      c172code |       male |     female |   <NA> | Total
-      ---------+------------+------------+--------+------
-      1        |  5 (10.9%) |  3  (5.6%) | 0 (0%) |     8
-      2        | 32 (69.6%) | 34 (63.0%) | 0 (0%) |    66
-      3        |  4  (8.7%) | 12 (22.2%) | 0 (0%) |    16
-      <NA>     |  5 (10.9%) |  5  (9.3%) | 0 (0%) |    10
-      ---------+------------+------------+--------+------
-      Total    |         46 |         54 |      0 |   100
+      c172code |       male |     female |     <NA> | Total
+      ---------+------------+------------+----------+------
+      1        |  5 (10.9%) |  3  (5.6%) | 0 (0.0%) |     8
+      2        | 32 (69.6%) | 34 (63.0%) | 0 (0.0%) |    66
+      3        |  4  (8.7%) | 12 (22.2%) | 0 (0.0%) |    16
+      <NA>     |  5 (10.9%) |  5  (9.3%) | 0 (0.0%) |    10
+      ---------+------------+------------+----------+------
+      Total    |         46 |         54 |        0 |   100
 
 # data_tabulate, table methods
 
diff --git a/tests/testthat/test-data_tabulate.R b/tests/testthat/test-data_tabulate.R
index 20193d68d..5ac4bad63 100644
--- a/tests/testthat/test-data_tabulate.R
+++ b/tests/testthat/test-data_tabulate.R
@@ -818,7 +818,7 @@ test_that("data_tabulate, correct 0% for proportions", {
   out <- data_tabulate(efc, "c172code", by = "e16sex", proportions = "column")
   expect_identical(
     format(out[[1]])[[4]],
-    c("0 (0%)", "0 (0%)", "0 (0%)", "0 (0%)", "", "0")
+    c("0 (0.0%)", "0 (0.0%)", "0 (0.0%)", "0 (0.0%)", "", "0")
   )
   expect_snapshot(print(out[[1]]))
 })
@@ -1052,3 +1052,54 @@ test_that("data_tabulate, table methods, only warn if necessary", {
   d <- data_group(mtcars, "am")
   expect_silent(as.table(data_tabulate(d, "cyl", by = "gear")))
 })
+
+
+test_that("data_tabulate, cross tables, extract proportions", {
+  data(efc, package = "datawizard")
+  out <- data_tabulate(
+    efc,
+    "c172code",
+    by = "e16sex",
+    proportions = "row",
+    remove_na = TRUE
+  )
+  tab <- table(efc$c172code, efc$e16sex) /
+    rowSums(table(efc$c172code, efc$e16sex))
+  dimnames(tab) <- list(c("1", "2", "3"), c("male", "female"))
+  expect_equal(
+    as.prop.table(out, verbose = FALSE),
+    list(tab),
+    ignore_attr = TRUE,
+    tolerance = 1e-4
+  )
+  expect_equal(
+    as.prop.table(out, verbose = FALSE, simplify = TRUE),
+    tab,
+    ignore_attr = TRUE,
+    tolerance = 1e-4
+  )
+  out <- data_tabulate(
+    efc,
+    "c172code",
+    by = "e16sex",
+    proportions = "col",
+    remove_na = TRUE
+  )
+  tab <- as.table(t(
+    t(table(efc$c172code, efc$e16sex)) /
+      colSums(table(efc$c172code, efc$e16sex))
+  ))
+  dimnames(tab) <- list(c("1", "2", "3"), c("male", "female"))
+  expect_equal(
+    as.prop.table(out, verbose = FALSE),
+    list(tab),
+    ignore_attr = TRUE,
+    tolerance = 1e-4
+  )
+  expect_equal(
+    as.prop.table(out, verbose = FALSE, simplify = TRUE),
+    tab,
+    ignore_attr = TRUE,
+    tolerance = 1e-4
+  )
+})