diff --git a/NAMESPACE b/NAMESPACE
index 400a054..a4f2994 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -41,6 +41,7 @@ S3method(vec_ptype_full,projectable_col)
 S3method(vec_ptype_full,projectable_col_row)
 export("col_shadow<-")
 export(col_binomial)
+export(col_binomial_unpack)
 export(col_binomial_vec)
 export(col_freq)
 export(col_row)
diff --git a/R/col_binomial.R b/R/col_binomial.R
index 85be5ef..2334a28 100644
--- a/R/col_binomial.R
+++ b/R/col_binomial.R
@@ -255,3 +255,79 @@ col_binomial_vec <- function(x,
     summarised = TRUE
   )
 }
+
+#' Unpack a col_binomial object into a vector or dataframe
+#'
+#' This function converts the output of `col_binomial()` into either a numeric
+#' vector (default) or a dataframe, depending on user preference. When returning
+#' a dataframe, you can specify which fields to include, such as confidence
+#' intervals.
+#'
+#' @param x A `projectable_col_binomial` object created by `col_binomial()`
+#' @param output Character string specifying the output format. Either "vector"
+#'   (default) or "dataframe".
+#' @param fields Character vector specifying which fields to include when
+#'   `output = "dataframe"`. Options include "p", "n", "N", "ci_lower",
+#'   "ci_upper", "ci_error", "population", and "note". Default is
+#'   `c("p", "ci_lower", "ci_upper")`.
+#'
+#' @return Either a numeric vector (when `output = "vector"`) containing the
+#'   point estimates, or a dataframe (when `output = "dataframe"`) containing
+#'   the requested fields.
+#' @export
+#'
+#' @examples
+#' # Create a binomial proportion
+#' b_trials <- stats::rbinom(1000, 1, 0.5)
+#' result <- col_binomial(b_trials)
+#'
+#' # Extract as vector (default)
+#' col_binomial_unpack(result)
+#'
+#' # Extract as dataframe with confidence intervals
+#' col_binomial_unpack(result, output = "dataframe")
+#'
+#' # Extract as dataframe with custom fields
+#' col_binomial_unpack(result, output = "dataframe", fields = c("p", "n", "N"))
+#'
+col_binomial_unpack <- function(x,
+                                output = c("vector", "dataframe"),
+                                fields = c("p", "ci_lower", "ci_upper")) {
+  # Check input
+  if (!is_col_binomial(x)) {
+    stop("`x` must be a projectable_col_binomial object", call. = FALSE)
+  }
+  
+  # Match output argument
+  output <- match.arg(output)
+  
+  # Return vector by default
+  if (output == "vector") {
+    return(vctrs::field(x, "p"))
+  }
+  
+  # Return dataframe with requested fields
+  if (output == "dataframe") {
+    # Available fields in col_binomial
+    available_fields <- c("n", "N", "population", "ci_error", "p", "ci_lower", "ci_upper", "note")
+    
+    # Check that requested fields are valid
+    invalid_fields <- setdiff(fields, available_fields)
+    if (length(invalid_fields) > 0) {
+      stop(
+        "Invalid field(s): ", paste(invalid_fields, collapse = ", "), 
+        ". Available fields are: ", paste(available_fields, collapse = ", "),
+        call. = FALSE
+      )
+    }
+    
+    # Extract requested fields
+    result <- lapply(fields, function(field) {
+      vctrs::field(x, field)
+    })
+    names(result) <- fields
+    
+    # Convert to dataframe
+    return(tibble::as_tibble(result))
+  }
+}
diff --git a/man/col_binomial_unpack.Rd b/man/col_binomial_unpack.Rd
new file mode 100644
index 0000000..393cc4f
--- /dev/null
+++ b/man/col_binomial_unpack.Rd
@@ -0,0 +1,49 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/col_binomial.R
+\name{col_binomial_unpack}
+\alias{col_binomial_unpack}
+\title{Unpack a col_binomial object into a vector or dataframe}
+\usage{
+col_binomial_unpack(
+  x,
+  output = c("vector", "dataframe"),
+  fields = c("p", "ci_lower", "ci_upper")
+)
+}
+\arguments{
+\item{x}{A \code{projectable_col_binomial} object created by \code{col_binomial()}}
+
+\item{output}{Character string specifying the output format. Either "vector"
+(default) or "dataframe".}
+
+\item{fields}{Character vector specifying which fields to include when
+\code{output = "dataframe"}. Options include "p", "n", "N", "ci_lower",
+"ci_upper", "ci_error", "population", and "note". Default is
+\code{c("p", "ci_lower", "ci_upper")}.}
+}
+\value{
+Either a numeric vector (when \code{output = "vector"}) containing the
+  point estimates, or a dataframe (when \code{output = "dataframe"}) containing
+  the requested fields.
+}
+\description{
+This function converts the output of \code{col_binomial()} into either a numeric
+vector (default) or a dataframe, depending on user preference. When returning
+a dataframe, you can specify which fields to include, such as confidence
+intervals.
+}
+\examples{
+# Create a binomial proportion
+b_trials <- stats::rbinom(1000, 1, 0.5)
+result <- col_binomial(b_trials)
+
+# Extract as vector (default)
+col_binomial_unpack(result)
+
+# Extract as dataframe with confidence intervals
+col_binomial_unpack(result, output = "dataframe")
+
+# Extract as dataframe with custom fields
+col_binomial_unpack(result, output = "dataframe", fields = c("p", "n", "N"))
+
+}
diff --git a/man/col_binomial_vec.Rd b/man/col_binomial_vec.Rd
new file mode 100644
index 0000000..4a7977b
--- /dev/null
+++ b/man/col_binomial_vec.Rd
@@ -0,0 +1,57 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/col_binomial.R
+\name{col_binomial_vec}
+\alias{col_binomial_vec}
+\title{Calculate binomial proportions from a vector}
+\usage{
+col_binomial_vec(
+  x,
+  success_value = 1,
+  range = c(0, 1),
+  ci_error = 0.05,
+  population = Inf,
+  method = "agresti.coull"
+)
+}
+\arguments{
+\item{x}{A numeric or integer vector containing the data. Can include NA values.}
+
+\item{success_value}{The value(s) that count as "success". Can be a single value
+or a vector of values. Default is 1.}
+
+\item{range}{The range of values to include in the calculation. Values outside
+this range are excluded (treated as NA). Default is c(0, 1). Set to NULL to
+include all non-NA values.}
+
+\item{ci_error}{A numeric vector, the error to be used for calculating
+confidence intervals. Default is 0.05.}
+
+\item{population}{A numeric vector, the number of individuals in the
+population to be used for calculating confidence intervals. Default is Inf.}
+
+\item{method}{The name of a method to be passed through to \code{asbio::ci.p()} for
+parameter estimation. The default is "agresti.coull", but other options
+include "asymptotic", "score", "LR" and "exact". See \code{asbio::ci.p()} for details.}
+}
+\value{
+An S3 vector of class \code{projectable_col_binomial}
+}
+\description{
+A wrapper function for \code{col_binomial()} that takes a single vector and
+automatically calculates the number of successes and trials. This is
+particularly useful when working with data that contains missing values.
+}
+\examples{
+# Calculate proportion of 1s in a 0-1 vector with missing data
+x <- c(0, 1, 1, 0, NA, 1, 0, 1)
+col_binomial_vec(x)
+
+# Calculate proportion of 2s in a 1-2 vector
+y <- c(1, 2, 2, 1, NA, 2, 1, 2)
+col_binomial_vec(y, success_value = 2, range = c(1, 2))
+
+# Calculate proportion with multiple success values
+z <- c(1, 2, 3, 4, 5, NA, 2, 3)
+col_binomial_vec(z, success_value = c(2, 3), range = c(1, 5))
+
+}
diff --git a/tests/testthat/test-col.R b/tests/testthat/test-col.R
index d010d15..29509e7 100644
--- a/tests/testthat/test-col.R
+++ b/tests/testthat/test-col.R
@@ -387,3 +387,115 @@ testthat::test_that("prj_project_col", {
   )
 })
 
+
+# col_binomial_unpack -----------------------------------------------------
+
+testthat::test_that("col_binomial_unpack: basic vector output", {
+  # Create a binomial proportion
+  b_trials <- c(1, 1, 0, 1, 0, 1, 1, 0, 1, 1)
+  result <- col_binomial(b_trials)
+  
+  # Test default vector output
+  unpacked <- col_binomial_unpack(result)
+  
+  # Should return a numeric vector
+  testthat::expect_type(unpacked, "double")
+  testthat::expect_length(unpacked, 1)
+  
+  # Should equal the p value
+  expected_p <- vctrs::field(result, "p")
+  testthat::expect_identical(unpacked, expected_p)
+})
+
+testthat::test_that("col_binomial_unpack: dataframe output with defaults", {
+  # Create a binomial proportion
+  result <- col_binomial(7, 10, summarised = TRUE)
+  
+  # Test dataframe output with default fields
+  unpacked <- col_binomial_unpack(result, output = "dataframe")
+  
+  # Should return a tibble
+  testthat::expect_s3_class(unpacked, "tbl_df")
+  
+  # Should have default fields: p, ci_lower, ci_upper
+  testthat::expect_identical(names(unpacked), c("p", "ci_lower", "ci_upper"))
+  
+  # Values should match the original
+  testthat::expect_identical(unpacked$p, vctrs::field(result, "p"))
+  testthat::expect_identical(unpacked$ci_lower, vctrs::field(result, "ci_lower"))
+  testthat::expect_identical(unpacked$ci_upper, vctrs::field(result, "ci_upper"))
+})
+
+testthat::test_that("col_binomial_unpack: dataframe output with custom fields", {
+  # Create a binomial proportion
+  result <- col_binomial(7, 10, summarised = TRUE)
+  
+  # Test dataframe output with custom fields
+  unpacked <- col_binomial_unpack(result, output = "dataframe", fields = c("p", "n", "N"))
+  
+  # Should return a tibble with requested fields
+  testthat::expect_s3_class(unpacked, "tbl_df")
+  testthat::expect_identical(names(unpacked), c("p", "n", "N"))
+  
+  # Values should match the original
+  testthat::expect_identical(unpacked$p, vctrs::field(result, "p"))
+  testthat::expect_identical(unpacked$n, vctrs::field(result, "n"))
+  testthat::expect_identical(unpacked$N, vctrs::field(result, "N"))
+})
+
+testthat::test_that("col_binomial_unpack: all available fields", {
+  # Create a binomial proportion with all parameters
+  result <- col_binomial(5, 20, ci_error = 0.01, population = 100, summarised = TRUE)
+  
+  # Test with all available fields
+  all_fields <- c("n", "N", "population", "ci_error", "p", "ci_lower", "ci_upper", "note")
+  unpacked <- col_binomial_unpack(result, output = "dataframe", fields = all_fields)
+  
+  # Should have all fields
+  testthat::expect_identical(names(unpacked), all_fields)
+  
+  # Check a few values
+  testthat::expect_identical(unpacked$n, vctrs::field(result, "n"))
+  testthat::expect_identical(unpacked$population, vctrs::field(result, "population"))
+  testthat::expect_identical(unpacked$ci_error, vctrs::field(result, "ci_error"))
+})
+
+testthat::test_that("col_binomial_unpack: error on invalid input", {
+  # Should error if input is not a col_binomial
+  testthat::expect_error(
+    col_binomial_unpack(1:10),
+    "must be a projectable_col_binomial"
+  )
+  
+  testthat::expect_error(
+    col_binomial_unpack(col_freq(1, 2, summarised = TRUE)),
+    "must be a projectable_col_binomial"
+  )
+})
+
+testthat::test_that("col_binomial_unpack: error on invalid fields", {
+  result <- col_binomial(7, 10, summarised = TRUE)
+  
+  # Should error on invalid field names
+  testthat::expect_error(
+    col_binomial_unpack(result, output = "dataframe", fields = c("p", "invalid_field")),
+    "Invalid field"
+  )
+})
+
+testthat::test_that("col_binomial_unpack: works with col_binomial_vec", {
+  # Test integration with col_binomial_vec
+  x <- c(0, 1, 1, 0, NA, 1, 0, 1)
+  result <- col_binomial_vec(x)
+  
+  # Should work with vector output
+  unpacked_vector <- col_binomial_unpack(result)
+  testthat::expect_type(unpacked_vector, "double")
+  testthat::expect_length(unpacked_vector, 1)
+  
+  # Should work with dataframe output
+  unpacked_df <- col_binomial_unpack(result, output = "dataframe")
+  testthat::expect_s3_class(unpacked_df, "tbl_df")
+  testthat::expect_true(all(c("p", "ci_lower", "ci_upper") %in% names(unpacked_df)))
+})
+