diff --git a/NAMESPACE b/NAMESPACE
index f7ffbaa..78729b0 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -1,5 +1,6 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(print,count_evt)
 S3method(print,or_ci)
 S3method(print,prop_ci)
 S3method(print,s_coxph)
@@ -11,6 +12,7 @@ export(derive_bor)
 export(h_pairwise_survdiff)
 export(h_prep_prop)
 export(rrPostProb)
+export(s_count_event)
 export(s_get_coxph)
 export(s_get_lsmeans)
 export(s_get_survfit)
@@ -39,6 +41,7 @@ importFrom(lubridate,days)
 importFrom(lubridate,ymd)
 importFrom(magrittr,"%>%")
 importFrom(magrittr,set_colnames)
+importFrom(magrittr,set_names)
 importFrom(magrittr,set_rownames)
 importFrom(rlang,":=")
 importFrom(rlang,.data)
diff --git a/R/count_event.R b/R/count_event.R
new file mode 100644
index 0000000..79fb1bb
--- /dev/null
+++ b/R/count_event.R
@@ -0,0 +1,154 @@
+# s_count_event ----
+
+#' Count the Number of Events for Specific Variable
+#'
+#' @description `r lifecycle::badge("experimental")`
+#'
+#' This function counts the number of events occur for specific variable with
+#' multiple conditions if needed, such as counting the different types of AE
+#' occurrence rates.
+#'
+#' @param data (`data.frame`)\cr a data frame as input.
+#' @param var (`string`)\cr target variable name for counting.
+#' @param by (`string`)\cr an optional variable to group by. If null, use the whole data.
+#' @param cond (`string`)\cr a listing contains multiple types of filtering conditions
+#'  that only support the equation such as `"TRTEMFL" = "Y"`. And the left side of the
+#'  equation is filtering id while the right should be a flag, string or numerical value.
+#' @param label (`string`)\cr an optional string vector for labeling each filtering
+#'  conditions. Default is the name of `cond` variable.
+#' @param denom (`string` or `data.frame`)\cr denominator for proportion can be a
+#'  numeric vector of denominators or a data frame where we can count the `var` inside.
+#'
+#' @return
+#' * `s_count_event` returns an object of class `count_evt` that is a data frame
+#'  contains percentages for each group for different conditions.
+#'
+#' @note
+#' - The order of `cond` names should be one-to-one respect to `label` you define.
+#' - The order of `denom` names should be one-to-one respect to `by` variable levels.
+#'
+#' @export
+#'
+#' @examples
+#' data("rand_adsl")
+#' data("rand_adae")
+#'
+#' # by TRTA groups
+#' s_count_event(
+#'   data = rand_adae, var = "SUBJID", by = "ARMCD",
+#'   cond = list(
+#'     "TEAEs" = c("TRTEMFL" = "Y"),
+#'     "TRAEs" = c("TRTEMFL" = "Y", "AEREL" = "Y"),
+#'     "SAE" = c("AESER" = "Y"),
+#'     "TRSAE" = c("AESER" = "Y", "AEREL" = "Y")
+#'   ),
+#'   label = c("Any TEAEs", "Any treatment-related TEAEs",
+#'             "Any serious TEAEs", "Any serious treatment-related TEAEs"),
+#'   denom = rand_adsl
+#' )
+#'
+#' # specify the denominator for each groups
+#' s_count_event(
+#'   data = rand_adae, var = "SUBJID", by = "ARMCD",
+#'   cond = list("TEAEs" = c("TRTEMFL" = "Y")),
+#'   label = c("Any TEAEs"),
+#'   denom = c(100, 100, 100)
+#' )
+#'
+#' # no grouping
+#' s_count_event(
+#'   data = rand_adae, var = "SUBJID",
+#'   cond = list(
+#'     "TEAEs" = c("TRTEMFL" = "Y"),
+#'     "TRAEs" = c("TRTEMFL" = "Y", "AEREL" = "Y")
+#'   ),
+#'   label = c("Any TEAEs", "Any treatment-related TEAEs"),
+#'   denom = 200
+#' )
+s_count_event <- function(data,
+                          var,
+                          by = NULL,
+                          cond,
+                          label = names(cond),
+                          denom) {
+  assert_class(data, "data.frame")
+  assert_subset(var, names(data), empty.ok = FALSE)
+  assert_subset(by, names(data))
+  assert_true(length(names(cond)) == length(label))
+  assert_multi_class(denom, c("numeric", "data.frame"))
+  if (is.null(by) & is.data.frame(denom)) {
+    stop("denom should be numeric vector if by is defined as NULL.")
+  }
+
+  cond_labels <- split(label, names(cond))
+  if (is.null(by)) {
+    by <- "Total"
+    data[["Total"]] <- factor(by, levels = by)
+  } else {
+    if (!is.factor(data[[by]])) {
+      data[[by]] <- factor(by, levels = unique(data[[by]]))
+    }
+  }
+  denom_vec <- if (is.data.frame(denom)) {
+    if (!is.null(by)) {
+      cnt <- denom %>% count(!!sym(by))
+      set_names(cnt[, "n", drop = TRUE], cnt[, 1, drop = TRUE])
+    } else {
+      set_names(length(denom[[var]]), levels(data[[by]]))
+    }
+  } else {
+    set_names(denom, levels(data[[by]]))
+  }
+
+  assert_numeric(denom_vec)
+  assert_true(all(names(denom_vec) == levels(data[[by]])))
+
+  cnt_tb <- cond %>%
+    purrr::imap(function(x, idx) {
+      filter_expr <- if (is.character(x)) {
+        paste0(names(x), " == '", x, "'", collapse = " & ")
+      } else if (is.numeric(x)) {
+        paste0(names(x), " == ", x, collapse = " & ")
+      } else {
+        stop("Each element of cond variable should be numeric or characteric.")
+      }
+      df <- data %>%
+        filter(!!rlang::parse_quo(filter_expr, env = rlang::global_env()))
+      attributes(df)$label <- NULL
+      res <- if (!is.null(by)) {
+        df %>%
+          distinct(!!sym(by), !!sym(var), .keep_all = TRUE) %>%
+          count(!!sym(by))
+      } else {
+        df %>%
+          distinct(!!sym(var), .keep_all = TRUE) %>%
+          count()
+      }
+      res %>%
+        mutate(
+          group = droplevels(!!sym(by)),
+          N = as.vector(denom_vec),
+          perc = .data$n / .data$N,
+          label_ = idx,
+          label = cond_labels[[idx]]
+        ) %>%
+        ungroup() %>%
+        select(c("group", "n", "N", "perc", "label_", "label"))
+    }) %>%
+    purrr::list_rbind()
+
+  structure(
+    list(
+      data = data,
+      cnt = cnt_tb,
+      params = list(
+        var = var,
+        by = by,
+        cond = cond,
+        label = label,
+        denom = denom_vec
+      )
+    ),
+    class = "count_evt"
+  )
+}
diff --git a/R/data.R b/R/data.R
index fbc9426..79e27be 100644
--- a/R/data.R
+++ b/R/data.R
@@ -34,3 +34,15 @@
 #' John Wiley and Sons Inc., New York, NY.
 #'
 "whas500"
+
+#' CDISC Random ADaM Data
+#'
+#' @description `r lifecycle::badge("experimental")`
+#'
+#' Random ADaM data created from `random.cdisc.data` package.
+#'
+#' @rdname cdisc_random_data
+"rand_adsl"
+
+#' @rdname cdisc_random_data
+"rand_adae"
diff --git a/R/package.R b/R/package.R
index 32ce4bd..ee57906 100644
--- a/R/package.R
+++ b/R/package.R
@@ -5,7 +5,7 @@
 "_PACKAGE"
 
 #' @import checkmate
-#' @importFrom magrittr set_colnames set_rownames
+#' @importFrom magrittr set_colnames set_rownames set_names
 #' @importFrom lifecycle deprecated
 #' @importFrom stats pbeta rbinom confint as.formula setNames coef quantile
 #' @importFrom dplyr add_count arrange bind_rows case_when count distinct filter
diff --git a/R/pkg-methods.R b/R/pkg-methods.R
index b6a822c..87f1435 100644
--- a/R/pkg-methods.R
+++ b/R/pkg-methods.R
@@ -496,3 +496,36 @@ print.s_coxph <- function(x, ...) {
 
   invisible(x)
 }
+
+
+#' @describeIn s_count_event prints the counts and percentages of events.
+#' @exportS3Method
+#' @keywords internal
+print.count_evt <- function(x, ...) {
+  grp_var <- x$params$by
+
+  a_evt_func <- function(df, .var, cnt_tb, lab) {
+    curgrp <- df[[.var]][1]
+    res <- cnt_tb %>%
+      filter(.data$group == curgrp & .data$label == lab)
+    in_rows(
+      rcell(unlist(res[, c("n", "perc"), drop = TRUE]), format = "xx (xx.x%)"),
+      .names = lab
+    )
+  }
+
+  labels <- x$params$label
+  tbl <- basic_table(show_colcounts = TRUE) %>%
+    split_cols_by(grp_var)
+  for (i in seq_along(labels)) {
+    tbl <- tbl %>%
+      analyze(grp_var, a_evt_func, show_labels = "hidden",
+              extra_args = list(cnt_tb = x$cnt, lab = labels[i]),
+              table_names = labels[i])
+  }
+  result <- tbl %>%
+    build_table(df = x$data, col_counts = x$params$denom)
+  print(result)
+
+  invisible(x)
+}
diff --git a/R/proportion.R b/R/proportion.R
index fac21db..6c54081 100644
--- a/R/proportion.R
+++ b/R/proportion.R
@@ -31,7 +31,7 @@ NULL
 #'
 #' @param data (`data.frame`)\cr a data frame as input.
 #' @param var (`string`)\cr target variable name for estimation.
-#' @param by (`string`)\cr a optional variable to group by. If null, use the whole data.
+#' @param by (`string`)\cr an optional variable to group by. If null, use the whole data.
 #' @param by.level (`vector`)\cr an optional vector for encoding `var` as a factor
 #'  and the first level will be as the reference group. If null, use the default
 #'  order to encode.
diff --git a/data-raw/create_random_cdisc.R b/data-raw/create_random_cdisc.R
new file mode 100644
index 0000000..fdb38be
--- /dev/null
+++ b/data-raw/create_random_cdisc.R
@@ -0,0 +1,9 @@
+rand_adsl <- random.cdisc.data::radsl(
+  N = 100, study_duration = 2,
+  with_trt02 = FALSE,
+  seed = 2
+)
+rand_adae <- random.cdisc.data::radae(adsl, seed = 2)
+
+usethis::use_data(rand_adsl, overwrite = TRUE)
+usethis::use_data(rand_adae, overwrite = TRUE)
diff --git a/data/rand_adae.rda b/data/rand_adae.rda
new file mode 100644
index 0000000..fb44b8f
Binary files /dev/null and b/data/rand_adae.rda differ
diff --git a/data/rand_adsl.rda b/data/rand_adsl.rda
new file mode 100644
index 0000000..982f586
Binary files /dev/null and b/data/rand_adsl.rda differ
diff --git a/inst/WORDLIST b/inst/WORDLIST
index 6ce892c..c443d0b 100644
--- a/inst/WORDLIST
+++ b/inst/WORDLIST
@@ -1,6 +1,7 @@
 ADRS
 ADT
 ADaM
+AE
 ANCOVA
 AVAL
 AVALC
diff --git a/man/cdisc_random_data.Rd b/man/cdisc_random_data.Rd
new file mode 100644
index 0000000..cf7ed1c
--- /dev/null
+++ b/man/cdisc_random_data.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/data.R
+\docType{data}
+\name{rand_adsl}
+\alias{rand_adsl}
+\alias{rand_adae}
+\title{CDISC Random ADaM Data}
+\format{
+An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 100 rows and 45 columns.
+
+An object of class \code{tbl_df} (inherits from \code{tbl}, \code{data.frame}) with 515 rows and 82 columns.
+}
+\usage{
+rand_adsl
+
+rand_adae
+}
+\description{
+\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
+
+Random ADaM data created from \code{random.cdisc.data} package.
+}
+\keyword{datasets}
diff --git a/man/prop_odds_ratio.Rd b/man/prop_odds_ratio.Rd
index cc286dc..3d70223 100644
--- a/man/prop_odds_ratio.Rd
+++ b/man/prop_odds_ratio.Rd
@@ -52,7 +52,7 @@ h_prep_prop(data, var, by, by.level, resp)
 
 \item{var}{(\code{string})\cr target variable name for estimation.}
 
-\item{by}{(\code{string})\cr a optional variable to group by. If null, use the whole data.}
+\item{by}{(\code{string})\cr an optional variable to group by. If null, use the whole data.}
 
 \item{by.level}{(\code{vector})\cr an optional vector for encoding \code{var} as a factor
 and the first level will be as the reference group. If null, use the default
diff --git a/man/s_count_event.Rd b/man/s_count_event.Rd
new file mode 100644
index 0000000..7cc57b4
--- /dev/null
+++ b/man/s_count_event.Rd
@@ -0,0 +1,90 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/count_event.R, R/pkg-methods.R
+\name{s_count_event}
+\alias{s_count_event}
+\alias{print.count_evt}
+\title{Count the Number of Events for Specific Variable}
+\usage{
+s_count_event(data, var, by = NULL, cond, label = names(cond), denom)
+
+\method{print}{count_evt}(x, ...)
+}
+\arguments{
+\item{data}{(\code{data.frame})\cr a data frame as input.}
+
+\item{var}{(\code{string})\cr target variable name for counting.}
+
+\item{by}{(\code{string})\cr an optional variable to group by. If null, use the whole data.}
+
+\item{cond}{(\code{string})\cr a listing contains multiple types of filtering conditions
+that only support the equation such as \code{"TRTEMFL" = "Y"}. And the left side of the
+equation is filtering id while the right should be a flag, string or numerical value.}
+
+\item{label}{(\code{string})\cr an optional string vector for labeling each filtering
+conditions. Default is the name of \code{cond} variable.}
+
+\item{denom}{(\code{string} or \code{data.frame})\cr denominator for proportion can be a
+numeric vector of denominators or a data frame where we can count the \code{var} inside.}
+}
+\value{
+\itemize{
+\item \code{s_count_event} returns an object of class \code{count_evt} that is a data frame
+contains percentages for each group for different conditions.
+}
+}
+\description{
+\ifelse{html}{\href{https://lifecycle.r-lib.org/articles/stages.html#experimental}{\figure{lifecycle-experimental.svg}{options: alt='[Experimental]'}}}{\strong{[Experimental]}}
+
+This function counts the number of events occur for specific variable with
+multiple conditions if needed, such as counting the different types of AE
+occurrence rates.
+}
+\section{Functions}{
+\itemize{
+\item \code{print(count_evt)}: prints the counts and percentages of events.
+
+}}
+\note{
+\itemize{
+\item The order of \code{cond} names should be one-to-one respect to \code{label} you define.
+\item The order of \code{denom} names should be one-to-one respect to \code{by} variable levels.
+}
+}
+\examples{
+data("rand_adsl")
+data("rand_adae")
+
+# by TRTA groups
+s_count_event(
+  data = rand_adae, var = "SUBJID", by = "ARMCD",
+  cond = list(
+    "TEAEs" = c("TRTEMFL" = "Y"),
+    "TRAEs" = c("TRTEMFL" = "Y", "AEREL" = "Y"),
+    "SAE" = c("AESER" = "Y"),
+    "TRSAE" = c("AESER" = "Y", "AEREL" = "Y")
+  ),
+  label = c("Any TEAEs", "Any treatment-related TEAEs",
+            "Any serious TEAEs", "Any serious treatment-related TEAEs"),
+  denom = rand_adsl
+)
+
+# specify the denominator for each groups
+s_count_event(
+  data = rand_adae, var = "SUBJID", by = "ARMCD",
+  cond = list("TEAEs" = c("TRTEMFL" = "Y")),
+  label = c("Any TEAEs"),
+  denom = c(100, 100, 100)
+)
+
+# no grouping
+s_count_event(
+  data = rand_adae, var = "SUBJID",
+  cond = list(
+    "TEAEs" = c("TRTEMFL" = "Y"),
+    "TRAEs" = c("TRTEMFL" = "Y", "AEREL" = "Y")
+  ),
+  label = c("Any TEAEs", "Any treatment-related TEAEs"),
+  denom = 200
+)
+}
+\keyword{internal}
diff --git a/tests/spelling.R b/tests/spelling.R
index 13f77d9..6713838 100644
--- a/tests/spelling.R
+++ b/tests/spelling.R
@@ -1,6 +1,3 @@
-if (requireNamespace("spelling", quietly = TRUE)) {
-  spelling::spell_check_test(
-    vignettes = TRUE, error = FALSE,
-    skip_on_cran = TRUE
-  )
-}
+if(requireNamespace('spelling', quietly = TRUE))
+  spelling::spell_check_test(vignettes = TRUE, error = FALSE,
+                             skip_on_cran = TRUE)
diff --git a/tests/testthat/test-count_event.R b/tests/testthat/test-count_event.R
new file mode 100644
index 0000000..1de64c7
--- /dev/null
+++ b/tests/testthat/test-count_event.R
@@ -0,0 +1,128 @@
+test_that("s_count_event works as expected with default arguments", {
+  data("rand_adsl")
+  data("rand_adae")
+
+  res <- s_count_event(
+    data = rand_adae, var = "SUBJID", by = "ARMCD",
+    cond = list(
+      "TEAEs" = c("TRTEMFL" = "Y"),
+      "TRAEs" = c("TRTEMFL" = "Y", "AEREL" = "Y"),
+      "SAE" = c("AESER" = "Y"),
+      "TRSAE" = c("AESER" = "Y", "AEREL" = "Y")
+    ),
+    label = c(
+      "Any TEAEs", "Any treatment-related TEAEs",
+      "Any serious TEAEs", "Any serious treatment-related TEAEs"
+    ),
+    denom = rand_adsl
+  )
+
+  expect_equal(
+    res$cnt,
+    tibble::tibble(
+      group = factor(rep(c("ARM A", "ARM B", "ARM C"), 4), levels = c("ARM A", "ARM B", "ARM C")),
+      n = c(34, 30, 26, 33, 28, 25, 25, 23, 22, 23, 19, 17),
+      N = rep(c(36, 34, 30), 4),
+      perc = c(
+        0.9444444, 0.8823529, 0.8666667, 0.9166667, 0.8235294, 0.8333333,
+        0.6944444, 0.6764706, 0.7333333, 0.6388889, 0.5588235, 0.5666667
+      ),
+      label_ = rep(c("TEAEs", "TRAEs", "SAE", "TRSAE"), each = 3),
+      label = rep(c(
+        "Any TEAEs", "Any treatment-related TEAEs",
+        "Any serious TEAEs", "Any serious treatment-related TEAEs"
+      ), each = 3)
+    ),
+    tolerance = 0.0001
+  )
+})
+
+test_that("s_count_event works as expected with default arguments", {
+  data("rand_adsl")
+  data("rand_adae")
+
+  res <- s_count_event(
+    data = rand_adae, var = "SUBJID", by = "ARMCD",
+    cond = list(
+      "TEAEs" = c("TRTEMFL" = "Y"),
+      "TRAEs" = c("TRTEMFL" = "Y", "AEREL" = "Y"),
+      "SAE" = c("AESER" = "Y"),
+      "TRSAE" = c("AESER" = "Y", "AEREL" = "Y")
+    ),
+    label = c(
+      "Any TEAEs", "Any treatment-related TEAEs",
+      "Any serious TEAEs", "Any serious treatment-related TEAEs"
+    ),
+    denom = rand_adsl
+  )
+
+  expect_equal(
+    res$cnt,
+    tibble::tibble(
+      group = factor(rep(c("ARM A", "ARM B", "ARM C"), 4), levels = c("ARM A", "ARM B", "ARM C")),
+      n = c(34, 30, 26, 33, 28, 25, 25, 23, 22, 23, 19, 17),
+      N = rep(c(36, 34, 30), 4),
+      perc = c(
+        0.9444444, 0.8823529, 0.8666667, 0.9166667, 0.8235294, 0.8333333,
+        0.6944444, 0.6764706, 0.7333333, 0.6388889, 0.5588235, 0.5666667
+      ),
+      label_ = rep(c("TEAEs", "TRAEs", "SAE", "TRSAE"), each = 3),
+      label = rep(c(
+        "Any TEAEs", "Any treatment-related TEAEs",
+        "Any serious TEAEs", "Any serious treatment-related TEAEs"
+      ), each = 3)
+    ),
+    tolerance = 0.0001
+  )
+})
+
+test_that("s_count_event works as expected with specific the denominator", {
+  data("rand_adsl")
+  data("rand_adae")
+
+  res <- s_count_event(
+    data = rand_adae, var = "SUBJID", by = "ARMCD",
+    cond = list("TEAEs" = c("TRTEMFL" = "Y")),
+    label = c("Any TEAEs"),
+    denom = c(100, 100, 100)
+  )
+
+  expect_equal(
+    res$cnt,
+    tibble::tibble(
+      group = factor(c("ARM A", "ARM B", "ARM C"), levels = c("ARM A", "ARM B", "ARM C")),
+      n = c(34, 30, 26),
+      N = rep(100, 3),
+      perc = c(0.34, 0.30, 0.26),
+      label_ = rep("TEAEs", 3),
+      label = rep("Any TEAEs", 3),
+    )
+  )
+})
+
+test_that("s_count_event works as expected with no grouping", {
+  data("rand_adsl")
+  data("rand_adae")
+
+  res <- s_count_event(
+    data = rand_adae, var = "SUBJID",
+    cond = list(
+      "TEAEs" = c("TRTEMFL" = "Y"),
+      "TRAEs" = c("TRTEMFL" = "Y", "AEREL" = "Y")
+    ),
+    label = c("Any TEAEs", "Any treatment-related TEAEs"),
+    denom = 200
+  )
+
+  expect_equal(
+    res$cnt,
+    tibble::tibble(
+      group = factor("Total", levels = "Total"),
+      n = c(90, 86),
+      N = rep(200, 2),
+      perc = c(0.45, 0.43),
+      label_ = c("TEAEs", "TRAEs"),
+      label = c("Any TEAEs", "Any treatment-related TEAEs"),
+    )
+  )
+})
diff --git a/tests/testthat/test-pkg-methods.R b/tests/testthat/test-pkg-methods.R
index 3a6bfed..dfc5e60 100644
--- a/tests/testthat/test-pkg-methods.R
+++ b/tests/testthat/test-pkg-methods.R
@@ -155,3 +155,38 @@ test_that("print.s_coxph works as expected", {
   )))
   expect_match(res3, "P-value method for HR: logtest", fixed = TRUE)
 })
+
+test_that("print.count_evt works as expected", {
+  data("rand_adsl")
+  data("rand_adae")
+
+  res <- capture_output(print(s_count_event(
+    data = rand_adae, var = "SUBJID", by = "ARMCD",
+    cond = list(
+      "TEAEs" = c("TRTEMFL" = "Y"),
+      "TRAEs" = c("TRTEMFL" = "Y", "AEREL" = "Y"),
+      "SAE" = c("AESER" = "Y"),
+      "TRSAE" = c("AESER" = "Y", "AEREL" = "Y")
+    ),
+    label = c(
+      "Any TEAEs", "Any treatment-related TEAEs",
+      "Any serious TEAEs", "Any serious treatment-related TEAEs"
+    ),
+    denom = rand_adsl
+  )))
+  expect_match(res, "ARM A        ARM B        ARM C", fixed = TRUE)
+  expect_match(res, "(N=36)       (N=34)       (N=30)", fixed = TRUE)
+
+  res2 <- capture_output(print(s_count_event(
+    data = rand_adae, var = "SUBJID",
+    cond = list(
+      "TEAEs" = c("TRTEMFL" = "Y"),
+      "TRAEs" = c("TRTEMFL" = "Y", "AEREL" = "Y")
+    ),
+    label = c("Any TEAEs", "Any treatment-related TEAEs"),
+    denom = 200
+  )))
+  expect_match(res2, "Total", fixed = TRUE)
+  expect_match(res2, "(N=200)", fixed = TRUE)
+})
+