diff --git a/CRAN-SUBMISSION b/CRAN-SUBMISSION
deleted file mode 100644
index 724a32c..0000000
--- a/CRAN-SUBMISSION
+++ /dev/null
@@ -1,3 +0,0 @@
-Version: 0.9.9
-Date: 2024-10-14 08:25:15 UTC
-SHA: 07d5eded069746483915cdb0e6f5240527d25c63
diff --git a/NAMESPACE b/NAMESPACE
index 56ac1e9..1c8bbbf 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -81,6 +81,7 @@ export(levels_add_na)
 export(levels_drop)
 export(levels_drop_na)
 export(levels_factor)
+export(levels_lump)
 export(levels_reorder)
 export(levels_unused)
 export(levels_used)
diff --git a/R/dots.R b/R/dots.R
index 49dd5e0..57b1285 100644
--- a/R/dots.R
+++ b/R/dots.R
@@ -30,6 +30,6 @@ named_list <- function(..., .keep_null = TRUE){
   dots
 }
 expr_names <- function(...){
-  as.character(substitute(c(...))[-1L])
-  # vapply(substitute(alist(...))[-1L], deparse2, "", USE.NAMES = FALSE)
+  # as.character(substitute(c(...))[-1L])
+  vapply(substitute(alist(...))[-1L], deparse2, "", USE.NAMES = FALSE)
 }
diff --git a/R/factors.R b/R/factors.R
index 80177e4..302ccd7 100644
--- a/R/factors.R
+++ b/R/factors.R
@@ -12,6 +12,8 @@
 #' `levels_add_na()` which adds an explicit `NA` level,
 #' `levels_drop_na()` which drops the `NA` level,
 #' `levels_drop()` which drops unused factor levels,
+#' `levels_lump()` which returns top n levels and lumps all others into the
+#' same category,
 #' and finally `levels_reorder()` which reorders the levels of `x`
 #' based on `y` using the ordered median values of `y` for each level.
 #'
@@ -32,6 +34,11 @@
 #' `order_by`.
 #' @param decreasing Should the reordered levels be in decreasing order?
 #' Default is `FALSE`.
+#' @param n Top n number of levels to calculate.
+#' @param prop Top proportion of levels to calculate.
+#' This is a proportion of the total unique levels in x.
+#' @param other_category Name of 'other' category.
+#' @param ties Ties method to use. See `?rank`.
 #'
 #' @details
 #' This operates similarly to `collapse::qF()`. \cr
@@ -45,6 +52,37 @@
 #' `factor_(as.POSIXct(1729984360, tz = "Europe/London") + 3600 *(1:5))`
 #' produces 5 levels.
 #'
+#' `levels_lump()` is a cheaper version of `forcats::lump_n()` but returns
+#' levels in order of highest frequency to lowest. This can be very useful
+#' for plotting.
+#'
+#'
+#' @examples
+#' library(cheapr)
+#'
+#' x <- factor_(sample(letters[sample.int(26, 10)], 100, TRUE), levels = letters)
+#' x
+#' # Used/unused levels
+#'
+#' levels_used(x)
+#' levels_unused(x)
+#'
+#' # Drop unused levels
+#' levels_drop(x)
+#'
+#' # Top 3 letters by by frequency
+#'
+#' table(levels_lump(x, 3))
+#'
+#' # We can use levels_lump to create a generic top n function for non-factors too
+#'
+#' get_top_n <- function(x, n){
+#'   f <- levels_lump(factor_(x, order = FALSE), n = n)
+#'   new_df(value = levels(f),
+#'          count = tabulate(f))
+#' }
+#'
+#' get_top_n(x, 5)
 #' @export
 #' @rdname factors
 factor_ <- function(
@@ -198,7 +236,7 @@ levels_drop <- function(x){
   if (length(which_used) == n_lvls){
     x
   } else {
-    out <- which_used[unclass(x)]
+    out <- collapse::fmatch(unclass(x), which_used, overid = 2L)
     attributes(out) <- attributes(x)
     attr(out, "levels") <- levels(x)[which_used]
     out
@@ -220,6 +258,72 @@ levels_reorder <- function(x, order_by, decreasing = FALSE){
     factor_(x, levels = ordered_levels)
   }
 }
+# levels_lump <- function(x, n, prop, other_category = "Other",
+#                           ties = c("min", "average", "first", "last", "random", "max")){
+#   check_is_factor(x)
+#   if (!missing(n) && !missing(prop)){
+#     stop("Please supply either n or prop, not both")
+#   }
+#   if (!missing(prop)){
+#     n <- floor(prop * length(levels(x)))
+#   }
+#   temp <- unclass(x)
+#   ties <- match.arg(ties)
+#   # counts <- collapse::GRPN(x, expand = FALSE)
+#   counts <- tabulate(x, length(levels(x)))
+#   if (ties == "min"){
+#     bound <- sort(counts, decreasing = TRUE)[min(n, length(counts))]
+#     top <- which_(counts >= bound)
+#   } else {
+#     rank <- rank(counts, ties.method = ties)
+#     top <- which_(rank <= n)
+#   }
+#   if (length(top) == length(counts)){
+#     x
+#   } else {
+#     lvls <- levels_factor(x)[top]
+#     out <- collapse::fmatch(x, lvls, nomatch = length(lvls) + 1L, overid = 2L)
+#     out[which_na(x)] <- NA
+#     out_levels <- c(factor_as_character(lvls), other_category)
+#     attr(out, "levels") <- out_levels
+#     class(out) <- "factor"
+#     out
+#   }
+# }
+#' @export
+#' @rdname factors
+levels_lump <- function(x, n, prop, other_category = "Other",
+                        ties = c("min", "average", "first", "last", "random", "max")){
+  check_is_factor(x)
+  if (!missing(n) && !missing(prop)){
+    stop("Please supply either n or prop, not both")
+  }
+  if (!missing(prop)){
+    n <- floor(prop * length(levels(x)))
+  }
+  ties <- match.arg(ties)
+  counts <- tabulate(x, length(levels(x)))
+  o <- order(counts, decreasing = TRUE)
+  sorted_counts <- counts[o]
+  if (ties == "min"){
+    bound <- sorted_counts[min(n, length(counts))]
+    top <- which_(sorted_counts >= bound)
+  } else {
+    rank <- rank(-sorted_counts, ties.method = ties)
+    top <- which_(rank <= n)
+  }
+  if (length(top) == length(counts)){
+    x
+  } else {
+    lvls <- levels_factor(x)[o][top]
+    out <- collapse::fmatch(x, lvls, nomatch = length(lvls) + 1L, overid = 2L)
+    out[which_na(x)] <- NA
+    out_levels <- c(factor_as_character(lvls), other_category)
+    attr(out, "levels") <- out_levels
+    class(out) <- "factor"
+    out
+  }
+}
 # Generic factor conversion to data representation
 factor_as_type <- function(x, type){
   check_length(type, 1)
diff --git a/R/overview.R b/R/overview.R
index e08eea9..a3f2dcc 100644
--- a/R/overview.R
+++ b/R/overview.R
@@ -36,71 +36,71 @@
 #' options(cheapr.digits = 2) # The default
 #' @rdname overview
 #' @export
-overview <- function(x, hist = FALSE, digits = getOption("cheapr.digits", 2)){
+overview <- function(x, hist = TRUE, digits = getOption("cheapr.digits", 2)){
   UseMethod("overview")
 }
 #' @rdname overview
 #' @export
-overview.default <- function(x, hist = FALSE, digits = getOption("cheapr.digits", 2)){
+overview.default <- function(x, hist = TRUE, digits = getOption("cheapr.digits", 2)){
   overview(new_df(x = x), hist = hist, digits = digits)
 }
 #' @rdname overview
 #' @export
-overview.logical <- function(x, hist = FALSE, digits = getOption("cheapr.digits", 2)){
+overview.logical <- function(x, hist = TRUE, digits = getOption("cheapr.digits", 2)){
   overview(new_df(x = as.logical(x)), hist = hist, digits = digits)
 }
 #' @rdname overview
 #' @export
-overview.integer <- function(x, hist = FALSE, digits = getOption("cheapr.digits", 2)){
+overview.integer <- function(x, hist = TRUE, digits = getOption("cheapr.digits", 2)){
   out <- overview(new_df(x = as.integer(x)), hist = hist, digits = digits)
   out$cols <- NA_integer_
   out
 }
 #' @rdname overview
 #' @export
-overview.numeric <- function(x, hist = FALSE, digits = getOption("cheapr.digits", 2)){
+overview.numeric <- function(x, hist = TRUE, digits = getOption("cheapr.digits", 2)){
   out <- overview(new_df(x = as.numeric(x)), hist = hist, digits = digits)
   out$cols <- NA_integer_
   out
 }
 #' @rdname overview
 #' @export
-overview.integer64 <- function(x, hist = FALSE, digits = getOption("cheapr.digits", 2)){
+overview.integer64 <- function(x, hist = TRUE, digits = getOption("cheapr.digits", 2)){
   out <- overview(cpp_int64_to_numeric(x), hist = hist, digits = digits)
   out$numeric$class <- class(x)[1]
   out
 }
 #' @rdname overview
 #' @export
-overview.character <- function(x, hist = FALSE, digits = getOption("cheapr.digits", 2)){
+overview.character <- function(x, hist = TRUE, digits = getOption("cheapr.digits", 2)){
   out <- overview(new_df(x = as.character(x)), hist = hist, digits = digits)
   out$cols <- NA_integer_
   out
 }
 #' @rdname overview
 #' @export
-overview.factor <- function(x, hist = FALSE, digits = getOption("cheapr.digits", 2)){
+overview.factor <- function(x, hist = TRUE, digits = getOption("cheapr.digits", 2)){
   out <- overview(new_df(x = as.factor(x)), hist = hist, digits = digits)
   out$cols <- NA_integer_
   out
 }
 #' @rdname overview
 #' @export
-overview.Date <- function(x, hist = FALSE, digits = getOption("cheapr.digits", 2)){
+overview.Date <- function(x, hist = TRUE, digits = getOption("cheapr.digits", 2)){
   out <- overview(new_df(x = as.Date(x)), hist = hist, digits = digits)
   out$cols <- NA_integer_
   out
 }
 #' @rdname overview
 #' @export
-overview.POSIXt <- function(x, hist = FALSE, digits = getOption("cheapr.digits", 2)){
+overview.POSIXt <- function(x, hist = TRUE, digits = getOption("cheapr.digits", 2)){
   out <- overview(new_df(x = as.POSIXct(x)), hist = hist, digits = digits)
   out$cols <- NA_integer_
   out
 }
 #' @rdname overview
 #' @export
-overview.ts <- function(x, hist = FALSE, digits = getOption("cheapr.digits", 2)){
+overview.ts <- function(x, hist = TRUE, digits = getOption("cheapr.digits", 2)){
   out <- overview(transform_all(as.data.frame(x), as.numeric), hist = hist, digits = digits)
   out$time_series <- out$numeric
   out$numeric <- sset(out$numeric, 0)
@@ -112,7 +112,7 @@ overview.ts <- function(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
 overview.zoo <- overview.ts
 #' @rdname overview
 #' @export
-overview.data.frame <- function(x, hist = FALSE, digits = getOption("cheapr.digits", 2)){
+overview.data.frame <- function(x, hist = TRUE, digits = getOption("cheapr.digits", 2)){
   check_is_df(x)
   N <- nrow(x)
   num_cols <- ncol(x)
@@ -340,75 +340,101 @@ overview.data.frame <- function(x, hist = FALSE, digits = getOption("cheapr.digi
 }
 #' @export
 print.overview <- function(x, max = NULL, ...){
-  digits <- x[["print_digits"]]
+  temp <- unclass(x)
+  digits <- temp[["print_digits"]]
   pretty_round <- function(x, decimal_digits = digits, ...){
     pretty_num(round(x, digits = decimal_digits), ...)
   }
-  cat(paste("obs:", x$obs, "\ncols:", x$cols), "\n")
-  if (nrow(x$logical)){
-    x$logical$p_complete <- pretty_round(x$logical$p_complete)
+  abbr <- function(x, min = 6, left = TRUE){
+    abbreviate(x, minlength = min, named = FALSE,
+               method = if (left) "left.kept" else "both.sides")
+  }
+  cat(paste("obs:", temp$obs, "\ncols:", temp$cols), "\n")
+  if (nrow(temp$logical)){
+    temp$logical$p_complete <- pretty_round(temp$logical$p_complete)
     cat("\n----- Logical -----\n")
-    print(x$logical)
+    names(temp$logical) <- abbr(names(temp$logical))
+    temp$logical$class <- abbr(temp$logical$class, 5, FALSE)
+    temp$logical$col <- abbr(temp$logical$col, 10, FALSE)
+    print(temp$logical)
   }
-  if (nrow(x$numeric)){
-    x$numeric$p_complete <- pretty_round(x$numeric$p_complete)
-    x$numeric$mean <- pretty_round(x$numeric$mean)
-    x$numeric$p0 <- pretty_round(x$numeric$p0)
-    x$numeric$p25 <- pretty_round(x$numeric$p25)
-    x$numeric$p50 <- pretty_round(x$numeric$p50)
-    x$numeric$p75 <- pretty_round(x$numeric$p75)
-    x$numeric$p100 <- pretty_round(x$numeric$p100)
-    x$numeric$iqr <- pretty_round(x$numeric$iqr)
-    x$numeric$sd <- pretty_round(x$numeric$sd)
+  if (nrow(temp$numeric)){
+    temp$numeric$p_complete <- pretty_round(temp$numeric$p_complete)
+    temp$numeric$mean <- pretty_round(temp$numeric$mean)
+    temp$numeric$p0 <- pretty_round(temp$numeric$p0)
+    temp$numeric$p25 <- pretty_round(temp$numeric$p25)
+    temp$numeric$p50 <- pretty_round(temp$numeric$p50)
+    temp$numeric$p75 <- pretty_round(temp$numeric$p75)
+    temp$numeric$p100 <- pretty_round(temp$numeric$p100)
+    temp$numeric$iqr <- pretty_round(temp$numeric$iqr)
+    temp$numeric$sd <- pretty_round(temp$numeric$sd)
     cat("\n----- Numeric -----\n")
-    print(x$numeric)
+    names(temp$numeric) <- abbr(names(temp$numeric))
+    temp$numeric$class <- abbr(temp$numeric$class, 5, FALSE)
+    temp$numeric$col <- abbr(temp$numeric$col, 10, FALSE)
+    print(temp$numeric)
   }
-  if (nrow(x$date)){
-    x$date$p_complete <- pretty_round(x$date$p_complete)
+  if (nrow(temp$date)){
+    temp$date$p_complete <- pretty_round(temp$date$p_complete)
     cat("\n----- Dates -----\n")
-    print(x$date)
+    names(temp$date) <- abbr(names(temp$date))
+    temp$date$class <- abbr(temp$date$class, 5, FALSE)
+    temp$date$col <- abbr(temp$date$col, 10, FALSE)
+    print(temp$date)
   }
-  if (nrow(x$datetime)){
-    x$datetime$p_complete <- pretty_round(x$datetime$p_complete)
+  if (nrow(temp$datetime)){
+    temp$datetime$p_complete <- pretty_round(temp$datetime$p_complete)
     # An overview list contains a 'min' & 'max' variable of date-times
     # This is UTC because R can't handle a date-time with multiple time-zones
     # And so we want to print it in local-time
-    datetime_chr_min <- character(nrow(x$datetime))
-    datetime_chr_max <- character(nrow(x$datetime))
-    mins <- x[["datetime"]][["min"]]
-    maxs <- x[["datetime"]][["max"]]
-    tzones <- x[["datetime"]][["tzone"]]
-    for (i in seq_len(nrow(x$datetime))){
+    datetime_chr_min <- character(nrow(temp$datetime))
+    datetime_chr_max <- character(nrow(temp$datetime))
+    mins <- temp[["datetime"]][["min"]]
+    maxs <- temp[["datetime"]][["max"]]
+    tzones <- temp[["datetime"]][["tzone"]]
+    for (i in seq_len(nrow(temp$datetime))){
       datetime_chr_min[i] <- format(mins[i], tz = tzones[i])
       datetime_chr_max[i] <- format(maxs[i], tz = tzones[i])
     }
-    x$datetime$min <- datetime_chr_min
-    x$datetime$max <- datetime_chr_max
+    temp$datetime$min <- datetime_chr_min
+    temp$datetime$max <- datetime_chr_max
     cat("\n----- Date-Times -----\n")
-    print(x$datetime)
+    names(temp$datetime) <- abbr(names(temp$datetime))
+    temp$datetime$class <- abbr(temp$datetime$class, 5, FALSE)
+    temp$datetime$col <- abbr(temp$datetime$col, 10, FALSE)
+    print(temp$datetime)
   }
-  if (nrow(x$time_series)){
-    x$time_series$p_complete <- pretty_round(x$time_series$p_complete)
-    x$time_series$mean <- pretty_round(x$time_series$mean)
-    x$time_series$p0 <- pretty_round(x$time_series$p0)
-    x$time_series$p25 <- pretty_round(x$time_series$p25)
-    x$time_series$p50 <- pretty_round(x$time_series$p50)
-    x$time_series$p75 <- pretty_round(x$time_series$p75)
-    x$time_series$p100 <- pretty_round(x$time_series$p100)
-    x$time_series$iqr <- pretty_round(x$time_series$iqr)
-    x$time_series$sd <- pretty_round(x$time_series$sd)
+  if (nrow(temp$time_series)){
+    temp$time_series$p_complete <- pretty_round(temp$time_series$p_complete)
+    temp$time_series$mean <- pretty_round(temp$time_series$mean)
+    temp$time_series$p0 <- pretty_round(temp$time_series$p0)
+    temp$time_series$p25 <- pretty_round(temp$time_series$p25)
+    temp$time_series$p50 <- pretty_round(temp$time_series$p50)
+    temp$time_series$p75 <- pretty_round(temp$time_series$p75)
+    temp$time_series$p100 <- pretty_round(temp$time_series$p100)
+    temp$time_series$iqr <- pretty_round(temp$time_series$iqr)
+    temp$time_series$sd <- pretty_round(temp$time_series$sd)
     cat("\n----- Time-Series -----\n")
-    print(x$time_series)
+    names(temp$time_series) <- abbr(names(temp$time_series))
+    temp$time_series$class <- abbr(temp$time_series$class, 5, FALSE)
+    temp$time_series$col <- abbr(temp$time_series$col, 10, FALSE)
+    print(temp$time_series)
   }
-  if (nrow(x$categorical)){
-    x$categorical$p_complete <- pretty_round(x$categorical$p_complete)
+  if (nrow(temp$categorical)){
+    temp$categorical$p_complete <- pretty_round(temp$categorical$p_complete)
     cat("\n----- Categorical -----\n")
-    print(x$categorical)
+    names(temp$categorical) <- abbr(names(temp$categorical))
+    temp$categorical$class <- abbr(temp$categorical$class, 5, FALSE)
+    temp$categorical$col <- abbr(temp$categorical$col, 10, FALSE)
+    print(temp$categorical)
   }
-  if (nrow(x$other)){
-    x$other$p_complete <- pretty_round(x$other$p_complete)
+  if (nrow(temp$other)){
+    temp$other$p_complete <- pretty_round(temp$other$p_complete)
     cat("\n----- Other -----\n")
-    print(x$other)
+    names(temp$other) <- abbr(names(temp$other))
+    temp$other$class <- abbr(temp$other$class, 5, FALSE)
+    temp$other$col <- abbr(temp$other$col, 10, FALSE)
+    print(temp$other)
   }
   invisible(x)
 }
@@ -455,10 +481,10 @@ pluck_row <- function(data, i = 1){
 spark_bar <- function(x){
   bars <- intToUtf8(c(9601L, 9602L, 9603L, 9605L, 9606L, 9607L),
                     multiple = TRUE)
-  bar_codes <- findInterval(
-    x, vec = seq.int(0, to = 1, length.out = length(bars) + 1L),
-    rightmost.closed = TRUE,
-    left.open = FALSE, all.inside = FALSE
+  bar_codes <- bin(
+    x, seq.int(0, to = 1, length.out = length(bars) + 1L),
+    left_closed = TRUE, include_oob = TRUE,
+    include_endpoint = TRUE
   )
   bar_codes[bar_codes == 0L] <- NA_integer_
   out <- bars[bar_codes]
@@ -470,7 +496,7 @@ inline_hist <- function(x, n_bins = 5L){
     return(" ")
   }
   if (is.infinite(max(abs(collapse::frange(x, na.rm = TRUE))))) {
-    x[is.infinite(x)] <- NA
+    x[which_(is.infinite(x))] <- NA
   }
   if (all_na(x)) {
     return(" ")
@@ -478,8 +504,10 @@ inline_hist <- function(x, n_bins = 5L){
   if (allv2(na_rm(x), 0)) {
     x <- x + 1
   }
-  hist_dt <- tabulate(cut_numeric(x, n_bins, labels = FALSE),
-                      nbins = n_bins)
+  hist_dt <- tabulate(
+    bin(x, r_cut_breaks(x, n_bins), left_closed = FALSE),
+    nbins = n_bins
+  )
   hist_dt <- hist_dt / max(hist_dt)
   spark_bar(hist_dt)
 }
diff --git a/R/utils.R b/R/utils.R
index c2ebba9..4a1c7f7 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -1,7 +1,7 @@
 #' @noRd
 
 # Like deparse1 but has a cutoff in case of massive strings
-deparse2 <- function(expr, collapse = " ", width.cutoff = 500L, nlines = 5L, ...){
+deparse2 <- function(expr, collapse = " ", width.cutoff = 500L, nlines = 10L, ...){
   paste(deparse(expr, width.cutoff, nlines = nlines, ...), collapse = collapse)
 }
 
@@ -148,3 +148,49 @@ n_dots <- function(...){
 
 # Keep this in-case anyone was using it
 fill_with_na <- na_insert
+
+r_cut_breaks <- function(x, n){
+  check_length(n, 1)
+  stopifnot(n >= 2)
+  breaks <- get_breaks(x, n, pretty = FALSE)
+  adj <- diff(range(breaks)) * 0.001
+  breaks[1] <- breaks[1] - adj
+  breaks[length(breaks)] <- breaks[length(breaks)] + adj
+  breaks
+}
+
+
+# str_to_factor_size <- function(x){
+#   size <- as.double(object.size(x))
+#   lvls_size <- size - (8 * (length(x) - collapse::fnunique(x)))
+#   int_size <- 48 + (4 * length(x))
+#   (48 * 7) + int_size + lvls_size
+# }
+# factor_to_str_size <- function(x){
+#   lvls_size <- as.double(object.size(levels(x)))
+#   lvls_size + (8 * (length(x) - length(levels(x))))
+# }
+
+# vec_compress <- function(x){
+#   if (inherits(x, "data.frame")){
+#     for (i in seq_along(x)){
+#       x[[i]] <- vec_compress(x[[i]])
+#     }
+#   }
+#   if (is.factor(x)){
+#     if (length(levels(x)) >= (length(x) / 2)){
+#       x <- factor_as_character(x)
+#     }
+#   } else if (is.character(x)){
+#     if (collapse::fnunique(x) < (length(x) / 2)){
+#       x <- as_factor(x)
+#     }
+#   } else if (
+#     is.double(x) && !is.object(x) &&
+#     all_integerable(x) &&
+#     cpp_all_whole_numbers(x, tol = sqrt(.Machine$double.eps), na_ignore = TRUE)
+#   ){
+#     storage.mode(x) <- "integer" # This keeps matrix structure intact
+#   }
+#   x
+# }
diff --git a/man/factors.Rd b/man/factors.Rd
index b2b8945..b3da87e 100644
--- a/man/factors.Rd
+++ b/man/factors.Rd
@@ -12,6 +12,7 @@
 \alias{levels_drop_na}
 \alias{levels_drop}
 \alias{levels_reorder}
+\alias{levels_lump}
 \title{A cheaper version of \code{factor()} along with cheaper utilities}
 \usage{
 factor_(
@@ -41,6 +42,14 @@ levels_drop_na(x)
 levels_drop(x)
 
 levels_reorder(x, order_by, decreasing = FALSE)
+
+levels_lump(
+  x,
+  n,
+  prop,
+  other_category = "Other",
+  ties = c("min", "average", "first", "last", "random", "max")
+)
 }
 \arguments{
 \item{x}{A vector.}
@@ -65,6 +74,15 @@ Default is \code{TRUE}.}
 
 \item{decreasing}{Should the reordered levels be in decreasing order?
 Default is \code{FALSE}.}
+
+\item{n}{Top n number of levels to calculate.}
+
+\item{prop}{Top proportion of levels to calculate.
+This is a proportion of the total unique levels in x.}
+
+\item{other_category}{Name of 'other' category.}
+
+\item{ties}{Ties method to use. See \code{?rank}.}
 }
 \value{
 A \code{factor} or \code{character} in the case of \code{levels_used} and \code{levels_unused}.
@@ -81,6 +99,8 @@ There are some additional utilities, most of which begin with the prefix
 \code{levels_add_na()} which adds an explicit \code{NA} level,
 \code{levels_drop_na()} which drops the \code{NA} level,
 \code{levels_drop()} which drops unused factor levels,
+\code{levels_lump()} which returns top n levels and lumps all others into the
+same category,
 and finally \code{levels_reorder()} which reorders the levels of \code{x}
 based on \code{y} using the ordered median values of \code{y} for each level.
 }
@@ -95,4 +115,35 @@ Using a daylight savings example where the clocks go back: \cr
 produces 4 levels whereas \cr
 \code{factor_(as.POSIXct(1729984360, tz = "Europe/London") + 3600 *(1:5))}
 produces 5 levels.
+
+\code{levels_lump()} is a cheaper version of \code{forcats::lump_n()} but returns
+levels in order of highest frequency to lowest. This can be very useful
+for plotting.
+}
+\examples{
+library(cheapr)
+
+x <- factor_(sample(letters[sample.int(26, 10)], 100, TRUE), levels = letters)
+x
+# Used/unused levels
+
+levels_used(x)
+levels_unused(x)
+
+# Drop unused levels
+levels_drop(x)
+
+# Top 3 letters by by frequency
+
+table(levels_lump(x, 3))
+
+# We can use levels_lump to create a generic top n function for non-factors too
+
+get_top_n <- function(x, n){
+  f <- levels_lump(factor_(x, order = FALSE), n = n)
+  new_df(value = levels(f),
+         count = tabulate(f))
+}
+
+get_top_n(x, 5)
 }
diff --git a/man/overview.Rd b/man/overview.Rd
index 4e745a0..ee1e726 100644
--- a/man/overview.Rd
+++ b/man/overview.Rd
@@ -16,31 +16,31 @@
 \alias{overview.data.frame}
 \title{An alternative to \code{summary()} inspired by the skimr package}
 \usage{
-overview(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
+overview(x, hist = TRUE, digits = getOption("cheapr.digits", 2))
 
-\method{overview}{default}(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
+\method{overview}{default}(x, hist = TRUE, digits = getOption("cheapr.digits", 2))
 
-\method{overview}{logical}(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
+\method{overview}{logical}(x, hist = TRUE, digits = getOption("cheapr.digits", 2))
 
-\method{overview}{integer}(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
+\method{overview}{integer}(x, hist = TRUE, digits = getOption("cheapr.digits", 2))
 
-\method{overview}{numeric}(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
+\method{overview}{numeric}(x, hist = TRUE, digits = getOption("cheapr.digits", 2))
 
-\method{overview}{integer64}(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
+\method{overview}{integer64}(x, hist = TRUE, digits = getOption("cheapr.digits", 2))
 
-\method{overview}{character}(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
+\method{overview}{character}(x, hist = TRUE, digits = getOption("cheapr.digits", 2))
 
-\method{overview}{factor}(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
+\method{overview}{factor}(x, hist = TRUE, digits = getOption("cheapr.digits", 2))
 
-\method{overview}{Date}(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
+\method{overview}{Date}(x, hist = TRUE, digits = getOption("cheapr.digits", 2))
 
-\method{overview}{POSIXt}(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
+\method{overview}{POSIXt}(x, hist = TRUE, digits = getOption("cheapr.digits", 2))
 
-\method{overview}{ts}(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
+\method{overview}{ts}(x, hist = TRUE, digits = getOption("cheapr.digits", 2))
 
-\method{overview}{zoo}(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
+\method{overview}{zoo}(x, hist = TRUE, digits = getOption("cheapr.digits", 2))
 
-\method{overview}{data.frame}(x, hist = FALSE, digits = getOption("cheapr.digits", 2))
+\method{overview}{data.frame}(x, hist = TRUE, digits = getOption("cheapr.digits", 2))
 }
 \arguments{
 \item{x}{A vector or data frame.}
diff --git a/src/utils.cpp b/src/utils.cpp
index 05027cb..d10e549 100644
--- a/src/utils.cpp
+++ b/src/utils.cpp
@@ -391,6 +391,10 @@ SEXP cpp_rev(SEXP x, bool set){
 //     break;
 //   }
 //   default: {
+//     if (is_int64(x)){
+//     out = true;
+//     break;
+//   }
 //     out = true;
 //     double *p_x = REAL(x);
 //     for (R_xlen_t i = 0; i < n; ++i) {
diff --git a/tests/testthat/_snaps/overview.md b/tests/testthat/_snaps/overview.md
index 49776e0..b49471a 100644
--- a/tests/testthat/_snaps/overview.md
+++ b/tests/testthat/_snaps/overview.md
@@ -7,20 +7,20 @@
       cols: 6 
       
       ----- Numeric -----
-            col   class n_missing p_complete n_unique   mean  p0    p25  p50    p75
-      1   Ozone integer        37       0.76       67  42.13   1     18 31.5  63.25
-      2 Solar.R integer         7       0.95      117 185.93   7 115.75  205 258.75
-      3    Wind numeric         0          1       31   9.96 1.7    7.4  9.7   11.5
-      4    Temp integer         0          1       40  77.88  56     72   79     85
-      5   Month integer         0          1        5   6.99   5      6    7      8
-      6     Day integer         0          1       31   15.8   1      8   16     23
-        p100   iqr    sd
-      1  168 45.25 32.99
-      2  334   143 90.06
-      3 20.7   4.1  3.52
-      4   97    13  9.47
-      5    9     2  1.42
-      6   31    15  8.86
+            col class n_mssn p_cmpl n_uniq   mean  p0    p25  p50    p75 p100   iqr
+      1   Ozone intgr     37   0.76     67  42.13   1     18 31.5  63.25  168 45.25
+      2 Solar.R intgr      7   0.95    117 185.93   7 115.75  205 258.75  334   143
+      3    Wind numrc      0      1     31   9.96 1.7    7.4  9.7   11.5 20.7   4.1
+      4    Temp intgr      0      1     40  77.88  56     72   79     85   97    13
+      5   Month intgr      0      1      5   6.99   5      6    7      8    9     2
+      6     Day intgr      0      1     31   15.8   1      8   16     23   31    15
+           sd
+      1 32.99
+      2 90.06
+      3  3.52
+      4  9.47
+      5  1.42
+      6  8.86
 
 ---
 
@@ -31,20 +31,15 @@
       cols: 5 
       
       ----- Numeric -----
-                 col   class n_missing p_complete n_unique mean  p0 p25  p50 p75 p100
-      1 Sepal.Length numeric         0          1       35 5.84 4.3 5.1  5.8 6.4  7.9
-      2  Sepal.Width numeric         0          1       23 3.06   2 2.8    3 3.3  4.4
-      3 Petal.Length numeric         0          1       43 3.76   1 1.6 4.35 5.1  6.9
-      4  Petal.Width numeric         0          1       22  1.2 0.1 0.3  1.3 1.8  2.5
-        iqr   sd
-      1 1.3 0.83
-      2 0.5 0.44
-      3 3.5 1.77
-      4 1.5 0.76
+               col class n_mssn p_cmpl n_uniq mean  p0 p25  p50 p75 p100 iqr   sd
+      1 Sepl.Lngth numrc      0      1     35 5.84 4.3 5.1  5.8 6.4  7.9 1.3 0.83
+      2 Sepal.Wdth numrc      0      1     23 3.06   2 2.8    3 3.3  4.4 0.5 0.44
+      3 Petl.Lngth numrc      0      1     43 3.76   1 1.6 4.35 5.1  6.9 3.5 1.77
+      4 Petal.Wdth numrc      0      1     22  1.2 0.1 0.3  1.3 1.8  2.5 1.5 0.76
       
       ----- Categorical -----
-            col  class n_missing p_complete n_unique n_levels    min       max
-      1 Species factor         0          1        3        3 setosa virginica
+            col class n_mssn p_cmpl n_uniq n_lvls    min       max
+      1 Species factr      0      1      3      3 setosa virginica
 
 ---
 
@@ -55,25 +50,20 @@
       cols: 7 
       
       ----- Logical -----
-          col   class n_missing p_complete n_true n_false p_true
-      1 large logical         0          1     24      76   0.24
+          col class n_mssn p_cmpl n_true n_fals p_true
+      1 large logcl      0      1     24     76   0.24
       
       ----- Numeric -----
-                 col   class n_missing p_complete n_unique mean  p0 p25  p50  p75
-      1 Sepal.Length numeric         0          1       28 5.47 4.3   5  5.4  5.9
-      2  Sepal.Width numeric         0          1       23  3.1   2 2.8 3.05  3.4
-      3 Petal.Length numeric         0          1       28 2.86   1 1.5 2.45 4.32
-      4  Petal.Width numeric         0          1       15 0.79 0.1 0.2  0.8  1.3
-        p100  iqr   sd
-      1    7  0.9 0.64
-      2  4.4  0.6 0.48
-      3  5.1 2.83 1.45
-      4  1.8  1.1 0.57
+               col class n_mssn p_cmpl n_uniq mean  p0 p25  p50  p75 p100  iqr   sd
+      1 Sepl.Lngth numrc      0      1     28 5.47 4.3   5  5.4  5.9    7  0.9 0.64
+      2 Sepal.Wdth numrc      0      1     23  3.1   2 2.8 3.05  3.4  4.4  0.6 0.48
+      3 Petl.Lngth numrc      0      1     28 2.86   1 1.5 2.45 4.32  5.1 2.83 1.45
+      4 Petal.Wdth numrc      0      1     15 0.79 0.1 0.2  0.8  1.3  1.8  1.1 0.57
       
       ----- Categorical -----
-             col     class n_missing p_complete n_unique n_levels    min        max
-      1  Species    factor         0          1        2        3 setosa versicolor
-      2 Species2 character         0          1        2       NA setosa versicolor
+             col class n_mssn p_cmpl n_uniq n_lvls    min        max
+      1  Species factr      0      1      2      3 setosa versicolor
+      2 Species2 chrct      0      1      2     NA setosa versicolor
 
 ---
 
@@ -84,15 +74,13 @@
       cols: 3 
       
       ----- Numeric -----
-           col   class n_missing p_complete n_unique  mean p0   p25 p50 p75 p100
-      1 breaks numeric         0          1       31 28.15 10 18.25  26  34   70
-          iqr   sd
-      1 15.75 13.2
+           col class n_mssn p_cmpl n_uniq  mean p0   p25 p50 p75 p100   iqr   sd
+      1 breaks numrc      0      1     31 28.15 10 18.25  26  34   70 15.75 13.2
       
       ----- Categorical -----
-            col  class n_missing p_complete n_unique n_levels min max
-      1    wool factor         0          1        2        2   A   B
-      2 tension factor         0          1        3        3   L   H
+            col class n_mssn p_cmpl n_uniq n_lvls min max
+      1    wool factr      0      1      2      2   A   B
+      2 tension factr      0      1      3      3   L   H
 
 ---
 
@@ -103,16 +91,13 @@
       cols: 3 
       
       ----- Numeric -----
-         col   class n_missing p_complete n_unique  mean  p0   p25   p50   p75 p100
-      1  len numeric         0          1       43 18.81 4.2 13.07 19.25 25.27 33.9
-      2 dose numeric         0          1        3  1.17 0.5   0.5     1     2    2
-         iqr   sd
-      1 12.2 7.65
-      2  1.5 0.63
+         col class n_mssn p_cmpl n_uniq  mean  p0   p25   p50   p75 p100  iqr   sd
+      1  len numrc      0      1     43 18.81 4.2 13.07 19.25 25.27 33.9 12.2 7.65
+      2 dose numrc      0      1      3  1.17 0.5   0.5     1     2    2  1.5 0.63
       
       ----- Categorical -----
-         col  class n_missing p_complete n_unique n_levels min max
-      1 supp factor         0          1        2        2  OJ  VC
+         col class n_mssn p_cmpl n_uniq n_lvls min max
+      1 supp factr      0      1      2      2  OJ  VC
 
 ---
 
@@ -123,22 +108,22 @@
       cols: 3 
       
       ----- Time-Series -----
-               col class n_missing p_complete n_unique mean    p0   p25  p50  p75
-      1          y    ts         0          1       25 0.05 -2.53 -0.44 0.24 0.82
-      2          x    ts         0          1       25 0.05 -2.53 -0.44 0.24 0.82
-      3 z_Series 1   mts         0          1       25 0.05 -2.53 -0.44 0.24 0.82
-      4 z_Series 2   mts         0          1       25 0.05 -2.53 -0.44 0.24 0.82
-      5 z_Series 3   mts         0          1       25 0.05 -2.53 -0.44 0.24 0.82
-      6 z_Series 4   mts         0          1       25 0.05 -2.53 -0.44 0.24 0.82
-      7 z_Series 5   mts         0          1       25 0.05 -2.53 -0.44 0.24 0.82
-        p100  iqr   sd
-      1 1.59 1.26 1.15
-      2 1.59 1.26 1.15
-      3 1.59 1.26 1.15
-      4 1.59 1.26 1.15
-      5 1.59 1.26 1.15
-      6 1.59 1.26 1.15
-      7 1.59 1.26 1.15
+               col class n_mssn p_cmpl n_uniq mean    p0   p25  p50  p75 p100  iqr
+      1          y    ts      0      1     25 0.05 -2.53 -0.44 0.24 0.82 1.59 1.26
+      2          x    ts      0      1     25 0.05 -2.53 -0.44 0.24 0.82 1.59 1.26
+      3 z_Series 1   mts      0      1     25 0.05 -2.53 -0.44 0.24 0.82 1.59 1.26
+      4 z_Series 2   mts      0      1     25 0.05 -2.53 -0.44 0.24 0.82 1.59 1.26
+      5 z_Series 3   mts      0      1     25 0.05 -2.53 -0.44 0.24 0.82 1.59 1.26
+      6 z_Series 4   mts      0      1     25 0.05 -2.53 -0.44 0.24 0.82 1.59 1.26
+      7 z_Series 5   mts      0      1     25 0.05 -2.53 -0.44 0.24 0.82 1.59 1.26
+          sd  hist
+      1 1.15 ▂▃▅▆▇
+      2 1.15 ▂▃▅▆▇
+      3 1.15 ▂▃▅▆▇
+      4 1.15 ▂▃▅▆▇
+      5 1.15 ▂▃▅▆▇
+      6 1.15 ▂▃▅▆▇
+      7 1.15 ▂▃▅▆▇
 
 ---
 
@@ -149,18 +134,18 @@
       cols: 5 
       
       ----- Time-Series -----
-             col class n_missing p_complete n_unique  mean    p0   p25   p50  p75
-      1 Series 1   mts         0          1        5   0.2 -1.24 -0.36  0.66 0.82
-      2 Series 2   mts         0          1        5  0.76 -0.39  0.77  0.81  1.3
-      3 Series 3   mts         0          1        5 -0.67 -2.53  -1.6 -0.11 0.24
-      4 Series 4   mts         0          1        5  0.21  -0.7 -0.44  0.08 0.53
-      5 Series 5   mts         0          1        5 -0.24 -2.36 -1.23  0.03 0.86
-        p100  iqr   sd
-      1 1.14 1.18 0.98
-      2 1.33 0.53  0.7
-      3 0.64 1.84 1.34
-      4 1.59 0.97 0.91
-      5 1.51 2.09 1.57
+             col class n_mssn p_cmpl n_uniq  mean    p0   p25   p50  p75 p100  iqr
+      1 Series 1   mts      0      1      5   0.2 -1.24 -0.36  0.66 0.82 1.14 1.18
+      2 Series 2   mts      0      1      5  0.76 -0.39  0.77  0.81  1.3 1.33 0.53
+      3 Series 3   mts      0      1      5 -0.67 -2.53  -1.6 -0.11 0.24 0.64 1.84
+      4 Series 4   mts      0      1      5  0.21  -0.7 -0.44  0.08 0.53 1.59 0.97
+      5 Series 5   mts      0      1      5 -0.24 -2.36 -1.23  0.03 0.86 1.51 2.09
+          sd  hist
+      1 0.98 ▅▅▁▅▇
+      2  0.7 ▅▁▁▇▇
+      3 1.34 ▅▅▁▅▇
+      4 0.91 ▇▅▅▁▅
+      5 1.57 ▅▅▁▅▇
 
 ---
 
@@ -171,14 +156,14 @@
       cols: 4 
       
       ----- Time-Series -----
-         col class n_missing p_complete n_unique    mean      p0     p25     p50
-      1  DAX   mts         0          1     1774 2530.66 1402.34  1744.1 2140.56
-      2  SMI   mts         0          1     1725 3376.22  1587.4 2165.62 2796.35
-      3  CAC   mts         0          1     1617 2227.83    1611 1875.15  1992.3
-      4 FTSE   mts         0          1     1729 3565.64    2281 2843.15  3246.6
-            p75    p100     iqr      sd
-      1 2722.37 6186.09  978.26 1084.79
-      2 3812.43    8412  1646.8 1663.03
-      3 2274.35  4388.5   399.2  580.31
-      4 3993.57    6179 1150.43  976.72
+         col class n_mssn p_cmpl n_uniq    mean      p0     p25     p50     p75
+      1  DAX   mts      0      1   1774 2530.66 1402.34  1744.1 2140.56 2722.37
+      2  SMI   mts      0      1   1725 3376.22  1587.4 2165.62 2796.35 3812.43
+      3  CAC   mts      0      1   1617 2227.83    1611 1875.15  1992.3 2274.35
+      4 FTSE   mts      0      1   1729 3565.64    2281 2843.15  3246.6 3993.57
+           p100     iqr      sd  hist
+      1 6186.09  978.26 1084.79 ▇▂▂▁▁
+      2    8412  1646.8 1663.03 ▇▃▁▁▁
+      3  4388.5   399.2  580.31 ▇▂▁▁▁
+      4    6179 1150.43  976.72 ▇▇▂▂▂