diff --git a/NEWS.md b/NEWS.md
index ba13ad1b..31e26729 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,10 @@
 # TidierData.jl updates
 
+## v0.12.0 - 2023-09-10
+- Fixes `!!` interpolation so that it works using normal Julia scoping rules. It no longer uses `Main.eval()` in the implementation. The way interpolation works contains some breaking changes, and the documentation has been updated accordingly.
+- Fixes name conflict with `Cleaner.rename()` and `DataFrames.rename()`
+- Adds `categorical()` to array of non-vectorized functions.
+
 ## v0.11.0 - 2023-08-22
 - Add `@fill_missing()`, `@slice_sample()`, `is_float()`, `is_integer()`, `is_string()`
 - Rename `@drop_na()` to `@drop_missing()` to be consistent with Julia data types.
diff --git a/Project.toml b/Project.toml
index 761ad800..3d052d13 100644
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "TidierData"
 uuid = "fe2206b3-d496-4ee9-a338-6a095c4ece80"
 authors = ["Karandeep Singh"]
-version = "0.11.0"
+version = "0.12.0"
 
 [deps]
 Chain = "8be319e6-bccf-4806-a6f7-6fae938471bc"
diff --git a/docs/examples/UserGuide/interpolation.jl b/docs/examples/UserGuide/interpolation.jl
index 6c7a11d4..4a48ae3d 100644
--- a/docs/examples/UserGuide/interpolation.jl
+++ b/docs/examples/UserGuide/interpolation.jl
@@ -1,10 +1,10 @@
-# The `!!` ("bang bang") operator can be used to interpolate values of variables from the global environment into your code. This operator is borrowed from the R `rlang` package. At some point, we may switch to using native Julia interpolation, but for a variety of reasons that introduce some complexity with native interpolation, we plan to continue to support `!!` interpolation.
+# The `!!` ("bang bang") operator can be used to interpolate values of variables from the parent environment into your code. This operator is borrowed from the R `rlang` package. At some point, we may switch to using native Julia interpolation, but for a variety of reasons that introduce some complexity with native interpolation, we plan to continue to support `!!` interpolation.
 
 # To interpolate multiple variables, the `rlang` R package uses the `!!!` "triple bang" operator. However, in `TidierData.jl`, the `!!` "bang bang" operator can be used to interpolate either single or multiple values as shown in the examples below.
 
-# Since the `!!` operator can only access variables in the global environment, we will set these variables in a somewhat roundabout way for the purposes of documentation. However, in interactive use, you can simply write `myvar = :b` instead of wrapping this code inside of an `@eval()` macro as is done here.
+# Note: You can only interpolate values from variables in the parent environment. If you would like to interpolate column names, you have two options: you can either use `across()` or you can use `@aside` with `@pull()` to create variables in the parent environment containing the values of those columns which can then be accessed using interpolatino.
 
-# Note: `myvar = :b`, `myvar = (:a, :b)`, and `myvar = [:a, :b]` all refer to *columns* with those names. On the other hand, `myvar = "b"`, `myvar = ("a", "b")` and `myvar = ["a", "b"]` will interpolate those *values*. See below for examples.
+# myvar = :b`, `myvar = (:a, :b)`, and `myvar = [:a, :b]` all refer to *columns* with those names. On the other hand, `myvar = "b"`, `myvar = ("a", "b")` and `myvar = ["a", "b"]` will interpolate those *values*. See below for examples.
 
 using TidierData
 
@@ -14,31 +14,23 @@ df = DataFrame(a = string.(repeat('a':'e', inner = 2)),
 
 # ## Select the column (because `myvar` contains a symbol)
 
-@eval(Main, myvar = :b)
+myvar = :b
 
 @chain df begin
   @select(!!myvar)
 end
 
-# ## Select multiple variables (tuple of symbols)
-
-@eval(Main, myvars_tuple = (:a, :b))
-
-@chain df begin
-  @select(!!myvars_tuple)
-end
-
 # ## Select multiple variables (vector of symbols)
 
-@eval(Main, myvars_vector = [:a, :b])
+myvars = [:a, :b]
 
 @chain df begin
-  @select(!!myvars_vector)
+  @select(!!myvars)
 end
 
-# ## Filter rows containing the *value* of `myvar_string` (because `myvar_string` does)
+# ## Filter rows containing the *value* of `myvar_string`
 
-@eval(Main, myvar_string = "b")
+myvar_string = "b"
 
 @chain df begin
   @filter(a == !!myvar_string)
@@ -48,15 +40,15 @@ end
 
 # Note that for `in` to work here, we have to wrap it in `[]` because otherwise, the string will be converted into a collection of characters, which are a different data type.
 
-@eval(Main, myvar_string = "b")
+myvar_string = "b"
 
 @chain df begin
   @filter(a in [!!myvar_string])
 end
 
-# ## You can also use this for a tuple or vector of strings.
+# ## You can also use this for a vector (or tuple) of strings.
 
-@eval(Main, myvars_string = ("a", "b"))
+myvars_string = ["a", "b"]
 
 @chain df begin
   @filter(a in !!myvars_string)
@@ -64,15 +56,29 @@ end
 
 # ## Mutate one variable
 
-@eval(Main, myvar = :b)
+# Remember: You cannot interpolate column names into `@mutate()` expressions. However, you *can* create a temporary variable containing the values of the column in question *or* you can use `@mutate()` with `across()`.
+
+# ### Option 1: Create a temporary variable containing the values of the column.
+
+myvar = :b
 
 @chain df begin
-  @mutate(!!myvar = !!myvar + 1)
+  @aside(myvar_values = @pull(_, !!myvar))
+  @mutate(d = !!myvar_values + 1)
+end
+
+# ### Option 2: Use `@mutate()` with `across()`
+
+# Note: when using `across()`, anonymous functions are not vectorized. This is intentional to allow users to specify their function exactly as desired.
+
+@chain df begin
+  @mutate(across(!!myvar, x -> x .+ 1))
+  @rename(d = b_function)
 end
 
 # ## Summarize across one variable
 
-@eval(Main, myvar = :b)
+myvar = :b
 
 @chain df begin
   @summarize(across(!!myvar, mean))
@@ -80,29 +86,38 @@ end
 
 # ## Summarize across multiple variables
 
-@eval(Main, myvars_tuple = (:b, :c))
+myvars = [:b, :c]
 
 @chain df begin
-  @summarize(across(!!myvars_tuple, (mean, minimum, maximum)))
+  @summarize(across(!!myvars, (mean, minimum, maximum)))
+end
+
+# ## Group by one interpolated variable
+
+myvar = :a
+
+@chain df begin
+  @group_by(!!myvar)
+  @summarize(c = mean(c))
 end
 
 # ## Group by multiple interpolated variables
 
-@eval(Main, myvars_tuple = (:a, :b))
+myvars = [:a, :b]
 
 @chain df begin
-  @group_by(!!myvars_tuple)
+  @group_by(!!myvars)
   @summarize(c = mean(c))
 end
 
+# Notice that `df` remains grouped by `a` because the `@summarize()` peeled off one layer of grouping.
+
 # ## Global constants
 
-# Because global constants like `pi` exist in the `Main` module, they can also be accessed using interpolation. For example, let's calculate the area of circles with a radius of 1 up to 5.
+# You can also use `!!` interpolation to access global variables like `pi`.
 
 df = DataFrame(radius = 1:5)
 
-# We can interpolate `pi` (from the `Main` module) to help with this.
-
 @chain df begin
   @mutate(area = !!pi * radius^2)
 end
@@ -112,28 +127,26 @@ end
 # While interpolation using `!!` is concise and handy, it's not required. You can also access user-defined globals and global constant variables using the following syntax:
 
 @chain df begin
-  @mutate(area = Main.pi * radius^2)
+  @mutate(area = esc(pi) * radius^2)
 end
 
-# The key lesson with interpolation is that any bare unquoted variable is assumed to refer to a column name in the DataFrame. If you are referring to any variable outside of the DataFrame, you need to either use `!!variable` or `Main.variable` syntax to refer to this variable.
-
-# ## There's one other situation when `!!` interpolation may not work correctly: inside a `for` loop.
+# Since we know that `pi` is defined in the `Main` module, we can also access it using `Main.pi`.
 
-# This is only a problem if the variable being interpolated using `!!` is the iterator. Because macros as expanded during *parsing* of the code (before it is compiled), the expanded code contains the last value of the global variable *before* the loop is run and does not update with each iteration of the loop.
+@chain df begin
+  @mutate(area = Main.pi * radius^2)
+end
 
-# To get around this, we can use `@eval(Main, variable)` inside our code, where `variable` refers to the iterator. Let's show a simple example of this where we print out each column one at a time using a `for` loop.
+# The key lesson with interpolation is that any bare unquoted variable is assumed to refer to a column name in the DataFrame. If you are referring to any variable outside of the DataFrame, you need to either use `!!variable`, `esc(variable)`, or `[Module_name_here].variable` syntax to refer to this variable.
 
-# We first need to initialize the global variable using `global_col = Symbol()`.
+# Note: You can use `!!` interpolation anywhere, including inside of functions and loops.
 
-# ```julia
-# global_col = Symbol()
-# for col in [:a, :b, :c]
-#     global global_col = col
-#     @chain df begin
-#         @select(@eval(Main, global_col))
-#         println
-#     end
-# end
-# ```
+df = DataFrame(a = string.(repeat('a':'e', inner = 2)),
+               b = [1,1,1,2,2,2,3,3,3,4],
+               c = 11:20)
 
-# The reason this works is because the `@eval()` macro inside `@select()` is not evaluated right away (unlike `!!`) but rather is evaluated at a later stage and thus is updated with each iteration. Instead of using the `@eval()` macro, we could instead have instead written `Main.eval(:global_col)`, which is functionally the same.
\ No newline at end of file
+for col in [:b, :c]
+  @chain df begin
+    @summarize(across(!!col, mean))
+    println
+  end
+end
diff --git a/src/TidierData.jl b/src/TidierData.jl
index f62f2e35..e6a0f141 100644
--- a/src/TidierData.jl
+++ b/src/TidierData.jl
@@ -5,7 +5,7 @@ using MacroTools
 using Chain
 using Statistics
 using StatsBase # primarily for `sample()`
-using Cleaner
+import Cleaner # changed from `using Cleaner` because of name conflict with `DataFrames.rename()`
 using Reexport
 
 # Exporting `Cols` because `summarize(!!vars, funs))` with multiple interpolated
@@ -26,7 +26,7 @@ const code = Ref{Bool}(false) # output DataFrames.jl code?
 const log = Ref{Bool}(false) # output tidylog output? (not yet implemented)
 
 # Expose the global do-not-vectorize "list"
-const not_vectorized = Ref{Vector{Symbol}}([:Ref, :Set, :Cols, :(:), :∘, :lag, :lead, :ntile, :repeat, :across, :desc, :mean, :std, :var, :median, :first, :last, :minimum, :maximum, :sum, :length, :skipmissing, :quantile, :passmissing, :cumsum, :cumprod, :accumulate, :is_float, :is_integer, :is_string, :cat_rev, :cat_relevel, :cat_infreq, :cat_lump, :cat_reorder, :cat_collapse, :cat_lump_min, :cat_lump_prop, :as_categorical, :is_categorical])
+const not_vectorized = Ref{Vector{Symbol}}([:esc, :Ref, :Set, :Cols, :(:), :∘, :lag, :lead, :ntile, :repeat, :across, :desc, :mean, :std, :var, :median, :first, :last, :minimum, :maximum, :sum, :length, :skipmissing, :quantile, :passmissing, :cumsum, :cumprod, :accumulate, :is_float, :is_integer, :is_string, :cat_rev, :cat_relevel, :cat_infreq, :cat_lump, :cat_reorder, :cat_collapse, :cat_lump_min, :cat_lump_prop, :categorical, :as_categorical, :is_categorical])
 
 # Includes
 include("docstrings.jl")
diff --git a/src/clean_names.jl b/src/clean_names.jl
index c79d4ecb..8366b738 100644
--- a/src/clean_names.jl
+++ b/src/clean_names.jl
@@ -11,13 +11,13 @@ macro clean_names(df, case)
       
       @chain $(esc(df)) begin
         DataFrame # remove grouping
-        polish_names(_; style = style)
+        Cleaner.polish_names(_; style = style)
         DataFrame # convert back to DataFrame
         groupby(col_names; sort = true) # regroup
       end
     else
       @chain $(esc(df)) begin
-        polish_names(_; style = style)
+        Cleaner.polish_names(_; style = style)
         DataFrame # convert back to DataFrame
       end
     end
@@ -33,13 +33,13 @@ macro clean_names(df)
       
       @chain $(esc(df)) begin
         DataFrame # remove grouping
-        polish_names
+        Cleaner.polish_names
         DataFrame # convert back to DataFrame
         groupby(col_names; sort = true) # regroup
       end
     else
       @chain $(esc(df)) begin
-        polish_names
+        Cleaner.polish_names
         DataFrame # convert back to DataFrame
       end
     end
diff --git a/src/parsing.jl b/src/parsing.jl
index 6b4c76b3..ae250efc 100644
--- a/src/parsing.jl
+++ b/src/parsing.jl
@@ -99,13 +99,13 @@ function parse_pivot_arg(tidy_expr::Union{Expr,Symbol,Number})
 end
 
 # Not exported
-function parse_function(lhs::Symbol, rhs::Expr; autovec::Bool=true, subset::Bool=false)
+function parse_function(lhs::Union{Symbol, Expr}, rhs::Expr; autovec::Bool=true, subset::Bool=false)
 
   lhs = QuoteNode(lhs)
 
   src = Symbol[]
   MacroTools.postwalk(rhs) do x
-    if @capture(x, (fn_(args__)) | (fn_.(args__)))
+    if @capture(x, (fn_(args__)) | (fn_.(args__))) && fn != :esc
       args = args[isa.(args, Symbol)]
       push!(src, args...)
     end
@@ -226,7 +226,9 @@ function parse_group_by(tidy_expr::Union{Expr,Symbol})
     return :(Cols($(args...),))
   elseif @capture(tidy_expr, lhs_ = rhs_)
     return QuoteNode(lhs)
-  else
+  elseif tidy_expr isa Expr
+    return tidy_expr
+  else # if it's a Symbol
     return QuoteNode(tidy_expr)
   end
 end
@@ -332,7 +334,7 @@ function parse_escape_function(rhs_expr::Union{Expr,Symbol})
     if @capture(x, fn_(args__))
 
       # `in`, `∈`, and `∉` should be vectorized in auto-vec but not escaped
-      if fn in [:in :∈ :∉ :Ref :Set :Cols :(:) :∘ :across :desc :mean :std :var :median :first :last :minimum :maximum :sum :length :skipmissing :quantile :passmissing :startswith :contains :endswith]
+      if fn in [:esc :in :∈ :∉ :Ref :Set :Cols :(:) :∘ :across :desc :mean :std :var :median :first :last :minimum :maximum :sum :length :skipmissing :quantile :passmissing :startswith :contains :endswith]
         return x
       elseif contains(string(fn), r"[^\W0-9]\w*$") # valid variable name
         return :($(esc(fn))($(args...)))
@@ -340,7 +342,7 @@ function parse_escape_function(rhs_expr::Union{Expr,Symbol})
         return x
       end
     elseif @capture(x, fn_.(args__))
-      if fn in [:in :∈ :∉ :Ref :Set :Cols :(:) :∘ :across :desc :mean :std :var :median :first :last :minimum :maximum :sum :length :skipmissing :quantile :passmissing :startswith :contains :endswith]
+      if fn in [:esc :in :∈ :∉ :Ref :Set :Cols :(:) :∘ :across :desc :mean :std :var :median :first :last :minimum :maximum :sum :length :skipmissing :quantile :passmissing :startswith :contains :endswith]
         return x
       elseif contains(string(fn), r"[^\W0-9]\w*$") # valid variable name
         return :($(esc(fn)).($(args...)))
@@ -361,19 +363,7 @@ function parse_interpolation(var_expr::Union{Expr,Symbol,Number,String}; summari
 
   var_expr = MacroTools.postwalk(var_expr) do x
     if @capture(x, !!variable_Symbol)
-      variable = Main.eval(variable)
-      if variable isa AbstractString
-        return variable # Strings are now treated as Strings and not columns
-      elseif variable isa Symbol
-        return variable
-      else # Tuple or Vector of columns
-        if variable[1] isa Symbol
-          variable = QuoteNode.(variable)
-          return :(Cols($(variable...),))
-        else
-          return variable
-        end
-      end
+      return esc(variable)
     # `hello` in Julia is converted to Core.@cmd("hello")
     # Since MacroTools is unable to match this pattern, we can directly
     # evaluate the expression to see if it matches. If it does, the 3rd argument
@@ -396,6 +386,8 @@ function parse_interpolation(var_expr::Union{Expr,Symbol,Number,String}; summari
       else
         return :($fn())
       end
+    elseif @capture(x, esc(variable_))
+     return esc(variable)
     end
     return x
   end