Added ROT references

docs/src/man/dirt_removal.md

@@ -8,9 +8,9 @@ Empty values can quickly become a big problem to handle when they come in differ
 such as `Julia`'s `missing`, `Python`'s `None`, `R`'s `NA` and a diversity of common strings
 like `""`, `' '`, etc.
 
-As an easy way to handle this common problems we got the `compact` functions, being them 
-`compact_table`, `compact_columns` and `compact_rows` with their mutating in-place variants
-i.e. `compact_table!` et al.
+As an easy way to handle this common problems we got the `compact` functions, being them
+`compact_table`, `compact_columns` and `compact_rows` with their mutating in-place and ROT variants
+i.e. `compact_table!`, `compact_table_ROT` et al.
 
 They all recieve a table as first argument and an optional keyword argument `empty_values`
 where you can pass a vector of what you consider being empty values present in your table.
@@ -64,8 +64,8 @@ julia> compact_table(ct; empty_values=[""])
 ```
 
 You might also feel that columns filled with just a constant value are not adding any value
-to your table and may prefer to remove them, for those cases we got the `delete_const_columns` and
-`delete_const_columns!` functions.
+to your table and may prefer to remove them, for those cases we got the `delete_const_columns`,
+`delete_const_columns!` and `delete_const_columns_ROT` functions.
 
 ```jldoctest removal
 julia> ct = CleanTable([:A, :B, :C], [[4, 5, 6], [1, 1, 1], String["7", "8", "9"]])
@@ -114,7 +114,7 @@ julia> ["1", 2.0]
 
 ```
 
-To solve this problem we have the `reinfer_schema` and `reinfer_schema!` functions that will try
+To solve this problem we have the `reinfer_schema`, `reinfer_schema!` and `reinfer_schema_ROT` functions that will try
 to make the column of type `Union` with, by default, up to 3 types stored in `Union` while also
 internally using `Base.promote_typejoin` on numeric types to reduce the final amount of numeric types.
 

docs/src/man/name_changing.md

@@ -6,7 +6,7 @@ Having repated column names, names with spaces in them, names where spaces are p
 end, names with inconsistent formating, etc can certainly become a trouble when trying to reference a certain
 column during your workflow.
 
-To tackle this problems directly, we have the functions `polish_names` and `polish_names!` used as follows:
+To tackle this problems directly, we have the functions `polish_names`, `polish_names!` and `polish_names_ROT` used as follows:
 
 ```jldoctest name_polish
 julia> using Cleaner
@@ -61,8 +61,8 @@ julia> generate_polished_names(["  _aName with_lotsOfProblems", "  _aName with_l
 
 ```
 
-If all you want is to change the column names to be your desired ones, you can always use the `rename` and 
-`rename!` functions.
+If all you want is to change the column names to be your desired ones, you can always use the `rename`, `rename!` and
+`rename_ROT` functions.
 
 ```jldoctest name_polish
 julia> rename(ct, [:A, :B])
@@ -79,10 +79,10 @@ julia> rename(ct, [:A, :B])
 ## Making a row be the column names
 
 When working with messy data you might end up having the row names being the second or third row of the table you have
-loaded. For this cases you can use the `row_as_names` and `row_as_names!` functions.
+loaded. For this cases you can use the `row_as_names`, `row_as_names!` and `row_as_names_ROT` functions.
 
-By default, `row_as_names` and `row_as_names!` will remove all rows above the index passed, but this behavior can be
-overwritten by passing the optional keyword argument `remove=false`.
+By default, `row_as_names`, `row_as_names!` and `row_as_names_ROT` will remove all rows above the index passed, but
+this behavior can be overwritten by passing the optional keyword argument `remove=false`.
 
 ```jldoctest promoting_rows; setup = :(using Cleaner)
 julia> ct = CleanTable([Symbol(" "), Symbol(" ")], [[" ", "A", 1], [" ", "B", 2]])