Merge pull request #7 from TidierOrg/updated-docs-without-tilde

Removed tilde and updated dependencies

.github/workflows/Documenter.yml

@@ -21,7 +21,7 @@ jobs:
       - uses: julia-actions/setup-julia@v1
       - uses: julia-actions/cache@v1
         with:
-          cache-registries: "true"
+          cache-registries: "false"
       - name: Install documentation dependencies
         run: julia --project=docs -e 'using Pkg; pkg"dev ."; Pkg.instantiate()'
       - name: Build and deploy

NEWS.md

@@ -0,0 +1,7 @@
+# TidierCats.jl updates
+
+## v0.1.1 - 2023-08-06
+- Added the `TidierCats.jl` functions to the `TidierData.jl` list of `not_vectorized[]` functions, which means that the user does *not* need to explicitly prefix them with a `~` when used inside of a `@mutate()` within `TidierData.jl`. Thus, all the `~` prefixes have been removed from the examples.
+
+## v0.1.0 - Initial commit
+- Released to Julia general registry

Project.toml

@@ -1,14 +1,13 @@
 name = "TidierCats"
 uuid = "79ddc9fe-4dbf-4a56-a832-df41fb326d23"
 authors = ["Daniel Rizk"]
-version = "0.1.0"
+version = "0.1.1"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-TidierData = "fe2206b3-d496-4ee9-a338-6a095c4ece80"
 
 [compat]
 CategoricalArrays = "0.10"

README.md

@@ -13,7 +13,6 @@
 
 `TidierCats.jl` has one main goal: to implement forcats's straightforward syntax and of ease of use while working with categorical variables for Julia users. While this package was develeoped to work seamelessly with `Tidier.jl` fucntions and macros, it can also work as a indepentenly as a standalone package. This package is powered by CateogricalArrays.jl 
 
-
 ## What functions does TidierCats.jl support?
 
 - `cat_rev()`
@@ -25,7 +24,6 @@
 - `cat_lump_prop()`
 - `as_categorical()`
 
-
 ## Installation
 
 For the development version:
@@ -59,7 +57,7 @@ This function changes the order of levels in a categorical variable. It accepts
 
 ```julia
 custom_order = @chain df begin
-    @mutate(CatVar = ~cat_relevel(CatVar, ["Zilch", "Medium", "High", "Low"]))
+    @mutate(CatVar = cat_relevel(CatVar, ["Zilch", "Medium", "High", "Low"]))
 end
 
 print(levels(df[!,:CatVar]))
@@ -76,7 +74,7 @@ This function reverses the order of levels in a categorical variable. It only re
 
 ```julia
 reversed_order = @chain df begin
-    @mutate(CatVar = ~cat_rev(CatVar))
+    @mutate(CatVar = cat_rev(CatVar))
 end
 
 print(levels(df[!,:CatVar]))
@@ -109,7 +107,7 @@ end
 
 ```julia
 orderedbyfrequency = @chain df begin
-    @mutate(CatVar = ~cat_infreq(CatVar))
+    @mutate(CatVar = cat_infreq(CatVar))
 end
 
 print(levels(df[!,:CatVar]))
@@ -126,7 +124,7 @@ This function lumps the least frequent levels into a new "Other" level. It accep
 
 ```julia
 lumped_cats = @chain df begin
-    @mutate(CatVar = ~cat_lump(CatVar,2))
+    @mutate(CatVar = cat_lump(CatVar,2))
 end
 
 print(levels(df[!,:CatVar]))
@@ -149,11 +147,11 @@ df3 = DataFrame(
 )
 
 df4 = @chain df3 begin
-    @mutate(cat_var= ~cat_reorder(cat_var, order_var, "median" ))
+    @mutate(cat_var= cat_reorder(cat_var, order_var, "median" ))
 end
 
 @chain df3 begin
-    @mutate(catty = ~as_categorical(cat_var))
+    @mutate(catty = as_categorical(cat_var))
     @group_by(cat_var)
     @summarise(median = median(order_var))
 end
@@ -179,7 +177,7 @@ This function collapses levels in a categorical variable according to a specifie
 
 ```julia
 df5 = @chain df begin
-    @mutate(CatVar = ~cat_collapse(CatVar, Dict("Low" => "bad", "Zilch" => "bad")))
+    @mutate(CatVar = cat_collapse(CatVar, Dict("Low" => "bad", "Zilch" => "bad")))
 end
 
 @chain df begin
@@ -215,7 +213,7 @@ This function converts a standard Julia array to a categorical array. The only a
 test = DataFrame( w = ["A", "B", "C", "D"])
 
 @chain test begin 
-    @mutate(w = ~as_categorical(w))
+    @mutate(w = as_categorical(w))
 end
 ```
 
@@ -234,7 +232,7 @@ This function wil lump any cargory with less than the minimum number of entries
 
 ```julia
 lumpedbymin = @chain df begin
-    @mutate(CatVar = ~cat_lump_min(CatVar, 14))
+    @mutate(CatVar = cat_lump_min(CatVar, 14))
 end
 
 print(levels(df[!,:CatVar]))
@@ -252,7 +250,7 @@ This function wil lump any cargory with less than the minimum proportion and rec
 
 ```julia
 lumpedbyprop = @chain df begin
-    @mutate(CatVar = ~cat_lump_prop(CatVar, .25, "new name"))
+    @mutate(CatVar = cat_lump_prop(CatVar, .25, "new name"))
 end
 
 print(levels(df[!,:CatVar]))

docs/Project.toml

@@ -1,10 +1,10 @@
 [deps]
-CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
-Chain = "8be319e6-bccf-4806-a6f7-6fae938471bc"
-DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DocumenterMarkdown = "997ab1e6-3595-5248-9280-8efb232c3433"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-Tidier = "f0413319-3358-4bb0-8e7c-0c83523a93bd"
+TidierData = "fe2206b3-d496-4ee9-a338-6a095c4ece80"
 TidierCats = "79ddc9fe-4dbf-4a56-a832-df41fb326d23"
+
+[compat]
+TidierData = ">=0.9.2"

docs/examples/UserGuide/supported_functions.jl

@@ -1,6 +1,5 @@
-using Tidier
+using TidierData
 using TidierCats
-using CategoricalArrays
 using Random
 
 Random.seed!(10)
@@ -9,7 +8,6 @@ categories = ["High", "Medium", "Low", "Zilch"]
 
 random_indices = rand(1:length(categories), 57)
 
-
 df = DataFrame(
     ID = 1:57,
     CatVar = categorical([categories[i] for i in random_indices], levels = categories)
@@ -20,27 +18,27 @@ first(df, 5)
 # This function changes the order of levels in a categorical variable. It accepts two arguments - a column name and an array of levels in the desired order.
 
 custom_order = @chain df begin
-    @mutate(CatVar = ~cat_relevel(CatVar, ["Zilch", "Medium", "High", "Low"]))
+    @mutate(CatVar = cat_relevel(CatVar, ["Zilch", "Medium", "High", "Low"]))
 end
 
-print(levels(df[!,:CatVar]))
+print(levels(df.CatVar))
 
 # and
 
-print(levels(custom_order[!,:CatVar]))
+print(levels(custom_order.CatVar))
 
 
 # ## `cat_rev()`
 # This function reverses the order of levels in a categorical variable. It only requires one argument - the column name whose levels are to be reversed
 reversed_order = @chain df begin
-    @mutate(CatVar = ~cat_rev(CatVar))
+    @mutate(CatVar = cat_rev(CatVar))
 end
 
-print(levels(df[!,:CatVar]))
+print(levels(df.CatVar))
 
 # and
 
-print(levels(reversed_order[!,:CatVar]))
+print(levels(reversed_order.CatVar))
 
 # ## `cat_infreq()`
 # This function reorders levels of a categorical variable based on their frequencies, with most frequent level first. The single argument is column name
@@ -50,14 +48,14 @@ print(levels(reversed_order[!,:CatVar]))
 end
 
 orderedbyfrequency = @chain df begin
-    @mutate(CatVar = ~cat_infreq(CatVar))
+    @mutate(CatVar = cat_infreq(CatVar))
 end
 
-print(levels(df[!,:CatVar]))
+print(levels(df.CatVar))
 
 # and 
 
-print(levels(orderedbyfrequency[!,:CatVar]))
+print(levels(orderedbyfrequency.CatVar))
 
 
 @chain df begin
@@ -68,14 +66,14 @@ end
 # This function lumps the least frequent levels into a new "Other" level. It accepts two arguments - a column name and an integer specifying the number of levels to keep.
 
 lumped_cats = @chain df begin
-    @mutate(CatVar = ~cat_lump(CatVar,2))
+    @mutate(CatVar = cat_lump(CatVar,2))
 end
 
-print(levels(df[!,:CatVar]))
+print(levels(df.CatVar))
 
 # and
 
-print(levels(lumped_cats[!,:CatVar]))
+print(levels(lumped_cats.CatVar))
 
 
 @chain lumped_cats begin
@@ -91,43 +89,42 @@ df3 = DataFrame(
 )
 
 df4 = @chain df3 begin
-    @mutate(cat_var= ~cat_reorder(cat_var, order_var, "median" ))
+    @mutate(cat_var= cat_reorder(cat_var, order_var, "median" ))
 end
 
 
-print(levels(df3[!,:cat_var]))
+print(levels(df3.cat_var))
 
 # and
 
-print(levels(df4[!,:cat_var]))
+print(levels(df4.cat_var))
 
 
 @chain df3 begin
-    @mutate(catty = ~as_categorical(cat_var))
+    @mutate(catty = as_categorical(cat_var))
     @group_by(catty)
-    #@summarise(median = median(order_var))
 end
 
 # ## `cat_collapse()`
 # This function collapses levels in a categorical variable according to a specified mapping. It requires two arguments - a categorical column and a dictionary that maps original levels to new ones.
 
 df5 = @chain df begin
-    @mutate(CatVar = ~cat_collapse(CatVar, Dict("Low" => "bad", "Zilch" => "bad")))
+    @mutate(CatVar = cat_collapse(CatVar, Dict("Low" => "bad", "Zilch" => "bad")))
 end
 
-print(levels(df[!,:CatVar]))
+print(levels(df.CatVar))
 
 # and
 
-print(levels(df5[!,:CatVar]))
+print(levels(df5.CatVar))
 
 # ## `as_categorical()`
 # This function converts a standard Julia array to a categorical array. The only argument it needs is the colunn name to be converted.
 
 test = DataFrame( w = ["A", "B", "C", "D"])
 
 @chain test begin 
-    @mutate(w = ~as_categorical(w))
+    @mutate(w = as_categorical(w))
 end
 
 # ## `cat_lump_min()`
@@ -137,28 +134,28 @@ end
     @count(CatVar)
 end
 lumpedbymin = @chain df begin
-    @mutate(CatVar = ~cat_lump_min(CatVar, 14))
+    @mutate(CatVar = cat_lump_min(CatVar, 14))
 end
 
-print(levels(df[!,:CatVar])) 
+print(levels(df.CatVar)) 
 
 # and
 
-print(levels(lumpedbymin[!,:CatVar]))
+print(levels(lumpedbymin.CatVar))
 
 # ## `cat_lump_min()`
 # This function wil lump any cargory with less than the minimum proportion and recateogrize it as "Other" as the default, or a category name chosen by the user
 
 lumpedbyprop = @chain df begin
-    @mutate(CatVar = ~cat_lump_prop(CatVar, .25, "wow"))
+    @mutate(CatVar = cat_lump_prop(CatVar, .25, "wow"))
 end
 
 
-print(levels(df[!,:CatVar]))
+print(levels(df.CatVar))
 
 # and
 
-print(levels(lumpedbyprop[!,:CatVar]))
+print(levels(lumpedbyprop.CatVar))
 
 
 # ## `cat_na_value_to_level()`

docs/make.jl

@@ -1,10 +1,10 @@
 using Documenter, DocumenterMarkdown
-using Tidier, TidierCats
-using CategoricalArrays
+using TidierCats
 
 DocTestMeta = quote
-    using Tidier, TidierCats, DataFrames, Chain, Statistics
+    using TidierData, TidierCats, Statistics
 end
+
 DocMeta.setdocmeta!(TidierCats,
     :DocTestSetup,
     DocTestMeta;

docs/src/index.md

@@ -1,18 +1,19 @@
 <img src="assets/TidierCats\_logo.png" align="left" style="padding-right:10px"; width="150"></img>
 
-## TidierCats
+## TidierCats.jl
 
-The goal of this package is to bring the convenience and simple usability of Forcats in R to Julia. This package was designed  to work with Tidier.jl, but can also work independently.
+The goal of this package is to bring the convenience and simple usability of `forcats` in R to Julia. This package was designed to work with `Tidier.jl` but can also work independently.
 
+This package re-exports `CategoricalArrays.jl`.
 
-This package includes:
+In addition, this package includes:
 
-- `cat_rev`
-- `cat_relevel`
-- `cat_infreq`
-- `cat_lump`
-- `cat_reorder`
-- `cat_collapse`
-- `cat_lump_min`
-- `cat_lump_prop`
-- `as_categorical`
+- `cat_rev()`
+- `cat_relevel()`
+- `cat_infreq()`
+- `cat_lump()`
+- `cat_reorder()`
+- `cat_collapse()`
+- `cat_lump_min()`
+- `cat_lump_prop()`
+- `as_categorical()`

docs/src/reference.md

@@ -1,5 +1,6 @@
 ```@meta
-DocTestSetup= quote
+DocTestSetup = quote
+using TidierData
 using TidierCats
 end
 ```

src/TidierCats.jl

@@ -10,13 +10,6 @@ using Reexport
 export cat_rev, cat_relevel, cat_infreq, cat_lump, cat_reorder, cat_collapse, cat_lump_min, cat_lump_prop, as_categorical
 include("catsdocstrings.jl")
 
-function __init__()
-try
-  append!(Main.TidierData.not_vectorized[], [:cat_rev, :cat_relevel, :cat_infreq, :cat_lump, :cat_reorder, :cat_collapse, :cat_lump_min, :cat_lump_prop, :as_categorical])
-catch
-end
-end
-
 """
 $docstring_cat_rev
 """