0.5 dev

.github/workflows/CI.yml

@@ -77,3 +77,18 @@ jobs:
           using Kezdi
           DocMeta.setdocmeta!(Kezdi, :DocTestSetup, :(using Kezdi); recursive=true)
           doctest(Kezdi)
+  trigger-docs-copy:
+    needs: docs # Ensure this runs after docs job completes
+    runs-on: ubuntu-latest
+    steps:
+      - name: Repository Dispatch
+        uses: peter-evans/repository-dispatch@v3
+        with:
+          token: ${{ secrets.DOCS_REPO_ACCESS_TOKEN }}
+          repository: gergelyattilakiss/docs.koren.dev
+          event-type: copy-kezdi-docs
+          client-payload: |
+            {
+              "ref": "${{ github.ref }}",
+              "sha": "${{ github.sha }}"
+            }

Project.toml

@@ -15,7 +15,6 @@ FreqTables = "da1fdf0e-e0ff-5433-a45f-9bb5ff651cb1"
 InteractiveUtils = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 Logging = "56ddb016-857b-54e1-b83d-db4d58db5568"
 Missings = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
-RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"
 ReadStatTables = "52522f7a-9570-4e34-8ac6-c005c74d4b84"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
@@ -33,14 +32,12 @@ FreqTables = "0.4"
 InteractiveUtils = "1"
 Logging = "1"
 Missings = "1"
-RDatasets = "0.7"
 ReadStatTables = "0.3"
 Reexport = "1"
 Statistics = "1"
 StatsBase = "0.34"
 julia = "1.9, 1.10, 1.11"
 
-
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 

README.md

@@ -11,7 +11,7 @@ It imports and reexports [CSV](https://csv.juliadata.org/stable/), [DataFrames](
 
 ## Getting started
 
-> `Kezdi.jl` is currently in beta. We have more than 500 unit tests and a large code coverage. [![Coverage](https://codecov.io/gh/codedthinking/Kezdi.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/codedthinking/Kezdi.jl) The package, however, is not guaranteed to be bug-free. If you encounter any issues, please report them as a [GitHub issue](https://github.com/codedthinking/Kezdi.jl/issues/new).
+> `Kezdi.jl` is currently in beta. We have more than 500 unit tests and a large code coverage. [![Coverage](https://codecov.io/gh/codedthinking/Kezdi.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/codedthinking/Kezdi.jl) The package, however, is not guaranteed to be bug-free. If you encounter any issues, please report them as a [GitHub issue](https://github.com/codedthinking/Kezdi.jl/issues/new). In any case you want to contribute to the software please do it in a form of pull request to the current development branch `0.5-dev`. In case you need support with installation or use please use the discussions forum on github.
 >
 > If you would like to receive updates on the package, please star the repository on GitHub and sign up for [email notifications here](https://relentless-producer-1210.ck.page/62d7ebb237).
 
@@ -217,7 +217,14 @@ To maintain compatibility with Julia, we had to rename some functions. For examp
 ## Acknowledgements
 [^stata]: Stata is a registered trademark of StataCorp LLC. Kezdi.jl is not affiliated with StataCorp LLC.
 
-Inspiration for the package came from [Tidier.jl](https://tidierorg.github.io/Tidier.jl/stable/), a similar package launched by Karandeep Singh that provides a dplyr-like interface for Julia. Johannes Boehm has also developed a similar package, [Douglass.jl](https://github.com/jmboehm/Douglass.jl).
+Inspiration for the package came from [Tidier.jl](https://tidierorg.github.io/Tidier.jl/stable/), a similar package launched by Karandeep Singh that provides a dplyr-like interface for Julia. Johannes Boehm has also developed a similar package, [Douglass.jl](https://github.com/jmboehm/Douglass.jl) although our package has a more extensive feature set and is more actively developed. We appreciate both previous work as they showed the possibility for creating and help us in formulating our idea.
+
+A comparison to Douglass.jl:
+1. The main difference is between how Douglass and Kezdi process the commands. Douglass uses a string parsing approach where a string using Stata-like syntax, but using Julia conventions for referencing variables in a dataframe, is used to implement the actions on the dataframe whereas Kezdi uses macros and Julia's macro parsing and processing to implement all the actions,
+2. Kezdi's reference to dataframe variables is using the Stata convention rather than using Julia symbols, 
+3. Kezdi has implemented commands for basic analysis of data, matching Stata commands and their syntax, 
+4. on data manipulation commands both packages have some that the other does not implemented yet, 
+5. and Douglass has implemented the `by:` and `bysort:` prefixes in general for commands.
 
 The package is built on top of [DataFrames.jl](https://dataframes.juliadata.org/stable/), [FreqTables.jl](https://github.com/nalimilan/FreqTables.jl) and [FixedEffectModels.jl](https://github.com/FixedEffects/FixedEffectModels.jl). The `@with` function relies on [Chain.jl](https://github.com/jkrumbiegel/Chain.jl) by Julius Krumbiegel.
 

src/functions.jl

@@ -50,7 +50,7 @@ getdf() = _global_dataframe
 Set the global data frame.
 """
 setdf(df::Union{AbstractDataFrame,Nothing}) = global _global_dataframe = isnothing(df) ? nothing : copy(df)
-display_and_return(x) = (display(x); x)
+display_and_return(x) = isinteractive() ? (display(x)) : (display(x); x)
 
 """
     distinct(x::AbstractVector) = unique(x)

src/side_effects.jl

@@ -39,7 +39,7 @@ function rewrite(::Val{:regress}, command::Command)
             display("Dropping $(sum(.!$additional_condition)) row(s) due to missing values.")
         end
         if length($(arguments[2:end])) == 1
-            reg(view($target_df, $additional_condition, :), @formula($(arguments[1]) ~ $(arguments[2])), $vcov) |> $teardown
+            reg(view($target_df, $additional_condition, :), @formula($(arguments[1]) ~ $(arguments[2])), $vcov) |> Kezdi.display_and_return |> $teardown
         else
             reg(view($target_df, $additional_condition, :), @formula($(arguments[1]) ~ $(Expr(:call, :+, arguments[2:end]...))), $vcov) |> Kezdi.display_and_return |> $teardown
         end

test/speed.jl

@@ -16,5 +16,5 @@
     end
 
     time = median(t).time / 1e9
-    @test time < 20.0
+    @test time < 60.0
 end