TidierData.jl is a 100% Julia implementation of the dplyr and tidyr R packages. Powered by the DataFrames.jl package and Julia’s extensive meta-programming capabilities, TidierData.jl is an R user’s love letter to data analysis in Julia.
TidierData.jl has two goals, which differentiate it from other data analysis meta-packages in Julia:
Stick as closely to dplyr and tidyr syntax as possible: Whereas other meta-packages introduce Julia-centric idioms for working with DataFrames, this package’s goal is to reimplement dplyr and tidyr in Julia. This means that TidierData.jl uses tidy expressions as opposed to idiomatic Julia expressions. An example of a tidy expression is a = mean(b).
Make broadcasting mostly invisible: Broadcasting trips up many R users switching to Julia because R users are used to most functions being vectorized. TidierData.jl currently uses a lookup table to decide which functions not to vectorize; all other functions are automatically vectorized. Read the documentation page on "Autovectorization" to read about how this works, and how to override the defaults.
For the stable version:
The ] character starts the Julia package manager. Press the backspace key to return to the Julia prompt.
or
using Pkg
Pkg.add("TidierData")
For the newest version:
or
using Pkg Pkg.add(url="https://github.com/TidierOrg/TidierData.jl")What functions does TidierData.jl support?
To support R-style programming, TidierData.jl is implemented using macros.
TidierData.jl currently supports the following top-level macros:
@glimpse() and @head()@select() and @distinct()@rename() and @rename_with()@mutate() and @transmute()@summarize() and @summarise()@filter()@slice(), @slice_sample(), @slice_min(), @slice_max(), @slice_head(), and @slice_tail()@group_by() and @ungroup()@arrange()@relocate()@pull()@count() and @tally()@left_join(), @right_join(), @inner_join(), @full_join(), @anti_join(), and @semi_join()@bind_rows() and @bind_cols()@pivot_wider() and @pivot_longer()@separate(), @separate_rows(), and @unite()@drop_missing() and @fill_missing()@unnest_longer(), @unnest_wider(), and @nest()@clean_names() (as in R's janitor::clean_names() function)@summary() (as in R's summary() function)TidierData.jl also supports the following helper functions:
across()where()desc()if_else() and case_when()n() and row_number()ntile()lag() and lead()everything(), starts_with(), ends_with(), matches(), and contains()as_float(), as_integer(), and as_string()is_number(), is_float(), is_integer(), and is_string()missing_if() and replace_missing()See the documentation Home page for a guide on how to get started, or the Reference page for a detailed guide to each of the macros and functions.
Let's select the first five movies in our dataset whose budget exceeds the mean budget. Unlike in R, where we pass an na.rm = TRUE argument to remove missing values, in Julia we wrap the variable with a skipmissing() to remove the missing values before the mean() is calculated.
using TidierData
using RDatasets
movies = dataset("ggplot2", "movies");
@chain movies begin
@mutate(Budget = Budget / 1_000_000)
@filter(Budget >= mean(skipmissing(Budget)))
@select(Title, Budget)
@slice(1:5)
end
5×2 DataFrame
Row │ Title Budget
│ String Float64?
─────┼──────────────────────────────────────
1 │ 'Til There Was You 23.0
2 │ 10 Things I Hate About You 16.0
3 │ 102 Dalmatians 85.0
4 │ 13 Going On 30 37.0
5 │ 13th Warrior, The 85.0
See NEWS.md for the latest updates.
Is there a tidyverse feature missing that you would like to see in TidierData.jl? Please file a GitHub issue. Because TidierData.jl primarily wraps DataFrames.jl, our decision to integrate a new feature will be guided by how well-supported it is within DataFrames.jl and how likely other users are to benefit from it.
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