phplyr

Data & Workflow

Statistics, Modeling & Visualization

Data & Workflow · Tables, pipelines, and notebooks

phplyr

A PHP port of the Tidyverse's dplyr and tidyr.

Read the docs Try an example Source
Install 
composer require phpstats/phplyr
Requires
phibble (installed by Composer) PDO extension mbstring extension
Optional
phit extension (lm(), glm(), lmer(), and glmer(), and native descriptive statistics) phpolars extension (phibble's PolarsTable backend) intl extension (janitor-matching transliteration in clean_names() and make_clean_names()) PDO driver for SQLite, PostgreSQL, or MySQL (database tables through Phibble\tbl())
Platforms
Linux macOS

phplyr covers most of dplyr and tidyr. Each function returns a closure, so steps compose with the PHP 8.5 pipe operator.

The dplyr side includes the single-table functions, mutating and filtering joins, set operations, window functions such as lag(), lead(), and row_number(), and across() with tidyselect-style selectors.

The tidyr side includes pivot_longer(), pivot_wider(), separate and unite functions, nesting, and functions for completing and filling missing values.

Summary helpers such as n(), mean(), sd(), and quantile() follow R conventions for missing values and use phit when the extension is loaded. Model formulas such as monthly_rent ~ area_m2 + neighborhood will fit models through phit.

Example

$schools = DataFrame::fromCsv('schools.csv');

echo $schools |>
    filter(fn ($r) => $r->study_hours >= 5) |>
    group_by('school') |>
    summarize(students: n(), avg_score: mean('score'), sd_score: sd('score')) |>
    arrange(desc('avg_score')) |>
    head(5);
$schools = DataFrame::fromCsv('schools.csv');

echo phplyr($schools)
    ->filter(fn ($r) => $r->study_hours >= 5)
    ->groupBy('school')
    ->summarize(students: n(), avg_score: mean('score'), sd_score: sd('score'))
    ->arrange(desc('avg_score'))
    ->head(5);

Output · 5 × 4

  school    students avg_score sd_score
  <chr>        <int>     <dbl>    <dbl>
1 school-07       14      86.9     6.32
2 school-06       20      80.9     8.90
3 school-02       12      79.7     8.22
4 school-09       11      79.3     6.67
5 school-11       13      77.6     7.31

Run it live

All vignettes →
Porting an R analysis A dplyr and tidyr analysis of 250 students, translated line by line and run live, with the pass mark and study-time bands as inputs. One pipeline, three backends Join, summarize, and rank sales on phibble's DataFrame, on SQLite, and on Polars, with timings and a check that every backend returns the same rows. Reshaping with tidyr Summarize to one row per school and band, pivot the bands into columns, split the school id, and pivot back. Simulated rents Linear regression with a factor, compared with the coefficients that generated the data.