emilyriederer / dbtplyr Goto Github PK

This add-on package enhances dbt by providing macros which programmatically select columns based on their column names. It is inspired by the across() function and the select helpers in the R package dplyr.

dplyr (>= 1.0.0) has helpful semantics for selecting and applying transformations to variables based on their names. For example, if one wishes to take the sum of all variables with name prefixes of N and the mean of all variables with name prefixes of IND in the dataset mydata, they may write:

summarize(
  mydata, 
  across( starts_with('N'), sum),
  across( starts_with('IND', mean)
)

This package enables us to similarly write dbt data models with commands like:

{% set cols = dbtplyr.get_column_names( ref('mydata') ) %}
{% set cols_n = dbtplyr.starts_with('N', cols) %}
{% set cols_ind = dbtplyr.starts_with('IND', cols) %}

select

  {{ dbtplyr.across(cols_n, "sum({{var}}) as {{var}}_tot") }},
  {{ dbtplyr.across(cols_ind, "mean({{var}}) as {{var}}_avg") }}

from {{ ref('mydata') }}

which dbt then compiles to standard SQL.

Alternatively, to protect against cases where no column names matched the pattern provided (e.g. no variables start with n so cols_n is an empty list), one may instead internalize the final comma so that it is only compiled to SQL when relevant by using the final_comma parameter of across.

  {{ dbtplyr.across(cols_n, "sum({{var}}) as {{var}}_tot", final_comma = true) }}

Note that, slightly more dplyr-like, you may also write:

select

  {{ dbtplyr.across(dbtplyr.starts_with('N', ref('mydata')), "sum({{var}}) as {{var}}_tot") }},
  {{ dbtplyr.across(dbtplyr.starts_with('IND', ref('mydata')), "mean({{var}}) as {{var}}_avg") }}

from {{ ref('mydata') }}

But, as each function call is a bit longer than the equivalent dplyr code, I personally find the first form more readable.

The complete list of macros included are:

Functions to apply operation across columns

• across(var_list, script_string, final_comma)
• c_across(var_list, script_string)

Functions to evaluation condition across columns

• if_any(var_list, script_string)
• if_all(var_list, script_string)

Functions to subset columns by naming conventions

• starts_with(string, relation or list)
• ends_with(string, relation or list)
• contains(string, relation or list)
• not_contains(string, relation or list)
• one_of(string_list, relation or list)
• not_one_of(string_list, relation or list)
• matches(string, relation)
• everything(relation)
• where(fn, relation) where fn is the string name of a Column type-checker (e.g. "is_number")

Note that all of the select-helper functions that take a relation as an argument can optionally be passed a list of names instead.

Documentation for these functions is available on the package website and in the macros/macro.yml file.