Translate R function calls into database-specific SQL for dbplyr backends.
npx skills add https://github.com/tidyverse/dbplyr --skill sql-translation
Please help me install this Agent Skill. Skill: sql-translation Source: https://github.com/tidyverse/dbplyr/tree/main/.claude/skills/sql-translation Command: npx skills add https://github.com/tidyverse/dbplyr --skill sql-translation
This Skill automates the complex process of converting R functions into their correct SQL equivalents for various database backends. It ensures that data operations are consistent, efficient, and accurate when working with different databases through dbplyr.
dbplyr backend files (e.g., SQLite, PostgreSQL, MySQL), extending dbplyr's capabilities.str_to_upper() needs to work with a PostgreSQL database via dbplyr, this skill guides you through adding the UPPER() SQL translation to the R/backend-postgres.R file, including the necessary research and testing steps.Implement a new SQL translation for the R function my_custom_sum(x) to SUM(x) for the SQLite backend, ensuring it handles NULLs correctly.
High-intent search queries and answers about installing and using this skill.
R-to-SQL translation in dbplyr maps R functions to database-specific SQL equivalents using sql_translator to define scalar, aggregate, and window translations. Add translations to backend files (e.g., R/backend-postgres.R) to extend dbplyr's capabilities across SQLite, PostgreSQL, MySQL, and MSSQL, ensuring consistent data operations.
Define the translation using sql_translator with the appropriate function category (scalar, aggregate, or window), research the target database's SQL syntax, add it to the backend file, document the change, run interactive tests, and format code. This enables R functions like str_to_upper() to work correctly with your chosen database.
Yes. dbplyr supports custom SQL translations for SQLite, PostgreSQL, MySQL, and MSSQL through backend-specific files. Each backend has its own translation definitions, allowing you to add support for new R functions tailored to each database's SQL dialect and NULL-handling behavior.
Scalar, aggregate, and window functions can be translated. Scalar functions operate on individual values, aggregate functions summarize data (like SUM or COUNT), and window functions perform row-by-row calculations within partitions. Each requires specific translation syntax matching your target database.
SQL translations push computation to the database for efficiency and correctness. Without translations, dbplyr cannot execute R functions remotely; adding translations ensures operations run natively in SQL, reducing data transfer and leveraging database optimization for large datasets.
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