Joining Data

Overview

The join block combines two datasets by matching rows on shared columns, the blockr equivalent of SQL joins or dplyr::left_join().

How it works

A join block has two input ports (unlike most blocks which have one). Connect one dataset to each port, then configure:

1. Join columns: which columns to match on. If the column names differ between datasets, you can map them explicitly.
2. Join type: how to handle non-matching rows:
   • Left join: keep all rows from the left dataset, fill NA for non-matches from the right
   • Right join: keep all rows from the right dataset
   • Inner join: keep only rows that match in both datasets
   • Full join: keep all rows from both datasets

When to use joins

Joins are essential when your data lives in multiple tables. Common scenarios:

• Enriching a transaction table with customer details from a lookup table
• Combining measurements from different instruments on a shared sample ID
• Merging survey responses with demographic data

The bind rows and bind columns blocks handle simpler cases where you just need to stack datasets vertically or horizontally.

Next steps

• Visualising data: chart your joined results
• Block reference: Wrangling: full reference for all wrangling blocks