R Data Transformation Cheat Sheet for Filtering and Joins
A Chinese R reference for selecting, filtering, deriving, grouping, summarizing, sorting and joining data while preserving a known data grain.
What this reference covers
This reference follows the path from an imported table to an analysis-ready table: select columns, filter rows, create derived values, sort, group, summarize and join. The key question is what each operation does to data grain. Code that runs successfully can still duplicate records or change a denominator.
Declare grain and keys
Write whether one row represents a person, event, order, day or group before transforming. Record candidate keys, required fields and expected row count. Before a join, count duplicate keys in both tables; after it, compare rows, unmatched records, ranges and important totals.
Separate missing states
Unknown, not applicable, unmatched and true zero are not interchangeable. Choose filter, imputation, preservation or aggregation based on the field meaning, and keep the decision in the script and project notes.
Maintenance note
This sheet is an operation index, not a substitute for business definitions, data lineage or statistical design. Content review date: 2026-08-23.
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R data transformation study guide
State the target table grain, apply short named transformations, verify keys and counts after joins or summaries, and preserve missing-value decisions for later review.
Before you start
- Prepare a PDF reader and a small data frame with a candidate key and a missing-value example.
- Record input rows, columns, key candidates and desired output grain.
- Create an analysis log for transformation rules and checks.
Quick start
- 01
Define the target table
State whether one row represents a record or a group, then list fields, filters and final grain.
- 02
Apply basic transformations
Select, filter, sort and derive columns in named stages, using units and meanings in new-column names.
- 03
Verify before grouping or joining
Check grouping fields and key uniqueness, because many-to-many joins can silently multiply rows.
- 04
Save verification checkpoints
Record rows, columns, duplicate keys, missing values and ranges after each major step and preserve a reproducible script.
Usage tips
- Short named stages make an unexpected result easier to trace.
- Count duplicate keys in each table before a join and compare row counts afterward.
- Distinguish unknown, not applicable, unmatched and true zero before filtering or imputing.
Troubleshooting and uninstall
Why did row count increase after a join?
Inspect key duplicates and join type in both tables, compare the relationship with the business rule and aggregate to a unique key when appropriate.
Why did a summary contain unexpected missing values?
Trace whether each missing value came from the source, an unmatched join or a calculation, then choose a field-specific rule instead of replacing all with zero.
Frequently asked questions
When is this R transformation sheet most useful?
It is useful after import when a raw table must become an analysis or reporting table through filtering, derivation, grouping and joins.
Why verify key uniqueness before a join?
Repeated keys can create one-to-many or many-to-many matches, increasing rows and repeating calculations without an obvious error.
Should every missing value become zero?
No. Unknown, not applicable, unmatched and true zero have different meanings and require field-specific handling.
How can transformations remain reproducible?
Write every stage in a script, freeze input and parameters, and record rows, columns, keys and key metrics after filtering, joining and summarizing.