Chinese tidyr Cheat Sheet for Data Reshaping
A Chinese-language reference for tidyr reshaping, splitting and combining columns, missing values and post-cleaning checks.
What this Chinese reference covers
This Chinese-language sheet presents common tidyr structure operations, including long and wide tables, splitting and combining fields, missing-value handling and checks after reshaping. Its central question is not which shape is universally best, but what one row, one key and one measurement mean for the next task.
A long table often works well for grouping, plotting and repeated operations, while a wide table may fit a matrix-like report or interface. A one-to-many key can legitimately add rows after expansion, but an accidental duplicate join can produce the same symptom, so expected grain and row counts should be written down first.
A small-sample workflow
Define the row grain and key, inspect column types and missing-value meaning, choose one structural transformation, and compare row count, key uniqueness, missing distribution and a known aggregate after each step.
Maintenance note
This Chinese reference supports concept review but does not replace current package documentation or a business data definition. It does not infer whether a duplicate or missing value is acceptable; keep sensitive samples sanitized. Content review date: 2026-08-23.
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Chinese tidyr data-wrangling study guide
Define the row grain and key in Chinese notes, choose a single reshape on a small sample and compare structure and known aggregates before scaling up.
Before you start
- Know R data frames, filtering and grouping.
- Prepare a sanitized sample with duplicate keys, missing values and multi-value fields.
- Write the current and target table structure before selecting a function.
Quick start
- 01
Define what one row means
Record the object, time grain and primary key represented by each row, then identify whether the table is long or wide.
- 02
Align field types
Check names, dates, numbers and strings, distinguishing true missing values, empty strings and unknown values.
- 03
Apply one structural change
Choose pivoting, splitting, combining or nesting for the analysis goal, and record expected rows and keys.
- 04
Review the result
Compare row count, key uniqueness, missing-value distribution and known aggregates to catch silent duplication or loss.
Usage tips
- Draw the input and target structure before choosing a function; use the Chinese sheet as a concept index.
- Convert to long format for many grouped or plotted workflows, and use wide format only when the next interface needs it.
- Keep before-and-after summaries and the rule description so the transformation can be reviewed and rerun.
Troubleshooting and uninstall
Why did the row count increase after reshaping?
Check one-to-many keys, duplicate records and multi-value fields, then decide whether expansion is expected or whether aggregation is needed first.
Why did a summary change after handling missing values?
Separate structural missingness from random missingness, keep an imputation flag and compare grouped totals before and after the change.
Frequently asked questions
Who benefits from the Chinese cheat sheet?
It is useful for R learners and analysts who want a Chinese explanation of tidyr concepts, long-wide reshaping and missing-value handling.
Why define a primary key before reshaping?
Grain and key determine duplicates, output shape and aggregation, so defining them prevents the statistical unit from changing silently.
How should long and wide formats be chosen?
Long format often suits grouping, plotting and repeated operations, while wide format may suit matrix-style reports or interfaces; the next use determines the choice.