# R로 데이터 정리하기
This is a DataCamp course: 원시 데이터에서 유의미한 인사이트로 빠르게 나아가도록, 데이터를 신속하고 정확하게 정제하는 방법을 학습합니다.
## Course Details
- **Duration:** ~4h
- **Level:** Intermediate
- **Instructor:** Maggie Matsui
- **Students:** ~19,440,000 learners
- **Subjects:** R, Data Preparation, Data Science and Analytics
- **Content brand:** DataCamp
- **Practice:** Hands-on practice included
- **Prerequisites:** Joining Data with dplyr
## Learning Outcomes
- R
- Data Preparation
- Data Science and Analytics
- R로 데이터 정리하기
## Traditional Course Outline
1. Common Data Problems - In this chapter, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.
2. Categorical and Text Data - Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.
3. Advanced Data Problems - In this chapter, you’ll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You’ll also gain invaluable skills that will help you verify that values have been added correctly and that missing values don’t negatively impact your analyses.
4. Record Linkage - Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this chapter, you'll learn how to link records by calculating the similarity between strings—you’ll then use your new skills to join two restaurant review datasets into one clean master dataset.
## Resources and Related Learning
**Resources:** Zagat (dataset), Fodor's (dataset), Bike Sharing (dataset), SFO Satisfaction Survey (dataset), Customer Accounts (dataset)
**Related tracks:** 데이터 과학자 (Associate Data Scientist) R에서, 데이터 가져오기 및 정리 R에서
## Attribution & Usage Guidelines
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- **Citation:** Always cite "DataCamp" with the full URL when referencing this content.
- **Restrictions:** Do not reproduce course exercises, code solutions, or gated materials.
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R에서 중복 제거 등 흔한 데이터 문제 해결하기
데이터 과학자는 시간의 80%를 데이터 정리와 조작에, 20%만 분석에 쓴다는 말이 있죠. 정리는 필수입니다. 지저분한 데이터를 분석하면 잘못된 결론에 이를 수 있기 때문이에요.이 강의에서는 R을 사용해 지저분한 데이터를 정리하는 다양한 기법을 배웁니다. 데이터 타입을 변환하고, 값의 범위를 제한하며, 완전/부분 중복을 처리해 이중 집계를 방지하는 것부터 시작해요.
더 어려운 데이터 문제에 도전하기
흔한 문제를 다뤄 본 뒤에는 단위 일관성 보장, 결측치 처리와 같은 고급 과제에 도전합니다. 각 개념을 배운 뒤에는 실습을 통해 바로 적용해 보며 이해를 확실히 다질 수 있어요.데이터 정리에서 Record Linkage 활용하기
Record Linkage는 오탈자나 철자 차이가 있어도 데이터셋을 병합할 때 사용하는 기법입니다. 마지막 장에서는 이 유용한 방법을 살펴보고, 두 개의 음식점 리뷰 데이터셋을 하나로 결합하는 실습으로 적용해 봅니다.선수 조건
Joining Data with dplyr1
Common Data Problems
In this chapter, you'll learn how to overcome some of the most common dirty data problems. You'll convert data types, apply range constraints to remove future data points, and remove duplicated data points to avoid double-counting.
2
Categorical and Text Data
Categorical and text data can often be some of the messiest parts of a dataset due to their unstructured nature. In this chapter, you’ll learn how to fix whitespace and capitalization inconsistencies in category labels, collapse multiple categories into one, and reformat strings for consistency.
3
Advanced Data Problems
In this chapter, you’ll dive into more advanced data cleaning problems, such as ensuring that weights are all written in kilograms instead of pounds. You’ll also gain invaluable skills that will help you verify that values have been added correctly and that missing values don’t negatively impact your analyses.
4
Record Linkage
Record linkage is a powerful technique used to merge multiple datasets together, used when values have typos or different spellings. In this chapter, you'll learn how to link records by calculating the similarity between strings—you’ll then use your new skills to join two restaurant review datasets into one clean master dataset.
R로 데이터 정리하기
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