How to clean data in R
Here, this involves various steps, as from the initial raw data have to move toward the consistent and highly efficient data which is ready to be implemented as per the requirements and produces highly precise and accurate statistical results. The steps vary from data to data in this case the user should be aware of the date he/she is using for the results. As there are many characteristics and common symptoms of messy data which totally depend on the data used by the user for analysis.
Characteristics of clean data include data are:
- Free of duplicate rows/values
- Error-free (misspellings free )
- Relevant (special characters free )
- The appropriate data type for analysis
- Free of outliers (or only contain outliers that have been identified/understood)
- Follows a “tidy data” structure
Common symptoms of messy data:
- Special characters (e.g. commas in numeric values)
- Numeric values stored as text/character data types
- Duplicate rows
- Misspellings
- Inaccuracies
- White space
- Missing data
- Zeros instead of null values vary.
Data Cleaning in R
In this article, we will briefly be going through Data cleaning with its application and its technique for implementation in the R programming language.
Contact Us