18  Reproducible Data Correction with R

18.1 Download the data we will be using in class

1. Open the messy data file demo_data.csv by following this link
2. the data will open as a tab in your web browser in .csv format; save them to the data_raw folder by going to ‘File’ on the menu bar of your web browser and selecting ‘Save page as’ from the drop-down menu.
3. save the file to the data_raw folder.

18.2 Data Cleaning: Practice

1. Review the .csv file

2. What things do you see that need to be corrected?

3. Make a list of the what you think needs to be corrected and the steps necessary to identify and implement each correction. Some of the things to look out for include:

   • Numeric values stored as character data types
   • Factors stred as characters
   • Duplicate rows
   • Spelling mistakes
   • inconsistent formatting (eg., codes, capitalizations)
   • White spaces
   • Missing data
   • Zeros instead of null values
   • Special characters (e.g. commas in numeric values instead of decimals)
   • column headings with spaces between words or that start with numerals

4. Write R code to implement the changes you have identified

5. Save this code as las_practice.r and submit it via the Canvas website.

Grading Rubric:

Assignment completed with data validation correctly programmed with useful error messages: 35 Most data validation properly programmed; some require instructor follow-up: 25 Many of the validation parameters need corrections, error messages not useful: 15 Incorrect data are able to be entered in all categories; Instructor follow-up required: 10