R: Checklist for data checking I am new to data science and new to R. I see a lot of resources on data cleaning (i.e., dplyr, tidyr, tidyverse), but not so much on raw value verification. What are the "best practices" I should apply to verify the accuracy of the raw values or detect possible coding errors before cleaning? For example, I have been told you should ensure NAs are true NAs, or ensure there are no spaces in front of numerical or character values after you import data into R.

Thanks in advance.
UPDDATED with second resource

In general as I understand this is question about 'data quality'. There are lot of manuals, i like suggestions from this one - this is lets say semantic level:

SIX CORE DATA QUALITY DIMENSIONS
    COMPLETENESS 
    UNIQUENESS 
    TIMELINESS
    VALIDITY
    ACCURACY
    CONSISTENCY


This M&amp;E Attribute: Data Quality Assessment checklist is more detailed.

Then, of course, you should go deeper, to the syntax level.