Crowdsourcing ideas for rmacroRDM basic data quality check strategy

rmacroRDM-#10-Basic_data_quality_checks.md

rmacroRDM issue #10

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There are a number of areas in the package that various checks are performed but could do with being more strategic about it. Should link in with developing tests #14 .

Q: What do you consider the most important elements of ensuring quality of your data? eg:

check all that apply. add your own

• handling of missing values?
• checking data.types against expectations?
• handling of white.space and blank lines
• consistency of variable names throughout file.system
• consistency of species names throughout file.system
• identifying typos
• identifying outliers
• identifying duplicates

Q: What tools in r and at what stage of data processing have you found these to be most effective?

Please feel free to fork and share thoughts and ideas!

Aure Moser

Q: What do you consider the most important elements of ensuring quality of your data? eg:

check all that apply. add your own

• handling of missing values?
• checking data.types against expectations?
• handling of white.space and blank lines
• consistency of variable names throughout file.system
• consistency of species names throughout file.system
• identifying typos
• identifying outliers
• identifying duplicates

I'm not sure I did this right, some of the issues seem to be more related to data clean-up and prep before analysis (handling white space/blank lines, naming conventions/consistency, and some seem like they would be more valuable when analyzing (identifying outliers, duplicates, missing values, checking against expectation, etc.)

Q: What tools in r and at what stage of data processing have you found these to be most effective?

• rrefine: and open refine-inspired R utility