18 cleaning
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This document discusses data cleaning techniques. It introduces the concept of clean data being complete, correct, concise and compatible. Specific techniques discussed include identifying incorrect values that cannot be fixed, like removing rows, and those that can be fixed, like making data more concise by removing duplicate information. The document demonstrates converting data from wide to long format using melt() to make it more compatible for analysis and visualization. Overall, the document provides an overview of assessing and improving data quality through cleaning.
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18 cleaning
- 1. Garrett Grolemund Phd Student / Rice University Department of Statistics Data cleaning
- 2. 1. Intro to data cleaning 2. What you can’t fix 3. What you can fix 4. Intro to reshape
- 3. Your turn Do you think men or women leave a larger tip when dining out? What data would you collect to test this belief? What would prompt you to change your belief?
- 10. 10 - 20% of an analysis
- 12. Data cleaning
- 13. “Happy families are all alike; every unhappy family is unhappy in its own way.” —Leo Tolstoy
- 14. “Clean datasets are all alike; every messy dataset is messy in its own way.” —Hadley Wickham
- 15. Clean data is: Complete Correct (factual and internally consistent) Concise Compatible (required variables: observations in rows, one column per variable)
- 17. Complete Correct
- 18. Correct Can’t restore incorrect values without original data but can remove clearly incorrect values Options: Remove entire row Mark incorrect value as missing (NA)
- 19. When two rows present the same information with different values, at least one row is wrong. Whenever there is inconsistency, you are going to have to make some tradeoff to ensure concision. Detecting inconsistency is not always easy. Inconsistency = incorrect
- 20. General strategy To find incorrect values you need to be creative, combining graphics and data processing.
- 21. Tipping data One waiter recorded information about each tip he received over a period of a few months 244 records Do men or women tip more?
- 22. Your turn Subset the tipping data to include only rows without NA’s. Judge whether you think all of the data points are correct. How will you make your decision?
- 23. tips <- read.csv("tipping.csv", stringsAsFactors = FALSE) summary(tips) tips <- subset(tips, !is.na(smoker) & !is.na(non_smoker)) qplot(tip, data = tips, binwidth = .5) qplot(total_bill, data = tips, binwidth = 2) qplot(total_bill, tip, data = tips)
- 25. What you can fix:
- 26. Concise (each fact represented once) Repeating facts: 1. wastes memory 2. creates opportunities for inconsistency
- 27. Compatible (Data is compatible with your analysis in both form and fact) 1. Do you have the relevant variables for your analysis?
- 28. This often requires some type of calculation. For example, proportion = sucesses / attempts Avg score per game per team = ? join(), transform(), summarise(), ddply(), plyr address this need
- 29. Compatible (Data is compatible with your analysis in both form and fact) 2. Is the data in the right form for your analysis and visualization tools? (reshape)
- 30. Rectangular
- 32. Variables in columns (1 column per variable)
- 33. Your turn What are the variables in tipping.csv? How are they arranged in rows and columns? Can you form the variables into two groups?
- 34. Reshape
- 36. Molten data We can use melt to put each variable into its own column. “Protect” the good columns. “Melt” the offending columns. Then subset.
- 37. 1. ID variables - identify the object that measurements will take place on (we know these before the experiment) 2. Measured variables - the features of the object that will be measured (we have to do an experiment to observe these) Two types of variables
- 38. object ID Variables Bruce Wayne Batman SSN: 555-89-3000 Measured Var. Height (6’1’’) IQ (180) Age (71)
- 39. ID Variables Gotham City + male + Top 1% tax bracket
- 40. Identifier variable Measured variable Index of random variable Random variable Dimension Measure Experimental design Measurement predictors (Xi) response (Y)
- 41. Molten data Molten data collapses all the measured variables into two columns: 1) the variable being measured and 2) the value. Sometimes called “long” form. To protect a column from being melted, label it as an id variable. reshape::melt(data, id)
- 42. tips1 <- melt(tips, id = c("customer_ID", "total_bill", "tip", "smoker", "non_smoker")) # assign an appropriate variable name names(tips1)[6] <- "sex" # subset out unwanted rows tips1 <- subset(tips1, value == 1) tips1 <- tips1[ , c(1,2,6,4,5,3)]
- 43. Use melt to fix the smoking variable. One column should be enough to record whether a person smokes or not. Your turn
- 44. Rectangular data are much easier to work with! qplot(total_bill, tip, data = tips1, color = sex) # vs. qplot(total_bill, tip, data = tip, colour = ?)
- 45. qplot(total_bill, tip, data = tips1, color = sex) + geom_smooth(method = lm)
- 46. Clean data is: Complete Correct (factual and internally consistent) Concise Compatible (required variables: observations in rows, one column per variable)
- 47. Resource Wickham, H. (2007) Reshaping data with the reshape package. Journal of Statistical Software. 22 (12) http://www.jstatsoft.org/v21/i12
- 48. Summary
- 49. Clean data is: Rectangular (observations in rows, one column per variable) Consistent Concise Complete Correct
- 56. This work is licensed under the Creative Commons Attribution-Noncommercial 3.0 United States License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/ 3.0/us/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA.