| as.data.frame,validatorComparison-method {validate} | R Documentation |
The performance of versions of a data set with regard to rule-based quality
requirements can be compared using using compare. The result is a
validatorComparison object, which can usefully be translated into a data
frame.
## S4 method for signature 'validatorComparison' as.data.frame(x, row.names = NULL, optional = FALSE, ...)
x |
Object to coerce |
row.names |
ignored |
optional |
ignored |
... |
arguments passed to other methods |
A data frame with the following columns.
status: Row names of the validatorComparison object.
version: Column names of the validatorComparison object.
count: Contents of the validatorComparison object.
Other comparing:
as.data.frame,cellComparison-method,
barplot,cellComparison-method,
barplot,validatorComparison-method,
cells(),
compare(),
match_cells(),
plot,cellComparison-method,
plot,validatorComparison-method
data(retailers) rules <- validator(turnover >=0, staff>=0, other.rev>=0) # start with raw data step0 <- retailers # impute turnovers step1 <- step0 step1$turnover[is.na(step1$turnover)] <- mean(step1$turnover,na.rm=TRUE) # flip sign of negative revenues step2 <- step1 step2$other.rev <- abs(step2$other.rev) # create an overview of differences, comparing to the previous step compare(rules, raw = step0, imputed = step1, flipped = step2, how="sequential") # create an overview of differences compared to raw data out <- compare(rules, raw = step0, imputed = step1, flipped = step2) out # graphical overview plot(out) barplot(out) # transform data to data.frame (easy for use with ggplot) as.data.frame(out)