as.data.frame,validatorComparison-method | 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)
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