validate (version 0.9.3)

barplot,validatorComparison-method: Barplot of validatorComparison object

Description

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. This method creates a stacked bar plot of the results. See also plot,validatorComparison-method for a line chart.

Usage

# S4 method for validatorComparison
barplot(
  height,
  las = 1,
  cex.axis = 0.8,
  cex.legend = cex.axis,
  wrap = TRUE,
  ...
)

Arguments

height

object of class validatorComparison

las

[numeric] in {0,1,2,3} determining axis label rotation

cex.axis

[numeric] Magnification with respect to the current setting of cex for axis annotation.

cex.legend

[numeric] Magnification with respect to the current setting of cex for legend annotation and title.

wrap

[logical] Toggle wrapping of x-axis labels when their width exceeds the width of the column.

...

Graphical parameters passed to barplot.default.

See Also

Other comparing: as.data.frame,cellComparison-method, as.data.frame,validatorComparison-method, barplot,cellComparison-method, cells(), compare(), match_cells(), plot,cellComparison-method, plot,validatorComparison-method

Examples

# NOT RUN {
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)


# }