R: Cell counts and differences for a series of datasets

cells {validate} | R Documentation

## Cell counts and differences for a series of datasets

### Description

Cell counts and differences for a series of datasets

### Usage

    cells(..., .list = NULL, compare = c("to_first", "sequential"))

### Arguments

`...` | For `cells`: data frames, comma separated. Names will become column names in the output. For `plot` or `barplot`: graphical parameters (see `par`).
`.list` | A `list` of data frames; will be concatenated with objects in `...`
`compare` | How to compare the datasets.

### Value

An object of class `cellComparison`, which is really an array with a few extra attributes. It counts the total number of cells, the number of missings, the number of altered values and changes therein as compared to the reference defined in `how`.

### Comparing datasets cell by cell

When comparing the contents of two data sets, the total number of cells in the current data set can be partitioned as in the following figure.

This function computes the partition for two or more datasets, comparing the current set to the first (default) or to the previous (by setting `compare='sequential'`).

### Details

This function assumes that the datasets have the same dimensions and that both rows and columns are ordered similarly.

### References

The figure is reproduced from MPJ van der Loo and E. De Jonge (2018) Statistical Data Cleaning with applications in R (John Wiley & Sons).

### See Also

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

### Examples

    data(retailers)

    # 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
    cells(raw = step0, imputed = step1, flipped = step2, compare="sequential")

    # create an overview of differences compared to raw data
    out <- cells(raw = step0, imputed = step1, flipped = step2)
    out

    # Graphical overview of the changes
    plot(out)
    barplot(out)

    # transform data to data.frame (easy for use with ggplot)
    as.data.frame(out)

[Package validate version 0.2.6 Index]