Calculate or plot the amount of missing/imputed values in each variable and the amount of missing/imputed values in certain combinations of variables.

Print method for objects of class `"aggr"`

.

Summary method for objects of class `"aggr"`

.

Print method for objects of class `"summary.aggr"`

.

```
aggr(x, delimiter = NULL, plot = TRUE, ...)
# S3 method for class 'aggr'
plot(
x,
col = c("skyblue", "red", "orange"),
bars = TRUE,
numbers = FALSE,
prop = TRUE,
combined = FALSE,
varheight = FALSE,
only.miss = FALSE,
border = par("fg"),
sortVars = FALSE,
sortCombs = TRUE,
ylabs = NULL,
axes = TRUE,
labels = axes,
cex.lab = 1.2,
cex.axis = par("cex"),
cex.numbers = par("cex"),
gap = 4,
...
)
# S3 method for class 'aggr'
print(x, ..., digits = NULL)
# S3 method for class 'aggr'
summary(object, ...)
# S3 method for class 'summary.aggr'
print(x, ...)
```

- x
an object of class

`"summary.aggr"`

.- delimiter
a character-vector to distinguish between variables and imputation-indices for imputed variables (therefore,

`x`

needs to have`colnames()`

). If given, it is used to determine the corresponding imputation-index for any imputed variable (a logical-vector indicating which values of the variable have been imputed). If such imputation-indices are found, they are used for highlighting and the colors are adjusted according to the given colors for imputed variables (see`col`

).- plot
a logical indicating whether the results should be plotted (the default is

`TRUE`

).- ...
Further arguments, currently ignored.

- col
a vector of length three giving the colors to be used for observed, missing and imputed data. If only one color is supplied, it is used for missing and imputed data and observed data is transparent. If only two colors are supplied, the first one is used for observed data and the second color is used for missing and imputed data.

- bars
a logical indicating whether a small barplot for the frequencies of the different combinations should be drawn.

- numbers
a logical indicating whether the proportion or frequencies of the different combinations should be represented by numbers.

- prop
a logical indicating whether the proportion of missing/imputed values and combinations should be used rather than the total amount.

- combined
a logical indicating whether the two plots should be combined. If

`FALSE`

, a separate barplot on the left hand side shows the amount of missing/imputed values in each variable. If`TRUE`

, a small version of this barplot is drawn on top of the plot for the combinations of missing/imputed and non-missing values. See “Details” for more information.- varheight
a logical indicating whether the cell heights are given by the frequencies of occurrence of the corresponding combinations.

- only.miss
a logical indicating whether the small barplot for the frequencies of the combinations should only be drawn for combinations including missing/imputed values (if

`bars`

is`TRUE`

). This is useful if most observations are complete, in which case the corresponding bar would dominate the barplot such that the remaining bars are too compressed. The proportion or frequency of complete observations (as determined by`prop`

) is then represented by a number instead of a bar.- border
the color to be used for the border of the bars and rectangles. Use

`border=NA`

to omit borders.- sortVars
a logical indicating whether the variables should be sorted by the number of missing/imputed values.

- sortCombs
a logical indicating whether the combinations should be sorted by the frequency of occurrence.

- ylabs
if

`combined`

is`TRUE`

, a character string giving the y-axis label of the combined plot, otherwise a character vector of length two giving the y-axis labels for the two plots.- axes
a logical indicating whether axes should be drawn.

- labels
either a logical indicating whether labels should be plotted on the x-axis, or a character vector giving the labels.

- cex.lab
the character expansion factor to be used for the axis labels.

- cex.axis
the character expansion factor to be used for the axis annotation.

- cex.numbers
the character expansion factor to be used for the proportion or frequencies of the different combinations

- gap
if

`combined`

is`FALSE`

, a numeric value giving the distance between the two plots in margin lines.- digits
the minimum number of significant digits to be used (see

`print.default()`

).- object
an object of class

`"aggr"`

.

for `aggr`

, a list of class `"aggr"`

containing the
following components:

x the data used.

combinations a character vector representing the combinations of variables.

count the frequencies of these combinations.

percent the percentage of these combinations.

missings a

`data.frame`

containing the amount of missing/imputed values in each variable.tabcomb the indicator matrix for the combinations of variables.

a list of class `"summary.aggr"`

containing the following
components:

missings a

`data.frame`

containing the amount of missing or imputed values in each variable.combinations a

`data.frame`

containing a character vector representing the combinations of variables along with their frequencies and percentages.

Often it is of interest how many missing/imputed values are contained in each variable. Even more interesting, there may be certain combinations of variables with a high number of missing/imputed values.

If `combined`

is `FALSE`

, two separate plots are drawn for the
missing/imputed values in each variable and the combinations of
missing/imputed and non-missing values. The barplot on the left hand side
shows the amount of missing/imputed values in each variable. In the
*aggregation plot* on the right hand side, all existing combinations of
missing/imputed and non-missing values in the observations are visualized.
Available, missing and imputed data are color coded as given by `col`

.
Additionally, there are two possibilities to represent the frequencies of
occurrence of the different combinations. The first option is to visualize
the proportions or frequencies by a small bar plot and/or numbers. The
second option is to let the cell heights be given by the frequencies of the
corresponding combinations. Furthermore, variables may be sorted by the
number of missing/imputed values and combinations by the frequency of
occurrence to give more power to finding the structure of missing/imputed
values.

If `combined`

is `TRUE`

, a small version of the barplot showing
the amount of missing/imputed values in each variable is drawn on top of the
aggregation plot.

The graphical parameter `oma`

will be set unless supplied as an
argument.

Some of the argument names and positions have changed with version 1.3
due to extended functionality and for more consistency with other plot
functions in `VIM`

. For back compatibility, the arguments `labs`

and `names.arg`

can still be supplied to `...{}`

and are handled
correctly. Nevertheless, they are deprecated and no longer documented. Use
`ylabs`

and `labels`

instead.

M. Templ, A. Alfons, P. Filzmoser (2012) Exploring incomplete
data using visualization tools. *Journal of Advances in Data Analysis
and Classification*, Online first. DOI: 10.1007/s11634-011-0102-y.

`print.aggr()`

, `summary.aggr()`

`aggr()`

`print.summary.aggr()`

, `aggr()`

`summary.aggr()`

, `aggr()`

Other plotting functions:
`barMiss()`

,
`histMiss()`

,
`marginmatrix()`

,
`marginplot()`

,
`matrixplot()`

,
`mosaicMiss()`

,
`pairsVIM()`

,
`parcoordMiss()`

,
`pbox()`

,
`scattJitt()`

,
`scattMiss()`

,
`scattmatrixMiss()`

,
`spineMiss()`

```
data(sleep, package="VIM")
## for missing values
a <- aggr(sleep)
a
#>
#> Missings in variables:
#> Variable Count
#> NonD 14
#> Dream 12
#> Sleep 4
#> Span 4
#> Gest 4
summary(a)
#>
#> Missings per variable:
#> Variable Count
#> BodyWgt 0
#> BrainWgt 0
#> NonD 14
#> Dream 12
#> Sleep 4
#> Span 4
#> Gest 4
#> Pred 0
#> Exp 0
#> Danger 0
#>
#> Missings in combinations of variables:
#> Combinations Count Percent
#> 0:0:0:0:0:0:0:0:0:0 42 67.741935
#> 0:0:0:0:0:0:1:0:0:0 3 4.838710
#> 0:0:0:0:0:1:0:0:0:0 2 3.225806
#> 0:0:0:0:0:1:1:0:0:0 1 1.612903
#> 0:0:1:0:1:0:0:0:0:0 2 3.225806
#> 0:0:1:1:0:0:0:0:0:0 9 14.516129
#> 0:0:1:1:0:1:0:0:0:0 1 1.612903
#> 0:0:1:1:1:0:0:0:0:0 2 3.225806
## for imputed values
sleep_IMPUTED <- kNN(sleep)
a <- aggr(sleep_IMPUTED, delimiter="_imp")
a
#>
#> Imputed missings in variables:
#> Variable Count
#> NonD 14
#> Dream 12
#> Sleep 4
#> Span 4
#> Gest 4
summary(a)
#>
#> Imputed missings per variables:
#> Variable Count
#> BodyWgt 0
#> BrainWgt 0
#> NonD 14
#> Dream 12
#> Sleep 4
#> Span 4
#> Gest 4
#> Pred 0
#> Exp 0
#> Danger 0
#>
#> Imputed missings in combinations of variables:
#> Combinations Count Percent
#> 0:0:0:0:0:0:0:0:0:0 42 67.741935
#> 0:0:0:0:0:0:2:0:0:0 3 4.838710
#> 0:0:0:0:0:2:0:0:0:0 2 3.225806
#> 0:0:0:0:0:2:2:0:0:0 1 1.612903
#> 0:0:2:0:2:0:0:0:0:0 2 3.225806
#> 0:0:2:2:0:0:0:0:0:0 9 14.516129
#> 0:0:2:2:0:2:0:0:0:0 1 1.612903
#> 0:0:2:2:2:0:0:0:0:0 2 3.225806
data(sleep, package = "VIM")
a <- aggr(sleep, plot=FALSE)
a
#>
#> Missings in variables:
#> Variable Count
#> NonD 14
#> Dream 12
#> Sleep 4
#> Span 4
#> Gest 4
data(sleep, package = "VIM")
summary(aggr(sleep, plot=FALSE))
#>
#> Missings per variable:
#> Variable Count
#> BodyWgt 0
#> BrainWgt 0
#> NonD 14
#> Dream 12
#> Sleep 4
#> Span 4
#> Gest 4
#> Pred 0
#> Exp 0
#> Danger 0
#>
#> Missings in combinations of variables:
#> Combinations Count Percent
#> 0:0:0:0:0:0:0:0:0:0 42 67.741935
#> 0:0:0:0:0:0:1:0:0:0 3 4.838710
#> 0:0:0:0:0:1:0:0:0:0 2 3.225806
#> 0:0:0:0:0:1:1:0:0:0 1 1.612903
#> 0:0:1:0:1:0:0:0:0:0 2 3.225806
#> 0:0:1:1:0:0:0:0:0:0 9 14.516129
#> 0:0:1:1:0:1:0:0:0:0 1 1.612903
#> 0:0:1:1:1:0:0:0:0:0 2 3.225806
data(sleep, package = "VIM")
s <- summary(aggr(sleep, plot=FALSE))
s
#>
#> Missings per variable:
#> Variable Count
#> BodyWgt 0
#> BrainWgt 0
#> NonD 14
#> Dream 12
#> Sleep 4
#> Span 4
#> Gest 4
#> Pred 0
#> Exp 0
#> Danger 0
#>
#> Missings in combinations of variables:
#> Combinations Count Percent
#> 0:0:0:0:0:0:0:0:0:0 42 67.741935
#> 0:0:0:0:0:0:1:0:0:0 3 4.838710
#> 0:0:0:0:0:1:0:0:0:0 2 3.225806
#> 0:0:0:0:0:1:1:0:0:0 1 1.612903
#> 0:0:1:0:1:0:0:0:0:0 2 3.225806
#> 0:0:1:1:0:0:0:0:0:0 9 14.516129
#> 0:0:1:1:0:1:0:0:0:0 1 1.612903
#> 0:0:1:1:1:0:0:0:0:0 2 3.225806
```