Aggregations for missing/imputed values

Source: R/aggr.R

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, ...)

Arguments

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".

Value

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.

Details

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.

Note

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.

References

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.

See also

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()

Author

Andreas Alfons, Matthias Templ, modifications for displaying imputed values by Bernd Prantner

Matthias Templ, modifications by Andreas Alfons and Bernd Prantner

Matthias Templ, modifications by Andreas Alfons

Andreas Alfons, modifications by Bernd Prantner

Examples


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