Growing dot map with information about missing/imputed values

Source: R/growdotMiss.R

Map with dots whose sizes correspond to the values in a certain variable. Observations with missing/imputed values in additional variables are highlighted.

growdotMiss(
  x,
  coords,
  map,
  pos = 1,
  delimiter = NULL,
  selection = c("any", "all"),
  log = FALSE,
  col = c("skyblue", "red", "skyblue4", "red4", "orange", "orange4"),
  border = par("bg"),
  alpha = NULL,
  scale = NULL,
  size = NULL,
  exp = c(0, 0.95, 0.05),
  col.map = grey(0.5),
  legend = TRUE,
  legtitle = "Legend",
  cex.legtitle = par("cex"),
  cex.legtext = par("cex"),
  ncircles = 6,
  ndigits = 1,
  interactive = TRUE,
  ...
)

Arguments

x

a vector, matrix or data.frame.

coords

a matrix or data.frame with two columns giving the spatial coordinates of the observations.

map

a background map to be passed to bgmap().

pos

a numeric value giving the index of the variable determining the dot sizes.

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).

selection

the selection method for highlighting missing/imputed values in multiple additional variables. Possible values are "any" (highlighting of missing/imputed values in any of the additional variables) and "all" (highlighting of missing/imputed values in all of the additional variables).

log

a logical indicating whether the variable given by pos should be log-transformed.

col

a vector of length six giving the colors to be used in the plot. If only one color is supplied, it is used for the borders of non-highlighted dots and the surface area of highlighted dots. Else if two colors are supplied, they are recycled.

border

a vector of length four giving the colors to be used for the borders of the growing dots. Use NA to omit borders.

alpha

a numeric value between 0 and 1 giving the level of transparency of the colors, or NULL. This can be used to prevent overplotting.

scale

scaling factor of the map.

size

a vector of length two giving the sizes for the smallest and largest dots.

exp

a vector of length three giving the factors that define the shape of the exponential function (see ‘Details’).

col.map

the color to be used for the background map.

legend

a logical indicating whether a legend should be plotted.

legtitle

the title for the legend.

cex.legtitle

the character expansion factor to be used for the title of the legend.

cex.legtext

the character expansion factor to be used in the legend.

ncircles

the number of circles displayed in the legend.

ndigits

the number of digits displayed in the legend. Note that \ this is just a suggestion (see format()).

interactive

a logical indicating whether information about certain observations can be displayed interactively (see ‘Details’).

...

for growdotMiss, further arguments and graphical parameters to be passed to bgmap(). For bubbleMiss, the arguments to be passed to growdotMiss.

Details

The smallest dots correspond to the 10\ the 99\ defining the shape of the exponential function. Missings/imputed missings in the variable of interest will be drawn as rectangles.

If interactive=TRUE, detailed information for an observation can be printed on the console by clicking on the corresponding point. Clicking in a region that does not contain any points quits the interactive session.

Note

The function was renamed to growdotMiss in version 1.3. bubbleMiss is a (deprecated) wrapper for growdotMiss for back compatibility with older versions. However, due to extended functionality, some of the argument positions have changed.

The code is based on (removed from CRAN) bubbleFIN from package StatDA.

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.

Author

Andreas Alfons, Matthias Templ, Peter Filzmoser, Bernd Prantner

Examples


data(chorizonDL, package = "VIM")
data(kola.background, package = "VIM")
coo <- chorizonDL[, c("XCOO", "YCOO")]
## for missing values
x <- chorizonDL[, c("Ca","As", "Bi")]
growdotMiss(x, coo, kola.background, border = "white")

#> 
#> Click on a point to get more information.
#> To regain use of the VIM GUI and the R console, click in a region that does not contain any points.
#> 

## for imputed values
x_imp <- kNN(chorizonDL[,c("Ca","As","Bi" )])
growdotMiss(x_imp, coo, kola.background, delimiter = "_imp", border = "white")

#> 
#> Click on a point to get more information.
#> To regain use of the VIM GUI and the R console, click in a region that does not contain any points.
#>