Description Usage Arguments Details Value Examples
View source: R/dataDiscretize.R
These functions discretize continuous input data into classes. Classes can be defined
by the user or, if the user provides the number of expected classes, calculated
from quantiles (default option) or by equal intervals.
dataDiscretize
processes a single variable at a time, provided as vector.
bulkDiscretize
discretizes multiple input rasters, by using parallel processing.
1 2 3 4  dataDiscretize(data, classBoundaries = NULL, classStates = NULL,
method = "quantile")
bulkDiscretize(formattedLst, xy, inparallel = FALSE)

data 
numeric vector. The continuous data to be discretized. 
classBoundaries 
numeric vector or single integer. Interval boundaries to be used for data discretization.
Outer values (minimum and maximum) required. 
classStates 
vector. The state labels to be assigned to the discretized data. 
method 
character. What splitting method should be used? This argument is ignored if
a vector of values is passed to

formattedLst 
A formatted list as returned by 
xy 
matrix. A matrix of spatial coordinates; first column is x (longitude), second column is y (latitude) of locations (in rows). 
inparallel 
logical or integer. Should the function use parallel processing facilities? Default is FALSE: a single process will be launched. If TRUE, all cores/processors but one will be used. Alternatively, an integer can be provided to dictate the number of cores/processors to be used. 
dataDiscretize
dataDiscretize
returns a named list of 4 vectors:
$discreteData
the discretized data, labels are applied accordingly if classStates
argument is provided
$classBoundaries
the class boundaries, i.e. values splitting the classes
$midValues
the mid point for each class (the mean of its lower and upper boundaries)
$classStates
the labels assigne to each class
bulkDataDiscretize
returns a matrix: in columns each node associated to input spatial data,
in rows their discretized values at coordinates specified by argument xy
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27  s < runif(30)
# Split by user defined values. Values out of boundaries are set to NA:
dataDiscretize(s, classBoundaries = c(0.2, 0.5, 0.8))
# Split by quantiles (default):
dataDiscretize(s, classStates = c('a', 'b', 'c'))
# Split by equal intervals:
dataDiscretize(s, classStates = c('a', 'b', 'c'), method = "equal")
# When Inf and Inf are provided as external boundaries, $midValues of outer classes
# are calculated on the minimum and maximum values:
dataDiscretize(s, classBoundaries=c(0, 0.5, 1), classStates=c("first", "second"))[c(2,3)]
dataDiscretize(s, classBoundaries=c(Inf, 0.5, Inf), classStates=c("first", "second"))[c(2,3)]
## Discretize multiple spatial data by location
data(ConwyData)
list2env(ConwyData, environment())
network < LandUseChange
spatialData < c(ConwyLU, ConwySlope, ConwyStatus)
# Link multiple spatial data to the network nodes and discretize
spDataLst < linkMultiple(spatialData, network, LUclasses, verbose = FALSE)
coord < aoi(ConwyLU, xy=TRUE)
head( bulkDiscretize(spDataLst, coord) )

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