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The {portion} R package offers convenient
tools to extract data portions from common R objects:
works for vector, matrix,
data.frame, and list objects
the relative portion size can be selected
allows extracting first, last, random, similar, or dissimilar data
can portion row- and column-wise
provides selected indices as an attribute
preserves object attributes
You can install the released version from CRAN with:
install.packages("portion")Portion a vector by selecting similar or dissimilar
values:
set.seed(1)
x <- c(1, 1, 2, 2)
portion(x, proportion = 0.5, how = "similar")
#> [1] 1 1
#> attr(,"indices")
#> [1] 1 2
portion(x, proportion = 0.5, how = "dissimilar")
#> [1] 1 2
#> attr(,"indices")
#> [1] 1 3Portion a matrix row-wise or column-wise:
x <- matrix(LETTERS[1:24], nrow = 4)
portion(x, proportion = 0.5, how = "first")
#> [,1] [,2] [,3] [,4] [,5] [,6]
#> [1,] "A" "E" "I" "M" "Q" "U"
#> [2,] "B" "F" "J" "N" "R" "V"
#> attr(,"indices")
#> [1] 1 2
portion(x, proportion = 0.5, how = "first", byrow = FALSE)
#> [,1] [,2] [,3]
#> [1,] "A" "E" "I"
#> [2,] "B" "F" "J"
#> [3,] "C" "G" "K"
#> [4,] "D" "H" "L"
#> attr(,"indices")
#> [1] 1 2 3Portion a data.frame at random. The selected row or
column indices are stored in the "indices" attribute.
set.seed(1)
x <- as.data.frame(diag(8))
portion(x, proportion = 0.3, how = "random")
#> V1 V2 V3 V4 V5 V6 V7 V8
#> 1 1 0 0 0 0 0 0 0
#> 4 0 0 0 1 0 0 0 0
#> 8 0 0 0 0 0 0 0 1
portion(x, proportion = 0.3, how = "random", byrow = FALSE)
#> V2 V3 V5
#> 1 0 0 0
#> 2 1 0 0
#> 3 0 1 0
#> 4 0 0 0
#> 5 0 0 1
#> 6 0 0 0
#> 7 0 0 0
#> 8 0 0 0For clustering data frame rows, all non-ignored columns must be
numeric or logical. Use ignore to exclude identifiers,
labels, or grouping columns from the clustering data while keeping them
in the returned object.
x <- data.frame(value = c(1, 1, 5, 5), group = c("a", "a", "b", "b"))
portion(x, proportion = 0.5, how = "similar", ignore = 2)
#> value group
#> 1 1 a
#> 2 1 aPortion each element of a list:
x <- list(1:5, diag(3), data.frame(1:3, 2:4))
portion(x, proportion = 0.5, how = "last")
#> [[1]]
#> [1] 3 4 5
#> attr(,"indices")
#> [1] 3 4 5
#>
#> [[2]]
#> [,1] [,2] [,3]
#> [1,] 0 1 0
#> [2,] 0 0 1
#> attr(,"indices")
#> [1] 2 3
#>
#> [[3]]
#> X1.3 X2.4
#> 2 2 3
#> 3 3 4These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.