The {portion} R package offers convenient
tools to extract data portions from common R objects:
works for vector, matrix,
data.frame, ts, 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 based on clustering via stats::kmeans():
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 a time series (class ts):
portion(ts(1:8, start = 2000), proportion = 0.5, how = "last")
#> Time Series:
#> Start = 2004
#> End = 2007
#> Frequency = 1
#> [1] 5 6 7 8
#> attr(,"indices")
#> [1] 5 6 7 8Portion each element of a list:
x <- list(1:5, diag(3), data.frame(a = 1:3, b = 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]]
#> a b
#> 2 2 3
#> 3 3 4