Sometimes we need to extract and analyse parts of a dataset (e.g. a vector, or the columns or rows of a data frame) along a moving (*aka* sliding, rolling, running) window. The *jumping.window* function, included in the newly released *DeadCanMove* package (Barbosa et al. 2014), extracts a **moving window with **(currently) **no overlap** between windows, and with the possibility of** a gap** (whose size is defined by the user) **between windows**:

jumping.window <- function(x, window.size, gap.size) {
window.indices <- integer(0)
for (i in 1 : window.size) {
window.indices <- c(window.indices, seq(from = i, to = length(x), by = window.size + gap.size))
}
window.indices <- sort(window.indices)
sampl.windows <- vector[window.indices]
return(sampl.windows)
} # end jumping.window function

[presented with Pretty R]

**Usage examples:**

Paste the whole text of the function above in R, press *enter*, and then paste the following commands to check out how the function works:

jumping.window(x = 1:20, window.size = 1, gap.size = 0)
jumping.window(x = 1:20, window.size = 1, gap.size = 1)
jumping.window(x = 6:52, window.size = 5, gap.size = 10)
jumping.window(x = c(1, 3, 4, 6, 9, 10, 11), window.size = 3, gap.size = 2)
jumping.window(x = c("air", "bird", "cloud", "deep", "elephant", "goat"), window.size = 2, gap.size = 2)
# to extract jumping-window columns from a table:
head(CO2)
CO2jump <- CO2[ , jumping.window(x = 1:ncol(CO2), window.size = 2, gap.size = 1)]
head(CO2jump)

**References:**

Barbosa A.M., Marques J.T., Santos S.M., Lourenço A., Medinas D., Beja P. & Mira A. (2014) DeadCanMove: Assess how spatial roadkill patterns change with temporal sampling scheme. R package version 0.1 (available at http://deadcanmove.r-forge.r-project.org)

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