inst/doc/magclass.R

In magclass: Data Class and Tools for Handling Spatial-Temporal Data

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knitr::opts_chunk$set(collapse = TRUE, comment = "#>")

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library(magclass)

# creating a magpie object with 2 regions, 2 years and 2 different values
m <- new.magpie(cells_and_regions = c("AFR", "CPA"),
                years = c(1995, 2000),
                names = c("bla", "blub"),
                sets = c("region", "year", "value"),
                fill = 0)
print(m)

# converting a simple vector with one value per region to a magpie object
v <- c(ENG = 10, USA = 20, BRA = 30, CHN = 40, IND = 50)
m2 <- as.magpie(v)
str(m2)

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m3 <- as.magpie(v, spatial = 0)
str(m3)

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pm <- maxample("pop")

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str(pm)

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head(pm)

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tail(pm)

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getItems(pm)

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getItems(pm, dim = 1)

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getItems(pm, dim = 3)

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getItems(pm, dim = "scenario")

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getSets(pm)

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getComment(pm)

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a <- maxample("animal")

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getItems(a)

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getItems(a, split = TRUE)

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getComment(pm) <- "This is a comment!"
getComment(pm)

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pm2 <- setComment(pm, "This is comment for pm2!")
getComment(pm2)

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getItems(pm, dim = 1)[1] <- "RRR"

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getSets(pm)[2] <- "year"

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mplot(pm)

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pm["LAM", c(2005, 2015), "A2"]

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pm["AS", , , pmatch = TRUE]

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mselect(pm, scenario = "B1", i = c("FSU", "LAM"))

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pm[list(i = c("FSU", "LAM")), , list(scenario = "B1")]

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d <- head(pm)

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d2 <- d^2 + 12 * d + 99 / exp(d)
getItems(d2, dim = 3) <- c("NEWSCEN1", "NEWSCEN2")
getSets(d2)[3] <- "newscen"
d2

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d <- d * d2
d

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d2 * d2

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dimSums(d, dim = 3)

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dimSums(d, dim = 3.2)

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dimSums(d, dim = "newscen")

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dimSums(d, dim = c(1, 3.1))

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lowpass(d)

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time_interpolate(d[, , 1], 2005:2030)

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d1 <- d[, 1:3, ] * 100
d2 <- d[, 4:6, ] * (-1)
dd <- mbind(d1, d2)
dd

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d[d > 0.5] <- 0.51
d

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round(d, 0)