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## ----echo=FALSE---------------------------------------------------------------
library(BiocStyle)
self <- Biocpkg("alabaster.base");
knitr::opts_chunk$set(error=FALSE, warning=FALSE, message=FALSE)
## -----------------------------------------------------------------------------
library(S4Vectors)
df <- DataFrame(X=1:10, Y=letters[1:10])
df
## -----------------------------------------------------------------------------
tmp <- tempfile()
library(alabaster.base)
saveObject(df, tmp)
## -----------------------------------------------------------------------------
readObject(tmp)
## -----------------------------------------------------------------------------
tmp <- tempfile()
saveObject(df, tmp)
list.files(tmp, recursive=TRUE)
## -----------------------------------------------------------------------------
readObject(tmp)
## -----------------------------------------------------------------------------
validateObject(tmp)
## -----------------------------------------------------------------------------
tmp <- tempfile()
saveObject(df, tmp)
tmp2 <- tempfile()
file.rename(tmp, tmp2)
readObject(tmp2)
## -----------------------------------------------------------------------------
# Creating a nested DF to be a little spicy:
df2 <- DataFrame(Z=factor(1:5), AA=I(DataFrame(B=runif(5), C=rnorm(5))))
tmp <- tempfile()
meta2 <- saveObject(df2, tmp)
# Now reading in the nested DF:
list.files(tmp, recursive=TRUE)
readObject(file.path(tmp, "other_columns/1"))
## -----------------------------------------------------------------------------
library(Matrix)
setMethod("saveObject", "dgTMatrix", function(x, path, ...) {
# Create a directory to stash our contents.
dir.create(path)
# Saving a DataFrame with the triplet data.
df <- DataFrame(i = x@i, j = x@j, x = x@x)
write.csv(df, file.path(path, "matrix.csv"), row.names=FALSE)
# Adding some more information.
write(dim(x), file=file.path(path, "dimensions.txt"), ncol=1)
# Creating an object file.
saveObjectFile(path, "triplet_sparse_matrix")
})
## -----------------------------------------------------------------------------
readSparseTripletMatrix <- function(path, metadata, ...) {
df <- read.table(file.path(path, "matrix.csv"), header=TRUE, sep=",")
dims <- readLines(file.path(path, "dimensions.txt"))
sparseMatrix(
i=df$i + 1L,
j=df$j + 1L,
x=df$x,
dims=as.integer(dims),
repr="T"
)
}
registerReadObjectFunction("triplet_sparse_matrix", readSparseTripletMatrix)
validateSparseTripletMatrix <- function(path, metadata) {
df <- read.table(file.path(path, "matrix.csv"), header=TRUE, sep=",")
dims <- as.integer(readLines(file.path(path, "dimensions.txt")))
stopifnot(is.integer(df$i), all(df$i >= 0 & df$i < dims[1]))
stopifnot(is.integer(df$j), all(df$j >= 0 & df$j < dims[2]))
stopifnot(is.numeric(df$x))
}
registerValidateObjectFunction("triplet_sparse_matrix", validateSparseTripletMatrix)
## -----------------------------------------------------------------------------
x <- sparseMatrix(
i=c(1,2,3,5,6),
j=c(3,6,1,3,8),
x=runif(5),
dims=c(10, 10),
repr="T"
)
x
tmp <- tempfile()
saveObject(x, tmp)
list.files(tmp, recursive=TRUE)
readObject(tmp)
## -----------------------------------------------------------------------------
setGeneric("appSaveObject", function(x, path, ...) {
ans <- standardGeneric("appSaveObject")
# File names with leading underscores are reserved for application-specific
# use, so they won't clash with anything produced by saveObject.
metapath <- file.path(path, "_metadata.json")
write(jsonlite::toJSON(ans, auto_unbox=TRUE), file=metapath)
})
setMethod("appSaveObject", "ANY", function(x, path, ...) {
saveObject(x, path, ...) # does the real work
list(authors=I(Sys.info()[["user"]])) # adds the desired metadata
})
# We can specialize the behavior for specific classes like DataFrames.
setMethod("appSaveObject", "DFrame", function(x, path, ...) {
ans <- callNextMethod()
ans$columns <- I(colnames(x))
ans
})
## -----------------------------------------------------------------------------
# Create a friendly user-visible function to handle the generic override; this
# is reversed on function exit to avoid interfering with other applications.
saveForApplication <- function(x, path, ...) {
old <- altSaveObjectFunction(appSaveObject)
on.exit(altSaveObjectFunction(old))
altSaveObject(x, path, ...)
}
# Saving our mocked up DataFrame with our overrides active.
df2 <- DataFrame(Z=factor(1:5), AA=I(DataFrame(B=runif(5), C=rnorm(5))))
tmp <- tempfile()
saveForApplication(df2, tmp)
# Both the parent and child DataFrames have new metadata.
cat(readLines(file.path(tmp, "_metadata.json")), sep="\n")
cat(readLines(file.path(tmp, "other_columns/1/_metadata.json")), sep="\n")
## -----------------------------------------------------------------------------
# Defining the override for altReadObject().
appReadObject <- function(path, metadata=NULL, ...) {
if (is.null(metadata)) {
metadata <- readObjectFile(path)
}
# Print custom message based on the type and application-specific metadata.
appmeta <- jsonlite::fromJSON(file.path(path, "_metadata.json"))
cat("I am a ", metadata$type, " created by ", appmeta$authors[1], ".\n", sep="")
if (metadata$type == "data_frame") {
all.cols <- paste(appmeta$columns, collapse=", ")
cat("I have the following columns: ", all.cols, ".\n", sep="")
}
readObject(path, metadata=metadata, ...)
}
# Creating a user-friendly function to set the override before the read.
readForApplication <- function(path, metadata=NULL, ...) {
old <- altReadObjectFunction(appReadObject)
on.exit(altReadObjectFunction(old))
altReadObject(path, metadata, ...)
}
# This diverts to the override with printing of custom messages.
readForApplication(tmp)
## -----------------------------------------------------------------------------
sessionInfo()
|
