prepInputs: Download and optionally post-process files

Description

maturing

Usage

prepInputs(
  targetFile = NULL,
  url = NULL,
  archive = NULL,
  alsoExtract = NULL,
  destinationPath = getOption("reproducible.destinationPath", "."),
  fun = NULL,
  quick = getOption("reproducible.quick"),
  overwrite = getOption("reproducible.overwrite", FALSE),
  purge = FALSE,
  useCache = getOption("reproducible.useCache", 2),
  .tempPath,
  verbose = getOption("reproducible.verbose", 1),
  ...
)

Arguments

targetFile: Character string giving the path to the eventual file (raster, shapefile, csv, etc.) after downloading and extracting from a zip or tar archive. This is the file before it is passed to postProcess. Currently, the internal checksumming does not checksum the file after it is postProcessed (e.g., cropped/reprojected/masked). Using Cache around prepInputs will do a sufficient job in these cases. See table in preProcess.
url: Optional character string indicating the URL to download from. If not specified, then no download will be attempted. If not entry exists in the CHECKSUMS.txt (in destinationPath), an entry will be created or appended to. This CHECKSUMS.txt entry will be used in subsequent calls to prepInputs or preProcess, comparing the file on hand with the ad hoc CHECKSUMS.txt. See table in preProcess.
archive: Optional character string giving the path of an archive containing targetFile, or a vector giving a set of nested archives (e.g., c("xxx.tar", "inner.zip", "inner.rar")). If there is/are (an) inner archive(s), but they are unknown, the function will try all until it finds the targetFile. See table in preProcess. If it is NA, then it will not attempt to see it as an archive, even if it has archive-like file extension (e.g., .zip). This may be useful when an R function is expecting an archive directly.
alsoExtract: Optional character string naming files other than targetFile that must be extracted from the archive. If NULL, the default, then it will extract all files. Other options: "similar" will extract all files with the same filename without file extension as targetFile. NA will extract nothing other than targetFile. A character string of specific file names will cause only those to be extracted. See table in preProcess.
destinationPath: Character string of a directory in which to download and save the file that comes from url and is also where the function will look for archive or targetFile. NOTE (still experimental): To prevent repeated downloads in different locations, the user can also set options("reproducible.inputPaths") to one or more local file paths to search for the file before attempting to download. Default for that option is NULL meaning do not search locally.
fun: Function, character string, or quoted call with which to load the targetFile or an object created by dlFun into an R object. See details and examples below.
quick: Logical. This is passed internally to Checksums (the quickCheck argument), and to Cache (the quick argument). This results in faster, though less robust checking of inputs. See the respective functions.
overwrite: Logical. Should downloading and all the other actions occur even if they pass the checksums or the files are all there.
purge: Logical or Integer. 0/FALSE (default) keeps existing CHECKSUMS.txt file and prepInputs will write or append to it. 1/TRUE will deleted the entire CHECKSUMS.txt file. Other options, see details.
useCache: Passed to Cache in various places. Defaults to getOption("reproducible.useCache", 2L) in prepInputs, and getOption("reproducible.useCache", FALSE) if calling any of the inner functions manually. For prepInputs, this mean it will use Cache only up to 2 nested levels, which will generally including postProcess and the first level of *Input functions, e.g., cropInputs, projectInputs, maskInputs, but not fixErrors.
.tempPath: Optional temporary path for internal file intermediate steps. Will be cleared on.exit from this function.
verbose: Numeric, -1 silent (where possible), 0 being very quiet, 1 showing more messaging, 2 being more messaging, etc. Default is 1. Above 3 will output much more information about the internals of Caching, which may help diagnose Caching challenges. Can set globally with an option, e.g., options('reproducible.verbose' = 0) to reduce to minimal
...: Additional arguments passed to fun (i.e,. user supplied), postProcess and Cache. Since ... is passed to postProcess, these will ... will also be passed into the inner functions, e.g., cropInputs. Possibly useful other arguments include dlFun which is passed to preProcess. See details and examples.

Stage 1 - Getting data

See preProcess for combinations of arguments.

Download from the web via either drive_download, download.file;
Extract from archive using unzip or untar;
Load into R using raster, shapefile, or any other function passed in with fun;
Checksumming of all files during this process. This is put into a CHECKSUMS.txt file in the destinationPath, appending if it is already there, overwriting the entries for same files if entries already exist.

Stage 2 - Post processing

This will be triggered if either rasterToMatch or studyArea is supplied.

Fix errors. Currently only errors fixed are for SpatialPolygons using buffer(..., width = 0);
Crop using cropInputs;
Project using projectInputs;
Mask using maskInputs;
Determine file name determineFilename via filename2;
Optionally, write that file name to disk via writeOutputs.

NOTE: checksumming does not occur during the post-processing stage, as there are no file downloads. To achieve fast results, wrap prepInputs with Cache.

NOTE: sf objects are still very experimental.

postProcessing of `Raster` and `Spatial` objects:

If rasterToMatch or studyArea are used, then this will trigger several subsequent functions, specifically the sequence, Crop, reproject, mask, which appears to be a common sequence in spatial simulation. See postProcess.spatialClasses.

Understanding various combinations of rasterToMatch and/or studyArea: Please see postProcess.spatialClasses.

<code>fun</code>

fun offers the ability to pass any custom function with which to load the object obtained by `preProcess` into the session. There are two cases that are dealt with: when the `preProcess` downloads a file (including via `dlFun`), `fun` must deal with a file; and, when `preProcess` creates an R object (e.g., raster::getData returns an object), `fun` must deal with an object.

fun can be supplied in three ways: a function, a character string (i.e., a function name as a string), of a quoted expression. If a character string or function, is should have the package name e.g., "raster::raster" or as an actual function, e.g., base::readRDS. In these cases, it will evaluate this function call while passing `targetFile` as the first argument. These will only work in the simplest of cases.

When more precision is required, the full call can be written, surrounded by quote, and where the object can be referred to as `targetFile` if the function is loading a file or as `x` if it is loading the object that was returned by `preProcess`. If `preProcess` returns an object, this must be used by `fun`; if `preProcess` is only getting a file, then there will be no object, so `targetFile` is the only option.

If there is a custom function call, is not in a package, `prepInputs` may not find it. In such cases, simply pass the function as a named argument (with same name as function) to `prepInputs`. See examples. NOTE: passing NA will skip loading object into R. Note this will essentially replicate the functionality of simply calling preProcess directly.

<code>purge</code>

In options for control of purging the CHECKSUMS.txt file are:

0: keep file
1: delete file
2: delete entry for targetFile
4: delete entry for alsoExtract
3: delete entry for archive
5: delete entry for targetFile & alsoExtract
6: delete entry for targetFile, alsoExtract & archive
7: delete entry that is failing (i.e., for the file downloaded by the url)

will only remove entries in the CHECKSUMS.txt that are associated with targetFile, alsoExtract or archive When prepInputs is called, it will write or append to a (if already exists) CHECKSUMS.txt file. If the CHECKSUMS.txt is not correct, use this argument to remove it.

Author

Eliot McIntire, Jean Marchal, and Tati Micheletti

Details

This function can be used to prepare R objects from remote or local data sources. The object of this function is to provide a reproducible version of a series of commonly used steps for getting, loading, and processing data. This function has two stages: Getting data (download, extracting from archives, loading into R) and post-processing (for Spatial* and Raster* objects, this is crop, reproject, mask/intersect). To trigger the first stage, provide url or archive. To trigger the second stage, provide studyArea or rasterToMatch. See examples.

Examples

Run this code

# This function works within a module; however, currently,
#   \cde{sourceURL} is not yet working as desired. Use \code{url}.
if (FALSE) {
# download a zip file from internet, unzip all files, load as shapefile, Cache the call
# First time: don't know all files - prepInputs will guess, if download file is an archive,
#   then extract all files, then if there is a .shp, it will load with raster::shapefile
dPath <- file.path(tempdir(), "ecozones")
shpEcozone <- prepInputs(destinationPath = dPath,
                         url = "http://sis.agr.gc.ca/cansis/nsdb/ecostrat/zone/ecozone_shp.zip")

# Robust to partial file deletions:
unlink(dir(dPath, full.names = TRUE)[1:3])
shpEcozone <- prepInputs(destinationPath = dPath,
                         url = "http://sis.agr.gc.ca/cansis/nsdb/ecostrat/zone/ecozone_shp.zip")
unlink(dPath, recursive = TRUE)

# Once this is done, can be more precise in operational code:
#  specify targetFile, alsoExtract, and fun, wrap with Cache
ecozoneFilename <- file.path(dPath, "ecozones.shp")
ecozoneFiles <- c("ecozones.dbf", "ecozones.prj",
                  "ecozones.sbn", "ecozones.sbx", "ecozones.shp", "ecozones.shx")
shpEcozone <- prepInputs(targetFile = ecozoneFilename,
                         url = "http://sis.agr.gc.ca/cansis/nsdb/ecostrat/zone/ecozone_shp.zip",
                         alsoExtract = ecozoneFiles,
                         fun = "shapefile", destinationPath = dPath)
unlink(dPath, recursive = TRUE)

#' # Add a study area to Crop and Mask to
# Create a "study area"
library(sp)
library(raster)
coords <- structure(c(-122.98, -116.1, -99.2, -106, -122.98, 59.9, 65.73, 63.58, 54.79, 59.9),
                    .Dim = c(5L, 2L))
Sr1 <- Polygon(coords)
Srs1 <- Polygons(list(Sr1), "s1")
StudyArea <- SpatialPolygons(list(Srs1), 1L)
crs(StudyArea) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"

#  specify targetFile, alsoExtract, and fun, wrap with Cache
ecozoneFilename <- file.path(dPath, "ecozones.shp")
# Note, you don't need to "alsoExtract" the archive... if the archive is not there, but the
#   targetFile is there, it will not redownload the archive.
ecozoneFiles <- c("ecozones.dbf", "ecozones.prj",
                  "ecozones.sbn", "ecozones.sbx", "ecozones.shp", "ecozones.shx")
shpEcozoneSm <- Cache(prepInputs,
                      url = "http://sis.agr.gc.ca/cansis/nsdb/ecostrat/zone/ecozone_shp.zip",
                      targetFile = reproducible::asPath(ecozoneFilename),
                      alsoExtract = reproducible::asPath(ecozoneFiles),
                      studyArea = StudyArea,
                      fun = "shapefile", destinationPath = dPath,
                      filename2 = "EcozoneFile.shp") # passed to determineFilename

plot(shpEcozone)
plot(shpEcozoneSm, add = TRUE, col = "red")
unlink(dPath)

# Big Raster, with crop and mask to Study Area - no reprojecting (lossy) of raster,
#   but the StudyArea does get reprojected, need to use rasterToMatch
dPath <- file.path(tempdir(), "LCC")
lcc2005Filename <- file.path(dPath, "LCC2005_V1_4a.tif")
url <- file.path("ftp://ftp.ccrs.nrcan.gc.ca/ad/NLCCLandCover",
                 "LandcoverCanada2005_250m/LandCoverOfCanada2005_V1_4.zip")

# messages received below may help for filling in more arguments in the subsequent call
LCC2005 <- prepInputs(url = url,
                      destinationPath = asPath(dPath),
                      studyArea = StudyArea)

plot(LCC2005)

# if wrapped with Cache, will be very fast second time (via memoised copy)
LCC2005 <- Cache(prepInputs, url = url,
                 targetFile = lcc2005Filename,
                 archive = asPath("LandCoverOfCanada2005_V1_4.zip"),
                 destinationPath = asPath(dPath),
                 studyArea = StudyArea)
# Using dlFun -- a custom download function -- passed to preProcess
test1 <- prepInputs(targetFile = "GADM_2.8_LUX_adm0.rds", # must specify currently
                    dlFun = "raster::getData", name = "GADM", country = "LUX", level = 0,
                    path = dPath)
}

# Using quoted dlFun and fun
if (FALSE) {
  prepInputs(..., fun = quote(customFun(x = targetFilePath)), customFun = customFun)
  # or more complex
  test5 <- prepInputs(
    targetFile = targetFileLuxRDS,
    dlFun = quote({
      getDataFn(name = "GADM", country = "LUX", level = 0) # preProcess keeps file from this!
    }),
    fun = quote({
      out <- readRDS(targetFilePath)
      out <- as(out, "SpatialPolygonsDataFrame")
      sf::st_as_sf(out)})
   )
}

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