Nothing
R/fetch.R
In fetch: Fetch Data from Various Data Sources
Defines functions
load_data
fetch
Documented in
fetch
#' @title Fetch data from many data sources
#' @encoding UTF-8
#' @description
#' The \strong{fetch} package allows you to retrieve data from many different
#' data sources. The package retrieves data in a memory-efficient manner.
#' You first identify the data by defining a data catalog. Then fetch
#' the data from the catalog. Catalogs can be defined for many popular
#' data formats: csv, rds, sas7bdat, excel, etc.
#'
#' The functions contained in the \strong{fetch} package are as follows:
#' \itemize{
#' \item{\code{\link{catalog}}: Creates a data library}
#' \item{\code{\link{fetch}}: Creates a data dictionary}
#' \item{\code{\link{import_spec}}: Defines an import spec for a specific dataset}
#' }
#' @name fetch
#' @aliases fetch-package
#' @keywords internal
"_PACKAGE"
#' @title Fetch a dataset from a data catalog
#' @encoding UTF-8
#' @description The \code{fetch} function retrieves a dataset from a data
#' catalog. The function accepts a catalog item as the first parameter. The
#' catalog item is the only required parameter. The "select" parameter allows
#' you to pull only some of the columns. The "where" and "top" parameters
#' may be used to define a subset of the data to retrieve. The "import_specs"
#' parameter accepts an \code{\link{import_spec}} object, which can be used
#' to control how data is read into the data frame.
#' @param catalog The catalog item to fetch data for. Catalog items
#' are created using the \code{\link{catalog}} function.
#' @param select A vector of column names or column numbers to extract from the
#' data item. Note that the column names can be easily obtained as a vector
#' from the catalog item, and then manipulated to suit your needs.
#' @param where An optional expression to be used to filter the fetched data.
#' Use the base R \code{\link{expression}} function to define the expression.
#' The expression allows logical operators and Base R functions. Column names
#' can be unquoted.
#' @param top A number of records to return from the head of the data item.
#' Valid value is an integer.
#' @param import_specs The import specs to use for the fetch operation. Import
#' specs can be used to control the data types of the fetched dataset.
#' An import specification is created with the \code{\link{import_spec}}
#' function. See the documentation of this function for additional details
#' and an example.
#' @return The desired dataset, returned as a tibble.
#' @seealso The \code{\link{catalog}} function to create a data catalog.
#' Also see the \code{\link{import_spec}} function to create import specifications.
#' @examples
#' # Get data directory
#' pkg <- system.file("extdata", package = "fetch")
#'
#' # Create catalog
#' ct <- catalog(pkg, engines$csv)
#'
#' # View catalog
#' ct
#' # data catalog: 6 items
#' # - Source: C:/packages/fetch/inst/extdata
#' # - Engine: csv
#' # - Items:
#' # data item 'ADAE': 56 cols 150 rows
#' # data item 'ADEX': 17 cols 348 rows
#' # data item 'ADPR': 37 cols 552 rows
#' # data item 'ADPSGA': 42 cols 695 rows
#' # data item 'ADSL': 56 cols 87 rows
#' # data item 'ADVS': 37 cols 3617 rows
#'
#' # Example 1: Fetch Entire Dataset
#'
#' # Get data from the catalog
#' dat1 <- fetch(ct$ADEX)
#'
#' # View Data
#' dat1
#' # A tibble: 348 × 17
#' # STUDYID USUBJID SUBJID SITEID TRTP TRTPN TRTA TRTAN RANDFL SAFFL
#' # <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr> <chr>
#' # 1 ABC ABC-01-0… 049 01 ARM D 4 ARM D 4 Y Y
#' # 2 ABC ABC-01-0… 049 01 ARM D 4 ARM D 4 Y Y
#' # 3 ABC ABC-01-0… 049 01 ARM D 4 ARM D 4 Y Y
#' # 4 ABC ABC-01-0… 049 01 ARM D 4 ARM D 4 Y Y
#' # 5 ABC ABC-01-0… 050 01 ARM B 2 ARM B 2 Y Y
#' # 6 ABC ABC-01-0… 050 01 ARM B 2 ARM B 2 Y Y
#' # 7 ABC ABC-01-0… 050 01 ARM B 2 ARM B 2 Y Y
#' # 8 ABC ABC-01-0… 050 01 ARM B 2 ARM B 2 Y Y
#' # 9 ABC ABC-01-0… 051 01 ARM A 1 ARM A 1 Y Y
#' # 10 ABC ABC-01-0… 051 01 ARM A 1 ARM A 1 Y Y
#' # 338 more rows
#' # 7 more variables: MITTFL <chr>, PPROTFL <chr>, PARAM <chr>,
#' # PARAMCD <chr>, PARAMN <dbl>, AVAL <dbl>, AVALCAT1 <chr>
#' # Use `print(n = ...)` to see more rows
#'
#' # Example 2: Fetch a Subset
#'
#' # Get data with selected columns and where expression
#' dat2 <- fetch(ct$ADEX, select = c("SUBJID", "TRTA", "RANDFL", "SAFFL"),
#' where = expression(SUBJID == '051'))
#'
#' # View Data
#' dat2
#' # A tibble: 4 x 4
#' # SUBJID TRTA RANDFL SAFFL
#' # <chr> <chr> <chr> <chr>
#' # 1 051 ARM A Y Y
#' # 2 051 ARM A Y Y
#' # 3 051 ARM A Y Y
#' # 4 051 ARM A Y Y
#'
#' @export
fetch <- function(catalog, select = NULL, where = NULL, top = NULL, import_specs = NULL) {
ret <- NULL
if ("dinfo" %in% class(catalog)) {
ret <- load_data(catalog, where, top, import_specs, select)
} else {
stop("Function requires a data catalog item as input.")
}
return(ret)
}
#' @import tibble
load_data <- function(dinfo, where = NULL, top = NULL, import_specs = NULL,
select = NULL) {
# Get the file list according to the engine type
if (!"dinfo" %in% class(dinfo))
stop("Class must by 'dinfo'")
dat <- NULL
eng <- attr(dinfo, "engine")
pth <- attr(dinfo, "path")
nm <- attr(dinfo, "name")
fl <- attr(dinfo, "where")
if (is.null(import_specs)) {
spc <- attr(dinfo, "import_specs")
if (!is.null(spc))
import_specs <- spc
}
# Combine filters if necessary
if (!is.null(fl)) {
if (!is.null(where)) {
where <- str2expression(paste(fl, "&",
as.character(where), collapse = ""))
} else {
where <- str2expression(fl)
}
}
if (eng == engines$csv) {
dat <- get_data_csv(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng == engines$rds) {
dat <- get_data_rds(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng %in% c(engines$rdata, engines$rda)) {
dat <- get_data_rda(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng == engines$sas7bdat) {
dat <- get_data_sas7bdat(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng == engines$dbf) {
dat <- get_data_dbf(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng == engines$xpt) {
dat <- get_data_xpt(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng == engines$xlsx) {
dat <- get_data_xlsx(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng == engines$xls) {
dat <- get_data_xls(pth, nm, where = where, top = top,
import_specs = import_specs)
}
# Deal with select parameter
if (!is.null(select)) {
nms <- names(dat)
if (typeof(select) == "character") {
snms <- select
if (all(snms %in% nms)) {
dat <- dat[ , snms]
} else {
rnms <- c()
for (nm in snms) {
if (!nm %in% nms)
rnms[length(rnms) + 1] <- nm
}
stop(paste0("Select parameter names not found in data: ",
paste(rnms, collapse = " ")))
}
} else if (typeof(select) %in% c("integer", "numeric")) {
snms <- nms[select]
if (any(is.na(snms))) {
pos <- select[is.na(snms)]
stop(paste0("Select parameter positions not found in data: ",
paste(pos, collapse = " ")))
} else {
dat <- dat[ , snms]
}
} else {
stop("Select parameter type invalid.")
}
}
return(dat)
}
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fetch documentation built on May 29, 2024, 4:37 a.m.
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Defines functions load_data fetch
Documented in fetch
#' @title Fetch data from many data sources
#' @encoding UTF-8
#' @description
#' The \strong{fetch} package allows you to retrieve data from many different
#' data sources. The package retrieves data in a memory-efficient manner.
#' You first identify the data by defining a data catalog. Then fetch
#' the data from the catalog. Catalogs can be defined for many popular
#' data formats: csv, rds, sas7bdat, excel, etc.
#'
#' The functions contained in the \strong{fetch} package are as follows:
#' \itemize{
#' \item{\code{\link{catalog}}: Creates a data library}
#' \item{\code{\link{fetch}}: Creates a data dictionary}
#' \item{\code{\link{import_spec}}: Defines an import spec for a specific dataset}
#' }
#' @name fetch
#' @aliases fetch-package
#' @keywords internal
"_PACKAGE"
#' @title Fetch a dataset from a data catalog
#' @encoding UTF-8
#' @description The \code{fetch} function retrieves a dataset from a data
#' catalog. The function accepts a catalog item as the first parameter. The
#' catalog item is the only required parameter. The "select" parameter allows
#' you to pull only some of the columns. The "where" and "top" parameters
#' may be used to define a subset of the data to retrieve. The "import_specs"
#' parameter accepts an \code{\link{import_spec}} object, which can be used
#' to control how data is read into the data frame.
#' @param catalog The catalog item to fetch data for. Catalog items
#' are created using the \code{\link{catalog}} function.
#' @param select A vector of column names or column numbers to extract from the
#' data item. Note that the column names can be easily obtained as a vector
#' from the catalog item, and then manipulated to suit your needs.
#' @param where An optional expression to be used to filter the fetched data.
#' Use the base R \code{\link{expression}} function to define the expression.
#' The expression allows logical operators and Base R functions. Column names
#' can be unquoted.
#' @param top A number of records to return from the head of the data item.
#' Valid value is an integer.
#' @param import_specs The import specs to use for the fetch operation. Import
#' specs can be used to control the data types of the fetched dataset.
#' An import specification is created with the \code{\link{import_spec}}
#' function. See the documentation of this function for additional details
#' and an example.
#' @return The desired dataset, returned as a tibble.
#' @seealso The \code{\link{catalog}} function to create a data catalog.
#' Also see the \code{\link{import_spec}} function to create import specifications.
#' @examples
#' # Get data directory
#' pkg <- system.file("extdata", package = "fetch")
#'
#' # Create catalog
#' ct <- catalog(pkg, engines$csv)
#'
#' # View catalog
#' ct
#' # data catalog: 6 items
#' # - Source: C:/packages/fetch/inst/extdata
#' # - Engine: csv
#' # - Items:
#' # data item 'ADAE': 56 cols 150 rows
#' # data item 'ADEX': 17 cols 348 rows
#' # data item 'ADPR': 37 cols 552 rows
#' # data item 'ADPSGA': 42 cols 695 rows
#' # data item 'ADSL': 56 cols 87 rows
#' # data item 'ADVS': 37 cols 3617 rows
#'
#' # Example 1: Fetch Entire Dataset
#'
#' # Get data from the catalog
#' dat1 <- fetch(ct$ADEX)
#'
#' # View Data
#' dat1
#' # A tibble: 348 × 17
#' # STUDYID USUBJID SUBJID SITEID TRTP TRTPN TRTA TRTAN RANDFL SAFFL
#' # <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <dbl> <chr> <chr>
#' # 1 ABC ABC-01-0… 049 01 ARM D 4 ARM D 4 Y Y
#' # 2 ABC ABC-01-0… 049 01 ARM D 4 ARM D 4 Y Y
#' # 3 ABC ABC-01-0… 049 01 ARM D 4 ARM D 4 Y Y
#' # 4 ABC ABC-01-0… 049 01 ARM D 4 ARM D 4 Y Y
#' # 5 ABC ABC-01-0… 050 01 ARM B 2 ARM B 2 Y Y
#' # 6 ABC ABC-01-0… 050 01 ARM B 2 ARM B 2 Y Y
#' # 7 ABC ABC-01-0… 050 01 ARM B 2 ARM B 2 Y Y
#' # 8 ABC ABC-01-0… 050 01 ARM B 2 ARM B 2 Y Y
#' # 9 ABC ABC-01-0… 051 01 ARM A 1 ARM A 1 Y Y
#' # 10 ABC ABC-01-0… 051 01 ARM A 1 ARM A 1 Y Y
#' # 338 more rows
#' # 7 more variables: MITTFL <chr>, PPROTFL <chr>, PARAM <chr>,
#' # PARAMCD <chr>, PARAMN <dbl>, AVAL <dbl>, AVALCAT1 <chr>
#' # Use `print(n = ...)` to see more rows
#'
#' # Example 2: Fetch a Subset
#'
#' # Get data with selected columns and where expression
#' dat2 <- fetch(ct$ADEX, select = c("SUBJID", "TRTA", "RANDFL", "SAFFL"),
#' where = expression(SUBJID == '051'))
#'
#' # View Data
#' dat2
#' # A tibble: 4 x 4
#' # SUBJID TRTA RANDFL SAFFL
#' # <chr> <chr> <chr> <chr>
#' # 1 051 ARM A Y Y
#' # 2 051 ARM A Y Y
#' # 3 051 ARM A Y Y
#' # 4 051 ARM A Y Y
#'
#' @export
fetch <- function(catalog, select = NULL, where = NULL, top = NULL, import_specs = NULL) {
ret <- NULL
if ("dinfo" %in% class(catalog)) {
ret <- load_data(catalog, where, top, import_specs, select)
} else {
stop("Function requires a data catalog item as input.")
}
return(ret)
}
#' @import tibble
load_data <- function(dinfo, where = NULL, top = NULL, import_specs = NULL,
select = NULL) {
# Get the file list according to the engine type
if (!"dinfo" %in% class(dinfo))
stop("Class must by 'dinfo'")
dat <- NULL
eng <- attr(dinfo, "engine")
pth <- attr(dinfo, "path")
nm <- attr(dinfo, "name")
fl <- attr(dinfo, "where")
if (is.null(import_specs)) {
spc <- attr(dinfo, "import_specs")
if (!is.null(spc))
import_specs <- spc
}
# Combine filters if necessary
if (!is.null(fl)) {
if (!is.null(where)) {
where <- str2expression(paste(fl, "&",
as.character(where), collapse = ""))
} else {
where <- str2expression(fl)
}
}
if (eng == engines$csv) {
dat <- get_data_csv(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng == engines$rds) {
dat <- get_data_rds(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng %in% c(engines$rdata, engines$rda)) {
dat <- get_data_rda(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng == engines$sas7bdat) {
dat <- get_data_sas7bdat(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng == engines$dbf) {
dat <- get_data_dbf(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng == engines$xpt) {
dat <- get_data_xpt(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng == engines$xlsx) {
dat <- get_data_xlsx(pth, nm, where = where, top = top,
import_specs = import_specs)
} else if (eng == engines$xls) {
dat <- get_data_xls(pth, nm, where = where, top = top,
import_specs = import_specs)
}
# Deal with select parameter
if (!is.null(select)) {
nms <- names(dat)
if (typeof(select) == "character") {
snms <- select
if (all(snms %in% nms)) {
dat <- dat[ , snms]
} else {
rnms <- c()
for (nm in snms) {
if (!nm %in% nms)
rnms[length(rnms) + 1] <- nm
}
stop(paste0("Select parameter names not found in data: ",
paste(rnms, collapse = " ")))
}
} else if (typeof(select) %in% c("integer", "numeric")) {
snms <- nms[select]
if (any(is.na(snms))) {
pos <- select[is.na(snms)]
stop(paste0("Select parameter positions not found in data: ",
paste(pos, collapse = " ")))
} else {
dat <- dat[ , snms]
}
} else {
stop("Select parameter type invalid.")
}
}
return(dat)
}
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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