Package 'y3628'

Read 'table' file with flexible file extension

Description

Read 'table' file with flexible file extension

Usage

flexTableReader(col_names, col_types, skip, ...)

Arguments

col_names	Column names, character vector
col_types	Column types, same as readr::read_csv(). See details.
skip	Number of lines to skip before reading data
...	These dots are for future extensions and must be empty.

Details

Depending on the actual file format provided by the user, column types may not be respected. For example, if the file is excel spreadsheet, then the column types provided is mapped to that supported by the readxl package.

Value

Reader function that accepts a file path

Examples

# TODO

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Get specific parameters for a table reader

Description

Get specific parameters for a table reader

Usage

getNormalizedParams(ext, ...)

Arguments

ext	File extension.
...	Parameters, see details.

Details

Currently the following parameters are supported: col_names, col_types, skip. For excel files, col_types is not supported yet.

Value

Named list of normalized parameters.

Examples

# Not exported

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Get filetype-specific table reader

Description

Get filetype-specific table reader

Usage

getTableReader(ext)

Arguments

ext	File extension.

Value

A suitable reader function.

Examples

# Not exported.

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Grouping of data frame without taking up the ellipsis

Description

Motivation: the original dplyr::group_by() uses ellipsis to allow flexibility in specifying grouping variables. However, sometimes a function might need the ellipsis for other purposes. In those cases, it is desirable to "save" the ellipsis by allowing the user to provide all grouping variables in a single parameter.

Usage

grouper(.data, var.group, ...)

Arguments

.data	Data frame to perform dplyr::group_by().
var.group	Quoted group variable(s). Multiple variables must be inside gvars(). To quote group variables use rlang::enexpr() in the caller function.
...	Must be empty.

Details

This function is not intended for end users.

Value

Grouped data frame.

Examples

# Correct use
f <- function(df, var.group){
    var.group <- rlang::enexpr(var.group)
    return(grouper(df, var.group))
}
# Single group variable
f(mtcars, cyl)
# Multiple group variable
f(mtcars, gvars(cyl,vs))

# Wrong use
f <- function(df, var.group){ return(grouper(df, var.group)) }
# f(mtcars, cyl) will error

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Customizable summary of data frame variables

Description

Customizable summary of data frame variables

Usage

groupThenSummarize(.data, var.group, .fns, ...)

Arguments

.data	a data frame (extension)
var.group	variables to group, either single name or gvars()
.fns	named list of summary functions
...	<tidy-select> Selection of variables to summarize on

Value

a data frame (extension) of summary

Examples

# Single group variable
groupThenSummarize(mtcars, cyl, list(m=mean, s=sd), disp:wt)
# Multiple group variable
groupThenSummarize(mtcars, gvars(cyl,vs), list(m=mean, s=sd), disp:wt)
#   cyl == 4 & vs == 0 group will have NA sd values.
#   This is because there is only one row in this group.
mtcars[mtcars$cyl == 4 & mtcars$vs == 0,]

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metaRcrd record-style vector

Description

This creates a vector where each item is a record of data and metadata fields. Data fields are used for record equality/comparison operations. Metadata fields are conceptually "attributes" attached to the data fields.

Usage

metaRcrd(fields, meta.fields, ...)

Arguments

fields	A named list or data.frame where each row is a record. Names of this list are the field names for the record vector.
meta.fields	A character vector giving fields that should be considered as "metadata" fields.
...	For future extensions. Must be empty.

Value

An S3 vector of class y3628_metaRcrd.

Note

Set operations must use vctrs methods (e.g., vctrs::vec_set_union()). Base set operations are not generic and hence invalid.

See Also

vctrs::new_rcrd()

Examples

require(vctrs)
require(tibble)

## Representing metadata of experimental samples

today <- Sys.Date()
dates <- c(today, today, today, today+1) # when
conds <- factor(c("L", "M", "H", "L"), levels = c("L", "M", "H")) # condition
who <- c("me", "me", "me", "Ahri") # personnel
eg1 <- metaRcrd(list(date=dates, condition=conds, personnel=who), "personnel")

dates <- c(today) # when
conds <- factor("L", levels = c("L", "M", "H")) # condition
who <- "Ahri" # personnel
eg2 <- metaRcrd(list(date=dates, condition=conds, personnel=who), "personnel")

### concatenate
eg_full <- vec_c(eg1, eg2) # c() works just fine too
eg_full

### equality/set operation
eg1 == eg2
vec_set_difference(eg1, eg2) # use vec_set_* methods

### comparison/sort
eg1 <= eg2
sort(eg_full)

### use in a tibble

proj <- c("fancy project 1", "ok project 2") # what project
rec <- list(eg1, eg2) # experiment record for each project
project_record <- tibble(project = proj, record = rec)
project_record

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Filtering metaRcrd objects

Description

Filtering metaRcrd objects

Usage

mR_filter(mRcrd, what)

Arguments

mRcrd	An S3 vector of class y3628_metaRcrd.
what	Filtering criteria. Quoted and evaluated to subset the record.

Value

Subsetted y3628_metaRcrd.

Examples

require(datasets)
my_mtcars <- datasets::mtcars[c("cyl", "hp", "mpg")]
my_mtcars <- metaRcrd(my_mtcars, meta.fields = "mpg")
mR_filter(my_mtcars, mpg > 20)

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Applying summary function to string splits

Description

Applying summary function to string splits

Usage

str_split_summary(string, pattern, summary)

Arguments

string	Input vector, passed to stringr::str_split().
pattern	Pattern to look for, passed to stringr::str_split().
summary	Summary function that will be applied to each split.

Value

Vector same length as input vector

Examples

t <- c("0.1,0.3,0.7", "NA", "0.2", "")
str_split_summary(t, ",", \(x) mean(as.numeric(x)))

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