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Type: Package
Title: Statistical Tests for the Production of Reference Materials
Version: 0.8.5
Date: 2025-03-28
Maintainer: Jan Lisec <jan.lisec@bam.de>
Description: The production of certified reference materials (CRMs) requires various statistical tests depending on the task and recorded data to ensure that reported values of CRMs are appropriate. Often these tests are performed according to the procedures described in 'ISO GUIDE 35:2017'. The 'eCerto' package contains a 'Shiny' app which provides functionality to load, process, report and backup data recorded during CRM production and facilitates following the recommended procedures. It is described in Lisec et al (2023) <doi:10.1007/s00216-023-05099-3> and can also be accessed online https://apps.bam.de/shn00/eCerto/ without package installation.
URL: https://github.com/janlisec/eCerto
BugReports: https://github.com/janlisec/eCerto/issues
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-US
LazyData: true
LazyDataCompression: bzip2
Depends: R (≥ 4.1.0)
Imports: bslib, config, dplyr, DT, golem, knitr, magick, markdown (≥ 2.0), moments, openxlsx, plyr, purrr, R6, rmarkdown (≥ 1.5), shiny, shinyjs, shinyWidgets, tidyxl, xml2
Suggests: covr, curl, fs, jsonlite, rlang, shinytest2, testthat (≥ 3.0.0), vdiffr, webshot2
RoxygenNote: 7.3.2
Config/testthat/edition: 3
NeedsCompilation: no
Packaged: 2025-03-28 12:31:10 UTC; jlisec
Author: Jan Lisec ORCID iD [cre, aut], Frederik Kreß [ctb]
Repository: CRAN
Date/Publication: 2025-03-28 13:00:02 UTC

An example set of data collected for a CRM.

Description

An example set of data collected for a CRM.

Usage

data(CRM001)

Format

A list of length = 6 containing CRM test data.

Source

jan.lisec@bam.de

An example set of data collected for a LTS monitoring.

Description

An example set of data collected for a LTS monitoring.

Usage

data(LTS001)

Format

A list of lists of length = 2 containing LTS test data.

Source

jan.lisec@bam.de

Assert a specific column (type and position) in a data frame.

Description

assert_col will check in a data.frame for name, position, type of a specific column and ensure that the return value (data frame) contains a respective column. If possible, the current values are converted into the specified type.

Usage

assert_col(
  df,
  name,
  pos = NULL,
  type = c("character", "integer", "numeric", "factor", "logical", "Date"),
  fuzzy_name = TRUE,
  default_value = NULL
)

Arguments

df

Input data frame.

name

Name of the column to ensure (and to search for).

pos

Position of this column. NULL to keep position where found in df.

type

Desired data type of this column.

fuzzy_name

Allow fuzzy matching (additional blanks and case insensitive search allowed).

default_value

Default value if column needs to be created or can not be converted to specified type. Keep NULL to use pre defined default values.

Details

tbd.

Value

A data frame with a column of the specified name and type at the specified position. An error message is attached to the result as an attribute in case of unexpected events.

Examples

x <- data.frame(
  "analyte" = c("A", "B"),
  "tmp" = rep(0L, 2),
  "unit" = c("x", "y")
)
str(x)
ac <- eCerto::assert_col
str(ac(df = x, name = "analyte", pos = 1, type = "factor"))
str(ac(df = x, name = "Analyte", pos = 3, type = "character"))
str(ac(df = x, name = " Analyte", pos = 2, type = "factor"))
str(ac(df = x, name = "Analyte", pos = 2, type = "factor", fuzzy_name = FALSE))
str(ac(df = x, name = "test", type = "factor", default_value = "test"))
# this will lead to NAs in column unit because the conversion does not lead to an error
# hence the default value is not used
str(ac(df = x, name = "unit", type = "numeric", default_value = 10))
# this will lead to the specified default data in column unit because the
# conversion attempt does lead to an error
str(ac(df = x, name = "unit", type = "Date"))
str(ac(df = data.frame("test" = "2022-03-31"), name = "test", type = "Date"))

# show type and class of internal default values
x <- data.frame(
  "character" = "", "integer" = 0L, "numeric" = 0, "factor" = factor(NA),
  "logical" = NA, "date" = Sys.Date(), NA
)
sapply(1:ncol(x), function(i) {
  typeof(x[, i])
})
sapply(1:ncol(x), function(i) {
  class(x[, i])
})

Calculate time differences for suitable vectors.

Description

Calculation of a time difference between time points in a vector x and a specific start date d_start in month (days or years).

Usage

calc_time_diff(
  x = NULL,
  d_start = NULL,
  type = c("mon", "day", "year"),
  origin = "1900-01-01",
  exact = FALSE
)

Arguments

x

A vector of dates or character in format 'yyyy-mm-dd'.

d_start

A specific start date (if unspecified the minimum of x will be used to ensure positive values).

type

You may specify 'year' or 'day' instead of month here.

origin

The origin used.

exact

Function will return exact values instead of full month and year if this is set to TRUE.

Value

A numeric vector of length x containing calculated time differences in the unit specified by type. Not a difftime object.

Examples

x <- c("2022-02-01", "2022-02-03", "2022-03-01", "2024-02-01")
calc_time_diff(x = x)
calc_time_diff(x = x, exact = TRUE)
calc_time_diff(x = x, type = "day")
calc_time_diff(x = x, type = "year")
calc_time_diff(x = x, type = "year", d_start = "2021-12-31")
calc_time_diff(x = 1:3, type = "day", origin = Sys.Date())

Dixon critical values table.

Description

Dixon critical values table.

Usage

data(cvals_Dixon)

Format

A data frame containing Dixon critical values with n in rows and alpha in cols.

Source

http://www.statistics4u.com/fundstat_eng/cc_outlier_tests_dixon.html

Grubbs2 critical values table.

Description

Grubbs2 critical values table.

Usage

data(cvals_Grubbs2)

Format

A data frame containing critical values for Double Grubbs test with n in rows and alpha in cols.

Source

outliers package and https://link.springer.com/article/10.1007/s10182-011-0185-y.

A reactive class based on an R6 object.

Description

Builds a class, which allows only restricted access to the contained 'reactiveValues'. Elements should be accessed via getValue(). Possible advantages are that (1) structure of 'reactiveValues' is clear from the beginning (no function like "addVariable" should exist!) and that (2) functions to calculate the mean or plot current data can be implemented here directly.

General access to data object (so data object can maybe get changed without that much code edit)

Returns element. If 'key' is used, reactivity not working correctly. Preferable way for calling getValue(df, key), see example

Usage

setValue(df, key, value)

getValue(df, key = NULL)

Arguments

df

An object of class R6.

key

Key value within R6 object 'df'.

value

Value to set.

Value

Nothing. The R6 object is updated automatically.

Value of 'key' from 'df'.

Active bindings

cur_an

Set or return the current analyte (reactiveVal) via an active binding.

Methods

Public methods

Method new()

Write the (reactive) value of element 'keys' from list 'l'.

Usage
eCerto$new(rv)
Arguments
rv

'reactiveValues' object.

Returns

A new 'eCerto' object.

Method get()

Read the value of field element of R6 object.

Usage
eCerto$get(keys = NULL)
Arguments
keys

Name of list element.

Returns

Current value of field.

Method set()

Set element of R6 object defined by 'keys' to new value.

Usage
eCerto$set(keys = NULL, value)
Arguments
keys

Name(s) of list element.

value

New value.

Returns

New value of element (invisible).

Method c_plot()

Plot the certification data either provided by the user or from the private slot of self.

Usage
eCerto$c_plot(data, annotate_id = FALSE, filename_labels = FALSE)
Arguments
data

data.frame containing columns 'value', 'Lab' and 'L_flt' for a specific analyte.

annotate_id

T/F to overlay the plot with ID as text if column 'ID' is present.

filename_labels

T/F to use imported file names as labels on x-axes.

Returns

A plot.

Method c_lab_means()

Compute the analyte means for a data set filtered for a specific analyte.

Usage
eCerto$c_lab_means(data)
Arguments
data

data.frame containing columns 'analyte', 'value', 'Lab', 'S_flt' and 'L_flt'.

Returns

A data.frame of lab means.

Method c_analytes()

Return analyte names currently in apm.

Usage
eCerto$c_analytes()
Returns

A named character vector.

Method c_lab_codes()

Return lab codes currently in C data.

Usage
eCerto$c_lab_codes()
Returns

A named character vector.

Method a_p()

Return currently specified values of a type for all analytes.

Usage
eCerto$a_p(
  val = c("precision", "precision_export", "pooling", "confirmed", "unit", "name")
)
Arguments
val

A character value indicating the item of the apm list to be extracted

Returns

A named vector.

Method e_present()

Return modules with existing data.

Usage
eCerto$e_present()
Returns

A named logical vector.

Method c_fltData()

Filter the full data set for a specific analyte and remove all 'S_flt' but keep 'L_flt'.

Usage
eCerto$c_fltData(recalc = FALSE)
Arguments
recalc

If TRUE triggers a recalculation and returns current object if FALSE..

Returns

A data.frame with filtered data of a single analyte.

Method clone()

The objects of this class are cloneable with this method.

Usage
eCerto$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

Examples


if (interactive()) {
  # establish new Shiny session and new eCerto object
  ShinySession <- shiny::MockShinySession$new()
  test <- eCerto::eCerto$new()
  # view current value stored in specific eCerto slot and register observer
  shiny::isolate(eCerto::getValue(test, c("Certification", "data")))
  shiny::observeEvent(eCerto::getValue(test, c("Certification", "data")), {
    message("Certification$data changed:", eCerto::getValue(test, "Certification")$data)
  })
  # change value of specific eCerto slot and flush reactivity to trigger observer
  shiny::isolate(eCerto::setValue(test, c("Certification", "data"), 5))
  ShinySession$flushReact()
  shiny::isolate(eCerto::getValue(test, c("Certification", "data")))
}
tmp <- eCerto$new()
shiny::isolate(tmp$c_plot())
shiny::isolate(tmp$c_lab_means())
tmp$c_analytes()
tmp$c_lab_codes()
tmp$a_p()
tmp$a_p("pooling")
ca <- shiny::isolate(tmp$cur_an)
tmp$a_p("pooling")[ca]
shiny::isolate(tmp$e_present())
tmp$c_fltData()
shiny::isolate(tmp$cur_an <- "Fe")
shiny::isolate(tmp$cur_an)
tmp$c_fltData()
x <- shiny::isolate(eCerto::getValue(tmp, c("General", "apm")))
x[[shiny::isolate(tmp$cur_an)]][["lab_filter"]] <- "L2"
shiny::isolate(eCerto::setValue(tmp, c("General", "apm"), x))
tmp$c_fltData()
tmp$c_fltData(recalc = TRUE)

# Only run examples in interactive R sessions
if (interactive()) {
  rv <- eCerto$new(init_rv())
  setValue(rv, c("Certification", "data"), 5)
  getValue(rv, c("Certification", "data")) # is 5?
  setValue(rv, c("General", "user"), "Franz")
  getValue(rv, c("General", "user"))
}

Run the Shiny Application

Description

Run the Shiny Application

Usage

run_app(
  onStart = NULL,
  options = list(),
  enableBookmarking = NULL,
  uiPattern = "/",
  ...
)

Arguments

onStart

A function that will be called before the app is actually run. This is only needed for shinyAppObj, since in the shinyAppDir case, a global.R file can be used for this purpose.

options

Named options that should be passed to the runApp call (these can be any of the following: "port", "launch.browser", "host", "quiet", "display.mode" and "test.mode"). You can also specify width and height parameters which provide a hint to the embedding environment about the ideal height/width for the app.

enableBookmarking

Can be one of "url", "server", or "disable". The default value, NULL, will respect the setting from any previous calls to enableBookmarking(). See enableBookmarking() for more information on bookmarking your app.

uiPattern

A regular expression that will be applied to each GET request to determine whether the ui should be used to handle the request. Note that the entire request path must match the regular expression in order for the match to be considered successful.

...

arguments to pass to golem_opts. See ?golem::get_golem_options for more details.

Implementation of the STEYX function from Excel.

Description

Translation of STEYX function from Excel to R. It is implemented according to the formula described in http://office.microsoft.com/en-au/excel-help/steyx-function-HP010062545.aspx. At least 3 finite pairs of data points are required for the calculation.

Usage

steyx(x, y)

Arguments

x

x values as numeric vector.

y

y values as numeric vector of similar length as x.

Value

The standard error of the predicted y-value for each x in the regression.

Examples

steyx(x = 1:3, y = 2:4)
steyx(x = 1:3, y = c(2, 3.1, 3.9))

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.