| 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 onlinehttps://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 [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 convertedinto 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 thespecified position. An error message is attached to the result as anattribute 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_colstr(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 usedstr(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 errorstr(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 valuesx <- 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 vectorx anda specific start dated_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 lengthx containing calculated time differencesin the unit specified bytype. 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 andhttps://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 thecontained 'reactiveValues'. Elements should be accessed viagetValue().Possible advantages are that (1) structure of 'reactiveValues' is clearfrom the beginning (no function like "addVariable" should exist!) and that(2) functions to calculate the mean or plot current data can be implementedhere directly.
General access to data object (so data object can maybe getchanged without that much code edit)
Returns element. If 'key' is used, reactivity not working correctly.Preferable way for callinggetValue(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_anSet or return the current analyte (reactiveVal) via an active binding.
Methods
Public methods
Methodnew()
Write the (reactive) value of element 'keys' from list 'l'.
Usage
eCerto$new(rv)
Arguments
rv'reactiveValues' object.
Returns
A new 'eCerto' object.
Methodget()
Read the value of field element of R6 object.
Usage
eCerto$get(keys = NULL)
Arguments
keysName of list element.
Returns
Current value of field.
Methodset()
Set element of R6 object defined by 'keys' to new value.
Usage
eCerto$set(keys = NULL, value)
Arguments
keysName(s) of list element.
valueNew value.
Returns
New value of element (invisible).
Methodc_plot()
Plot the certification data either provided by the user or from the private slot of self.
Usage
eCerto$c_plot(data, annotate_id = FALSE, filename_labels = FALSE)
Arguments
datadata.frame containing columns 'value', 'Lab' and 'L_flt' for a specific analyte.
annotate_idT/F to overlay the plot with ID as text if column 'ID' is present.
filename_labelsT/F to use imported file names as labels on x-axes.
Returns
A plot.
Methodc_lab_means()
Compute the analyte means for a data set filtered for a specific analyte.
Usage
eCerto$c_lab_means(data)
Arguments
datadata.frame containing columns 'analyte', 'value', 'Lab', 'S_flt' and 'L_flt'.
Returns
A data.frame of lab means.
Methodc_analytes()
Return analyte names currently in apm.
Usage
eCerto$c_analytes()
Returns
A named character vector.
Methodc_lab_codes()
Return lab codes currently in C data.
Usage
eCerto$c_lab_codes()
Returns
A named character vector.
Methoda_p()
Return currently specified values of a type for all analytes.
Usage
eCerto$a_p( val = c("precision", "precision_export", "pooling", "confirmed", "unit", "name"))Arguments
valA character value indicating the item of the apm list to be extracted
Returns
A named vector.
Methode_present()
Return modules with existing data.
Usage
eCerto$e_present()
Returns
A named logical vector.
Methodc_fltData()
Filter the full data set for a specific analyte and remove all 'S_flt' but keep 'L_flt'.
Usage
eCerto$c_fltData(recalc = FALSE)
Arguments
recalcIf TRUE triggers a recalculation and returns current object if FALSE..
Returns
A data.frame with filtered data of a single analyte.
Methodclone()
The objects of this class are cloneable with this method.
Usage
eCerto$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
if (interactive()) { # establish new Shiny session and new eCerto object ShinySession <- shiny::MockShinySession$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$c_fltData(recalc = TRUE)# Only run examples in interactive R sessionsif (interactive()) { rv <- eCerto$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 |
options | Named options that should be passed to the |
enableBookmarking | Can be one of |
uiPattern | A regular expression that will be applied to each |
... | arguments to pass to golem_opts.See |
Implementation of the STEYX function from Excel.
Description
Translation ofSTEYX function from Excel to R. It is implementedaccording to the formula described inhttp://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))