Package 'eCerto'

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

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])
})

Description

A raster representation of the BAM logo to be used in Reports.

Usage

data(BAMlogo_raster)

Format

A raster representation of the BAM logo png file.

Source

[email protected]

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

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())

Description

An example set of data collected for a CRM.

Usage

data(CRM001)

Format

A list of length = 6 containing CRM test data.

Source

[email protected]

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

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.

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

Value

Nothing. The R6 object is updated automatically.

Value of 'key' from 'df'.

Active bindings

Methods

Public methods

• eCerto$new()

• eCerto$get()

• eCerto$set()

• eCerto$c_plot()

• eCerto$c_lab_means()

• eCerto$c_analytes()

• eCerto$c_lab_codes()

• eCerto$a_p()

• eCerto$e_present()

• eCerto$c_fltData()

• eCerto$clone()

Method new()

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

Usage

eCerto$new(rv)

Arguments

Returns

A new 'eCerto' object.

Method get()

Read the value of field element of R6 object.

Usage

eCerto$get(keys = NULL)

Arguments

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

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

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

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

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

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

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"))
}

Description

A base R implementation of plyr::ldply

Usage

ldply_base(.data, .fun = identity, .progress = "none", .id = NA, ...)

Arguments

Examples

x <- list(a = data.frame(x = 1:2, y = 5:6), b = data.frame(x = 3:4, y = 7:8))
ldply_base(x)
ldply_base(x, .id = NULL)
ldply_base(unname(x))
ldply_base(x, .id = "test")
# compare against standard plyr::ldply
#plyr::ldply(x, .id="test")
str(ldply_base(x))
#str(plyr::ldply(x))
x <- c("01.01.2025","02.01.2025")
ldply_base(x, as.Date.character, tryFormats = "%d.%m.%Y")
#plyr::ldply(x, as.Date.character, tryFormats = "%d.%m.%Y")

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

[email protected]

Description

Parses a character string containing markdown-style formatting syntax and converts it into an R plotmath expression suitable for use in plot labels and annotations.

Usage

markdown2expression(x, vectorize = FALSE)

Arguments

Details

The following markdown syntax is supported:

• **text** — bold

• *text* — italic

• base^sup^ — superscript

• base~sub~ — subscript

Formatting can be nested (e.g., bold inside superscript). The returned expression can be passed directly to plot() arguments such as main, xlab, or ylab, or to expression().

Value

A language object (R expression) suitable for use in plotting functions that support plotmath.

See Also

plotmath

Examples

markdown2expression("**bold text**")
markdown2expression("*italic text*")
markdown2expression("x^2^")
markdown2expression("H~2~O")
markdown2expression("**H~2~O**")
plot(1,1, main = markdown2expression("R^2^ = 0.99"), pch=".")
text(1,1, markdown2expression("Water = **H~2~O**"))

Description

Run the Shiny Application

Usage

run_app(
  onStart = NULL,
  options = list(port = 3838),
  enableBookmarking = NULL,
  uiPattern = "/",
  ...
)

Arguments

Description

Provide save link tag

Usage

save_link(text, link)

Arguments

Examples

save_link("KS", "https://rdrr.io/r/stats/ks.test.html")

Description

Translation of STEYX function from Excel to R. It is implemented according to the formula described in https://support.microsoft.com/en-us/office/steyx-function-6ce74b2c-449d-4a6e-b9ac-f9cef5ba48ab. At least 3 finite pairs of data points are required for the calculation.

Usage

steyx(x, y)

Arguments

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))