library(magrittr) library(knitr) knitr::opts_chunk$set(echo = TRUE) x <- params$x result <- params$result ## only for debugging ## if (is.null(x)) { if (!require("dmdScheme")) { devtools::load_all() } x <- dmdScheme::dmdScheme_raw() result <- validate(x = dmdScheme_raw(), validateData = TRUE, report = "none", path = system.file("example_data", "basic", "data", "archiving_data", package = "dmdScheme")) } ## ## ## Define colours for results of evaluation
r params$author
r attr(x, "fileName")
add this
This validation report validates the most likely an excelsheet containing the data. The headers do represent sections of validations, while the bullets reperesent individual validations. Sections of the validation and are hierarchical, i.e. a higher level validation result depends on the lower level validation results. The headers and validations follow the structure of
Error_Level : Name of the validation
The error level can be: - OK: the validation passed - note: the validation contains an inconsistency which migh be an error or on purpose. Please check these! - warning: the validation contains an inconsistency which could be an error or on purpose. Please check these! - error: the validation failed and contains an error - needs to be fixewd before the data can be exported!
An ideal validation will not contain any errors or warnings.
Validation results are accumulated by forwarding the highest error to the validation of the next level. If validation 1.3. contains an error, 1. will be classified as an error as well, irrespective of 1.1, 1.2, ....
The details do contain the individual tests and are explained in detail in the sections
Details Error_Level : Name of the validation
valErr_extract(result) %>% table %>% set_names(valErr_info(names(.))$text)
print(result, level = 2, listLevel = 20, type = "details", error = 3)
print(result, level = 2, listLevel = 20, type = "details", error = 2)
print(result, level = 2, listLevel = 20, type = "details", error = 1)
print(result, level = 2, listLevel = 20, type = "details", error = 0)
attributes(x)
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