| Title: | Machine-Readable Data Analysis Results with Function Wrappers |
| Version: | 1.0.0 |
| Description: | You can use the set of wrappers for analytical schemata to reduce the effort in writing machine-readable data. The set of all-in-one wrappers will cover widely used functions from data analysis packages. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.3 |
| URL: | https://gitlab.com/TIBHannover/lki/knowledge-loom/mrap-r |
| BugReports: | https://gitlab.com/TIBHannover/lki/knowledge-loom/mrap-r/-/issues |
| Imports: | dtreg, jsonlite, stringr |
| Suggests: | knitr, lme4, rmarkdown, testthat (≥ 3.0.0) |
| Config/testthat/edition: | 3 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2025-11-20 17:43:48 UTC; LezhninaO |
| Author: | Olga Lezhnina 
[aut, cre],
Manuel Prinz
[aut],
Markus Stocker
[aut],
Open Research Knowledge Graph Project and Contributors [cph] |
| Maintainer: | Olga Lezhnina <olga.lezhnina@tib.eu> |
| Repository: | CRAN |
| Date/Publication: | 2025-11-25 07:00:08 UTC |
mrap: Machine-Readable Data Analysis Results with Function Wrappers
Description
You can use the set of wrappers for analytical schemata to reduce the effort in writing machine-readable data. The set of all-in-one wrappers will cover widely used functions from data analysis packages.
Author(s)
Maintainer: Olga Lezhnina olga.lezhnina@tib.eu (ORCID)
Authors:
Manuel Prinz manuel.prinz@tib.eu (ORCID)
Markus Stocker markus.stocker@tib.eu (ORCID)
Other contributors:
Open Research Knowledge Graph Project and Contributors [copyright holder]
See Also
Useful links:
Create an algorithm_evaluation instance
Description
Create an algorithm_evaluation instance
Usage
algorithm_evaluation(code_string, input_data, named_list_results)
Arguments
code_string |
A line of code as a string, or "N/A" if not given |
input_data |
A data frame, a named list, or a URL as a string |
named_list_results |
A named list with metrics and values |
Value
An algorithm_evaluation instance
Examples
res <- list(F1= 0.46, recall = 0.51)
inst_ae <- algorithm_evaluation("N/A", "data_url", res)
Create a class_discovery instance
Description
Create a class_discovery instance
Usage
class_discovery(code_string, input_data, test_results)
Arguments
code_string |
A line of code as a string, or "N/A" if not given |
input_data |
A data frame, a named list, or a URL as a string |
test_results |
A data frame or a list of data frames |
Value
A class_discovery instance
Examples
clust_data <- iris[-5]
res <- data.frame(result_1 = 1, result_2 = 2)
inst_cd <- class_discovery(
"stats::kmeans(clust_data, 3)",
iris,
res
)
Create a class_prediction instance
Description
Create a class_prediction instance
Usage
class_prediction(code_string, input_data, test_results)
Arguments
code_string |
A line of code as a string, or "N/A" if not given |
input_data |
A data frame, a named list, or a URL as a string |
test_results |
A data frame or a list of data frames |
Value
A class_prediction instance
Examples
res <- data.frame(result_1 = 1, result_2 = 2)
inst_cp <- class_prediction(
"stats::glm(Species ~ Petal.Width + Petal.Length, family='binomial', iris)",
iris,
res
)
Create a correlation_analysis instance
Description
Create a correlation_analysis instance
Usage
correlation_analysis(code_string, input_data, test_results)
Arguments
code_string |
A line of code as a string, or "N/A" if not given |
input_data |
A data frame, a named list, or a URL as a string |
test_results |
A data frame or a list of data frames |
Value
A correlation_analysis instance
Examples
res <- data.frame(result_1 = 1, result_2 = 2)
inst_ca <- correlation_analysis(
"stats::cor.test(iris$Petal.Length, iris$Sepal.Length)",
iris,
res
)
Create a data_analysis instance
Description
Create a data_analysis instance
Usage
data_analysis(instances, code_reference = NULL)
Arguments
instances |
Analytic instance or a list of instances |
code_reference |
A URL of the code implementing data analysis |
Value
A data analysis instance
Examples
res <- data.frame(mean = 3.758)
inst_ds <- descriptive_statistics(
"base::mean(iris$Petal.Length)",
iris,
res
)
inst_da <- data_analysis(inst_ds)
Create a descriptive_statistics instance
Description
Create a descriptive_statistics instance
Usage
descriptive_statistics(code_string, input_data, test_results)
Arguments
code_string |
A line of code as a string, or "N/A" if not given |
input_data |
A data frame, a named list, or a URL as a string |
test_results |
A data frame or a list of data frames |
Value
A descriptive_statistics instance
Examples
res <- data.frame(mean = 3.758)
inst_ds <- descriptive_statistics(
"base::mean(iris$Petal.Length)",
iris,
res
)
Create a factor_analysis instance
Description
Create a factor_analysis instance
Usage
factor_analysis(code_string, input_data, test_results)
Arguments
code_string |
A line of code as a string, or "N/A" if not given |
input_data |
A data frame, a named list, or a URL as a string |
test_results |
A data frame or a list of data frames |
Value
A factor_analysis instance
Examples
fa_data <- iris[-5]
res <- data.frame(result_1 = 1, result_2 = 2)
inst_fa <- factor_analysis(
"stats::princomp(fa_data)",
iris,
res
)
Create a group_comparison instance
Description
Create a group_comparison instance
Usage
group_comparison(code_string, input_data, test_results)
Arguments
code_string |
A line of code as a string, or "N/A" if not given |
input_data |
A data frame, a named list, or a URL as a string |
test_results |
A data frame or a list of data frames |
Value
A group_comparison instance
Examples
res <- data.frame(result_1 = 1, result_2 = 2)
inst_gc <- group_comparison(
"stats::aov(Petal.Length ~ Species, data = iris)",
iris,
res
)
Create a multilevel_analysis instance
Description
Create a multilevel_analysis instance
Usage
multilevel_analysis(code_string, input_data, test_results)
Arguments
code_string |
A line of code as a string, or "N/A" if not given |
input_data |
A data frame, a named list, or a URL as a string |
test_results |
A data frame or a list of data frames |
Value
A multilevel_analysis instance
Examples
code_string <- "lme4::lmer(math ~ homework + (1 | schid))"
res <- data.frame(result_1 = 1, result_2 = 2)
inst <- multilevel_analysis(code_string, "data_url", res)
Create a regression_analysis instance
Description
Create a regression_analysis instance
Usage
regression_analysis(code_string, input_data, test_results)
Arguments
code_string |
A line of code as a string, or "N/A" if not given |
input_data |
A data frame, a named list, or a URL as a string |
test_results |
A data frame or a list of data frames |
Value
A regression_analysis instance
Examples
res <- data.frame(result_1 = 1, result_2 = 2)
inst_ra <- regression_analysis(
"stats::lm(Petal.Length ~ Sepal.Length, data = iris)",
iris,
res
)
Wrap stats::aov function
Description
Wrap stats::aov function
Usage
stats_aov(...)
Arguments
... |
the same arguments as in the wrapped function |
Value
a list of ANOVA object and R6 class instance
Examples
results <- stats_aov(Petal.Length ~ Species, data = iris)
Write an instance in JSON-LD format
Description
This function is imported from dtreg for ease-of-use
Usage
to_jsonld(instance)
Arguments
instance |
An instance of an R6 class |
Value
JSON string in JSON-LD format
Examples
res <- data.frame(mean = 3.758)
inst_ds <- descriptive_statistics(
"base::mean(iris$Petal.Length)",
iris,
res
)
json <- to_jsonld(inst_ds)