| Title: | 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. |
| Authors: | Olga Lezhnina [aut] (ORCID: <https://orcid.org/0000-0003-2237-7725>), Manuel Prinz [aut] (ORCID: <https://orcid.org/0000-0003-2151-4556>), Markus Stocker [aut, cre] (ORCID: <https://orcid.org/0000-0001-5492-3212>), Open Research Knowledge Graph Project and Contributors [cph] |
| Maintainer: | Markus Stocker <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.0.1 |
| Built: | 2026-05-07 08:25:21 UTC |
| Source: | https://github.com/cran/mrap |
Create an algorithm_evaluation instance
algorithm_evaluation(code_string, input_data, named_list_results)algorithm_evaluation(code_string, input_data, named_list_results)
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 |
An algorithm_evaluation instance
res <- list(F1= 0.46, recall = 0.51) inst_ae <- algorithm_evaluation("N/A", "data_url", res)res <- list(F1= 0.46, recall = 0.51) inst_ae <- algorithm_evaluation("N/A", "data_url", res)
Create a class_discovery instance
class_discovery(code_string, input_data, test_results)class_discovery(code_string, input_data, test_results)
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 |
A class_discovery instance
clust_data <- iris[-5] res <- data.frame(result_1 = 1, result_2 = 2) inst_cd <- class_discovery( "stats::kmeans(clust_data, 3)", iris, res )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
class_prediction(code_string, input_data, test_results)class_prediction(code_string, input_data, test_results)
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 |
A class_prediction instance
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 )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
correlation_analysis(code_string, input_data, test_results)correlation_analysis(code_string, input_data, test_results)
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 |
A correlation_analysis instance
res <- data.frame(result_1 = 1, result_2 = 2) inst_ca <- correlation_analysis( "stats::cor.test(iris$Petal.Length, iris$Sepal.Length)", iris, res )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
data_analysis(instances, code_reference = NULL)data_analysis(instances, code_reference = NULL)
instances |
Analytic instance or a list of instances |
code_reference |
A URL of the code implementing data analysis |
A data analysis instance
res <- data.frame(mean = 3.758) inst_ds <- descriptive_statistics( "base::mean(iris$Petal.Length)", iris, res ) inst_da <- data_analysis(inst_ds)res <- data.frame(mean = 3.758) inst_ds <- descriptive_statistics( "base::mean(iris$Petal.Length)", iris, res ) inst_da <- data_analysis(inst_ds)
Create a descriptive_statistics instance
descriptive_statistics(code_string, input_data, test_results)descriptive_statistics(code_string, input_data, test_results)
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 |
A descriptive_statistics instance
res <- data.frame(mean = 3.758) inst_ds <- descriptive_statistics( "base::mean(iris$Petal.Length)", iris, res )res <- data.frame(mean = 3.758) inst_ds <- descriptive_statistics( "base::mean(iris$Petal.Length)", iris, res )
Create a factor_analysis instance
factor_analysis(code_string, input_data, test_results)factor_analysis(code_string, input_data, test_results)
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 |
A factor_analysis instance
fa_data <- iris[-5] res <- data.frame(result_1 = 1, result_2 = 2) inst_fa <- factor_analysis( "stats::princomp(fa_data)", iris, res )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
group_comparison(code_string, input_data, test_results)group_comparison(code_string, input_data, test_results)
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 |
A group_comparison instance
res <- data.frame(result_1 = 1, result_2 = 2) inst_gc <- group_comparison( "stats::aov(Petal.Length ~ Species, data = iris)", iris, res )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
multilevel_analysis(code_string, input_data, test_results)multilevel_analysis(code_string, input_data, test_results)
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 |
A multilevel_analysis instance
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)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
regression_analysis(code_string, input_data, test_results)regression_analysis(code_string, input_data, test_results)
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 |
A regression_analysis instance
res <- data.frame(result_1 = 1, result_2 = 2) inst_ra <- regression_analysis( "stats::lm(Petal.Length ~ Sepal.Length, data = iris)", iris, res )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
stats_aov(...)stats_aov(...)
... |
the same arguments as in the wrapped function |
a list of ANOVA object and R6 class instance
results <- stats_aov(Petal.Length ~ Species, data = iris)results <- stats_aov(Petal.Length ~ Species, data = iris)
This function is imported from dtreg for ease-of-use
to_jsonld(instance)to_jsonld(instance)
instance |
An instance of an R6 class |
JSON string in JSON-LD format
res <- data.frame(mean = 3.758) inst_ds <- descriptive_statistics( "base::mean(iris$Petal.Length)", iris, res ) json <- to_jsonld(inst_ds)res <- data.frame(mean = 3.758) inst_ds <- descriptive_statistics( "base::mean(iris$Petal.Length)", iris, res ) json <- to_jsonld(inst_ds)