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labourR

Codecov test coverage R-CMD-check CRAN status Total Downloads

The goal of labourR is to map multilingual free-text of occupations, such as a job title in a Curriculum Vitae, to hierarchical ontologies provided by ESCO, the multilingual classification of European Skills, Competences, Qualifications and Occupations, and ISCO, the International Standard Classification of Occupations.

Fig.1 - ESCO is mapped to the 4th level of the ISCO hierarchical model.

Computations are vectorised and the data.table package is used for high performance and memory efficiency.

See Articles section for details.

Installation

You can install the released version of labourR from CRAN with,

install.packages("labourR")

Examples

library(labourR)
corpus <- data.frame(
  id = 1:3,
  text = c("Data Scientist", "Junior Architect Engineer", "Cashier at McDonald's")
)
  • If the ISCO level is specified, the top suggested ISCO group is returned. num_leaves specifies the number of ESCO occupations used for the k-NN classifier to perform a plurality vote,
classify_occupation(corpus = corpus, isco_level = 3, lang = "en", num_leaves = 5)
#>    id iscoGroup                                          preferredLabel
#> 1:  1       251       Software and applications developers and analysts
#> 2:  2       214 Engineering professionals (excluding electrotechnology)
#> 3:  3       523                              Cashiers and ticket clerks
  • If the ISCO level is not specified, the top num_leaves, in this case 5, suggested ESCO occupations are returned for each id,
classify_occupation(corpus = corpus, isco_level = NULL, lang = "en", num_leaves = 5)
#>     id                           conceptUri                      preferredLabel
#>  1:  1 258e46f9-0075-4a2e-adae-1ff0477e0f30                      data scientist
#>  2:  1 1562c7a3-c7d9-419d-b9b6-db26610bcf84             data warehouse designer
#>  3:  1 f470b785-643c-46f9-8b31-6085427ab7b8 aeronautical information specialist
#>  4:  1 7086d0ca-1e77-4690-89c9-7ed1a0478fa3             data quality specialist
#>  5:  1 d3edb8f8-3a06-47a0-8fb9-9b212c006aa2                        data analyst
#>  6:  2 76abbb82-c103-4d7a-a4c0-14dba4d6199a              commissioning engineer
#>  7:  2 c8fa93eb-7c2c-42c3-b135-c2e825a6615e                       test engineer
#>  8:  2 e12f08fb-4748-4388-9489-b647df60332a                 hydropower engineer
#>  9:  2 9dbbeb2c-0d51-4c03-8ef6-8dfa7360db22             ship assistant engineer
#> 10:  2 2f26a52b-cf45-4282-9138-478252161f00            food production engineer
#> 11:  3 3f32394b-f1b1-48ef-96ee-74405fb7c6b6                     lottery cashier
#> 12:  3 961cbd1f-2a9b-4756-b227-67a1c23c94b6                      casino cashier
#> 13:  3 2ff9e53c-6e7f-42af-8d71-b5dd7f283089                         bank teller
#> 14:  3 b7fc1cd1-0d6d-4e9e-8a9f-c3270201be81            foreign exchange cashier
#> 15:  3 2b871272-bd61-4206-bd1a-0b96d7023098                             cashier