README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# labourR <a href="http://eworx-org.github.io/labourR/" rel="nofollow"><img src='man/figures/logo.png' align="right" height="130"/></a>

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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](https://ec.europa.eu/esco/portal), the multilingual classification of European Skills, Competences, Qualifications and Occupations, and [ISCO](https://ec.europa.eu/esco/portal/escopedia/International_Standard_Classification_of_Occupations__40_ISCO_41_), the International Standard Classification of Occupations. 


```{r pressure, echo=FALSE, fig.cap="Fig.1 - ESCO is mapped to the 4th level of the ISCO hierarchical model.", out.width = '80%'}
knitr::include_graphics("man/figures/ESCO_ISCO_hierarchy.png")
```

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

See [Articles](https://eworx-org.github.io/labourR/articles/occupations_retrieval.html) section for details.

## Installation

You can install the released version of labourR from [CRAN](https://CRAN.R-project.org) with,

``` r
install.packages("labourR")
```

## Examples

```{r example}
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,

```{r with_isco_level}
classify_occupation(corpus = corpus, isco_level = 3, lang = "en", num_leaves = 5)
```


- If the ISCO level is not specified, the top num_leaves, in this case 5, suggested ESCO occupations are returned for each id,

```{r without_isco_level}
classify_occupation(corpus = corpus, isco_level = NULL, lang = "en", num_leaves = 5)
```