---
title: "Integration with existing packages"
author: "Maciej Beręsewicz"
execute:
  warning: false
  message: false
lang: en
output: 
    html_vignette:
        df_print: kable
        toc: true
        number_sections: true
        fig_width: 6
        fig_height: 4
vignette: >
  %\VignetteIndexEntry{Integration with existing packages}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Setup

```{r setup}
library(blocking)
library(reclin2)
```

# Data

In the example we will use the same dataset as in the *Blocking records for record linkage* vignette.

```{r}
data(census)
data(cis)
census[, x:=1:.N]
cis[, y:=1:.N]
```

# Integration with the `reclin2` package

The package contains function `pair_ann` which aims at integration with `reclin2` package. This function works as follows.

```{r}
pair_ann(x = census[1:1000], 
         y = cis[1:1000], 
         on = c("pername1", "pername2", "sex", "dob_day", "dob_mon", "dob_year", "enumcap", "enumpc"), 
         deduplication = FALSE) |>
  head()
```

Which provides you information on the total number of pairs. This can be further included in the pipeline of the `reclin2` package (note that we use a different ANN this time).

```{r}
pair_ann(x = census[1:1000], 
         y = cis[1:1000], 
         on = c("pername1", "pername2", "sex", "dob_day", "dob_mon", "dob_year", "enumcap", "enumpc"), 
         deduplication = FALSE,
         ann = "hnsw") |>
  compare_pairs(on = c("pername1", "pername2", "sex", "dob_day", "dob_mon", "dob_year", "enumcap", "enumpc"),
                comparators = list(cmp_jarowinkler())) |>
  score_simple("score",
               on = c("pername1", "pername2", "sex", "dob_day", "dob_mon", "dob_year", "enumcap", "enumpc")) |>
  select_threshold("threshold", score = "score", threshold = 6) |>
  link(selection = "threshold") |>
  head()
```

# Usage with `fastLink` package

Just use the `block` column in the function `fastLink::blockData()`. As a result you will obtain a list of records blocked for further processing. 

# Usage with `RecordLinkage` package

Just use the `block` column in the argument `blockfld` in the `compare.dedup()` or `compare.linkage()` function. Please note that `block` column for the `RecordLinkage` package should be stored as a `character` not a `numeric/integer` vector.