README.RMD

---
title: "TokenLink"
output: github_document
date: '2022-05-05'
editor_options: 
  chunk_output_type: console
---



# TokenLink
link two dataset using tokens or words in common between them


```{r setup, include=FALSE} 
knitr::opts_chunk$set(warning = FALSE, message = FALSE) 

```


# Install
```{r Install, eval = FALSE}
devtools::install_github("csps-efpc/TokenLink")
```




# Example Basic Usage

## Load Libraries and Data from Internet
```{r load_data}
source('R/tokenify.R')
library(tidyr)
library(dplyr)
library(readr)
library(magrittr)
library(TokenLink)

ceo_url <- 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-04-27/departures.csv'
ceo_url <- 'https://tinyurl.com/2p8etjr6'
alb_url <- 'https://open.alberta.ca/dataset/a2b1fc9b-aac4-4718-8645-b0466ca5ec57/resource/3da9a7f9-bd34-48c0-841f-19c856b551ad/download/foodindustry.csv'
alb_url <- 'https://tinyurl.com/2p8ap4ad'

# Load Data From internet
dat_ceo <- readr::read_csv(ceo_url)
dat_alb <- readr::read_csv(alb_url)
```

### CEO Resignation data from [Tidy Tuesday](https://github.com/rfordatascience/tidytuesday/blob/master/data/2021/2021-04-27/readme.md)
```{r echo=FALSE}
knitr::kable(dplyr::select(dplyr::sample_n(dat_ceo, 3), coname, exec_fullname), floating.environment="sidewaystable")
```

### Data from [open.alberta.ca](https://open.alberta.ca/dataset/a2b1fc9b-aac4-4718-8645-b0466ca5ec57)
```{r echo=FALSE}
knitr::kable(dplyr::select(dplyr::sample_n(dat_alb, 3), companyName, address, town, province ), floating.environment="sidewaystable")
```




# Show Tokenization
```{r basic_tokenization}
dat_ceo_tokes <- 
  dat_ceo |> 
  tokenize_ations(col_nms = 'coname', token_types = 'company_name') 

dat_ceo_tokes |>
  magrittr::extract2('tokens') |> 
  group_by(row_name) |>
  nest() |> ungroup() |>
  sample_n(3) |>
  unnest() |>
  knitr::kable(caption = 'Tokens')
```

# Count Tokenization
```{r count_tokens}
nsamp <- 4

dat_ceo_tokes |> 
  magrittr::extract2('token_counts') |> 
  {\(.) bind_rows(head(., nsamp), sample_n(.,nsamp), tail(., nsamp))}() |>
  arrange(desc(n)) |>
  knitr::kable(caption = 'Token Counts') 
```


# Create a t_dat Object
```{r basic_linking}
t_dat <- token_links(
  dat_x = dat_ceo,
  dat_y = dat_alb,
  args_x = list(col_nms = 'coname'),
  args_y = list(col_nms = 'companyName'),
  token_types = 'company_name',
  token_index = '',
  suffix = c('ceo', 'alb')
)

t_dat |>
  extract2('tokens_all') |>
  {\(.) bind_rows(head(., nsamp), sample_n(.,nsamp), tail(., nsamp))}() |>
  knitr::kable(caption = 'All Tokens')
```



# Find Posteriors
```{r find_posterior}
t_dat <- 
  t_dat |> 
  find_posterior()

t_dat$all_evidence |> 
  {\(.) bind_rows(head(., nsamp), sample_n(.,nsamp), tail(., nsamp))}() |>
  arrange(desc(posterior)) |>
  knitr::kable()
```



# Vissual Compare Of Results
```{r joind_result}
t_dat |>  joined_results(include_row_numbers = TRUE, link_col_nms = c('posterior', 'tokens_in_favour', 'tokens_against')) |>
  {\(.) bind_rows(head(., nsamp), sample_n(.,nsamp), tail(., nsamp))}() |>
  arrange(desc(posterior)) |>
  knitr::kable()
```