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TokenLink

2022-05-05

TokenLink

link two dataset using tokens or words in common between them

Install

devtools::install_github("csps-efpc/TokenLink")

Example Basic Usage

Load Libraries and Data from Internet

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

coname exec_fullname
MEDQUIST INC David A. Cohen
ATC TECHNOLOGY CORP Donald T. Johnson Jr.
E TRADE FINANCIAL CORP Karl A. Roessner

Data from open.alberta.ca

companyName address town province
Ryley Sausage 1991 Ltd. Box 205 Ryley AB
Capital Packers Inc. 12907 - 57 St. Edmonton AB
Parmalat Canada 3410 - 24 Ave. N Lethbridge AB

Show 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')
row_name token token_type
5648 regency company_name
5648 centers company_name
5648 corp company_name
3317 fresh company_name
3317 choice company_name
3317 incorporated company_name
5244 wec company_name
5244 energy company_name
5244 group company_name
5244 incorporated company_name

Tokens

Count Tokenization

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')
token token_type n
incorporated company_name 5064
corp company_name 2436
company company_name 728
group company_name 470
sprint company_name 7
internap company_name 6
blount company_name 4
mn company_name 4
zions company_name 1
zoetis company_name 1
zumiez company_name 1
zynex company_name 1

Token Counts

Create a t_dat Object

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')
token token_type n.ceo n.alb n_comparisons u_prob m_prob
incorporated company_name 5064 102 516528 0.1081177 0.9900000
company company_name 728 37 26936 0.0056381 0.9920208
limited company_name 114 197 22458 0.0047008 0.9921452
corp company_name 2436 9 21924 0.0045890 0.9921616
trptn company_name 2 0 0 0.0000000 0.9990000
karamel company_name 0 1 0 0.0000000 0.9990000
mcdermott company_name 7 0 0 0.0000000 0.9990000
online company_name 3 0 0 0.0000000 0.9990000
yat company_name 0 1 0 0.0000000 0.9990000
yeg company_name 0 1 0 0.0000000 0.9990000
yogurt company_name 0 1 0 0.0000000 0.9990000
zinter company_name 0 1 0 0.0000000 0.9990000

All Tokens

Find Posteriors

t_dat <- 
  t_dat |> 
  find_posterior()

t_dat$all_evidence |> 
  {\(.) bind_rows(head(., nsamp), sample_n(.,nsamp), tail(., nsamp))}() |>
  arrange(desc(posterior)) |>
  knitr::kable()
row_name.ceo row_name.alb tokens_in_favour tokens_against priori posterior
3115 40 3 1 3.9e-06 1.0000000
59 40 3 1 3.9e-06 1.0000000
5962 40 3 1 3.9e-06 1.0000000
7464 40 3 1 3.9e-06 1.0000000
171 133 3 1 3.9e-06 0.9999739
172 133 3 1 3.9e-06 0.9999739
171 134 2 3 3.9e-06 0.0136264
6032 241 3 4 3.9e-06 0.0103901
3041 241 3 4 3.9e-06 0.0103901
4404 241 3 4 3.9e-06 0.0103901
6032 241 3 4 3.9e-06 0.0103901
6247 241 3 4 3.9e-06 0.0103901

Vissual Compare Of Results

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()
row_name.ceo row_name.alb posterior tokens_in_favour tokens_against coname.ceo companyName.alb
3115 40 1.0000000 3 1 ARCHER-DANIELS-MIDLAND CO Archer Daniels Midland
59 40 1.0000000 3 1 ARCHER-DANIELS-MIDLAND CO Archer Daniels Midland
5962 40 1.0000000 3 1 ARCHER-DANIELS-MIDLAND CO Archer Daniels Midland
7464 40 1.0000000 3 1 ARCHER-DANIELS-MIDLAND CO Archer Daniels Midland
5962 40 1.0000000 3 1 ARCHER-DANIELS-MIDLAND CO Archer Daniels Midland
3167 133 0.9999739 3 1 COCA-COLA CO Coca-Cola Bottling Company
171 134 0.0136264 2 3 COCA-COLA CO Coca-Cola Bottling Ltd.
3041 241 0.0103901 3 4 STEWART ENTERPRISES -CL A J & A Stewart Enterprises Ltd.
3041 241 0.0103901 3 4 STEWART ENTERPRISES -CL A J & A Stewart Enterprises Ltd.
4404 241 0.0103901 3 4 STEWART ENTERPRISES -CL A J & A Stewart Enterprises Ltd.
6032 241 0.0103901 3 4 STEWART ENTERPRISES -CL A J & A Stewart Enterprises Ltd.
6247 241 0.0103901 3 4 STEWART ENTERPRISES -CL A J & A Stewart Enterprises Ltd.