Объедините аккуратный текст с синонимами, чтобы создать фрейм данных - PullRequest
0 голосов
/ 03 апреля 2019

У меня есть образец кадра данных, как показано ниже:

 quoteiD <- c("q1","q2","q3","q4", "q5")
 quote <- c("Unthinking respect for authority is the greatest enemy of truth.",
      "In the middle of difficulty lies opportunity.",
      "Intelligence is the ability to adapt to change.",
      "Science is not only a disciple of reason but, also, one of romance and passion.", 
      "If I have seen further it is by standing on the shoulders of Giants.")

 library(dplyr)
  quotes <- tibble(quoteiD = quoteiD, quote= quote)
   quotes

Я создал небольшой текст, как показано ниже

library(tidytext)
 data(stop_words)
   tidy_words <- quotes %>%
      unnest_tokens(word, quote) %>%
        anti_join(stop_words) %>% 
         count( word, sort = TRUE)
tidy_words

Далее, я искал синонимы, используя пакет qdap , как показано ниже

 library(qdap)
  syns <- synonyms(tidy_words$word)

qdap out put - это список, и я собираюсь выбрать первые 5 синонимов для каждого слова во фрейме аккуратных данных и создать столбец с именем синонимов, как показано ниже:

word       n    synonyms
ability    1    adeptness, aptitude, capability, capacity, competence 
adapt      1    acclimatize, accommodate, adjust, alter, apply,
authority  1    ascendancy, charge, command, control, direction

Что представляет собой элегантный способ объединения списка из 5 слов из функции синонима qdap и разделения их запятыми?

1 Ответ

1 голос
/ 03 апреля 2019

Один из способов сделать это с помощью решения tidyverse -

library(plyr)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:plyr':
#> 
#>     arrange, count, desc, failwith, id, mutate, rename, summarise,
#>     summarize
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(tidytext)
library(qdap)
#> Loading required package: qdapDictionaries
#> Loading required package: qdapRegex
#> 
#> Attaching package: 'qdapRegex'
#> The following object is masked from 'package:dplyr':
#> 
#>     explain
#> Loading required package: qdapTools
#> 
#> Attaching package: 'qdapTools'
#> The following object is masked from 'package:dplyr':
#> 
#>     id
#> The following object is masked from 'package:plyr':
#> 
#>     id
#> Loading required package: RColorBrewer
#> 
#> Attaching package: 'qdap'
#> The following object is masked from 'package:dplyr':
#> 
#>     %>%
#> The following object is masked from 'package:base':
#> 
#>     Filter
library(tibble)
library(tidyr)
#> 
#> Attaching package: 'tidyr'
#> The following object is masked from 'package:qdap':
#> 
#>     %>%

quotes <- tibble(quoteiD = paste0("q", 1:5), 
                 quote=  c(".\n\nthe ebodac consortium consists of partners: janssen (efpia), london school of hygiene and tropical medicine (lshtm),", 
                           "world vision) mobile health software development and deployment in resource limited settings grameen\n\nas such, the ebodac consortium is well placed to tackle.", 
                           "Intelligence is the ability to adapt to change.", 
                           "Science is a of reason of romance and passion.", 
                           "If I have seen further it is by standing on ."))
quotes
#> # A tibble: 5 x 2
#>   quoteiD quote                                                            
#>   <chr>   <chr>                                                            
#> 1 q1      ".\n\nthe ebodac consortium consists of partners: janssen (efpia~
#> 2 q2      "world vision) mobile health software development and deployment~
#> 3 q3      Intelligence is the ability to adapt to change.                  
#> 4 q4      Science is a of reason of romance and passion.                   
#> 5 q5      If I have seen further it is by standing on .

data(stop_words)
tidy_words <- quotes %>%
  unnest_tokens(word, quote) %>%
  anti_join(stop_words) %>% 
  count( word, sort = TRUE)
#> Joining, by = "word"
tidy_words
#> # A tibble: 33 x 2
#>    word            n
#>    <chr>       <int>
#>  1 consortium      2
#>  2 ebodac          2
#>  3 ability         1
#>  4 adapt           1
#>  5 change          1
#>  6 consists        1
#>  7 deployment      1
#>  8 development     1
#>  9 efpia           1
#> 10 grameen         1
#> # ... with 23 more rows

syns <- synonyms(tidy_words$word)
#> no match for the following:
#> consortium, ebodac, consists, deployment, efpia, grameen, janssen, london, lshtm, partners, settings, software, tropical
#> ========================

syns %>% 
  plyr::ldply(data.frame) %>% # Change the list to a dataframe (See /2784256/r-spisok-k-freimu-dannyh)
  rename("Word_DefNumber" = 1, "Syn" = 2) %>% # Rename the columns with a name that is more intuitive
  separate(Word_DefNumber, c("Word", "DefNumber"), sep = "\\.") %>% # Find the word part of the word and definition number
  group_by(Word) %>% # Group by words, so that when we select rows it is done for each word
  slice(1:5) %>% # Keep the first 5 rows for each word
  summarise(synonyms = paste(Syn, collapse = ", ")) %>% # Combine the synonyms together comma separated using paste 
  ungroup() # So there are not unintended effects of having the data grouped when using the data later
#> # A tibble: 20 x 2
#>    Word         synonyms                                                   
#>    <chr>        <chr>                                                      
#>  1 ability      adeptness, aptitude, capability, capacity, competence      
#>  2 adapt        acclimatize, accommodate, adjust, alter, apply             
#>  3 change       alter, convert, diversify, fluctuate, metamorphose         
#>  4 development  advance, advancement, evolution, expansion, growth         
#>  5 health       fitness, good condition, haleness, healthiness, robustness 
#>  6 hygiene      cleanliness, hygienics, sanitary measures, sanitation      
#>  7 intelligence acumen, alertness, aptitude, brain power, brains           
#>  8 limited      bounded, checked, circumscribed, confined, constrained     
#>  9 medicine     cure, drug, medicament, medication, nostrum                
#> 10 mobile       ambulatory, itinerant, locomotive, migrant, motile         
#> 11 passion      animation, ardour, eagerness, emotion, excitement          
#> 12 reason       apprehension, brains, comprehension, intellect, judgment   
#> 13 resource     ability, capability, cleverness, ingenuity, initiative     
#> 14 romance      affair, affaire (du coeur), affair of the heart, amour, at~
#> 15 school       academy, alma mater, college, department, discipline       
#> 16 science      body of knowledge, branch of knowledge, discipline, art, s~
#> 17 standing     condition, credit, eminence, estimation, footing           
#> 18 tackle       accoutrements, apparatus, equipment, gear, implements      
#> 19 vision       eyes, eyesight, perception, seeing, sight                  
#> 20 world        earth, earthly sphere, globe, everybody, everyone

Создано в 2019-04-05 пакетом Представить (v0.2.1)

Обратите внимание, что plyr должен быть загружен до dplyr

...