我们可以使用dplyr执行以下操作

library(dplyr)
df %>% 
  group_by(syn_group) %>%
  mutate(sum_word_count = sum(word_count)) %>% 
  filter(rank == 1)

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数据：

df <- read.table(text = "id  word    word_count  syn_group   rank
1   accept  15          1           1
5   admit   8           1           2
2   dad     14          2           1
8   daddy   4           2           2
9   papa    2           2           3
3   happy   9           3           1
7   glad    4           3           2
4   large   8           4           1
6   big     6           4           2", header = T)

型
请下次发布dput的输出。

• 编辑：* 这里有一些代码可以帮助你开始循环单词和词性，并存储同义词。剩下的就是确定当前术语是否是前一个术语的同义词，在这种情况下，你已经有了同义词，你可以分配一个唯一的同义词组。接下来，你需要存储一些结果。最后，你需要计算排名，这只是seq_along同义词和grep来确定排名位置。注解是提示你可能需要在哪里包含这些提示的代码。

d <- data.frame(Term = row.names(m), word_count = m[,1])
all_pos <- c("ADJECTIVE", "ADVERB", "NOUN","VERB")
syns <- vector("list", length(all_pos))
for(w in seq(nrow(d))){
  # if sysns of (d$Term[w]) has been calculated skip over current w 
  emf <- getTermFilter("ExactMatchFilter", d$Term[w], TRUE)  
  for(i in seq_along(syns)){
    terms <- getIndexTerms(all_pos[i], 1, emf)
    if(is.null(terms)){
      syns[i] <- NA
    } else{
      syns[[i]] <-  getSynonyms(terms[[1]])
    }
  }
  # store the results of syns for current w 
}

型

我已经能够提取法语同义词自动从一个网站如下：

library(stringr)
library(pagedown)
library(pdftools)
path_Save_PDF <- "D:\\"
base_Url <- "https://dictionary.reverso.net/french-synonyms/"
words <- c("fâché")
nb_Words <- length(words)
list_Text <- list()

for(i in 1 : nb_Words)
{
  print(i)
  pdf_File <- paste0(path_Save_PDF, words[i], ".pdf")
  chrome_print(input = paste0(base_Url, words[i]), output = pdf_File)
  list_Text[[i]] <- pdftools::pdf_text(pdf_File)
  list_Text[[i]] <- strsplit(x = list_Text[[i]], split = "\n")
}

save(list_Text, file = "list_Text.RData")

list_Synonymes <- list()
for(i in 1 : nb_Words)
{
  print(i)
  id_Lines_Synonymes <- which(str_detect(string = list_Text[[i]][[1]], pattern = "[:space:]{4,8}\\d{1,2}"))
  text_Synonymes <- list_Text[[i]][[1]][id_Lines_Synonymes]
  text_Synonymes <- stringr::str_remove_all(text_Synonymes, pattern = "Facebook®(.*)Visit Site")
  text_Synonymes <- stringr::str_remove_all(text_Synonymes, pattern = "and post updates\\.")
  text_Synonymes <- stringr::str_remove_all(text_Synonymes, pattern = "par extension au sens figuré")
  text_Synonymes <- stringr::str_remove_all(text_Synonymes, pattern = "details")
  text_Synonymes <- stringr::str_remove_all(text_Synonymes, pattern = "\\d")
  text_Synonymes <- stringr::str_squish(text_Synonymes)
  text_Synonymes <- paste0(text_Synonymes, collapse = ",")
  text_Synonymes <- stringr::str_replace_all(string = text_Synonymes, pattern = "\\,\\,", replacement = "\\,")
  text_Synonymes <- base::strsplit(text_Synonymes, ",")[[1]]
  list_Synonymes[[i]] <- text_Synonymes
}

names(list_Synonymes) <- words
list_Synonymes

list_Synonymes
$fâché
 [1] "dépité"                                       
 [2] " grognon"                                     
 [3] " mécontent"                                   
 [4] " morfondu"                                    
 [5] " transi"                                      
 [6] " horripilé"                                   
 [7] " irrité"                                      
 [8] " contrarié"                                   
 [9] " ennuyé"                                      
[10] " frissonnant"                                 
[11] "navré"                                        
[12] " désolé"                                      
[13] "vexé"                                         
[14] " indisposé"                                   
[15] " piqué"                                       
[16] "en colère"                                    
[17] " mécontent"                                   
[18] "désolé"                                       
[19] " navré"                                       
[20] "brouillé avec quelqu'un"                      
[21] " en froid"                                    
[22] " être incompétent dans un domaine particulier"
[23] " ne rien comprendre"

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之后，它可以用来将同义词分组在一起。