R mapply返回列表中的函数调用不是数据帧

我有一个自定义函数。当我手动运行该函数时，它返回一个数据帧：

> create_sentiment_df('taggreason', 'republican', 'lost')
Joining, by = "word"
Joining, by = "word"
      sentiment prop.sentiment twitter.name      party election.result
1         anger     0.04721931   taggreason republican            lost
2  anticipation     0.14375656   taggreason republican            lost
3       disgust     0.01259182   taggreason republican            lost
4          fear     0.06190976   taggreason republican            lost
5           joy     0.09024134   taggreason republican            lost
6      negative     0.10073452   taggreason republican            lost
7      positive     0.26862539   taggreason republican            lost
8       sadness     0.03777545   taggreason republican            lost
9      surprise     0.03882476   taggreason republican            lost
10        trust     0.19832109   taggreason republican            lost

但是，我希望多次运行此操作，因此我在数据帧的每一行上使用

mappy

。以下是数据，数据框仅为1行（用于测试）：

或：

下面是函数。它需要twitter授权，所以我不确定如何轻松地重新运行它。函数本身是否会使

mapply

返回列表而不是数据帧

library(rtweet)
library(tidytext)
library(tidyverse)
library(BBmisc)
library(reshape)

create_token(
  app = "Flippable Sentiment Analysis",
  consumer_key = c_k,
  consumer_secret = c_s,
  access_token <- a_t,
  access_secret <- a_s)

create_sentiment_df <- function(twitter.name, party, election.result) {
  va_stop_words <- stop_words %>% select(-lexicon) %>% 
    bind_rows(data.frame(word = c("https", "t.co", "rt", "amp")))
  nrc_lex <- get_sentiments("nrc") # many sentiments

  dat <- get_timeline(twitter.name, n=3200)
  dat$created_at <- as.Date(dat$created_at)
  dat_2017 <- subset(dat, created_at > as.Date('2017-01-01') & created_at < as.Date('2017-11-06'))

  dat_words <- dat_2017 %>% 
    select(status_id, text) %>% 
    unnest_tokens(word,text)

  dat_words_interesting <- dat_words %>% anti_join(va_stop_words)
  dat_sentiment <- dat_words_interesting %>% left_join(nrc_lex)
  dat_sentiment_count <- dat_sentiment %>% 
    filter(!is.na(sentiment)) %>% 
    group_by(sentiment) %>% 
    summarise(prop.sentiment=n())
  dat_sentiment_count <- na.omit(dat_sentiment_count)

  dat_sentiment_count <- cbind(dat_sentiment_count[1], 
                            prop.table(data.matrix(dat_sentiment_count[-1]), margin=2))

  # dat_sentiment_count$twitter.name <- NA
  dat_sentiment_count$twitter.name <- twitter.name
  dat_sentiment_count$party <- party
  dat_sentiment_count$election.result <- election.result

  return(as.data.frame(dat_sentiment_count))
}

库（rtweet）
图书馆（tidytext）
图书馆（tidyverse）
图书馆（BBmisc）
图书馆（重塑）
创建令牌(
app=“可翻转情绪分析”，
消费者密钥=c_k，
消费者秘密=消费者秘密，
access_token您的
create_token_df
函数返回data.frame，默认情况下，
mapply
会简化它

如果需要data.frames列表，可以执行以下操作：

mapply(create_sentiment_df, datt1$twtr_handle, datt1$party, datt1$result, SIMPLIFY = FALSE)

如果所有data.frame输出都需要一个data.frame，请使用：

do.call(rbind, mapply(create_sentiment_df, datt1$twtr_handle,
                      datt1$party, datt1$result, SIMPLIFY = FALSE))

太好了，谢谢。我不知道关于mapply的事
library(rtweet)
library(tidytext)
library(tidyverse)
library(BBmisc)
library(reshape)

create_token(
  app = "Flippable Sentiment Analysis",
  consumer_key = c_k,
  consumer_secret = c_s,
  access_token <- a_t,
  access_secret <- a_s)

create_sentiment_df <- function(twitter.name, party, election.result) {
  va_stop_words <- stop_words %>% select(-lexicon) %>% 
    bind_rows(data.frame(word = c("https", "t.co", "rt", "amp")))
  nrc_lex <- get_sentiments("nrc") # many sentiments

  dat <- get_timeline(twitter.name, n=3200)
  dat$created_at <- as.Date(dat$created_at)
  dat_2017 <- subset(dat, created_at > as.Date('2017-01-01') & created_at < as.Date('2017-11-06'))

  dat_words <- dat_2017 %>% 
    select(status_id, text) %>% 
    unnest_tokens(word,text)

  dat_words_interesting <- dat_words %>% anti_join(va_stop_words)
  dat_sentiment <- dat_words_interesting %>% left_join(nrc_lex)
  dat_sentiment_count <- dat_sentiment %>% 
    filter(!is.na(sentiment)) %>% 
    group_by(sentiment) %>% 
    summarise(prop.sentiment=n())
  dat_sentiment_count <- na.omit(dat_sentiment_count)

  dat_sentiment_count <- cbind(dat_sentiment_count[1], 
                            prop.table(data.matrix(dat_sentiment_count[-1]), margin=2))

  # dat_sentiment_count$twitter.name <- NA
  dat_sentiment_count$twitter.name <- twitter.name
  dat_sentiment_count$party <- party
  dat_sentiment_count$election.result <- election.result

  return(as.data.frame(dat_sentiment_count))
}

mapply(create_sentiment_df, datt1$twtr_handle, datt1$party, datt1$result, SIMPLIFY = FALSE)

do.call(rbind, mapply(create_sentiment_df, datt1$twtr_handle,
                      datt1$party, datt1$result, SIMPLIFY = FALSE))