如何解决以下错误:输入必须是任意长度的字符向量或字符向量列表，每个字符向量的长度为1. [英] How do I solve the following error?Input must be a character vector of any length or a list of character vectors, each of which has a length of 1.

问题描述

我正在研究R项目.我使用的数据集可从以下链接获得 https://www.kaggle.com/ranjitha1/hotel-reviews-city -chennai/data

I am working on a R project. The data set I used is available at the following link https://www.kaggle.com/ranjitha1/hotel-reviews-city-chennai/data

我使用的代码是

df1 = read.csv("chennai.csv", header = TRUE)
library(tidytext)
tidy_books <- df1 %>% unnest_tokens(word,Review_Text)

Review_Text是文本列.但是，出现以下错误.

Here Review_Text is the text column. Yet, I get the following error.

Error in check_input(x) : 
Input must be a character vector of any length or a list of character
vectors, each of which has a length of 1.

推荐答案

stringsAsFactors再次罢工！

stringsAsFactors strikes again!

您的Review_Text列是一个因素，而不是错误提示功能所要求的字符向量.

Your Review_Text column is a factor, not a character vector as the error message says the function requires.

我强烈建议您使用默认值read.csv上的readr::read_csv，因为它更快，并且默认值不会导致此问题.否则，只需将stringsAsFactors设置为FALSE，就可以了:

I would strongly recommend using readr::read_csv over the default read.csv as it's faster and its defaults don't cause this problem. Otherwise, just set stringsAsFactors to FALSE and you're good:

> tidytext::unnest_tokens(readr::read_csv("chennai_reviews.csv"), word, Review_Text)
Parsed with column specification:
cols(
  Hotel_name = col_character(),
  Review_Title = col_character(),
  Review_Text = col_character(),
  Sentiment = col_character(),
  Rating_Percentage = col_character(),
  X6 = col_integer(),
  X7 = col_integer(),
  X8 = col_character(),
  X9 = col_character()
)
Warning: 1 parsing failure.
row # A tibble: 1 x 5 col     row   col   expected                                                                                                       actual expected   <int> <chr>      <chr>                                                                                                        <chr> actual 1  2262    X7 an integer "Expedia Booking  availability was  , only  for  Non-  AC ; ON REQUEST  OVER  PHONE got  it.\n\nRecommended" file # ... with 1 more variables: file <chr>

# A tibble: 179,883 x 9
            Hotel_name                          Review_Title Sentiment Rating_Percentage    X6    X7    X8    X9       word
                 <chr>                                 <chr>     <chr>             <chr> <int> <int> <chr> <chr>      <chr>
 1 Accord Metropolitan Excellent comfortableness during stay         3               100    NA    NA  <NA>  <NA>        its
 2 Accord Metropolitan Excellent comfortableness during stay         3               100    NA    NA  <NA>  <NA>     really
 3 Accord Metropolitan Excellent comfortableness during stay         3               100    NA    NA  <NA>  <NA>       nice
 4 Accord Metropolitan Excellent comfortableness during stay         3               100    NA    NA  <NA>  <NA>      place
 5 Accord Metropolitan Excellent comfortableness during stay         3               100    NA    NA  <NA>  <NA>         to
 6 Accord Metropolitan Excellent comfortableness during stay         3               100    NA    NA  <NA>  <NA>       stay
 7 Accord Metropolitan Excellent comfortableness during stay         3               100    NA    NA  <NA>  <NA> especially
 8 Accord Metropolitan Excellent comfortableness during stay         3               100    NA    NA  <NA>  <NA>        for
 9 Accord Metropolitan Excellent comfortableness during stay         3               100    NA    NA  <NA>  <NA>   business
10 Accord Metropolitan Excellent comfortableness during stay         3               100    NA    NA  <NA>  <NA>        and
# ... with 179,873 more rows
Warning message:
Missing column names filled in: 'X6' [6], 'X7' [7], 'X8' [8], 'X9' [9] 

或

> tidytext::unnest_tokens(read.csv("chennai_reviews.csv", stringsAsFactors = FALSE), word, Review_Text)
                                                Hotel_name
1                                      Accord Metropolitan
                                                                                                                                                                                                                                                        Review_Title
...snip...

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