R 从pdf文本到文件列中文件名的整齐数据框
我想分析近300个pdf文档中的文本。现在,我使用R 从pdf文本到文件列中文件名的整齐数据框,r,pdf,text-mining,corpus,tidytext,R,Pdf,Text Mining,Corpus,Tidytext,我想分析近300个pdf文档中的文本。现在,我使用pdftools和tm,tidytext包来阅读文本,将其转换为语料库,然后转换为文档术语矩阵,最后我希望在一个整洁的数据框中对其进行结构构建 我有几个问题: 如何删除页面数据(在每个pdf页面的顶部和/或底部) 我更希望文件名作为文档列中的值,而不是索引数字 以下代码仅包含2个pdf文件,以确保再现性。当我运行所有文件时,我在corpus对象中得到294个文档,但当我整理它时,我似乎丢失了一些文件,因为converted%>%distinct
pdftools
和tm
,tidytext
包来阅读文本,将其转换为语料库,然后转换为文档术语矩阵,最后我希望在一个整洁的数据框中对其进行结构构建
我有几个问题:
- 如何删除页面数据(在每个pdf页面的顶部和/或底部)
- 我更希望文件名作为
列中的值,而不是索引数字文档
- 以下代码仅包含2个pdf文件,以确保再现性。当我运行所有文件时,我在
对象中得到294个文档,但当我整理它时,我似乎丢失了一些文件,因为corpus
返回了275个文档。我想知道这是为什么converted%>%distinct(document)
library(tidyverse)
library(tidytext)
library(pdftools)
library(tm)
library(broom)
# Create a temporary empty directory
# (don't worry at the end of this script I'll remove this directory and its files)
dir.create("~/Desktop/sample-pdfs")
# Fill directory with 2 pdf files from my github repo
download.file("https://github.com/thomasdebeus/colourful-facts/raw/master/projects/sample-data/'s-Gravenhage_coalitieakkoord.pdf", destfile = "~/Desktop/sample-pdfs/'s-Gravenhage_coalitieakkoord.pdf")
download.file("https://github.com/thomasdebeus/colourful-facts/raw/master/projects/sample-data/Aa%20en%20Hunze_coalitieakkoord.pdf", destfile = "~/Desktop/sample-pdfs/Aa en Hunze_coalitieakkoord.pdf")
# Create vector of file paths
dir <- "~/Desktop/sample-pdfs"
pdfs <- paste(dir, "/", list.files(dir, pattern = "*.pdf"), sep = "")
# Read the text from pdf's with pdftools package
pdfs_text <- map(pdfs, pdf_text)
# Convert to document-term-matrix
converted <- Corpus(VectorSource(pdfs_text)) %>%
DocumentTermMatrix()
# Now I want to convert this to a tidy format
converted %>%
tidy() %>%
filter(!grepl("[0-9]+", term))
从运行以下行的桌面删除下载的文件及其目录:
unlink("~/Desktop/sample-pdfs", recursive = TRUE)
非常感谢您的帮助 我建议为您想要执行的操作编写一个包装函数,这样您就可以将每个文件名添加为一列
read_PDF <- function(file){
pdfs_text <- pdf_text(file)
converted <- Corpus(VectorSource(pdfs_text)) %>%
DocumentTermMatrix()
converted %>%
tidy() %>%
filter(!grepl("[0-9]+", term)) %>%
# add FileName as a column
mutate(FileName = file)
}
final <- map(pdfs, read_PDF) %>% data.table::rbindlist()
read\u PDF%
过滤器(!grepl(“[0-9]+”,术语))%>%
#将文件名添加为列
变异(文件名=文件)
}
最终%data.table::rbindlist()
我建议为要执行的操作编写一个包装函数,这样您就可以将每个文件名添加为一列
read_PDF <- function(file){
pdfs_text <- pdf_text(file)
converted <- Corpus(VectorSource(pdfs_text)) %>%
DocumentTermMatrix()
converted %>%
tidy() %>%
filter(!grepl("[0-9]+", term)) %>%
# add FileName as a column
mutate(FileName = file)
}
final <- map(pdfs, read_PDF) %>% data.table::rbindlist()
read\u PDF%
过滤器(!grepl(“[0-9]+”,术语))%>%
#将文件名添加为列
变异(文件名=文件)
}
最终%data.table::rbindlist()
很好的例子
- 我添加了几行来添加名称李>
- 我不确定是否会丢失文件,我没有这种行为
- 仅提及您的文件名不是很标准,建议再次检查名称,并且您在第一个文件的开头有一个撇号。还将建议对空间进行清洁
- 我用英语文档做了测试,你可以在语料库中添加不同的语言
library(tidyverse)
library(tidytext)
library(pdftools)
library(tm)
library(broom)
# Create a temporary empty directory
dir <- "PDFs/"
pdfs <- paste0(dir, list.files(dir, pattern = "*.pdf"))
names <- list.files(dir, pattern = "*.pdf")
# create a table of names
namesDocs <-
names %>%
str_remove(pattern = ".pdf") %>%
as.tibble() %>%
mutate(ids = as.character(seq_along(names)))
namesDocs
# Read the text from pdf's with pdftools package
pdfs_text <- map(pdfs, pdftools::pdf_text)
# Convert to document-term-matrix
# add cleaning process
converted <-
Corpus(VectorSource(pdfs_text)) %>%
DocumentTermMatrix(
control = list(removeNumbers = TRUE,
stopwords = TRUE,
removePunctuation = TRUE))
converted
# Now I want to convert this to a tidy format
# add names of documents
mytable <-
converted %>%
tidy() %>%
arrange(desc(count)) %>%
left_join(y = namesDocs, by = c("document" = "ids"))
head(mytable)
View(mytable)
库(tidyverse)
图书馆(tidytext)
图书馆(pdftools)
图书馆(tm)
图书馆(扫帚)
#创建一个临时空目录
dir很好的例子
- 我添加了几行来添加名称李>
- 我不确定是否会丢失文件,我没有这种行为
- 仅提及您的文件名不是很标准,建议再次检查名称,并且您在第一个文件的开头有一个撇号。还将建议对空间进行清洁
- 我用英语文档做了测试,你可以在语料库中添加不同的语言
代码如下:
library(tidyverse)
library(tidytext)
library(pdftools)
library(tm)
library(broom)
# Create a temporary empty directory
dir <- "PDFs/"
pdfs <- paste0(dir, list.files(dir, pattern = "*.pdf"))
names <- list.files(dir, pattern = "*.pdf")
# create a table of names
namesDocs <-
names %>%
str_remove(pattern = ".pdf") %>%
as.tibble() %>%
mutate(ids = as.character(seq_along(names)))
namesDocs
# Read the text from pdf's with pdftools package
pdfs_text <- map(pdfs, pdftools::pdf_text)
# Convert to document-term-matrix
# add cleaning process
converted <-
Corpus(VectorSource(pdfs_text)) %>%
DocumentTermMatrix(
control = list(removeNumbers = TRUE,
stopwords = TRUE,
removePunctuation = TRUE))
converted
# Now I want to convert this to a tidy format
# add names of documents
mytable <-
converted %>%
tidy() %>%
arrange(desc(count)) %>%
left_join(y = namesDocs, by = c("document" = "ids"))
head(mytable)
View(mytable)
库(tidyverse)
图书馆(tidytext)
图书馆(pdftools)
图书馆(tm)
图书馆(扫帚)
#创建一个临时空目录
dir您可以使用tm将文档直接读入语料库。readPDF阅读器使用pdftools作为引擎。无需首先创建一组文本,通过语料库获取输出。我创建了两个示例。第一个与你所做的一致,但首先要通过语料库。第二个完全基于tidyverse+tidytext。不需要在tm、tidytext等之间切换
示例之间令牌数量的差异是由于tidytext/tokenizer中的自动清理造成的
如果您有很多文档要做,您可能希望使用quanteda
作为您的主力,因为它可以在多个核心上即时工作,并可能加快标记器部分的速度。不要忘记使用stopwords
软件包来获得荷兰stopwords的良好列表。如果您需要荷兰语单词的词性标注,请检查updipe
软件包
library(tidyverse)
library(tidytext)
library(tm)
directory <- "D:/sample-pdfs"
# create corpus from pdfs
converted <- VCorpus(DirSource(directory), readerControl = list(reader = readPDF)) %>%
DocumentTermMatrix()
converted %>%
tidy() %>%
filter(!grepl("[0-9]+", term))
# A tibble: 5,707 x 3
document term count
<chr> <chr> <dbl>
1 's-Gravenhage_coalitieakkoord.pdf "\ade" 4
2 's-Gravenhage_coalitieakkoord.pdf "\adeze" 1
3 's-Gravenhage_coalitieakkoord.pdf "\aeen" 2
4 's-Gravenhage_coalitieakkoord.pdf "\aer" 2
5 's-Gravenhage_coalitieakkoord.pdf "\aextra" 2
6 's-Gravenhage_coalitieakkoord.pdf "\agroei" 1
7 's-Gravenhage_coalitieakkoord.pdf "\ahet" 1
8 's-Gravenhage_coalitieakkoord.pdf "\amet" 1
9 's-Gravenhage_coalitieakkoord.pdf "\aonderwijs," 1
10 's-Gravenhage_coalitieakkoord.pdf "\aop" 11
# ... with 5,697 more rows
库(tidyverse)
图书馆(tidytext)
图书馆(tm)
目录%
整洁()%>%
过滤器(!grepl(“[0-9]+”,术语))
#A tibble:5707 x 3
文件期限计数
1 s-Gravenhage_coalitieakkoord.pdf“\ade”4
2 s-Gravenhage_coaliteakoord.pdf“\adeze”1
3 s-Gravenhage_coalitieakkoord.pdf“\aeen”2
4 s-Gravenhage_coaliteakoord.pdf“\aer”2
5 s-Gravenhage_coaliteakoord.pdf“\aextra”2
6 s-Gravenhage_coalitieakkoord.pdf“\agroei”1
7's-Gravenhage_coaliteakoord.pdf“\ahet”1
8 s-Gravenhage_coalitieakkoord.pdf“\amet”1
9's-Gravenhage_coaliteakoord.pdf“\aonderwijs,”1
10 s-Gravenhage_coalitieakkoord.pdf“\aop”11
# ... 还有5697行
只使用tidytext而不是tm
directory <- "D:/sample-pdfs"
pdfs <- paste(directory, "/", list.files(directory, pattern = "*.pdf"), sep = "")
pdf_names <- list.files(directory, pattern = "*.pdf")
pdfs_text <- map(pdfs, pdftools::pdf_text)
my_data <- data_frame(document = pdf_names, text = pdfs_text)
my_data %>%
unnest %>% # pdfs_text is a list
unnest_tokens(word, text, strip_numeric = TRUE) %>% # removing all numbers
group_by(document, word) %>%
summarise(count = n())
# A tibble: 4,646 x 3
# Groups: document [?]
document word count
<chr> <chr> <int>
1 's-Gravenhage_coalitieakkoord.pdf 1e 2
2 's-Gravenhage_coalitieakkoord.pdf 2e 2
3 's-Gravenhage_coalitieakkoord.pdf 3e 1
4 's-Gravenhage_coalitieakkoord.pdf 4e 1
5 's-Gravenhage_coalitieakkoord.pdf aan 164
6 's-Gravenhage_coalitieakkoord.pdf aanbesteding 2
7 's-Gravenhage_coalitieakkoord.pdf aanbestedingen 1
8 's-Gravenhage_coalitieakkoord.pdf aanbestedingsprocedures 1
9 's-Gravenhage_coalitieakkoord.pdf aanbevelingen 1
10 's-Gravenhage_coalitieakkoord.pdf aanbieden 4
# ... with 4,636 more rows
directory您可以使用tm将文档直接读入语料库。readPDF阅读器使用pdftools作为引擎。无需首先创建一组文本,通过语料库获取输出。我创建了两个示例。第一个与你所做的一致,但首先要通过语料库。第二个完全基于tidyverse+tidytext。不需要在tm、tidytext等之间切换
示例之间令牌数量的差异是由于tidytext/tokenizer中的自动清理造成的
如果您有很多文档要做,您可能希望使用quanteda
作为您的主力,因为它可以在多个核心上即时工作,并可能加快标记器部分的速度。不要忘记使用stopwords
软件包来获得荷兰stopwords的良好列表。如果您需要荷兰语单词的词性标注,请检查updipe
软件包
library(tidyverse)
library(tidytext)
library(tm)
directory <- "D:/sample-pdfs"
# create corpus from pdfs
converted <- VCorpus(DirSource(directory), readerControl = list(reader = readPDF)) %>%
DocumentTermMatrix()
converted %>%
tidy() %>%
filter(!grepl("[0-9]+", term))
# A tibble: 5,707 x 3
document term count
<chr> <chr> <dbl>
1 's-Gravenhage_coalitieakkoord.pdf "\ade" 4
2 's-Gravenhage_coalitieakkoord.pdf "\adeze" 1
3 's-Gravenhage_coalitieakkoord.pdf "\aeen" 2
4 's-Gravenhage_coalitieakkoord.pdf "\aer" 2
5 's-Gravenhage_coalitieakkoord.pdf "\aextra" 2
6 's-Gravenhage_coalitieakkoord.pdf "\agroei" 1
7 's-Gravenhage_coalitieakkoord.pdf "\ahet" 1
8 's-Gravenhage_coalitieakkoord.pdf "\amet" 1
9 's-Gravenhage_coalitieakkoord.pdf "\aonderwijs," 1
10 's-Gravenhage_coalitieakkoord.pdf "\aop" 11
# ... with 5,697 more rows
库(tidyverse)
图书馆(tidytext)
图书馆(tm)
目录%
整洁()%>%
过滤器(!grepl(“[0-9]+”,术语))
#A tibble:5707 x 3
文件期限计数
1 s-Gravenhage_coalitieakkoord.pdf“\ade”4
2 s-Gravenhage_coaliteakoord.pdf“\adeze”1
3's-Gravenhage_coalitieakkoord.pdf“\aeen”
# set path to the PDF
pdf_path <- "PDFs/"
# List the PDFs
pdfs <- list.files(path = pdf_path, pattern = 'pdf$', full.names = TRUE)
# Import the PDFs into R
spill_texts <- readtext(pdfs,
docvarsfrom = "filenames",
sep = "_",
docvarnames = c("First_author", "Year"))
# Transform the pdfs into a corpus object
spill_corpus <- corpus(spill_texts)
spill_corpus
# Some stats about the pdfs
tokenInfo <- summary(spill_corpus)
tokenInfo