我有一个txt文件的URL列表。txt 文件的结构使得某些部分是纯文本，某些部分是表格。我想提取表并将它们导出到数据框。下面是一个网址示例：
https://www.sec.gov/Archives/edgar/data/1000275/0001140361-13-007449.txt
txt 文件的结构使得表以<TABLE>开头并以</TABLE>结尾。我想合并所有表格。我尝试过使用 read.delim，但我不知道如何将其用于表格。下面是预期输出的示例。我将不胜感激有关如何进行我的项目的任何指导。
Example of current df:
+----+--------------------------------------------------------------------------+
| ID | URL |
+----+--------------------------------------------------------------------------+
| 1 | https://www.sec.gov/Archives/edgar/data/1000097/0000919574-13-001835.txt |
| 2 | https://www.sec.gov/Archives/edgar/data/1000275/0001140361-13-007449.txt |
| 3 | https://www.sec.gov/Archives/edgar/data/1000742/0000898432-13-000218.txt |
+----+--------------------------------------------------------------------------+
Example of txt file from url:
text text text
text text text
text text text
<TABLE>
+---------------------+----------------+-----------+--------------+-----------------+--------+----------+-----------------------+-------------+------------------+
| NAME OF ISSUER | TITLE OF CLASS | CUSIP | VALUE (x1000 | SHRS OR PRN AMT | SH/PRN | PUT/CALL | INVESTMENT DISCRETION | OTHER MNGRS | VOTING AUTHORITY |
+---------------------+----------------+-----------+--------------+-----------------+--------+----------+-----------------------+-------------+------------------+
| ABBVIE INC | COM | 00287Y109 | 1,547 | 45,300 | SHS | | Shared-Defined | 1/2/3 | 45,300 |
| ABERCROMBIE & FITCH | CL A | 002896207 | 4,797 | 100,000 | SHS | | Shared-Defined | 1/2/3 | 100,000 |
+---------------------+----------------+-----------+--------------+-----------------+--------+----------+-----------------------+-------------+------------------+
</TABLE>
<TABLE>
+---------------------+----------------+-----------+--------------+-----------------+--------+----------+-----------------------+-------------+------------------+
| NAME OF ISSUER | TITLE OF CLASS | CUSIP | VALUE (x1000 | SHRS OR PRN AMT | SH/PRN | PUT/CALL | INVESTMENT DISCRETION | OTHER MNGRS | VOTING AUTHORITY |
+---------------------+----------------+-----------+--------------+-----------------+--------+----------+-----------------------+-------------+------------------+
| ABBVIE INC | COM | 00287Y109 | 1,547 | 45,300 | SHS | | Shared-Defined | 1/2/3 | 45,300 |
| ABERCROMBIE & FITCH | CL A | 002896207 | 4,797 | 100,000 | SHS | | Shared-Defined | 1/2/3 | 100,000 |
+---------------------+----------------+-----------+--------------+-----------------+--------+----------+-----------------------+-------------+------------------+
</TABLE>
Expected output:
+----+----------------+----------------+-------+--------------+-----------------+--------+----------+-----------------------+-------------+------------------+
| ID | NAME OF ISSUER | TITLE OF CLASS | CUSIP | VALUE (x1000 | SHRS OR PRN AMT | SH/PRN | PUT/CALL | INVESTMENT DISCRETION | OTHER MNGRS | VOTING AUTHORITY |
+----+----------------+----------------+-------+--------------+-----------------+--------+----------+-----------------------+-------------+------------------+
| 1 | x | x | x | x | x | x | x | x | x | x |
| 1 | x | x | x | x | x | x | x | x | x | x |
| 1 | x | x | x | x | x | x | x | x | x | x |
| 2 | x | x | x | x | x | x | x | x | x | x |
| 2 | x | x | x | x | x | x | x | x | x | x |
| 2 | x | x | x | x | x | x | x | x | x | x |
+----+----------------+----------------+-------+--------------+-----------------+--------+----------+-----------------------+-------------+------------------+
这是一个粗略的解决方案。
# Read the text files from the web
fileContents <- readr::read_file("https://www.sec.gov/Archives/edgar/data/1000275/0001140361-13-007449.txt")
# Extract the tables. The regex isn't quite right, as it includes the starting <TABLE>
# and ending </TABLE> tags, but more complicated regexes failed. Regex isn't my
# strong point, and I can handle the extra work
tables <- stringr::str_extract_all(
fileContents,
stringr::regex("(?s)<TABLE>(.*?)</TABLE>",
multiline=TRUE,
dotall=TRUE
)
)
# Function to process a single tibble
toTibble <- function(y) {
lines <- str_split_fixed(y, "n", n=Inf)
colStarts <- c()
colEnds <- c()
# Scroll through to final table header
for (i in 1:(length(lines)-1)) { # Final line is '</TABLE>' because of initial regex
# Could probably to this with regexes, but my head is hurting
if (any(!is.na(stringr::str_locate(lines[i], "<\w>")))) {
# Define column widths based on locations of type markers. THIS IS AN ASSUMPTION
colStarts <- stringr::str_locate_all(lines[i], "<\w>")[[1]][,"start"]
for (i in 1:(length(colStarts)-1)) colEnds[i] <- colStarts[i+1] -1
colEnds[length(colStarts)] <- stringr::str_length(lines[i])
lines <- lines[(i+1):(length(lines)-1)]
data <- dplyr::bind_rows(
lapply(
lines, # For each data line
function(line)
tibble::enframe( # Split in to columns and convert to a tibble of name/value pairs
stringr::str_trim(
stringr::str_sub(
line,
colStarts,
colEnds
)
)
) %>% # Convert from name/value pairs to columns
tidyr::pivot_wider(
values_from="value",
names_from="name",
names_prefix="Column"
)
)
)
# Finished
return(data)
}
}
}
要处理单个文件：
firstTable <- toTibble(tables[[1]][[1]])
firstTable
给
# A tibble: 59 x 12
Column1 Column2 Column3 Column4 Column5 Column6 Column7 Column8 Column9 Column10 Column11 Column12
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 AAR CORP COM 000361105 190 10158 SH "" DEFINED "2" 10158 0 0
2 AAR CORP COM 000361105 15 803 SH "" DEFINED "3" 0 0 803
3 AAR CORP COM 000361105 37 2000 SH "" DEFINED "5" 2000 0 0
4 AAR CORP COM 000361105 78 4200 SH "" DEFINED "" 4200 0 0
5 ABB LTD SPONSORED ADR 000375204 2164 104112 SH "" DEFINED "3" 257 0 103855
6 ABB LTD SPONSORED ADR 000375204 10774 518215 SH "" DEFINED "5" 518215 0 0
7 ABB LTD SPONSORED ADR 000375204 64 3100 SH "" DEFINED "7" 0 3100 0
8 ABB LTD SPONSORED ADR 000375204 1044 50200 SH "" DEFINED "8" 50200 0 0
9 ABB LTD SPONSORED ADR 000375204 9 410 SH "" DEFINED "9" 410 0 0
10 ABB LTD SPONSORED ADR 000375204 103 4958 SH "" DEFINED "15" 4958 0 0
# … with 49 more rows
在我的系统上
system.time({firstTable <- toTibble(tables[[1]][[1]])})
user system elapsed
0.843 0.004 0.849
所以在一秒钟内处理一个表。
length(tables[[1]])
[1] 299
文件中只有不到 300 个表，因此将所有表绑定到一个 tibble 中
alldata <- bind_rows(lapply(tables[[1]], function(t) toTibble(t)))
提取表大约需要五分钟，再加上将它们全部绑定在一起。 [此代码未经过测试或计时。
进入此阶段后，您可以使用列类型和名称来获得所需的内容。 这应该是直截了当的。