我的本地驱动器中有900多个文件夹，每个文件夹都有一个.dat扩展名文件。我想遍历每个文件夹以访问其中的文件，以仅获取特定数据并将该数据写入新文件中。每个.dat文件看起来都像这样-
Authors:
# Pallavi Subhraveti
# Quang Ong
# Tim Holland
# Anamika Kothari
# Ingrid Keseler
# Ron Caspi
# Peter D Karp
# Please see the license agreement regarding the use of and distribution of
this file.
# The format of this file is defined at http://bioinformatics.ai.sri.com
# Version: 21.5
# File Name: compounds.dat
# Date and time generated: October 24, 2017, 14:52:45
# Attributes:
# UNIQUE-ID
# TYPES
# COMMON-NAME
# ABBREV-NAME
# ACCESSION-1
# ANTICODON
# ATOM-CHARGES
# ATOM-ISOTOPES
# CATALYZES
# CFG-ICON-COLOR
# CHEMICAL-FORMULA
# CITATIONS
# CODONS
# COFACTORS-OF
# MOLECULAR-WEIGHT
# MONOISOTOPIC-MW
[Data Chunk 1]
UNIQUE-ID - CPD0-1108
TYPES - D-Ribofuranose
COMMON-NAME - β-D-ribofuranose
ATOM-CHARGES - (9 -1)
ATOM-CHARGES - (6 1)
CHEMICAL-FORMULA - (C 5)
CHEMICAL-FORMULA - (H 14)
CHEMICAL-FORMULA - (N 1)
CHEMICAL-FORMULA - (O 6)
CHEMICAL-FORMULA - (P 1)
CREDITS - SRI
CREDITS - kaipa
DBLINKS - (CHEBI "10647" NIL |kothari| 3594051403 NIL NIL)
DBLINKS - (BIGG "37147" NIL |kothari| 3584718837 NIL NIL)
DBLINKS - (PUBCHEM "25200464" NIL |taltman| 3466375284 NIL NIL)
DBLINKS - (LIGAND-CPD "C01233" NIL |keseler| 3342798255 NIL NIL)
INCHI - InChI=1S/C5H14NO6P/c6-1-2-11-13(9,10)12-4-5(8)3-7/h5,7-8H,1-4,6H2,(H,9,10)
MOLECULAR-WEIGHT - 215.142
MONOISOTOPIC-MW - 216.0636987293
NON-STANDARD-INCHI - InChI=1S/C5H14NO6P/c6-1-2-11-13(9,10)12-4-5(8)3-7/h5,7-8H,1-4,6H2,(H,9,10)
SMILES - C(OP([O-])(OCC(CO)O)=O)C[N+]
SYNONYMS - sn-Glycero-3-phosphoethanolamine
SYNONYMS - 1-glycerophosphorylethanolamine\
[Data Chunk 2]
//
UNIQUE-ID - URIDINE
TYPES - Pyrimidine
....
....
每个文件中大约包含18000行（查看Notepad ++中的数据）。现在，我想创建一个新文件，并仅复制数据中的特定列。我只希望将这些列复制到新创建的文件中，并且该文件应如下所示-
UNIQUE-ID TYPES COMMON-NAME CHEMICAL-FORMULA BIGG ID CHEMSPIDER ID CAS ID CHEBI ID PUBCHEM ID MOLECULAR-WEIGHT MONOISOTOPIC-MW
CPD0-1108 D-Ribofuranose β-D-ribofuranose C5H14N1O6P1 37147 NA NA 10647 25200464 215.142 216.0636987293
URIDINE Pyrimidine ...
每个文件中的每个数据块不一定都具有我需要的所有列的信息，这就是为什么我在想要的输出表中为那些列提到了NA的原因。尽管在这些列中获取空白值是完全可以的，因为以后我可以分别处理这些空白。
这是具有数据的目录-
File 1] -> C:\Users\robbie\Desktop\Organism_Data\aact1035194-hmpcyc\compounds.dat
File 2] -> C:\Users\robbie\Desktop\Organism_Data\aaph679198-hmpcyc\compounds.dat
File 3] -> C:\Users\robbie\Desktop\Organism_Data\yreg1002368-hmpcyc\compounds.dat
File 4] -> C:\Users\robbie\Desktop\Organism_Data\tden699187-hmpcyc\compounds.dat
...
...
我真的很倾向于在R引用this的帖子中使用dir函数，但是当编写代码作为生物体名称（文件夹名称）时，我很困惑在函数的模式参数中添加什么）很奇怪，而且不一致。
非常感谢获得所需输出的任何帮助。我正在考虑在R中执行此操作的方法，但是如果我有很好的建议以及在python中处理此问题的方法，我也愿意在python中尝试此操作。在此先感谢您的帮助！
编辑： 链接到数据-Data
答案 0 :(得分：1)
将其分解为一些逻辑操作：
text2chunks <- function(txt) {
chunks <- split(txt, cumsum(grepl("^\\[Data Chunk.*\\]$", txt)))
Filter(function(a) grepl("^\\[Data Chunk.*\\]$", a[1]), chunks)
}
chunk2dataframe <- function(vec, hdrs = NULL, sep = " - ") {
s <- stringi::stri_split(vec, fixed=sep, n=2L)
s <- Filter(function(a) length(a) == 2L, s)
df <- as.data.frame(setNames(lapply(s, `[[`, 2), sapply(s, `[[`, 1)),
stringsAsFactors=FALSE)
if (! is.null(hdrs)) df <- df[ names(df) %in% make.names(hdrs) ]
df
}
hdrs是要保留的列名的可选向量；如果未提供（或NULL），则所有键/值对将作为列返回。
hdrs <- c("UNIQUE-ID", "TYPES", "COMMON-NAME")
使用数据（如下），我有lines，它是来自单个文件的character向量：
head(lines)
# [1] "Authors:"
# [2] "# Pallavi Subhraveti"
# [3] "# Quang Ong"
# [4] "# Please see the license agreement regarding the use of and distribution of this file."
# [5] "# The format of this file is defined at http://bioinformatics.ai.sri.com"
# [6] "# Version: 21.5"
str(text2chunks(lines))
# List of 2
# $ 1: chr [1:5] "[Data Chunk 1]" "UNIQUE-ID - CPD0-1108" "TYPES - D-Ribofuranose" "COMMON-NAME - β-D-ribofuranose" ...
# $ 2: chr [1:6] "[Data Chunk 2]" "// something out of place here?" "UNIQUE-ID - URIDINE" "TYPES - Pyrimidine" ...
str(lapply(text2chunks(lines), chunk2dataframe, hdrs=hdrs))
# List of 2
# $ 1:'data.frame': 1 obs. of 3 variables:
# ..$ UNIQUE.ID : chr "CPD0-1108"
# ..$ TYPES : chr "D-Ribofuranose"
# ..$ COMMON.NAME: chr "β-D-ribofuranose"
# $ 2:'data.frame': 1 obs. of 3 variables:
# ..$ UNIQUE.ID : chr "URIDINE"
# ..$ TYPES : chr "Pyrimidine"
# ..$ COMMON.NAME: chr "β-D-ribofuranose or something"
最终产品：
dplyr::bind_rows(lapply(text2chunks(lines), chunk2dataframe, hdrs=hdrs))
# UNIQUE.ID TYPES COMMON.NAME
# 1 CPD0-1108 D-Ribofuranose β-D-ribofuranose
# 2 URIDINE Pyrimidine β-D-ribofuranose or something
由于要在许多函数上进行迭代，因此为此创建一个便捷函数很有意义：
text2dataframe <- function(txt) {
dplyr::bind_rows(lapply(text2chunks(txt), chunk2dataframe, hdrs=hdrs))
}
未经测试，但应该可以工作：
files <- list.files(path="C:/Users/robbie/Desktop/Organism_Data/",
pattern="compounds.dat", recursive=TRUE, full.names=TRUE)
alldata <- lapply(files, readLines)
allframes <- lapply(alldata, text2dataframe)
oneframe <- dplyr::bind_rows(allframes)
注意：
stringi::stri_split而不是strsplit来使用n=；使用几行额外的代码，在R中进行相同的操作并不难。dplyr::bind_rows是因为它可以很好地处理缺少的列和不同的顺序；基本rbind.data.frame可以省力/省心。data.frame-大小化趋向于使列名稍微增加一点，请注意。数据：
# lines <- readLines("some_filename.dat")
fulltext <- 'Authors:
# Pallavi Subhraveti
# Quang Ong
# Please see the license agreement regarding the use of and distribution of this file.
# The format of this file is defined at http://bioinformatics.ai.sri.com
# Version: 21.5
# File Name: compounds.dat
# Date and time generated: October 24, 2017, 14:52:45
# Attributes:
# UNIQUE-ID
# TYPES
[Data Chunk 1]
UNIQUE-ID - CPD0-1108
TYPES - D-Ribofuranose
COMMON-NAME - β-D-ribofuranose
DO-NOT-CARE - 42
[Data Chunk 2]
// something out of place here?
UNIQUE-ID - URIDINE
TYPES - Pyrimidine
COMMON-NAME - β-D-ribofuranose or something
DO-NOT-CARE - 43
'
lines <- strsplit(fulltext, '[\r\n]+')[[1]]
答案 1 :(得分：1)
另一种方法，在这种情况下，它仅读取您提供的文件，但可以读取多个文件。
我添加了一些中间结果以显示代码的实际作用...
library(tidyverse)
library(data.table)
library(zoo)
# create a data.frame with the desired files
filenames <- list.files( path = getwd(), pattern = "*.dat$", recursive = TRUE, full.names = TRUE )
# > filenames
#[1] "C:/Users/********/Documents/Git/udls2/test.dat"
#read in the files, using data.table's fread.. here I grep lines starting with UNIQUE-ID or TYPES. create your desired regex-pattern
pattern <- "^UNIQUE-ID|^TYPES"
content.list <- lapply( filenames, function(x) fread( x, sep = "\n", header = FALSE )[grepl( pattern, V1 )] )
# > content.list
# [[1]]
# V1
# 1: UNIQUE-ID - CPD0-1108
# 2: TYPES - D-Ribofuranose
# 3: UNIQUE-ID - URIDINE
# 4: TYPES - Pyrimidine
#add all content to a single data.table
dt <- rbindlist( content.list )
# > dt
# V1
# 1: UNIQUE-ID - CPD0-1108
# 2: TYPES - D-Ribofuranose
# 3: UNIQUE-ID - URIDINE
# 4: TYPES - Pyrimidine
#split the text in a variable-name and it's content
dt <- dt %>% separate( V1, into = c("var", "content"), sep = " - ")
# > dt
# var content
# 1: UNIQUE-ID CPD0-1108
# 2: TYPES D-Ribofuranose
# 3: UNIQUE-ID URIDINE
# 4: TYPES Pyrimidine
#add an increasing id for every UNIQUE-ID
dt[var == "UNIQUE-ID", id := seq.int( 1: nrow( dt[var=="UNIQUE-ID", ]))]
# > dt
# var content id
# 1: UNIQUE-ID CPD0-1108 1
# 2: TYPES D-Ribofuranose NA
# 3: UNIQUE-ID URIDINE 2
# 4: TYPES Pyrimidine NA
#fill down id vor all variables found
dt[, id := na.locf( dt$id )]
# > dt
# var content id
# 1: UNIQUE-ID CPD0-1108 1
# 2: TYPES D-Ribofuranose 1
# 3: UNIQUE-ID URIDINE 2
# 4: TYPES Pyrimidine 2
#cast
dcast(dt, id ~ var, value.var = "content")
# id TYPES UNIQUE-ID
# 1: 1 D-Ribofuranose CPD0-1108
# 2: 2 Pyrimidine URIDINE