我有一个文本文件，包含4个独立的组件（与数据集关联的源，用法和实际数据）。我想将每个组件读入一个单独的R对象。
以下是文件格式的示例。每个文件都有关键字SOURCE，STORY，USAGE和DATASET作为分隔符。
SOURCE Boxofficemojo.com STORY These lines, of variable length and number, would contain the story behind the dataset. USAGE "Course" "Year" "Section" "Exercise" "Course1" 5 9 "ex 3" "Course1" 5 9 "ex 4" "Course1" 5 9 "ex 5" "Course2" 5 9 "ex 3" "Course2" 5 9 "ex 4" DATASET Dataset with headers follows.
我的问题仅在于将USAGE部分作为数据框阅读。我写了一个快速的逐行解析器，它扫描文件中的关键字USAGE和DATASET并返回它们的行号。但是，此代码有效：
Usage <- read.table(Output.File, skip= 9, nrows = 6, header = TRUE)
但此代码不是
Usage <- read.table(Output.File, skip= Beginrow, nrows = Endrow - Beginr4w, header = TRUE)
如何使read.table（）或任何其他函数允许使用变量skip和行数？或者，是否有更简单的方法将USAGE和DATASET之间的数据作为数据表读入？
USAGE将始终具有4列，具有与上述文件中相同的标题名称，但使用行数可以从1到任意任意数字。
答案 0 :(得分：0)
这个想法是，首先你必须设法为你选择包含相关数据的字符串的所需部分，然后从你读取的子字符串中选择csv。在下面的解决方案中，strsplit函数用于获取USAGE和DATASE之间的部分，无论行数多少。我基本上把字符串分成了方便的部分。您可以在strsplit了解更多信息：
str <- 'SOURCE
Boxofficemojo.com
STORY
These lines, of variable length and number, would contain the story behind the dataset.
USAGE
"Course" "Year" "Section" "Exercise"
"Course1" 5 9 "ex 3"
"Course1" 5 9 "ex 4"
"Course1" 5 9 "ex 5"
"Course2" 5 9 "ex 3"
"Course2" 5 9 "ex 4"
DATASET
Dataset with headers follows.'
# get the desired part of the string
datasetStr <- strsplit(paste0(strsplit(str, 'USAGE')[[1]][2]), 'DATASET')[[1]][1]
# read it as data frame
df <- read.csv(text = datasetStr, sep = '\t')
输出
> df
Course....Year..Section...Exercise
1 Course1 5 9 ex 3
2 Course1 5 9 ex 4
3 Course1 5 9 ex 5
4 Course2 5 9 ex 3
5 Course2 5 9 ex 4
答案 1 :(得分：0)
这是一种有点可扩展的方法。首先，使用readLines将整个文件读入变量。我会在这里使用textConnection来表示再现性，但你应该从文件中读取。
x <- readLines(con=textConnection('
SOURCE
Boxofficemojo.com
STORY
These lines, of variable length and number, would contain the story behind the dataset.
USAGE
"Course" "Year" "Section" "Exercise"
"Course1" 5 9 "ex 3"
"Course1" 5 9 "ex 4"
"Course1" 5 9 "ex 5"
"Course2" 5 9 "ex 3"
"Course2" 5 9 "ex 4"
DATASET
Dataset with headers follows.'))
过滤掉我介绍的前一个空行：
head(x)
# [1] ""
# [2] "SOURCE"
# [3] "Boxofficemojo.com"
# [4] ""
# [5] "STORY"
# [6] "These lines, of variable length and number, would contain the story behind the dataset."
allcaps <- grep("^[A-Z]+$", x)
if (allcaps[1] > 1) x <- x[-(1:(allcaps[1]-1))]
我推断出只有大写字母的行表示“标题”。这也可以使用cumsum(x %in% c("USAGE",...))：
str( x2 <- split(x, cumsum(grepl("^[A-Z]+$", x))) )
# List of 4
# $ 1: chr [1:3] "SOURCE" "Boxofficemojo.com" ""
# $ 2: chr [1:3] "STORY" "These lines, of variable length and number, would contain the story behind the dataset." ""
# $ 3: chr [1:8] "USAGE" "\"Course\" \"Year\" \"Section\" \"Exercise\"" "\"Course1\" 5 9 \"ex 3\"" "\"Course1\" 5 9 \"ex 4\"" ...
# $ 4: chr [1:2] "DATASET" "Dataset with headers follows."
（您也可以选择删除尾随空字符串，也许使用类似x2 <- lapply(x2, head, n=-1)的字符串，但最后会因为没有它而受到影响。使用Filter(nchar, x2)也可能有用，但它假定没有“故意”的空白行。对你说。）
下一步可能是装饰性的，但是将“标题”作为列表元素名称，后续行是其数据：
str( x3 <- setNames(lapply(x2, `[`, -1L),
sapply(x2, `[`, 1L)) )
# List of 4
# $ SOURCE : chr [1:2] "Boxofficemojo.com" ""
# $ STORY : chr [1:2] "These lines, of variable length and number, would contain the story behind the dataset." ""
# $ USAGE : chr [1:7] "\"Course\" \"Year\" \"Section\" \"Exercise\"" "\"Course1\" 5 9 \"ex 3\"" "\"Course1\" 5 9 \"ex 4\"" "\"Course1\" 5 9 \"ex 5\"" ...
# $ DATASET: chr "Dataset with headers follows."
最后，您可以对嵌入元素执行任何操作：
x3$USAGE <- read.table(textConnection(x3$USAGE), header=TRUE)
str(x3)
# List of 4
# $ SOURCE : chr [1:2] "Boxofficemojo.com" ""
# $ STORY : chr [1:2] "These lines, of variable length and number, would contain the story behind the dataset." ""
# $ USAGE :'data.frame': 5 obs. of 4 variables:
# ..$ Course : Factor w/ 2 levels "Course1","Course2": 1 1 1 2 2
# ..$ Year : int [1:5] 5 5 5 5 5
# ..$ Section : int [1:5] 9 9 9 9 9
# ..$ Exercise: Factor w/ 3 levels "ex 3","ex 4",..: 1 2 3 1 2
# $ DATASET: chr "Dataset with headers follows."