此解决方案涉及很多，并且依赖于多个Tidyverse库和功能的知识。我不确定它是否可以满足您的需求，但是对于您发布的示例确实可以。但是，我认为折叠块，创建函数以解析较小的块，然后展开结果的方法非常有用。

第一部分涉及找到“ $”标记，将以下几行分组在一起，然后将数据块“嵌套”在一起。然后，我们有一个只有几行的数据框-每节一个。

library(tidyverse)
txt_lns <- readLines("ocean-sample.txt") 

txt <- tibble(txt = txt_lns)

# Start by finding new sections,and nesting the data
nested_txt <- txt %>%
  mutate(row_number = row_number()) %>%
  mutate(new_section = str_detect(txt,"\\$")) %>%            # Mark new sections
  mutate(starting = ifelse(new_section,row_number,NA)) %>%  # Index with row num
  tidyr::fill(starting) %>%                                   # Fill index down
                                                              # where missing
  select(-new_section) %>%                                    # Clean up
  filter(!str_detect(txt,"\\$")) %>%                         
  nest(data = c(txt,row_number))                             # "Nest" the data

# Take a quick look
nested_txt

然后，我们需要能够处理那些嵌套的块。此处的例程通过识别标头行，然后将字段分为自己的数据帧来解析这些块。在这里，标题行与较短的较小行的逻辑不同。

# Deal with the records within a section
parse_inner_block <- function(x,header_ind) {
  if (header_ind) {
    df <- x %>%
      mutate(txt = str_trim(txt)) %>%
      # Separate the header row into 22 variables
      separate(txt,into = LETTERS[1:22],sep = "\\s+")
  } else {
    df <- x %>%
      mutate(txt = str_trim(txt)) %>% 
      # Separate the lesser rows into 6 variables
      separate(txt,into  = letters[1:6],sep = "\\s+")
  }
  return(df)
}

parse_outer_block <- function(x) {
  df <- x %>%
    # Determine if it's a header row with 22 variables or lesser row with 6
    mutate(leading_row = (row_number == min(row_number))) %>%
    # Fold by header row vs. not
    nest(data = c(txt,row_number)) %>%
    # Create data frames for both header and lesser rows
    mutate(processed = purrr::map2(data,leading_row,parse_inner_block)) %>%
    unnest(processed) %>%
    # Copy header row values to lesser rows
    tidyr::fill(A:V) %>%
    # Drop header row
    filter(!leading_row)
  return(df)
}

然后，我们可以将它们放在一起-从嵌套数据开始，处理每个块，取消嵌套返回的字段，并准备完整的输出。

# Actually put all this together and generate an output dataframe
output <- nested_txt %>%
  mutate(proc_out = purrr::map(data,parse_outer_block)) %>%
  select(-data) %>%
  unnest(proc_out) %>%
  select(-starting,-leading_row,-data,-row_number)

output 

希望有帮助。我建议您也参考一些purrr教程，以解决一些类似的问题。

这更简单，仅取决于基数R。我假设您已经首先使用x <- readLines(....)读取了文本文件：

start <- which(x == "$") + 1             # Find header indices
rows <- diff(c(start,length(x)+2)) - 2  # Find number of lines per group
# Function to read header and rows and cbind
getdata <- function(begin,end) {
    cbind(read.table(text=x[begin]),read.table(text=x[(begin+1):(begin+end)]))
}
dta.list <- lapply(1:(length(start)),function(i) getdata(start[i],rows[i]))
dta.df <- do.call(rbind,dta.list)

这适用于您帖子中包含的两个组。您将需要修复列名，因为在开头和结尾重复了V1-V6。