Скачать или смотреть How to Read Messy Tab-Delimited .DAT Files with Grouped Lines in R

Learn a clean and efficient method to read irregular tab-delimited .DAT files in R by grouping related lines and parsing them into structured data.
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Introduction

When working with .DAT files that are supposed to be tab-delimited but include irregular lines (such as free text without tabs), standard functions like read_tsv() may fail or produce incorrect output. This often happens when data rows span multiple lines or contain notes embedded beneath main records.

The Challenge

You have a .DAT file where:

Each record should have five columns:

Numeric ID

Date (MM/DD/YYYY)

Time (HH:MM or HH:MM:SS)

Free text field

Free text field

However, the file also contains lines without tabs that belong to the previous record's last column.

For example:

[[See Video to Reveal this Text or Code Snippet]]

Here, the lines without tabs ("UNKNOWN", "CONTRAINDICATION, STOP") are continuation lines for the first record's last column.

The Solution: Group and Collapse Related Lines

We can solve this by:

Reading all lines as strings using readLines() or readr::read_lines().

Identifying record starts: Lines containing tabs indicate a new record start.

Grouping lines: Use cumulative sums on presence of tabs to group related lines.

Collapsing lines in each group: Concatenate all lines belonging to the same record, separating continuation lines with ", ".

Parsing the cleaned data: Apply readr::read_tsv() on the collapsed strings.

Concise R Code Implementation

[[See Video to Reveal this Text or Code Snippet]]

Explanation

grepl("\t", line) returns a logical vector identifying lines with tabs (record starts).

cumsum() turns this into a grouping integer that increments only when a new record starts.

summarise(paste(...)) joins all lines of a record into one string with comma-separated continuation texts.

Finally, read_tsv() easily parses the well-structured tab-delimited data.

Result

The output dataframe will have five columns:

X1: Numeric identifier

X2: Date

X3: Time

X4: Free text

X5: Concatenated free text from continuation lines

This method is robust as long as continuation lines never contain tabs themselves.

Summary

Handling irregular tab-delimited files with continuation lines can be tricky, but simple grouping based on tab presence combined with collapsing lines enables clean parsing into tidy data frames.

Keep this pattern handy when your data doesn't fit neatly into standard delimited formats!