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Compute diffs between text strings

Source: R/RcppExports.R, R/diff.R
diff.Rd

The following functions are used to construct or work with diff(s) between text strings. Specifically, diff_make() computes the character level differences between the source string (x) and destination string (y). These diffs can be made more human friendly via a secondary cleaning process via the cleanup argument.

Once computed, diffs are represented using diff_df data frames, which consist of just two columns: text and op. Basic convenience functions for pretty printing of these are provided by the package.

The following helper functions are provided:

  • print() - prints a diff using ANSI colors if available.

  • as.character() - converts a diff (using ANSI colors if available) to a character vector.

  • diff_levenshtein() calculates the Levenshtein distance of a diff.

  • diff_to_delta() converts a diff to a delta string.

  • diff_from_delta() creates a diff from a source string (x) and a delta string.

  • diff_to_html() converts a diff to pretty HTML string.

  • diff_to_patch() converts a diff to a patch string.

  • diff_text_source() recovers the source string from a diff.

  • diff_text_dest() recovers the destination string from a diff.

Usage

diff_make(x, y, cleanup = "semantic", checklines = TRUE)

diff_levenshtein(diff)

diff_to_delta(diff)

diff_from_delta(x, delta)

diff_to_html(diff)

diff_to_patch(diff)

diff_text_source(diff)

diff_text_dest(diff)

Arguments

x

The source string

y

The destination string

cleanup

Determines the cleanup method applied to the diffs. Allowed values include: semantic, lossless, efficiency, merge and none. See Details for the behavior of these methods.

checklines

Performance flag - if FALSE, then don't run a line-level diff first to identify the changed areas. If TRUE, run a faster slightly less optimal diff. Default: TRUE.

diff

A diff_df data frame.

delta

A delta string.

Value

  • diff_make() returns a diff_df data frame containing the diffs.

  • diff_make() returns the Levenshtein distance as an integer.

  • diff_to_delta() returns an character string.

  • diff_from_delta() returns a diff_df data frame.

  • diff_to_html() returns a character string.

  • diff_to_patch() returns a character string.

  • diff_text_source() returns a character string.

  • diff_text_dest() returns a character string.

Details

Cleanup methods

  • semantic - Reduce the number of edits by eliminating semantically trivial equalities.

  • semantic lossless - Look for single edits surrounded on both sides by equalities which can be shifted sideways to align the edit to a word boundary. e.g: The cat came. -> The **cat **came.

  • efficiency - Reduce the number of edits by eliminating operationally trivial equalities.

  • merge - Reorder and merge like edit sections. Merge equalities. Any edit section can move as long as it doesn't cross an equality.

  • none - Do not apply any cleanup methods to the diffs.

Examples

(d = diff_make("abcdef", "abchij"))
#> abcdefhij

diff_levenshtein(d)
#> [1] 3

diff_to_html(d)
#> [1] "<span>abc</span><del style=\"background:#ffe6e6;\">def</del><ins style=\"background:#e6ffe6;\">hij</ins>"

diff_text_source(d) 
#> [1] "abcdef"

diff_text_dest(d) 
#> [1] "abchij"

diff_to_patch(d)
#> [1] "@@ -1,6 +1,6 @@\n abc\n-def\n+hij\n"

(delta = diff_to_delta(d))
#> [1] "=3\t-3\t+hij"

diff_from_delta("abcdef", delta)
#> abcdefhij