diff: Compute diffs between text strings
In diffmatchpatch: String Diff, Match, and Patch Utilities

Description Usage Arguments Details Value Examples

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.

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)

`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.

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.

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.

(d = diff_make("abcdef", "abchij"))

diff_levenshtein(d)

diff_to_html(d)

diff_text_source(d) 

diff_text_dest(d) 

diff_to_patch(d)

(delta = diff_to_delta(d))

diff_from_delta("abcdef", delta)