Package {cncleanr}

Title: Parse Compact Numbers in Chinese Data
Version: 0.2.5
Description: Parses compact numeric values, qualified quantities, and ranges commonly found in Chinese tables and spreadsheets. It handles Chinese magnitude suffixes, currencies, percentages, full-width characters, financial negatives, and configurable missing-value markers while reporting values that cannot be parsed safely.
License: MIT + file LICENSE
URL: https://github.com/Jorungandr/cncleanr
BugReports: https://github.com/Jorungandr/cncleanr/issues
Encoding: UTF-8
Language: en-US
Suggests: spelling, testthat (≥ 3.0.0)
Config/testthat/edition: 3
Config/roxygen2/version: 8.1.0
NeedsCompilation: no
Packaged: 2026-09-21 04:37:14 UTC; ASUS
Author: Haotian Liu [aut, cre, cph]
Maintainer: Haotian Liu <wyvern06@163.com>
Repository: CRAN
Date/Publication: 2026-09-30 09:30:02 UTC

cncleanr: Parse Compact Numbers in Chinese Data

Description

cncleanr parses compact numeric values commonly found in Chinese tables and spreadsheets. It favors explicit rules and visible parse failures over guessing.

Author(s)

Maintainer: Haotian Liu wyvern06@163.com [copyright holder]

Authors:

See Also

Useful links:

Extract Problems From a cncleanr Result

Description

Returns parsing failures attached to an object produced by a cncleanr parser. Successful results return an empty data frame with the same stable schema.

Usage

cn_problems(x)

Arguments

x

An object returned by a cncleanr parsing function.

Value

A data frame with columns index, value, and reason.

Examples

result <- suppressWarnings(parse_cn_number(c("bad", "2\u4e07")))
cn_problems(result)

Parse Compact Numbers Used in Chinese Data

Description

Converts character values such as "1.25\u4e07", "\uffe53\u4ebf", "1.2e5", and "12.5%" to doubles. Parsing is deliberately conservative: every non-missing value must match the supported syntax in full. Spaces between digits are accepted only when they form valid three-digit grouping.

Usage

parse_cn_number(
  x,
  na = c(
    "", "NA", "N/A", "\u6682\u65e0", "\u672a\u516c\u5e03",
    "\u2014", "\u2013", "-", "...", "\u2026"
  ),
  strict = FALSE
)

Arguments

x

A character, factor, or numeric vector. Numeric input is converted to double and otherwise passed through unchanged, including Inf, -Inf, and NaN.

na

A character vector containing values that should be interpreted as missing. Matching happens after whitespace and full-width normalization.

strict

A single logical value. If FALSE, invalid values become NA_real_, a warning is emitted, and a data frame is stored in the problems attribute. If TRUE, invalid values cause an error.

Details

Finite-range validation applies to parsed character and factor input. Existing numeric Inf, -Inf, and NaN values are preserved.

Value

A double vector with the same length and names as x. When invalid values occur in non-strict mode, the result has a problems attribute with columns index, value, and reason.

Examples

parse_cn_number(c("1.25\u4e07", "3\u4ebf\u5143", "12.5%", "\u6682\u65e0"))
parse_cn_number(c("\uff11\uff12\uff0e\uff15\uff05", "(2.5\u4e07)"))

Parse Qualified Quantities Used in Chinese Data

Description

Parses exact values and values qualified by language or symbols, such as "\u7ea63\u4e07", "\u5927\u4e8e2\u4ebf", "\u4e0d\u5c0f\u4e8e5\u4e07", "10\u4e07+", "50\u4f59", or "50\u4f59\u4e07\u5143". The qualifier is retained instead of silently discarded.

Usage

parse_cn_quantity(
  x,
  na = c(
    "", "NA", "N/A", "\u6682\u65e0", "\u672a\u516c\u5e03",
    "\u2014", "\u2013", "-", "...", "\u2026"
  ),
  strict = FALSE
)

Arguments

x

A character, factor, or numeric vector. Numeric input is converted to double and otherwise passed through unchanged, including Inf, -Inf, and NaN.

na

A character vector containing values that should be interpreted as missing. Matching happens after whitespace and full-width normalization.

strict

A single logical value. If FALSE, invalid values become NA_real_, a warning is emitted, and a data frame is stored in the problems attribute. If TRUE, invalid values cause an error.

Value

A data frame with double column value and character column qualifier. Qualifiers are exact, approx, greater_than, at_least, less_than, and at_most. Numeric finite and infinite values have the qualifier exact; NA and NaN have a missing qualifier. Numeric values, including Inf, -Inf, and NaN, are preserved in value.

Examples

parse_cn_quantity(c(
  "\u7ea63\u4e07", "\u5927\u4e8e2\u4ebf", "10\u4e07+", "50\u4f59", "50\u4f59\u4e07\u5143"
))

Parse Numeric Ranges Used in Chinese Data

Description

Converts closed ranges such as "3\u4e07-5\u4e07" and open bounds such as "10\u4e07\u5143\u4ee5\u4e0a" into explicit lower and upper bounds. A shared suffix in "3-5\u4e07" is applied to both endpoints. Signs used by negative numbers and scientific notation are distinguished from range separators.

Usage

parse_cn_range(
  x,
  na = c(
    "", "NA", "N/A", "\u6682\u65e0", "\u672a\u516c\u5e03",
    "\u2014", "\u2013", "-", "...", "\u2026"
  ),
  strict = FALSE
)

Arguments

x

A character, factor, or numeric vector. Numeric input is converted to double and otherwise passed through unchanged, including Inf, -Inf, and NaN.

na

A character vector containing values that should be interpreted as missing. Matching happens after whitespace and full-width normalization.

strict

A single logical value. If FALSE, invalid values become NA_real_, a warning is emitted, and a data frame is stored in the problems attribute. If TRUE, invalid values cause an error.

Value

A data frame with columns lower, upper, lower_inclusive, and upper_inclusive. Numeric input becomes an inclusive point range, including numeric Inf and -Inf; NA and NaN have missing inclusivity. Infinite bounds created from textual inequalities represent open-ended bounds and are distinct from numeric point inputs.

Examples

parse_cn_range(c("3\u4e07-5\u4e07", "3-5\u4e07", "10\u4e07\u5143\u4ee5\u4e0a"))

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