janitor (version 1.2.0)

clean_names: Cleans names of a data.frame.

Description

Resulting names are unique and consist only of the _ character, numbers, and letters. Capitalization preferences can be specified using the case parameter.

Accented characters are transliterated to ASCII. For example, an "o" with a German umlaut over it becomes "o", and the Spanish character "enye" becomes "n".

This function takes and returns a data.frame, for ease of piping with `%>%`. For the underlying function that works on a character vector of names, see make_clean_names.

Usage

clean_names(dat, case)
# S3 method for default
clean_names(dat, case = c("snake", "lower_camel",
  "upper_camel", "screaming_snake", "lower_upper", "upper_lower",
  "all_caps", "small_camel", "big_camel", "old_janitor", "parsed", "mixed",
  "none"))

Arguments

dat

the input data.frame.

case

The desired target case (default is "snake"), indicated by these possible values:

• "snake" produces snake_case

• "lower_camel" or "small_camel" produces lowerCamel

• "upper_camel" or "big_camel" produces UpperCamel

• "screaming_snake" or "all_caps" produces ALL_CAPS

• "lower_upper" produces lowerUPPER

• "upper_lower" produces UPPERlower

• old_janitor: legacy compatibility option to preserve behavior of clean_names prior to addition of the "case" argument(janitor versions <= 0.3.1 ). Provided as a quick fix for old scripts broken by the changes to clean_names in janitor v1.0.

• "parsed", "mixed", "none": less-common cases offered by snakecase::to_any_case. See to_any_case for details.

Value

Returns the data.frame with clean names.

Examples

# NOT RUN {
# not run:
# clean_names(poorly_named_df)

# or pipe in the input data.frame:
# poorly_named_df %>% clean_names()

# if you prefer camelCase variable names:
# poorly_named_df %>% clean_names(., "small_camel")

# not run:
# library(readxl)
# read_excel("messy_excel_file.xlsx") %>% clean_names()
# }

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