clean_names
Cleans names of a data.frame.
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".
Usage
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"))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 UPPERlowerold_janitor: legacy compatibility option to preserve behavior ofclean_namesprior to addition of the "case" argument(janitor versions <= 0.3.1 ). Provided as a quick fix for old scripts broken by the changes toclean_namesin janitor v1.0."parsed","mixed","none","internal_parsing": less-common cases offered bysnakecase::to_any_case. Seeto_any_casefor 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)
# readxl("messy_excel_file.xlsx") %>% clean_names()
# }