cleaner 1.5.0

• New function format_names() to quickly and easily change names of data.frame columns, lists or character vectors.

  format_names(df, snake_case = TRUE)
  format_names(df, c(old.name = "new_name", value = "measurement"))
  
  library(dplyr)
  starwars %>% 
    format_names(camelCase = TRUE) %>% # column names
    mutate(name = name %>% 
             format_names(snake_case = TRUE)) # values in column

• New generic function na_replace() to replace NA values in any data type. Its default replacement value is dependent on the data type that is given as input: 0 for numeric values and class matrix, FALSE for class logical, today for class Date, and "" otherwise.

  na_replace(c(1, 2, NA, NA))
  #> [1] 1 2 0 0
  na_replace(c(1, 2, NA, NA), replacement = -1)
  #> [1]  1  2 -1 -1
  
  library(dplyr)
  starwars %>% 
    na_replace(hair_color) # only replace NAs in this column
  
  starwars %>% 
    na_replace() # replace NAs in all columns ("" for hair_color and 0 for birth_year)

• Support for the upcoming R 4.1.0

cleaner 1.4.0

• New function rdate() to generate random dates (in analogy to e.g. runif())

• Frequency tables (freq()):

  • Added availability of data to header

  • Fix for using na.rm

  • Fix for transforming to a visual histogram with hist()

  • New method for using format() on a frequency table

  • New method for transforming the values of a frequency table to a vector with as.vector(), which also supports dates

    library(dplyr)
    library(cleaner)
    data.frame(dates = rdate(100)) %>% 
      freq(dates) %>% 
      as.vector()

• Fix for clean_Date() not accepting already POSIX or Date input

• When using clean_Date(..., guess_each = TRUE) it now accepts the format parameter as a vector of options to let it choose from

• clean_Date() and clean_POSIXct gained a parameter max_date (that defaults to today), so that they will never return years beyond a specified date:

  # old
  clean_Date("23-01-67")
  #> [1] "2067-01-23"
  
  # new
  clean_Date("23-01-67")
  #> [1] "1967-01-23"
  #> Warning: Some years were decreased by 100 to not exceed today.
  #>          Use clean_Date(..., max_date = Inf) to prevent this.
  clean_Date("23-01-67", max_date = Inf)
  #> [1] "2067-01-23"

• Cleaned all code using the lintr package

cleaner 1.3.1

• Fixed a bug when using a percentage class into the percentage() function, i.e. percentage(as.percentage(1)) would fail
• Fixed extremely small percentages, like as.percentage(2.5e-14)

cleaner 1.3.0

• Added functions clean_double() and clean_integer()
• Added a method for median() in percentages
• Fixed a bug where NA in percentages would not be formatted correctly
• Fixed a bug in frequency tables where sometimes the number of digits used for percentages would be astronomical

cleaner 1.2.0

• DUE TO CRAN POLICY: RENAMED TO PACKAGE TO cleaner
• Added support for percentages as a new class: as.percentage() and clean_percentage(). They also come with ‘S3 methods’ for print, format, sum, min and max.
• More robust coercing of dates
• Support for negative values for clean_numeric(), clean_percentage() and clean_currency()
• Fix for clean_character() on R v3.5 and lower
• Fix for digits in frequency tables for numeric values

clean 1.1.0

• Added support for currency as a new class: as.currency() and clean_currency(). They also come with ‘S3 methods’ for print, format, sum, min and max.
• Added clean_POSIXct() to clean date/time objects
• top_freq() now correctly selects bottoms items using negative a number for n
• freq.default() is now exported for use in other packages
• All numeric calculation in the header of frequency tables now use the same algorithm as used by Minitab and SPSS (see ‘Type 6’ on stats::quantile())
• More robust results for clean_character(), it also keeps in-between spaces now
• clean_numeric() now supports currency
• Fix for freq() where the precentage of NAs in the header was not calculated right

clean 1.0.0

• First release