lama_translate_all: Assign new labels to all variables of a data.frame
In labelmachine: Make Labeling of R Data Sets Easy

Description Usage Arguments Details Value See Also Examples

The functions lama_translate_all() and lama_to_factor_all() converts all variables (which have a translation in the given lama-dictionary) of a data frame .data into factor variables with new labels. These functions are special versions of the functions lama_translate() and lama_to_factor(). The difference to lama_translate() and lama_to_factor() is, that when using lama_translate_all() and lama_to_factor_all() the used translations in dictionary must have the exact same names as the corresponding columns in the data frame .data.

lama_translate_all(.data, dictionary, prefix = "", suffix = "",
  fn_colname = function(x) x, keep_order = FALSE, to_factor = TRUE)

## S3 method for class 'data.frame'
lama_translate_all(.data, dictionary, prefix = "",
  suffix = "", fn_colname = function(x) x, keep_order = FALSE,
  to_factor = TRUE)

lama_to_factor_all(.data, dictionary, prefix = "", suffix = "",
  fn_colname = function(x) x, keep_order = FALSE)

## S3 method for class 'data.frame'
lama_to_factor_all(.data, dictionary, prefix = "",
  suffix = "", fn_colname = function(x) x, keep_order = FALSE)

`.data`	Either a data frame, a factor or a vector.
`dictionary`	A lama_dictionary object, holding the translations for various variables.
`prefix`	A character string, which is used as prefix for the new column names.
`suffix`	A character string, which is used as suffix for the new column names.
`fn_colname`	A function, which transforms character string into a new character string. This function will be used to transform the old column names into new column names under which the labeled variables will then be stored.
`keep_order`	A logical of length one, defining if the original order (factor order or alphanumerical order) of the data frame variables should be preserved.
`to_factor`	A logical of length one, defining if the resulting labeled variables should be factor variables (`to_factor = TRUE`) or plain character vectors (`to_factor = FALSE`).

The difference between lama_translate_all() and lama_to_factor_all() is the following:

lama_translate_all(): Assign new labels to the variables and turn them into factor variables with the order given in the corresponding translations (keep_order = FALSE) or in the same order as the original variable (keep_order = TRUE).
lama_to_factor_all(): The variables are character vectors or factors already holding the right label strings. The variables are turned into a factor variables with the order given in the corresponding translation (keep_order = FALSE) or in the same order as the original variable (keep_order = TRUE).

An extended data.frame, that has a factor variable holding the assigned labels.

lama_translate(), lama_to_factor(), new_lama_dictionary(), as.lama_dictionary(), lama_rename(), lama_select(), lama_mutate(), lama_merge(), lama_read(), lama_write()

  ## initialize lama_dictinoary
  dict <- new_lama_dictionary(
    subject = c(en = "English", ma = "Mathematics"),
    result = c("1" = "Very good", "2" = "Good", "3" = "Not so good")
  )
  ## data frame which should be translated
  df <- data.frame(
    pupil = c(1, 1, 2, 2, 3),
    subject = c("en", "ma", "ma", "en", "en"),
    result = c(1, 2, 3, 2, 2)
  )
  
  ## Example-1: 'lama_translate_all''
  df_new <- lama_translate_all(
    df,
    dict,
    prefix = "pre_",
    fn_colname = toupper,
    suffix = "_suf"
  )
  str(df_new)

  ## Example-2: 'lama_translate_all' with 'to_factor = FALSE'
  # The resulting variables are plain character vectors
  df_new <- lama_translate_all(df, dict, suffix = "_new", to_factor = TRUE)
  str(df_new)

  ## Example-3: 'lama_to_factor_all'
  # The variables 'subject' and 'result' are turned into factor variables
  # The ordering is taken from the translations 'subject' and 'result'
  df_2 <- data.frame(
    pupil = c(1, 1, 2, 2, 3),
    subject = c("English", "Mathematics", "Mathematics", "English", "English"),
    result = c("Very good", "Good", "Good", "Very good", "Good")
  )
  df_2_new <- lama_to_factor_all(
    df_2, dict,
    prefix = "pre_",
    fn_colname = toupper,
    suffix = "_suf"
  )
  str(df_new)