R/lama_translate_all.R
lama_translate_all.RdThe 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 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 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 ( |
An extended data.frame, that has a factor variable holding the assigned labels.
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).
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)#> 'data.frame': 5 obs. of 5 variables: #> $ pupil : num 1 1 2 2 3 #> $ subject : Factor w/ 2 levels "en","ma": 1 2 2 1 1 #> $ result : num 1 2 3 2 2 #> $ pre_SUBJECT_suf: Factor w/ 2 levels "English","Mathematics": 1 2 2 1 1 #> $ pre_RESULT_suf : Factor w/ 3 levels "Very good","Good",..: 1 2 3 2 2## 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)#> 'data.frame': 5 obs. of 5 variables: #> $ pupil : num 1 1 2 2 3 #> $ subject : Factor w/ 2 levels "en","ma": 1 2 2 1 1 #> $ result : num 1 2 3 2 2 #> $ subject_new: Factor w/ 2 levels "English","Mathematics": 1 2 2 1 1 #> $ result_new : Factor w/ 3 levels "Very good","Good",..: 1 2 3 2 2## 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)#> 'data.frame': 5 obs. of 5 variables: #> $ pupil : num 1 1 2 2 3 #> $ subject : Factor w/ 2 levels "en","ma": 1 2 2 1 1 #> $ result : num 1 2 3 2 2 #> $ subject_new: Factor w/ 2 levels "English","Mathematics": 1 2 2 1 1 #> $ result_new : Factor w/ 3 levels "Very good","Good",..: 1 2 3 2 2