R/02-dictionaries_functions.R
data_dict_pivot_wider.RdTransforms column(s) of a data dictionary from long format to wide format.
If a taxonomy is provided, the corresponding columns in the data
dictionary will be converted to a format with the taxonomy expanded.
This operation is equivalent to performing a tidyr::pivot_wider() on these
columns following the taxonomy structure provided. Variable names in the
data dictionary must be unique.
data_dict_pivot_wider(data_dict, taxonomy = NULL)A list of data frame(s) representing metadata to be transformed.
An optional data frame identifying a variable classification schema.
A list of data frame(s) identifying a data dictionary.
A data dictionary contains the list of variables in a dataset and metadata
about the variables and can be associated with a dataset. A data dictionary
object is a list of data frame(s) named 'Variables' (required) and
'Categories' (if any). To be usable in any function, the data frame
'Variables' must contain at least the name column, with all unique and
non-missing entries, and the data frame 'Categories' must contain at least
the variable and name columns, with unique combination of
variable and name.
A taxonomy is a classification schema that can be defined for variable
attributes. A taxonomy is usually extracted from an
Opal environment, and a
taxonomy object is a data frame that must contain at least the columns
taxonomy, vocabulary, and terms. Additional details about Opal
taxonomies are
available online.
{
library(dplyr)
# use madshapR_examples provided by the package
data_dict <- madshapR_examples$`data_dictionary_example`
taxonomy <- madshapR_examples$`taxonomy_example`
data_dict_longer <- data_dict_pivot_longer(data_dict, taxonomy)
data_dict_wider <- data_dict_pivot_wider(data_dict_longer, taxonomy)
glimpse(data_dict_wider)
}
#> List of 2
#> $ Variables : tibble [9 × 8] (S3: tbl_df/tbl/data.frame)
#> ..$ index : chr [1:9] "1" "2" "3" "4" ...
#> ..$ name : chr [1:9] "part_id" "gndr" "height" "weight_ms" ...
#> ..$ label:en : chr [1:9] "id of the participant" "gndr" "height" "weight_ms" ...
#> ..$ description:en : chr [1:9] "id of the participant" "gender of the participant" "height of the participant" "weight of the participant - measured" ...
#> ..$ valueType : chr [1:9] "text" "integer" "integer" "integer" ...
#> ..$ unit : chr [1:9] NA NA "cm" "kg" ...
#> ..$ datacollection::type : chr [1:9] "automatic" "declared" "declared" "measured" ...
#> ..$ datacollection::level: chr [1:9] "high" "high" "moderate" "moderate" ...
#> $ Categories: tibble [11 × 4] (S3: tbl_df/tbl/data.frame)
#> ..$ variable: chr [1:11] "gndr" "gndr" "gndr" "weight_ms" ...
#> ..$ name : chr [1:11] "1" "2" "-77" "-88" ...
#> ..$ label:en: chr [1:11] "Male" "Female" "Don’t want to answer" "Don’t want to answer" ...
#> ..$ missing : logi [1:11] FALSE FALSE TRUE TRUE TRUE FALSE ...