NBDCtools (version 1.0.2)

create_dataset: Create a dataset

Description

This high-level function simplifies the process of creating a dataset from the ABCD or HBCD Study data by allowing users to create an analysis-ready dataset in a single step. It executes the lower-level functions provided in the NBDCtools package in sequence to load, join, and transform the data.

The function expects study data to be stored as one .parquet or .tsv file per database table within a specified directory, provided as dir_data. Variables specified in vars and tables will be full-joined together, while variables specified in vars_add and tables_add will be left-joined to these variables. For more details, see join_tabulated().

In addition to the main create_dataset() function, there are two study-specific variations:

• create_dataset_abcd(): for the ABCD study.

• create_dataset_hbcd(): for the HBCD study.

They have the same arguments as the create_dataset() function, except that the study argument is set to the respective study by default, and should not be set by the user.

Usage

create_dataset(
  dir_data,
  study,
  vars = NULL,
  tables = NULL,
  vars_add = NULL,
  tables_add = NULL,
  release = "latest",
  format = "parquet",
  bypass_ram_check = FALSE,
  categ_to_factor = TRUE,
  add_labels = TRUE,
  value_to_label = FALSE,
  value_to_na = FALSE,
  time_to_hms = FALSE,
  bind_shadow = FALSE,
  ...
)
create_dataset_abcd(...)
create_dataset_hbcd(...)

Value

A tibble with the analysis-ready dataset.

Arguments

dir_data

character. Path to the directory with the data files in .parquet or .tsv format.

study

character. NBDC study (One of "abcd" or "hbcd").

vars

character (vector). Name(s) of variable(s) to be joined. (Default: NULL, i.e., no variables are selected; one of tables or vars has to be provided).

tables

character (vector). Name(s) of table(s) to be joined (Default: NULL, i.e., no tables are selected; one of tables or vars has to be provided).

vars_add

character (vector). Name(s) of additional variable(s) to be left-joined to the variables selected in vars and tables (Default: NULL, i.e., no additional variables are selected)

tables_add

character (vector). Name(s) of additional table(s) to be left-joined to the variables selected in vars and tables (Default: NULL, i.e., no additional tables are selected)

release

character. Release version (Default: "latest")

format

character. Data format (One of "parquet" or "tsv"; default: "parquet").

bypass_ram_check

logical. If TRUE, the function will not abort if the number of variables exceeds 10000 and current available RAM is less than 75% of the estimated RAM usage. This can prevent the long loading time of the data, but failing in the middle due to insufficient RAM. For large datasets, it is recommended to save 2 times or more of estimated RAM before running this function.

This argument is only used for the ABCD study, as the HBCD data is small enough to be loaded without RAM issues with most personal computers. As HBCD data grows in the future, this may change.

categ_to_factor

logical. Whether to convert categorical variables to factors class, see transf_factor() (Default: TRUE).

add_labels

logical. Whether to adds variable and value labels to the variables, see transf_label() (Default: TRUE).

value_to_label

logical. Whether to convert the categorical variables' numeric values to labels, see transf_value_to_label() (Default: FALSE). To run this process, categ_to_factor and add_labels must be TRUE.

value_to_na

logical. Whether to convert categorical missingness/non-response codes to NA, see transf_value_to_na() (Default: FALSE). To run this process, categ_to_factor and add_labels must be TRUE.

time_to_hms

logical. Whether to convert time variables to hms class, see transf_time_to_hms() (Default: FALSE).

bind_shadow

logical. Whether to bind the shadow matrix to the dataset (Default: FALSE). See more in details.

...

additional arguments passed to downstream functions after the join_tabulated() step. See examples for details.

Details

Order

This high-level function executes the different steps in the following order:

1. Read the data/shadow matrix using join_tabulated().

2. Convert categorical variables to factors using transf_factor().

3. Add labels to the variables and values using transf_label().

4. Convert categorical variables' numeric values to labels using transf_value_to_label().

5. Convert categorical missingness/non-response codes to NA using transf_value_to_na().

6. Convert time variables to hms class using transf_time_to_hms().

7. If bind_shadow and the study is "HBCD", replace the missing values in the shadow due to joining multiple datasets using shadow_replace_binding_missing().

8. Bind the shadow matrix to the data using shadow_bind_data().

Not all steps are executed by default. The above order represents the maximal order of execution.

bind_shadow

If bind_shadow is TRUE, the shadow matrix will be added to the data using shadow_bind_data().

• HBCD study: For the HBCD study, this function uses the shadow matrix from the dir_data directory by default (the HBCD Study releases a _shadow.parquet/_shadow.tsv file per table that accompanies the data). Alternatively, one can set naniar_shadow = TRUE as part of the ... arguments to use naniar::as_shadow() to create a shadow matrix from the data.

• ABCD study: The ABCD Study does not currently release shadow matrices. If bind_shadow is set to TRUE, the function will create the shadow matrix from the data using naniar::as_shadow(); no extra naniar_shadow = TRUE argument is needed.

Examples

if (FALSE) {
# most common use case
create_dataset(
  dir_data = "6_0/data",
  study = "abcd",
  vars = c("var1", "var2", "var3")
)

# to handle with tagged missingness
create_dataset(
  dir_data = "1_0/data",
  study = "hbcd",
  vars = c("var1", "var2", "var3"),
  value_to_na = TRUE
)

# to bind shadow matrices to the data
create_dataset(
  dir_data = "1_0/data/",
  study = "hbcd",
  vars = c("var1", "var2", "var3"),
  bind_shadow = TRUE
)

# to use the additional arguments
# for example in `value_to_na` option, the underlying function
# `transf_value_to_na()` has 2 more arguments,
# which can be passed to the `create_dataset()` function
create_dataset(
  dir_data = "6_0/data",
  study = "abcd",
  vars = c("var1", "var2", "var3"),
  value_to_na = TRUE,
  missing_codes = c("999", "888", "777", "666", "555", "444", "333", "222"),
  ignore_col_pattern = "__dk$|__dk__l$"
)

# use study specific functions
create_dataset_abcd(
  dir_data = "6_0/data",
  vars = c("var1", "var2", "var3")
)
}