wizaRdry: A Magical Framework for Collaborative & Reproducible Data Analysis

Description

A comprehensive data analysis framework for NIH-funded research that streamlines workflows for both data cleaning and preparing NIH Data Archive ('NDA') submission templates. Provides unified access to multiple data sources ('REDCap', 'MongoDB', 'Qualtrics') through interfaces to their APIs, with specialized functions for data cleaning, filtering, merging, and parsing. Features automatic validation, field harmonization, and memory-aware processing to enhance reproducibility in multi-site collaborative research as described in Mittal et al. (2021) doi:10.20900/jpbs.20210011.

Author(s)

Maintainer: Joshua G. Kenney joshua.kenney@yale.edu

Authors:

• Trevor F. Williams trevormsu@gmail.com

• Minerva K. Pappu minerva.pappu@yale.edu

• Michael J. Spilka michael.j.spilka@gmail.com

Other contributors:

• Danielle N. Pratt danielle.pratt33@gmail.com [contributor]

• Victor J. Pokorny victor.pokorny@northwestern.edu [contributor]

• Santiago Castiello de Obeso santiago.castiellodeobeso@yale.edu [contributor]

• Praveen Suthaharan praveen.suthaharan@yale.edu [contributor]

• Christian R. Horgan christian.horgan@yale.edu [contributor]

See Also

Useful links:

• https://github.com/belieflab/wizaRdry

• Report bugs at https://github.com/belieflab/wizaRdry/issues

Assign a Secret Value

Description

Assign a Secret Value

Usage

assign_secret(name, value)

Arguments

Value

Invisibly returns TRUE if successful

Generate clean data frames from cleaning scripts created in the ./clean directory

Description

This function processes requests for clean data sequentially for specified measures. It makes a request to the appropriate API for the named measure or measures and runs the associated data cleaning routines. It then runs a series of unit tests to verify that the data quality standards are met.

Usage

clean(..., csv = FALSE, rdata = FALSE, spss = FALSE, skip_prompt = TRUE)

Arguments

Value

Prints the time taken for the data request process.

Author(s)

Joshua Kenney joshua.kenney@yale.edu

Examples

## Not run: 
  clean("prl", csv=TRUE)
  clean("rgpts", "kamin", rdata=TRUE)

  # Skip confirmation prompts
  clean("prl", csv=TRUE, skip_prompt=TRUE)

## End(Not run)

Alias for 'to.csv'

Description

This is a legacy alias for the 'to.csv' function to maintain compatibility with older code.

Usage

createCsv(df, df_name = NULL, path = ".", skip_prompt = TRUE)

Arguments

Value

Invisible TRUE if successful. The function writes a CSV file to the specified path and prints a message indicating the file's location.

Examples

## Not run: 
createCsv(prl01)

## End(Not run)

Alias for 'to.rds'

Description

This is a legacy alias for the 'to.rds' function to maintain compatibility with older code.

Usage

createRds(df, df_name = NULL, path = ".", skip_prompt = TRUE)

Arguments

Value

Invisible TRUE if successful. The function writes an RDS file to the specified path and prints a message indicating the file's location.

Examples

## Not run: 
createRds(prl01)

## End(Not run)

Alias for 'to.sav'

Description

This is a legacy alias for the 'to.sav' function to maintain compatibility with older code.

Usage

createSpss(df, df_name = NULL, path = ".", skip_prompt = TRUE)

Arguments

Value

Invisible TRUE if successful. Writes an SPSS file to the designated path and prints a message indicating the file's location.

Examples

## Not run: 
createSpss(prl01)

## End(Not run)

Alias for 'sift'

Description

This is a legacy alias for the 'sift' function to maintain compatibility with older code.

Usage

dataFilter(
  df,
  rows = NULL,
  cols = NULL,
  record_id = NULL,
  src_subject_id = NULL,
  subjectkey = NULL,
  site = NULL,
  subsiteid = NULL,
  sex = NULL,
  race = NULL,
  ethnic_group = NULL,
  phenotype = NULL,
  phenotype_description = NULL,
  status = NULL,
  lost_to_followup = NULL,
  twins_study = NULL,
  sibling_study = NULL,
  family_study = NULL,
  sample_taken = NULL,
  visit = NULL,
  week = NULL,
  arm = NULL,
  interview_date = NULL
)

Arguments

Value

A filtered dataframe based on the provided parameters, and containing only the columns specified in 'cols'. If no columns are specified, returns the entire dataframe with applied row filters.

Examples

## Not run: 
filtered <- dataFilter(df, sex="F")

## End(Not run)

Alias for 'meld'

Description

This is a legacy alias for the 'meld' function to maintain compatibility with older code.

Usage

dataMerge(
  ...,
  by = NULL,
  all = TRUE,
  no.dups = FALSE,
  csv = FALSE,
  rdata = FALSE,
  spss = FALSE
)

Arguments

Value

A merged data frame based on the specified or common candidate keys.

Examples

## Not run: 
merged <- dataMerge(df1_clean, df2_clean)

## End(Not run)

Alias for 'clean'

Description

This is a legacy alias for the 'clean' function to maintain compatibility with older code.

Usage

dataRequest(..., csv = FALSE, rdata = FALSE, spss = FALSE, skip_prompt = TRUE)

Arguments

Value

Prints the time taken for the data request process.

Examples

## Not run: 
prl <- clean("prl")

## End(Not run)

Display a file tree structure similar to the Unix tree command

Description

Display a file tree structure similar to the Unix tree command

Usage

display_tree(path)

Arguments

Value

NULL (called for side effects)

Alias for 'redcap'

Description

This is a legacy alias for the 'redcap' function to maintain compatibility with older code.

Usage

getRedcap(
  instrument_name = NULL,
  ...,
  raw_or_label = "raw",
  redcap_event_name = NULL,
  batch_size = 1000,
  records = NULL,
  fields = NULL,
  exclude_pii = TRUE,
  interview_date = NULL,
  date_format = "ymd",
  complete = NULL
)

Arguments

Value

A data frame containing the requested REDCap data

Examples

## Not run: 
survey_data <- getRedcap("demographics")

## End(Not run)

Alias for 'qualtrics'

Description

This is a legacy alias for the 'qualtrics' function to maintain compatibility with older code.

Usage

getSurvey(
  qualtrics_alias,
  ...,
  institution = NULL,
  label = FALSE,
  interview_date = NULL
)

Arguments

Value

A cleaned and harmonized data frame containing the survey data with superkeys first.

Examples

## Not run: 
survey_data <- getSurvey("your_survey_alias")

## End(Not run)

Alias for 'mongo'

Description

This is a legacy alias for the 'mongo' function to maintain compatibility with older code.

Usage

getTask(
  collection_name,
  ...,
  db_name = NULL,
  identifier = NULL,
  chunk_size = NULL,
  verbose = FALSE,
  interview_date = NULL
)

Arguments

Value

A data frame containing the MongoDB data with superkeys first

Examples

## Not run: 
survey_data <- getTask("task_alias")

## End(Not run)

Merge two or more data frames magically according to their candidate key

Description

This function simplifies the process of merging multiple cleaned data frames by automatically determining common merge keys or utilizing user-specified keys. Supports both inner and outer join methods, and offers options for exporting the merged data.

Usage

meld(
  ...,
  by = NULL,
  all = TRUE,
  no.dups = FALSE,
  csv = FALSE,
  rdata = FALSE,
  spss = FALSE
)

Arguments

Value

A merged data frame based on the specified or common candidate keys.

Author(s)

Joshua Kenney joshua.kenney@yale.edu

Examples

## Not run: 
# Create sample dataframes for demonstration
df1 <- data.frame(
  src_subject_id = c("S001", "S002", "S003"),
  visit = c(1, 2, 1),
  measure1 = c(10, 15, 12),
  stringsAsFactors = FALSE
)

df2 <- data.frame(
  src_subject_id = c("S001", "S002", "S004"),
  visit = c(1, 2, 2),
  measure2 = c(85, 92, 78),
  stringsAsFactors = FALSE
)

# Perform an OUTER JOIN using default keys:
merged1 <- meld(df1, df2, all = TRUE)

# Perform an INNER JOIN using specified keys:
merged2 <- meld(df1, df2, by = "src_subject_id", all = FALSE)

## End(Not run)

Fetch data from MongoDB to be stored in a data frame

Description

Fetch data from MongoDB to be stored in a data frame

Usage

mongo(
  collection_name,
  ...,
  db_name = NULL,
  identifier = NULL,
  chunk_size = NULL,
  verbose = FALSE,
  interview_date = NULL
)

Arguments

Value

A data frame containing the MongoDB data with superkeys first

Examples

## Not run: 
# Get data from MongoDB collection
data <- mongo("collection_name")

## End(Not run)

Display table of available MongoDB collections

Description

Retrieves a list of all available collections in the configured MongoDB database.

Usage

mongo.index(db_name = NULL)

Arguments

Value

A character vector containing the names of all available collections in the configured MongoDB database.

Parse composite MongoDB collection into component data frames by variable prefix

Description

This function fetches a MongoDB collection containing multiple collections and separates it into individual data frames for each collection detected in the data. It identifies the appropriate identifier column (e.g., participantId, workerId) and splits the data based on column name prefixes.

Usage

mongo.rune(collection, db_name = NULL, lower = TRUE)

Arguments

Details

The function performs the following steps:

• Retrieves the raw Qualtrics data using the getSurvey() function

• Identifies which identifier column to use (participantId, workerId, PROLIFIC_PID, or src_subject_id)

• Determines survey prefixes by analyzing column names

• Creates separate dataframes for each survey prefix found

• Assigns each dataframe to the global environment with names matching the survey prefixes

Value

Creates multiple dataframes in the global environment, one for each survey detected in the data. Each dataframe is named after its survey prefix.

Examples

## Not run: 
# Parse a Qualtrics export containing multiple surveys
mongo.rune("combined_surveys", label = FALSE)

# After running, access individual survey dataframes directly:
head(pss)  # Access the PSS survey dataframe
head(cesd) # Access the CESD survey dataframe

## End(Not run)

Generate validated NDA submission templates created in the ./nda directory

Description

This function processes requests for clean data sequentially for specified measures. It makes a request to the NIH NDA API for the named data structures and runs the associated data remediation routines. It then runs a series of unit tests to verify that the data quality standards are met.

Usage

nda(
  ...,
  csv = FALSE,
  rdata = FALSE,
  spss = FALSE,
  limited_dataset = FALSE,
  skip_prompt = TRUE
)

Arguments

Value

Prints the time taken for the data request process.

Author(s)

Joshua Kenney joshua.kenney@yale.edu

Examples

## Not run: 
  nda("prl", csv=TRUE)
  nda("rgpts", "kamin", rdata=TRUE)

  # Skip confirmation prompts
  nda("prl", csv=TRUE, skip_prompt=TRUE)

## End(Not run)

Alias for 'nda'

Description

This is a legacy alias for the 'nda' function to maintain compatibility with older code.

Usage

ndaRequest(
  ...,
  csv = FALSE,
  rdata = FALSE,
  spss = FALSE,
  limited_dataset = FALSE,
  skip_prompt = TRUE
)

Arguments

Value

Prints the time taken for the data request process.

Examples

## Not run: 
prl01 <- ndaRequest("prl01")

## End(Not run)

Retrieve Survey Data from Qualtrics

Description

Retrieve Survey Data from Qualtrics

Usage

qualtrics(
  qualtrics_alias,
  ...,
  institution = NULL,
  label = FALSE,
  interview_date = NULL
)

Arguments

Value

A cleaned and harmonized data frame containing the survey data with superkeys first.

Examples

## Not run: 
# Get survey by alias (will search all institutions)
survey_data <- qualtrics("rgpts")

## End(Not run)

Fetch Qualtrics survey metadata to be stored in data frame

Description

This function extracts column mappings from the metadata of a Qualtrics survey data frame. It can accept either a data frame containing Qualtrics data, a variable name as string, or a survey alias string.

Usage

qualtrics.dict(survey_alias, exclude_embedded = TRUE)

Arguments

Value

A list containing the mappings of column names to survey questions.

Display table of available Qualtrics surveys

Description

Retrieves a list of all available surveys in the configured Qualtrics account.

Usage

qualtrics.index(institution = NULL)

Arguments

Value

A data frame containing the IDs and names of all available surveys in the configured Qualtrics account. Can be used to identify surveys for further data retrieval.

Parse composite Qualtrics survey into component data frames by variable prefix

Description

This function fetches a Qualtrics data frame containing multiple surveys and separates it into individual data frames for each survey detected in the data. It identifies the appropriate identifier column (e.g., participantId, workerId) and splits the data based on column name prefixes.

Usage

qualtrics.rune(
  qualtrics_alias,
  institution = NULL,
  label = FALSE,
  interview_date = NULL,
  lower = TRUE
)

Arguments

Details

The function performs the following steps:

• Retrieves the raw Qualtrics data using the getSurvey() function

• Identifies which identifier column to use (participantId, workerId, PROLIFIC_PID, or src_subject_id)

• Determines survey prefixes by analyzing column names

• Creates separate dataframes for each survey prefix found

• Assigns each dataframe to the global environment with names matching the survey prefixes

Value

Creates multiple dataframes in the global environment, one for each survey detected in the data. Each dataframe is named after its survey prefix.

Examples

## Not run: 
# Parse a Qualtrics export containing multiple surveys
qualtrics.rune("combined_surveys", label = FALSE)

# After running, access individual survey dataframes directly:
head(pss)  # Access the PSS survey dataframe
head(cesd) # Access the CESD survey dataframe

## End(Not run)

Fetch data from REDCap to be stored in a data frame

Description

Retrieves data from a REDCap instrument and ensures subject identifiers are propagated across all events

Usage

redcap(
  instrument_name = NULL,
  ...,
  raw_or_label = "raw",
  redcap_event_name = NULL,
  batch_size = 1000,
  records = NULL,
  fields = NULL,
  exclude_pii = TRUE,
  interview_date = NULL,
  date_format = "ymd",
  complete = NULL
)

Arguments

Value

A data frame containing the requested REDCap data

Examples

## Not run: 
# Get data from a specific instrument
data <- redcap("demographics")

## End(Not run)

Fetch REDCap data dictionary to be stored in data frame

Description

This function extracts metadata/dictionary information from REDCap. It can accept either an instrument name to fetch new data, an existing data frame with instrument attributes, or a variable name as string.

Usage

redcap.dict(instrument_name)

Arguments

Value

A data frame containing the data dictionary/metadata for the specified instrument

Display table of available REDCap instruments and their labels

Description

Retrieves a list of all available REDCap forms as a formatted table

Usage

redcap.index()

Value

A formatted table (kable) of available REDCap instruments/forms

Parse composite REDCap instrument into component data frames by variable prefix

Description

This function fetches a REDCap instrument and separates it into individual data frames for each survey/collection detected in the data based on column name prefixes. It identifies the appropriate identifier column and splits the data accordingly.

Usage

redcap.rune(
  instrument_name,
  raw_or_label = "raw",
  redcap_event_name = NULL,
  batch_size = 1000,
  records = NULL,
  fields = NULL,
  exclude_pii = TRUE,
  interview_date = NULL,
  date_format = "ymd",
  lower = TRUE
)

Arguments

Value

Creates multiple dataframes in the parent environment, one for each survey detected in the data. Each dataframe is named after its survey prefix.

Parse composite data frame into component data frames by variable prefix

Description

This function takes a data frame containing multiple measures and separates it into individual data frames for each measure detected in the data. It identifies the appropriate identifier column (e.g., participantId, workerId) and splits the data based on column name prefixes.

Usage

rune(df, lower = TRUE)

Arguments

Details

The function performs the following steps:

• Identifies which identifier column to use (participantId, workerId, PROLIFIC_PID, or src_subject_id)

• Determines survey prefixes by analyzing column names

• Creates separate dataframes for each survey prefix found

• Assigns each dataframe to the global environment with names matching the survey prefixes

Value

Creates multiple dataframes in the global environment, one for each survey detected in the data. Each dataframe is named after its survey prefix.

Examples

# Parse a data frame containing multiple surveys
combined_df <- data.frame(
  record_id = c("REC001", "REC002", "REC003", "REC004"),
  src_subject_id = c("SUB001", "SUB002", "SUB003", "SUB004"),
  subjectkey = c("KEY001", "KEY002", "KEY003", "KEY004"),
  site = c("Yale", "NU", "Yale", "NU"),
  phenotype = c("A", "B", "A", "C"),
  visit = c(1, 2, 2, 1),
  state = c("complete", "completed baseline", "in progress", NA),
  status = c(NA, NA, NA, "complete"),
  lost_to_followup = c(FALSE, FALSE, TRUE, NA),
  interview_date = c("2023-01-15", "2023/02/20", NA, "2023-03-10"),
  foo_1 = c(1, 3, 5, 7),
  foo_2 = c("a", "b", "c", "d"),
  bar_1 = c(2, 4, 6, 8),
  bar_2 = c("w", "x", "y", "z")
)
rune(combined_df)

# After running, access individual survey dataframes directly:
head(foo)  # Access the foo dataframe
head(bar)  # Access the bar dataframe

Initialize the wizaRdry directory structure inside an R project

Description

Creates the standard directory structure required for the wizaRdry package to function properly. This includes folders for data cleaning scripts, NDA submission templates, and temporary outputs. It can detect and repair incomplete directory structures, and optionally create an R project.

Usage

scry(
  study_alias = NULL,
  path = ".",
  overwrite = FALSE,
  repair = FALSE,
  show_tree = NULL,
  create_project = FALSE,
  examples = FALSE,
  skip_prompt = TRUE
)

Arguments

Details

The function creates the following directory structure:

• clean/

  • csv/

  • mongo/

  • qualtrics/

  • redcap/

  • sql/

• nda/

  • csv/

  • mongo/

  • qualtrics/

  • redcap/

  • sql/

• tmp/

It also creates template config.yml and secrets.R files, and optionally an R project file.

Value

Invisible TRUE if successful.

Examples

## Not run: 
# Initialize in current directory
scry()

# Repair structure in current directory
scry(repair = TRUE)

# Initialize in a specific directory with an R project
scry("path/to/project", create_project = TRUE, repair = TRUE)

# Skip the tree display
scry(repair = TRUE, show_tree = FALSE)

# Explicitly create example scripts when repairing
scry(repair = TRUE, examples = TRUE)

# Skip the confirmation prompt
scry(skip_prompt = TRUE)

## End(Not run)

Filter data frame by superkey parameters, rows, and columns

Description

Filter data frame by superkey parameters, rows, and columns

Usage

sift(
  df,
  rows = NULL,
  cols = NULL,
  record_id = NULL,
  src_subject_id = NULL,
  subjectkey = NULL,
  site = NULL,
  subsiteid = NULL,
  sex = NULL,
  race = NULL,
  ethnic_group = NULL,
  phenotype = NULL,
  phenotype_description = NULL,
  status = NULL,
  lost_to_followup = NULL,
  twins_study = NULL,
  sibling_study = NULL,
  family_study = NULL,
  sample_taken = NULL,
  visit = NULL,
  week = NULL,
  arm = NULL,
  interview_date = NULL
)

Arguments

Value

A filtered dataframe based on the provided parameters, and containing only the columns specified in 'cols'. If no columns are specified, returns the entire dataframe with applied row filters.

Examples

# Create a sample dataframe
sample_df <- data.frame(
  record_id = c("REC001", "REC002", "REC003", "REC004"),
  src_subject_id = c("SUB001", "SUB002", "SUB003", "SUB004"),
  subjectkey = c("KEY001", "KEY002", "KEY003", "KEY004"),
  site = c("Yale", "NU", "Yale", "NU"),
  phenotype = c("A", "B", "A", "C"),
  visit = c(1, 2, 2, 1),
  state = c("complete", "completed baseline", "in progress", NA),
  status = c(NA, NA, NA, "complete"),
  lost_to_followup = c(FALSE, FALSE, TRUE, NA),
  interview_date = c("2023-01-15", "2023/02/20", NA, "2023-03-10")
)

# Set row names for demonstration
rownames(sample_df) <- c("foo", "bar", "baz", "qux")

# Filter by specific date
filtered1 <- sift(sample_df, 
                 cols = c("src_subject_id", "phenotype"), 
                 visit = 2, 
                 interview_date = "01/31/2023")
                 
# Filter to include only rows with non-NA interview dates
filtered2 <- sift(sample_df, 
                 interview_date = TRUE)
                 
# Filter by status (works with either state or status column)
filtered3 <- sift(sample_df,
                 status = c("complete", "completed baseline"))
                 
# Filter with specific row names
filtered4 <- sift(sample_df,
                 rows = c("foo", "qux"))
                 
# Filter with vector of visit values
filtered6 <- sift(sample_df,
                 visit = c(1, 2))
                 
# Filter by lost_to_followup
filtered10 <- sift(sample_df,
                 lost_to_followup = FALSE)
                 
# Filter by src_subject_id
filtered11 <- sift(sample_df,
                 src_subject_id = c("SUB001", "SUB004"))
                 
# Multiple filters combined
filtered12 <- sift(sample_df,
                 site = "Yale",
                 visit = 1,
                 cols = c("record_id", "src_subject_id", "site"))

Create .csv file from a data frame

Description

This function exports a given R data frame to a CSV file format. The resulting file is saved in the "tmp" directory. If a filename is not specified, the function uses the name of the data frame variable. The ".csv" extension is appended automatically to the filename. The function will prompt for confirmation before creating the file, with an option to remember the user's preference for future calls.

Usage

to.csv(df, df_name = NULL, path = ".", skip_prompt = TRUE)

Arguments

Value

Invisible TRUE if successful. The function writes a CSV file to the specified path and prints a message indicating the file's location.

Author(s)

Joshua Kenney joshua.kenney@yale.edu

Examples

## Not run: 
# Create a sample data frame
sample_df <- data.frame(
  id = 1:3,
  name = c("Alice", "Bob", "Charlie")
)

# Basic usage with prompt
to.csv(sample_df)

# Custom filename
to.csv(sample_df, "participants_data")

# Skip the confirmation prompt
to.csv(sample_df, skip_prompt = TRUE)

# Save in a different directory
to.csv(sample_df, path = "path/to/project")

## End(Not run)

Create NDA Submission Template

Description

This function creates a CSV template file for National Data Archive (NDA) submissions. It extracts the data from a specified data frame and formats it according to NDA requirements, with the structure name split into base name and suffix in the first line. The function will prompt for confirmation before creating the file, with an option to remember the user's preference for future calls.

Usage

to.nda(df, path = ".", skip_prompt = TRUE)

Arguments

Details

The function will:

1. Create a 'tmp' directory if it doesn't exist

2. Parse the structure name into base and suffix components (e.g., "eefrt01" → "eefrt" and "01")

3. Write the structure name components as the first line

4. Write column headers as the second line

5. Write the data rows below

Value

Invisible TRUE if successful. Creates a CSV file at the specified path and prints a message with the file location.

Examples

## Not run: 
  # First create some sample data
  eefrt01 <- data.frame(
    src_subject_id = c("SUB001", "SUB002"),
    interview_age = c(240, 360),
    interview_date = c("01/01/2023", "02/15/2023"),
    response_time = c(450, 520)
  )

  # Create the NDA template using the data frame directly
  to.nda(eefrt01)

  # Or using the name as a string
  to.nda("eefrt01")

  # Skip the confirmation prompt
  to.nda(eefrt01, skip_prompt = TRUE)

## End(Not run)

Create .rds file from a data frame

Description

This function exports a given R data frame to an RDS file format. The resulting file is saved in the "tmp" directory. If a filename is not specified, the function uses the name of the data frame variable. The ".rds" extension is appended automatically to the filename. The function will prompt for confirmation before creating the file, with an option to remember the user's preference for future calls.

Usage

to.rds(df, df_name = NULL, path = ".", skip_prompt = TRUE)

Arguments

Value

Invisible TRUE if successful. The function writes an RDS file to the specified path and prints a message indicating the file's location.

Examples

## Not run: 
# Create a sample data frame
sample_df <- data.frame(
  id = 1:3,
  name = c("Alice", "Bob", "Charlie")
)

# Basic usage with prompt
to.rds(sample_df)

# Custom filename
to.rds(sample_df, "participants_data")

# Skip the confirmation prompt
to.rds(sample_df, skip_prompt = TRUE)

# Save in a different directory
to.rds(sample_df, path = "path/to/project")

## End(Not run)

Create .sav SPSS file from a data frame

Description

This function takes a R data frame and writes it to an SPSS file using the Haven package. The resulting file will be stored in the "tmp" directory with a default name derived from the data frame variable name, but can be customized if desired. The function will prompt for confirmation before creating the file, with an option to remember the user's preference for future calls.

Usage

to.sav(df, df_name = NULL, path = ".", skip_prompt = TRUE)

Arguments

Value

Invisible TRUE if successful. Writes an SPSS file to the designated path and prints a message indicating the file's location.

Examples

## Not run: 
# Create a sample data frame
sample_df <- data.frame(
  id = 1:3,
  score = c(85, 92, 78),
  group = c("A", "B", "A")
)

# Basic usage with prompt
to.sav(sample_df)

# Custom filename
to.sav(sample_df, "participants_data")

# Skip the confirmation prompt
to.sav(sample_df, skip_prompt = TRUE)

# Save in a different directory
to.sav(sample_df, path = "path/to/project")

## End(Not run)