Package {formr}

Title:	Companion R Package for the 'formr' Survey Framework
Description:	Serves as a companion toolkit for the 'formr' survey framework (https://rforms.org). The package acts as a bridge between a 'formr' server and a local R environment. Key features include an API client for fetching, type-casting, and automatically scoring data; a project management workflow for syncing study assets (surveys, CSS) for local editing; and functions for use within 'formr' runs to generate dynamic, personalized feedback plots and to simplify survey logic.
Version:	1.1.0
Depends:	R (≥ 4.1.0)
Imports:	stats, methods, tools, utils, dplyr, ggplot2, scales, haven, stringr, tidyr, knitr, httr, curl, jsonlite, lubridate, rmarkdown, otp, keyring, purrr, readr, rlang
Suggests:	testthat (≥ 3.0.0), vcr, mockery, withr, rstudioapi, plotly, commonmark
License:	MIT + file LICENSE
Encoding:	UTF-8
Language:	en-US
VignetteBuilder:	knitr
Config/testthat/edition:	3
URL:	https://rubenarslan.github.io/formr/, https://github.com/rubenarslan/formr
BugReports:	https://github.com/rubenarslan/formr/issues
Config/roxygen2/version:	8.0.0
RoxygenNote:	8.0.0
NeedsCompilation:	no
Packaged:	2026-06-08 20:38:00 UTC; rubenarslan
Author:	Ruben Arslan [aut, cre], Tim B Seidel [aut, ctb]
Maintainer:	Ruben Arslan <rubenarslan@gmail.com>
Repository:	CRAN
Date/Publication:	2026-06-16 20:10:13 UTC

check whether a character string begins with a string

Description

Escapes any special RegExp characters in the search term. A way to check whether the search term (e.g. a variable name) is the beginning. Just a simple shorthand so that inexperienced R users won't have to use somewhat complex functions such as grepl() and stringr::str_detect(). You can also use \%starts_with\%.

Usage

haystack %begins_with% needle

Arguments

Value

A logical vector, TRUE where haystack begins with needle.

Examples

"1, 3, 4" %begins_with% "1" # TRUE
"1, 3, 4" %begins_with% 1 # unlike str_detect casts all needles as characters
"1, 3, 4" %begins_with% "." # FALSE

check whether a character string contains another

Description

Just a simple shorthand so that inexperienced R users don't have to use somewhat complex functions such as grepl() and stringr::str_detect() with non-default arguments (e.g. fixed params).

Usage

haystack %contains% needle

Arguments

Value

A logical vector, TRUE where haystack contains needle as a fixed substring.

Examples

"1, 2, 3, 4, you" %contains% "you"
"1, 2, 3, 4, you" %contains% 1 # unlike str_detect casts all needles as characters
"1, 2, 3, 4, you" %contains% 343

check whether a character string contains another as a word

Description

Looks for a string appearing on its own. This is needed e.g. when checking whether the replies to a mmc item, stored as a comma-separated list from 1 to 12 contain option 1 - you wouldn't want to get a hit for 11 and 12. Only works for search terms containing alphanumeric characters. Just a simple shorthand so that inexperienced R users don't have to use somewhat complex functions such as grepl() and stringr::str_detect().

Usage

haystack %contains_word% needle

Arguments

Value

A logical vector, TRUE where haystack contains needle as a whole word.

Examples

"1, 3, 4" %contains_word% "1" # TRUE
"1, 3, 4" %contains_word% 1 # TRUE unlike str_detect casts all needles as characters
"12, 14, 17" %contains_word% "1" # FALSE even though 12 contains 1

check whether a character string ends with a string

Description

Escapes any special RegExp characters in the search term. A way to check whether the search term (e.g. a variable name) is the ending. Just a simple shorthand so that inexperienced R users don't have to use somewhat complex functions such as grepl() and stringr::str_detect().

Usage

haystack %ends_with% needle

Arguments

Value

A logical vector, TRUE where haystack ends with needle.

Examples

"1, 3, 4" %ends_with% "4" # TRUE
"1, 3, 4" %ends_with% 4 # unlike str_detect casts all needles as characters
"1, 3, 4" %ends_with% "." # FALSE

Per-request environment populated by rforms.org

Description

An environment that the rforms.org server fills with per-request state when R code runs inside an OpenCPU session on a formr study (for example, on a CalculateUnit page or an OverviewScriptPage). Useful fields the server may set:

Usage

.formr

Format

An environment.

Details

• .formr$run_name – the name of the current run.

• .formr$host – the API host (e.g. ⁠https://api.rforms.org⁠).

• .formr$access_token – a short-lived OAuth token for the request.

• .formr$last_action_time / .formr$last_action_date – timestamps used by time_passed() and the other shorthands.

Several user-facing functions (e.g. formr_api_authenticate(), formr_api_results(), formr_overview_sankey()) default their host / run_name / access_token arguments to these fields, so the same code runs unchanged locally and on a formr study. Outside a formr session the environment is empty.

Value

An environment holding per-request state in its bindings (the run_name, host, access_token, last_action_time and last_action_date fields listed above). .formr is a data object, not a function, so it does not itself return a value; the rforms.org server populates these bindings before user code runs and the environment is empty outside a formr/OpenCPU session.

Aggregate variables and remember which variables this were

Description

Copied from codebook. The resulting variables will have the attribute scale_item_names containing the basis for aggregation. Its label attribute will refer to the common stem of the aggregated variable names (if any), the number of variables, and the aggregation function.

Usage

aggregate_and_document_scale(items, fun = rowMeans, stem = NULL)

Arguments

Value

A numeric vector (the aggregated scale score) carrying scale_item_names and label attributes.

Examples

testdf <- data.frame(bfi_neuro_1 = rnorm(20), bfi_neuro_2 = rnorm(20),
                    bfi_neuro_3R = rnorm(20), age = rpois(20, 30))
item_names <- c('bfi_neuro_1', 'bfi_neuro_2', 'bfi_neuro_3R')
testdf$bfi_neuro <- aggregate_and_document_scale(testdf[, item_names])
testdf$bfi_neuro

Convert formr run structure to data.frame

Description

Convert formr run structure to data.frame

Usage

## S3 method for class 'formr_api_run_structure'
as.data.frame(x, ...)

Arguments

Value

A data.frame with one row per unit and columns position, type, description and details.

Transform formr_item_list into a data.frame for ease of use

Description

This function just turns a formr_item_list into a data.frame. The reason, these lists don't come as data.frames as default is because the 'choices' are a list themselves. When transforming, the choice column contains a collapsed choice list, which may be less useful for some purposes.

Usage

## S3 method for class 'formr_item_list'
as.data.frame(x, row.names, ...)

Arguments

Value

A data.frame of item metadata, one row per item, with choices collapsed to a comma-separated string.

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_connect(email = 'you@example.net', password = 'zebrafinch' )
as.data.frame(formr_items(survey_name = 'training_diary' ))

## End(Not run)
items = formr_items(path = 
system.file('extdata/gods_example_items.json', package = 'formr', mustWork = TRUE))
items_df = as.data.frame(items)
items_df[1,]

knit_child as is

Description

This slightly modifies the knitr::knit_child() function to have different defaults.

• the environment defaults to the calling environment.

• the output receives the class knit_asis, so that the output will be rendered "as is" by knitr when calling inside a chunk (no need to set results='asis' as a chunk option).

• defaults to quiet = TRUE

Usage

asis_knit_child(
  input = NULL,
  text = NULL,
  ...,
  quiet = TRUE,
  options = NULL,
  envir = parent.frame()
)

Arguments

Details

Why default to the calling environment? Typically this function defaults to the global environment. This makes sense if you want to use knit_children in the same context as the rest of the document. However, you may also want to use knit_children inside functions to e.g. summarise a regression using a set of commands (e.g. plot some diagnostic graphs and a summary for a regression nicely formatted).

Some caveats:

• the function has to return to the top-level. There's no way to cat() this from loops or an if-condition without without setting results='asis'. You can however concatenate these objects with paste.knit_asis()

Value

A length-1 character string of class knit_asis (the knitted child document).

Examples

## Not run: 
# Not run: requires a knitr session and an external child .Rmd document.
# an example of a wrapper function that calls asis_knit_child with an argument
# ensures distinct paths for cache and figures, so that these calls can be looped in parallel
regression_summary = function(model) {
   child_hash = digest::digest(model)
   options = list(
       fig.path = paste0(knitr::opts_chunk$get("fig.path"), child_hash, "-"), 
       cache.path = paste0(knitr::opts_chunk$get("cache.path"), child_hash, "-"))
   asis_knit_child("_regression_summary.Rmd", options = options)
}

## End(Not run)

switch choice values with labels

Description

formr display labels for multiple choice items, but stores their values. We assume you prefer to analyse the values (e.g. numeric values for Likert-type items, or English values for international surveys), but sometimes you may wish to switch this around.

Usage

choice_labels_for_values(survey, item_name)

Arguments

Value

A vector of choice labels mapped onto the supplied item values.

Examples

example(formr_post_process_results)
table(processed_results$BFIK_extra_4)
table(choice_labels_for_values(processed_results, "BFIK_extra_4"))

Gives the last element, doesn't omit missings

Description

Just a simple shorthand to get the current element (in a formr df, where the last element is always the one from the current session).

Usage

current(x)

Arguments

Value

A length-1 vector (the last element of x), keeping x's type.

Examples

current( c(1:10,NA) )
current( 1:10 )

generates valid email cids

Description

can be used as an argument to knitr::opts_knit. If you attach the images properly, you can then send knit emails including plots. See the formr OpenCPU module on Github for a sample implementation.

Usage

email_image(x, ext = ".png")

Arguments

Value

A length-1 character string holding a ⁠cid:⁠ reference for inline email images, carrying the source path in its link attribute.

Examples

## Not run: 
# Not run: meant to run inside a knitr session as the figure upload hook.
library(knitr); library(formr)
opts_knit$set(upload.fun=formr::email_image)

## End(Not run)

How many surveys were expired?

Description

Just a simple to check how many times a survey (e.g. diary) has expired (i.e. user missed it). It defaults to checking the "expired" variable for this.

Usage

expired(survey, variable = "expired")

Arguments

Value

An integer: the count of expired sessions (non-missing values in survey[[variable]]).

Examples

survey = data.frame(expired = c(NA, "2016-05-29 10:11:00", NA))
expired(survey = survey)

Text feedback based on groups

Description

If you pass in a z-standardised value (x - Mean)/SD, and a vector of feedback text chunks, that has either three or five elements, the text chunks will be used in this order [very low], low, average, high, [very high] corresponding to these intervals [low, -2], [-2, -1], [-1, 1], [1, 2], [2, high]

Usage

feedback_chunk(normed_value, chunks)

Arguments

Value

A length-1 character string: the chunk selected for the interval containing normed_value.

Examples

feedback_chunk(normed_value = 0.7, chunks = c("You are rather introverted.",
"You're approximately as extraverted as most people.","You are rather extraverted."))

How many surveys were finished?

Description

Just a simple to check how many times a survey (e.g. diary) was finished. It defaults to checking the "ended" variable for this.

Usage

finished(survey, variable = "ended")

Arguments

Value

An integer: the count of non-missing values in survey[[variable]] (0 when the survey or column is empty).

Examples

survey = data.frame(ended = c("2016-05-28 10:11:00", NA, "2016-05-30 11:18:28"))
finished(survey = survey)

Gives the first non-missing element

Description

Just a simple shorthand to get the first, non-missing argument per default. Can give more than one element and can include missing elements. The inverse of last().

Usage

first(x, n = 1, na.rm = TRUE)

Arguments

Value

A vector of the same type as x holding its first n elements (after dropping NAs when na.rm = TRUE); an empty vector if none remain.

Examples

first( c(NA,1:10) )
first( c(NA, 1:10), 2, TRUE )

Aggregate data based on item table

Description

If you've retrieved an item table using formr_items() you can use this function to aggregate your multiple choice items into mean scores. If you do not have a item table (e.g. your data was not collected using formr, you don't want another HTTP request in a time-sensitive process). Example: If your data contains Extraversion_1, Extraversion_2R and Extraversion_3, there will be two new variables in the result: Extraversion_2 (reversed to align with _1 and _2) and Extraversion, the mean score of the three.

Usage

formr_aggregate(
  survey_name,
  item_list = formr_items(survey_name, host = host),
  results = formr_raw_results(survey_name, host = host),
  host = formr_last_host(),
  compute_alphas = FALSE,
  fallback_max = 5,
  plot_likert = FALSE,
  quiet = FALSE,
  aggregation_function = rowMeans,
  ...
)

Arguments

Value

The results data.frame with one added numeric column per scale (the row mean of its items).

Examples

results = jsonlite::fromJSON(txt = 
	system.file('extdata/gods_example_results.json', package = 'formr', mustWork = TRUE))
items = formr_items(path = 
	system.file('extdata/gods_example_items.json', package = 'formr', mustWork = TRUE))
results = formr_recognise(item_list = items, results = results)
agg = formr_aggregate(item_list = items, results = results, 
	compute_alphas = FALSE, plot_likert = FALSE)
agg[, c('religiousness', 'prefer')]

Aggregate Scales

Description

Aggregate Scales

Usage

formr_api_aggregate(results, item_list, min_items = 2)

Arguments

Value

The results data.frame with one added numeric column per scale (the row mean of its items); scale reliability stored in attributes.

Authenticate with formr

Description

Connects to the API. If no credentials are provided, the auto-pickup chain is: the package's hidden .formr env (set automatically when the code runs inside an OpenCPU session on rforms.org), then the calling-frame chain (for legacy injectors that wrote bare locals into the wrapper scope), then the keyring.

Usage

formr_api_authenticate(
  host = "https://rforms.org",
  client_id = NULL,
  client_secret = NULL,
  access_token = NULL,
  account = NULL,
  verbose = TRUE
)

Arguments

Value

Invisibly NULL; called for its side effect of obtaining and caching an OAuth access token (errors on failure).

Backup a study

Description

Downloads the full run structure, all survey items, attached files, and results. Saves everything into a structured folder.

Usage

formr_api_backup_run(run_name, dir = NULL, prompt = TRUE, verbose = TRUE)

Arguments

Value

Invisibly NULL; called for its side effect of writing the run structure (JSON), surveys, files and results (results.rds) into dir.

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_api_backup_run("my_run", dir = tempdir())

## End(Not run)

Create a new run

Description

Creates one or more new runs on the server. Prints a confirmation message with the public link for each.

Usage

formr_api_create_run(name, verbose = TRUE)

Arguments

Value

Invisibly returns a data frame containing the name and link of the created runs.

Create Session(s)

Description

Creates one or more sessions. If codes is NULL, one random session is created. If codes is provided, tries to create sessions with those specific codes.

Usage

formr_api_create_session(
  run_name,
  codes = NULL,
  testing = FALSE,
  verbose = TRUE
)

Arguments

Value

Invisibly the API response: a list with the created sessions and, for any that failed, an errors data.frame to inspect.

Delete ALL files attached to a run

Description

CAUTION: This will permanently remove every file attached to the specified run. It first fetches the list of existing files, then iterates through them to delete.

Usage

formr_api_delete_all_files(run_name, prompt = TRUE, verbose = TRUE)

Arguments

Value

Invisibly TRUE on success; called to delete all files from the run.

Delete file(s) from a run

Description

Removes file attachment(s) from the run. Accepts a single filename, a vector of filenames, or a local directory path (which will delete files on the server that match the names of the files in the local directory).

Usage

formr_api_delete_file(run_name, file_name, verbose = TRUE)

Arguments

Value

Invisibly TRUE; called to delete the named file(s) from the run.

Delete a Run

Description

Permanently deletes a run and all associated data (sessions, results).

Usage

formr_api_delete_run(run_name, prompt = TRUE, verbose = TRUE)

Arguments

Value

Invisibly TRUE (single run) or a named logical vector (multiple runs) indicating per-run success; FALSE if the user declines the prompt.

Delete a Survey

Description

Permanently deletes a survey study. Note: The API may prevent deletion if this survey is currently used in an active run.

Usage

formr_api_delete_survey(survey_name, prompt = TRUE, verbose = TRUE)

Arguments

Value

Invisibly TRUE on success; FALSE if the user declines the prompt.

Lower-level API Result Fetcher

Description

Fetches raw results. Advanced users can use this if they want completely raw data without any type coercion or processing.

Usage

formr_api_fetch_results(
  run_name = .formr$run_name,
  surveys = NULL,
  session_ids = NULL,
  item_names = NULL,
  join = FALSE
)

Arguments

Value

A tibble of survey results, or (when join = FALSE) a named list of tibbles, one per survey.

List files attached to a run

Description

Returns a data frame of all files uploaded to a specific run, including their public URLs and timestamps.

Usage

formr_api_files(run_name, verbose = TRUE)

Arguments

Value

A data.frame containing: id, name, path, url, created, modified.

Check if currently authenticated

Description

Checks if there is a valid, non-expired session. Does NOT verify token validity with the server (use formr_api_session() for that).

Usage

formr_api_is_authenticated()

Value

TRUE if authenticated and token not expired, FALSE otherwise.

Revoke Access Token (Logout)

Description

Invalidates the current access token on the server and clears the local session state.

Usage

formr_api_logout(verbose = TRUE)

Arguments

Value

Invisibly TRUE on success (or FALSE if there was no active session); called to revoke the access token on the server and clear the local session.

Pull Project from Server Scaffolds folder structure if missing, then overwrites local files with Server state.

Description

Pull Project from Server Scaffolds folder structure if missing, then overwrites local files with Server state.

Usage

formr_api_pull_project(run_name, dir = NULL, prompt = TRUE, verbose = TRUE)

Arguments

Value

Invisibly NULL on success (or FALSE if the user declines the overwrite prompt); called for its side effect of scaffolding dir and writing the run's structure, settings, surveys and files from the server.

Push Project to Server

Description

Uploads local project files (surveys, assets, settings) to the formr server. Optionally monitors the directory for subsequent changes (Watcher mode).

Usage

formr_api_push_project(
  run_name,
  dir = NULL,
  watch = FALSE,
  background = TRUE,
  interval = 2,
  verbose = TRUE
)

Arguments

Value

Invisibly TRUE when the watcher is launched as a background RStudio job; otherwise invisibly NULL. Called for its side effect of uploading the local project in dir to the server (optionally starting a file-watcher).

Apply Type Definitions and Labels

Description

Apply Type Definitions and Labels

Usage

formr_api_recognise(item_list, results)

Arguments

Value

The results data.frame with item types applied: POSIXct timestamps and choice items as haven::labelled vectors.

Get and Process Run Results

Description

This is the main function for scientists. It fetches data from the API, automatically cleans types (dates/numbers), reverses items, computes scales, and joins everything into one dataframe.

Usage

formr_api_results(
  run_name = .formr$run_name,
  ...,
  compute_scales = TRUE,
  join = TRUE,
  remove_test_sessions = TRUE,
  verbose = TRUE
)

Arguments

Value

A processed tibble with class formr_results.

Reverse Items and Update Labels

Description

Reverses numeric items ending in 'R' based on metadata bounds. Critically, it also updates haven::labelled attributes so that the text labels point to the new, reversed values.

Usage

formr_api_reverse(results, item_list)

Arguments

Value

The results data.frame with reverse-keyed items (those ending in R) flipped and their value labels remapped.

Get or Update Run Settings

Description

Retrieve the settings for one or more runs as a tidy data frame, or update them by providing a named list of new values.

Usage

formr_api_run_settings(run_name, settings = NULL, verbose = TRUE)

Arguments

Value

• If settings is NULL: A data.frame/tibble with details for all requested runs.

• If settings is provided: Invisibly returns TRUE on success.

Get or Update Run Structure (Run Units)

Description

Export the current run structure as a list (GET) or replace it by importing a JSON file (PUT).

Usage

formr_api_run_structure(
  run_name,
  structure_json_path = NULL,
  file = NULL,
  verbose = TRUE
)

Arguments

Value

• GET (default): A formr_run_structure object (list) for inspection.

• GET (file provided): Invisibly returns the file path.

• PUT: Invisibly returns TRUE on success.

List all runs

Description

Returns a data frame of all runs accessible to the user, including status flags and timestamps.

Usage

formr_api_runs()

Value

A data.frame containing run details: id, name, title, public (bool), cron_active (bool), locked (bool), created (POSIXct), modified (POSIXct).

Get Current API session

Description

Returns the current session object or NULL if not authenticated.

Usage

formr_api_session()

Value

A list, or NULL if not authenticated:

• base_url: parsed URL (httr style).

• token: the bearer access token.

• scope: space-delimited string of scopes granted to this token. NA_character_ when the auth path couldn't introspect (direct access-token authentication, or older server). "" means the credential was issued with no scopes — every API call will 403 until the user picks scopes at admin/account#api.

• expires_at: POSIXct of token expiry (or NULL).

Perform Action on Session(s)

Description

Controls the flow of one or more sessions.

Usage

formr_api_session_action(
  run_name,
  session_codes,
  action,
  position = NULL,
  verbose = TRUE
)

Arguments

Value

A logical vector indicating success for each session.

List Sessions in a Run

Description

Returns a tidy data frame of sessions. Can either list all sessions (with filtering) or fetch specific sessions by their codes.

Usage

formr_api_sessions(
  run_name,
  session_codes = NULL,
  active = NULL,
  testing = NULL,
  limit = 1000,
  offset = 0,
  verbose = TRUE
)

Arguments

Value

A combined tibble of session states and details.

Get Survey Structure (Items)

Description

Retrieves the item table for a survey. Can return a tibble (JSON) or download the original Excel file (XLSX).

Usage

formr_api_survey_structure(survey_name, format = "json", file_path = NULL)

Arguments

Value

In JSON mode (the default) a tibble of the survey's items; in xlsx mode invisibly the file_path written.

List Surveys

Description

Returns a list of all surveys owned by the user.

Usage

formr_api_surveys(name_pattern = NULL, verbose = TRUE)

Arguments

Value

A tibble of surveys (id, name, created, modified, results_table).

Get token expiry information

Description

Returns information about when the current token expires.

Usage

formr_api_token_expiry()

Value

A list with:

• expires_at: POSIXct of expiry time (or NULL if unknown)

• seconds_left: Seconds until expiry (or NA if unknown)

• is_expired: TRUE if token has expired

List Per-Unit Sessions in a Run

Description

Returns one row per (participant × unit × iteration) for the run — the history view that complements formr_api_sessions() (which gives one row per participant with their current unit only).

Usage

formr_api_unit_sessions(
  run_name,
  session_codes = NULL,
  testing = NULL,
  since = NULL,
  limit = 1000,
  offset = 0,
  verbose = TRUE
)

Arguments

Details

Use this for trajectory plots (Sankey, alluvial), drop-off analytics, and debugging stuck participants. The rows arrive ordered by ⁠(session, created, unit_session_id)⁠, so dplyr::group_by(session) |> dplyr::mutate(next_unit = dplyr::lead(unit_description)) gives the edges of a trajectory plot directly.

Special units (OverviewScriptPage, ServiceMessagePage, ReminderEmail) surface with position = NA because they live outside the ordered run flow.

Value

A tidy tibble with columns: unit_session_id, session, testing, unit_id, unit_type, unit_description, position, iteration, created, expires, ended, expired, result, state.

Upload File(s) to Run

Description

Uploads local file(s) to the run. Accepts a single file path, a vector of file paths, or a directory path (which will upload all files within that directory).

Usage

formr_api_upload_file(run_name, path, verbose = TRUE)

Arguments

Value

Invisibly returns a list of server responses.

Upload/Update Survey

Description

Uploads a survey structure.

Usage

formr_api_upload_survey(
  file_path = NULL,
  google_sheet_url = NULL,
  verbose = TRUE
)

Arguments

Value

Invisibly the server response; called to upload or update a survey from a local file or Google Sheet.

Backup uploaded files from formr

Description

After connecting to formr using formr_connect() you can backup uploaded files using this command.

Usage

formr_backup_files(
  survey_name,
  overwrite = FALSE,
  save_path = NULL,
  host = formr_last_host()
)

Arguments

Value

Invisibly the file list with an updated downloaded field; called to download a survey's user-uploaded files into save_path.

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_backup_files(survey_name = 'training_diary', save_path = tempdir() )

## End(Not run)

Backup a study

Description

Backup a study by downloading all surveys, results, item displays, run shuffle, user overview and user details. This function will save the data in a folder named after the study.

Usage

formr_backup_study(
  study_name,
  save_path = NULL,
  host = formr_last_host(),
  overwrite = FALSE
)

Arguments

Value

Invisibly NULL; called for its side effect of downloading a whole study (run structure, surveys, files and results) into save_path.

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_default_dir(tempdir())
formr_backup_study(study_name = 'training_diary' )

## End(Not run)

Backup surveys

Description

Backup surveys by downloading item lists, results, item displays and file lists.

Usage

formr_backup_surveys(
  survey_names,
  surveys = list(),
  save_path = NULL,
  overwrite = FALSE,
  host = formr_last_host()
)

Arguments

Value

Invisibly NULL; called for its side effect of downloading surveys (items, results, item displays and files) into save_path.

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_backup_surveys(survey_names = 'training_diary', save_path = file.path(tempdir(), 'surveys'))

## End(Not run)

Connect to formr

Description

Connects to formr using your normal login and the httr library which supports persistent session cookies. Calling this function will persist the specified host (by default https://rforms.org) in further formr_ function calls. You can change this by calling formr_last_host()

Usage

formr_connect(
  email = NULL,
  password = NULL,
  host = formr_last_host(),
  keyring = NULL
)

Arguments

Value

Invisibly TRUE on success; called for its side effect of establishing an authenticated cookie session with the formr server (stored in httr's cookie jar).

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_connect(keyring = "formr_diary_study_account" )

## End(Not run)

Get or set the default directory for downloads and backups

Description

formr's file-writing functions (e.g. formr_backup_study(), formr_api_backup_run(), formr_api_pull_project()) never write to your working directory by default. Instead they default their destination to the value stored here, which is unset (NULL) until you choose one — so nothing is ever written until you opt in. Call this function with a path to set a session-wide default, or without arguments to read the current value. The path is held in memory for the current R session only; nothing is written to disk to persist it. There is no separate reset: the value lasts until you overwrite it with another path or your R session ends.

Usage

formr_default_dir(dir = NULL)

Arguments

Value

The current default directory as a length-1 character string, or NULL when none has been set.

Examples

formr_default_dir(tempdir())
formr_default_dir()

Disconnect from formr

Description

Disconnects from formr if connected.

Usage

formr_disconnect(host = formr_last_host())

Arguments

Value

Invisibly TRUE on a successful logout; called to log out and clear the active session.

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_disconnect()

## End(Not run)

render inline text for formr

Description

Render text

Usage

formr_inline_render(text, self_contained = TRUE, ...)

Arguments

Value

A length-1 character string of rendered inline HTML.

Download detailed result timings and display counts from formr

Description

After connecting to formr using formr_connect() you can download detailed times and display counts for each item using this command.

Usage

formr_item_displays(survey_name, host = formr_last_host())

Arguments

Value

A data.frame (parsed JSON) of item-display records with timing and display counts.

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_connect(email = 'you@example.net', password = 'zebrafinch' )
formr_item_displays(survey_name = 'training_diary' )

## End(Not run)

Download items from formr

Description

After connecting to formr using formr_connect() you can download items using this command. One of survey_name or path has to be specified, if both are specified, survey_name is preferred.

Usage

formr_items(survey_name = NULL, host = formr_last_host(), path = NULL)

Arguments

Value

A list of class formr_item_list (item metadata per item, named by item name).

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_connect(email = 'you@example.net', password = 'zebrafinch' )
formr_items(survey_name = 'training_diary' )

## End(Not run)
formr_items(path = 
	system.file('extdata/gods_example_items.json', package = 'formr', mustWork = TRUE))[1:2]

knit rmarkdown to markdown for formr

Description

Render text

Usage

formr_knit(text)

Arguments

Value

A length-1 character string of knitted markdown.

Get the last specified host

Description

This function returns the default or the last specified host if called without an argument. It changes the host when called with an argument.

Usage

formr_last_host(host = NULL)

Arguments

Value

the last specified host

Examples

formr_last_host("https://rforms.org")
formr_last_host()

Render a participant-flow Sankey for an overview script

Description

Pulls the run's per-unit interaction history via formr_api_unit_sessions() and renders a plotly Sankey diagram of how participants are moving through the ordered units.

Usage

formr_overview_sankey(
  run_name = .formr$run_name,
  testing = FALSE,
  orientation = "v",
  min_avg_visits_to_annotate = 1.1
)

Arguments

Details

Sankey diagrams can only draw acyclic graphs, but diary / longitudinal studies revisit the same units across iterations. To stay readable without losing information, the helper collapses each position to a single node (counting only the first time a participant visits it) and surfaces the average per-participant visit count as an "avg N visits" suffix on the node label. Single-pass runs stay clutter-free (no suffix when the average is ~1); diary studies show e.g. "p20: Daily mood (avg 14.2 visits)" so the loop density is visible without drawing it.

Designed to be called from an OverviewScriptPage on rforms.org, where the server injects the per-token .formr$access_token / .formr$host / .formr$run_name environment and formr_api_authenticate() picks them up automatically. Outside an Overview render, set run_name explicitly and call formr_api_authenticate() first.

Value

A plotly Sankey object, or NULL with a message when there are no rows to plot. Returning NULL lets the caller's knitr chunk gracefully display the message instead of erroring.

Processed, aggregated results

Description

This function chains formr_recognise() and formr_aggregate() in sequence. Useful if you want to post-process raw results before aggregating etc.

Usage

formr_post_process_results(
  item_list = NULL,
  results,
  compute_alphas = FALSE,
  fallback_max = 5,
  plot_likert = FALSE,
  quiet = FALSE,
  item_displays = NULL,
  tag_missings = !is.null(item_displays),
  remove_test_sessions = TRUE
)

Arguments

Value

A data.frame/tibble with recognised types, reverse-keyed items flipped, scales aggregated, and missing values tagged.

Examples

results = jsonlite::fromJSON(txt = 
	system.file('extdata/BFI_post.json', package = 'formr', mustWork = TRUE))
items = formr_items(path = 
	system.file('extdata/BFI_post_items.json', package = 'formr', mustWork = TRUE))
item_displays = jsonlite::fromJSON(
	system.file('extdata/BFI_post_itemdisplay.json', package = 'formr', mustWork = TRUE))
processed_results = formr_post_process_results(items, results, item_displays = item_displays,
compute_alphas = FALSE, plot_likert = FALSE)

Download data from formr

Description

After connecting to formr using formr_connect() you can download data using this command.

Usage

formr_raw_results(survey_name, host = formr_last_host())

Arguments

Value

The survey's results before processing: a data.frame (or the raw parsed list).

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_raw_results(survey_name = 'training_diary' )

## End(Not run)

Recognise data types based on item table

Description

Once you've retrieved an item table using formr_items() you can use this function to correctly type your variables based on the item table (e.g. formr free text types will be character, but select_add_one will be factor, dates are also typed as Date, datetimes as POSIXct).

Usage

formr_recognise(
  survey_name = NULL,
  item_list = formr_items(survey_name, host = host),
  results = formr_raw_results(survey_name, host = host),
  host = formr_last_host()
)

Arguments

Value

The results data.frame with POSIXct timestamps and numeric/factor/haven::labelled columns set according to item types.

Examples

results = jsonlite::fromJSON(txt = 
system.file('extdata/gods_example_results.json', package = 'formr', mustWork = TRUE))
class(results$created)
items = formr_items(path = 
system.file('extdata/gods_example_items.json', package = 'formr', mustWork = TRUE))
results = formr_recognise(item_list = items, results = results)
class(results$created)

render text for formr

Description

Render text

Usage

formr_render(text, self_contained = FALSE, ...)

Arguments

Value

A length-1 character string: the path to the rendered HTML file.

render inline text for formr

Description

Render text

Usage

formr_render_commonmark(text)

Arguments

Value

A length-1 character string of HTML rendered from CommonMark.

Examples

formr_render_commonmark("There are only `r sample(2:3, 1)` types of people.")

Download processed, aggregated results from formr

Description

After connecting to formr using formr_connect() you can download data and process it. This approach calls the following functions in the right sequence: formr_raw_results() formr_items(), formr_item_displays() and formr_post_process_results(). So, results are downloaded, metadata on items (labels etc.) is added, normal and missing values are labelled. In the end, items like bfi_extra_3R are reversed in place (maintaining labels but changing underlying numbers), and scales are aggregated (bfi_extra_1, bfi_extra_2, bfi_extra_3R become bfi_extra)

Usage

formr_results(survey_name, host = formr_last_host(), ...)

Arguments

Value

A tibble of processed, aggregated survey results (the output of formr_post_process_results()).

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_results(survey_name = 'training_diary' )

## End(Not run)

Reverse items based on item table or a fallback_max

Description

Example: If your data contains Extraversion_1, Extraversion_2R and Extraversion_3, there will be two new variables in the result: Extraversion_2 (reversed to align with _1 and _2) and Extraversion, the mean score of the three. If you supply an item table, the maximum possible answer to the item will be used to reverse it. If you don't, the maximum actually given answer or the fallback_max argument will be used to reverse it. It's faster to do this without an item table, but this can lead to problems, if you mis-specify the fallback max or the highest possible value does not occur in the data.

Usage

formr_reverse(results, item_list = NULL, fallback_max = 5)

Arguments

Value

The results data.frame with reverse-keyed (R-suffixed) items flipped and labelled items' value labels updated.

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_connect(email = 'you@example.net', password = 'zebrafinch' )
icar_items = formr_items(survey_name='ICAR',host = 'http://localhost:8888/formr/')
# get some simulated data and aggregate it
sim_results = formr_simulate_from_items(icar_items)
reversed_items = formr_reverse(item_list = icar_items, results = sim_results)

## End(Not run)
results = jsonlite::fromJSON(txt = 
	system.file('extdata/gods_example_results.json', package = 'formr', mustWork = TRUE))
items = formr_items(path = 
	system.file('extdata/gods_example_items.json', package = 'formr', mustWork = TRUE))
formr_reverse(results, items)

Download run structure from formr

Description

After connecting to formr using formr_connect() you can download the study/run structure using this command.

Usage

formr_run_structure(run_name, host = formr_last_host())

Arguments

Value

A list (parsed JSON) describing the run structure (its units).

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_run_structure(run_name = 'training_diary' )

## End(Not run)

Download random groups

Description

formr has a specific module for randomisation. After connecting using formr_connect() you can download the assigned random groups and merge them with your data.

Usage

formr_shuffled(run_name, host = formr_last_host())

Arguments

Value

A data.frame (parsed JSON) of random group assignments keyed by session.

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_connect(email = 'you@example.net', password = 'zebrafinch' )
formr_shuffled(run_name = 'different_drills' )

## End(Not run)

Simulate data based on item table

Description

Once you've retrieved an item table using formr_items() you can use this function to sample data from the possible choices. At the moment random data is only generated for choice-type items and numeric ones, as these are most likely to enter data analysis. Does not yet handle dates, times, text, locations, colors

Usage

formr_simulate_from_items(item_list, n = 300)

Arguments

Value

A data.frame of simulated survey data (columns id, created, modified, ended, plus sampled values for each item).

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_connect(email = 'you@example.net', password = 'zebrafinch' )
sim = formr_simulate_from_items(item_list = formr_items('training_diary'), n = 100)
summary(lm(pushups ~ pullups, data = sim))

## End(Not run)
items = formr_items(path = 
system.file('extdata/gods_example_items.json', package = 'formr', mustWork = TRUE))
fakedata = formr_simulate_from_items(items, n = 20)
fakedata[1:2,]

Store Credentials in Keyring

Description

Securely stores formr credentials in the system keyring. This function supports two modes:

1. Classic Mode: Stores email/password (and optional 2FA) for a specific account name.

2. API Mode: Stores OAuth credentials or Access Tokens for a specific host.

Usage

formr_store_keys(
  account_name = NULL,
  email = NULL,
  password = NULL,
  secret_2fa = NULL,
  host = "https://rforms.org",
  client_id = NULL,
  client_secret = NULL,
  access_token = NULL,
  account = NULL,
  verbose = TRUE
)

Arguments

Value

Invisibly NULL; called for its side effect of storing the password and 2FA seed in the system keyring.

Examples

## Not run: 
# Not run: prompts interactively and writes to the system keyring.
# --- Classic EXAMPLES ---
# Prompts for password interactively
formr_store_keys("formr_diary_study_account")

# --- NEW API EXAMPLES ---

# Store OAuth Credentials for a custom host
formr_store_keys(host = "http://localhost",
                 client_id = "my-id",
                 client_secret = "my-secret")
                 
# Store token for a specific secondary account
formr_store_keys(host = "http://localhost",
                 client_id = "my-id",
                 client_secret = "my-secret",
                 account = "project_b")

## End(Not run)

Upload new item table

Description

To automatically create surveys using formr, you can upload survey item tables from R. Only file uploads are available. The file name determines the survey name. Updating existing surveys is not implemented and not recommended (because of the sanity checks we require to prevent data deletion).

Usage

formr_upload_items(survey_file_path, host = formr_last_host())

Arguments

Value

Invisibly TRUE on success; called to upload an item-table file to the formr server.

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_connect(email = 'you@example.net', password = 'zebrafinch' )
items <- system.file('extdata/gods_example_items.json', package = 'formr', 
mustWork = TRUE)
formr_upload_items(items)

## End(Not run)

Download uploaded files from formr

Description

After connecting to formr using formr_connect() you can download uploaded files using this command.

Usage

formr_uploaded_files(survey_name, host = formr_last_host())

Arguments

Value

A list (parsed JSON) of uploaded-file metadata.

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_uploaded_files(survey_name = 'training_diary' )

## End(Not run)

Download random groups

Description

formr collects information about users' progression through the run After connecting using formr_connect() you can download a table showing their progression through the run.

Usage

formr_user_detail(run_name, host = formr_last_host())

Arguments

Value

A data.frame (parsed JSON) of detailed per-session progress through the run.

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_connect(email = 'you@example.net', password = 'zebrafinch' )
formr_user_detail(run_name = 'different_drills' )

## End(Not run)

Download random groups

Description

formr collects information about users' progression through the run After connecting using formr_connect() you can download a table showing where they are in the run.

Usage

formr_user_overview(run_name, host = formr_last_host())

Arguments

Value

A data.frame (parsed JSON) of per-session progress/overview data.

Examples

## Not run: 
# Not run: needs a live formr server and an authenticated session.
formr_connect(email = 'you@example.net', password = 'zebrafinch' )
formr_user_overview(run_name = 'different_drills' )

## End(Not run)

pass in the url to the RDS representation of a openCPU session object, get the object

Description

useful to programmatically access openCPU session object stored in character variables etc.

Usage

get_opencpu_rds(session_url, local = TRUE)

Arguments

Value

The R object deserialised from an OpenCPU session's RDS result (or loaded from a local .RData).

Examples

## Not run: 
# Not run: fetches a result from a remote OpenCPU server.
get_opencpu_rds('https://public.opencpu.org/ocpu/tmp/x02a93ec/R/.val/rds')

## End(Not run)

Replace NA values with something else

Description

Often, you want to substitute missing values with some implicit known value (e.g. if the question on number of sexual partners was skipped for sexually inactive people, you know the missing should turn into zero)

Usage

if_na(x, missing)

Arguments

Value

x with its NA values replaced by missing.

Examples

number_of_sex_partners <- c(1, 3, 5, 10, NA, 29)
if_na(number_of_sex_partners, 0)

This function makes sure you know what to expect when evaluating uncertain results in an if-clause. In most cases, you should not use this function, because it can lump a lot of very different cases together, but it may have some use for fool-proofing certain if-clauses on rforms.org, where a field in a survey may either not exist, be missing or have a value to check.

Description

This function makes sure you know what to expect when evaluating uncertain results in an if-clause. In most cases, you should not use this function, because it can lump a lot of very different cases together, but it may have some use for fool-proofing certain if-clauses on rforms.org, where a field in a survey may either not exist, be missing or have a value to check.

Usage

if_na_null(test, na = FALSE, null = FALSE)

Arguments

Value

A length-0-or-1 value: test when it is non-missing, na when test is NA, or null when test has length 0.

Examples

testdf = data.frame(test1 = 1, test2 = NA)
if ( if_na_null(testdf$test1 == 1) ) { print("go on") }
if ( if_na_null(testdf$test2 == 1) ) { print("not shown") }
if ( if_na_null(testdf$test3 == 1) ) { print("not shown") }
tryCatch({ if ( if_na_null(testdf2$test1 == 1) ) { print("causes error") } }, 
   error = function(e) { warning(e) }) 

Like ifelse(), but allows you to assign a third value to missings.

Description

Deprecated. Please use dplyr::if_else() in the future. Defaults to assigning the "no" value to missing values as well. Often missings encapsulate some sort of meaning for the variable you're trying to define.

Usage

ifelsena(test, yes, no, missing = no)

Arguments

Value

A vector like ifelse()'s result, with NA positions replaced by missing.

Examples

## Not run: 
# Not run: ifelsena() is deprecated; use dplyr::if_else() instead.
data(beavers)
beaver1$activ[1:10] = NA
beaver1$hyperactive = ifelse(beaver1$activ > 1, 1, 0)
table(beaver1$hyperactive)
beaver1$hyperactive = ifelsena(beaver1$activ > 1, 1, 0)
table(beaver1$hyperactive)

## End(Not run)

checks whether the current time is in a certain time window

Description

supply min,max as POSIXct

Usage

in_time_window(min, max)

Arguments

Value

A length-1 logical: TRUE if the current time lies between min and max.

Examples

in_time_window(Sys.time() - 1, Sys.time() + 1)

get item from survey attribute

Description

Shortcut for attributes(survey$item_name)$item. Fails with a warning.

Usage

item(survey, item_name)

Arguments

Value

The metadata list for a single item (from a survey variable's attributes), or NULL with a warning if not found.

Examples

example(formr_post_process_results)
item(processed_results, "BFIK_extra_4")

get item list from survey attributes

Description

get item list from survey attributes

Usage

items(survey)

Arguments

Value

A list of class formr_item_list extracted from a survey data.frame's variable attributes.

Examples

example(formr_post_process_results)
items(processed_results)[[1]]

knit prefixed

Description

Knit using knitr, but prefix file name to figure and cache folder (to knit in parallel on e.g. a cluster)

Usage

knit_prefixed(input, ...)

Arguments

Value

A length-1 character string of knitted output, with figure/cache paths prefixed by the input file name.

Gives the last non-missing element

Description

Just a simple shorthand to get the last, non-missing argument per default. Can give more than one element and can include missing elements. The inverse of first().

Usage

last(x, n = 1, na.rm = TRUE)

Arguments

Value

A vector of the same type as x holding its last n elements (after dropping NAs when na.rm = TRUE); an empty vector if none remain.

Examples

last( c(1:10,NA) )
last( c(1:10,NA), 2, TRUE )

custom markdown options for rmarkdown's pandoc

Description

custom markdown options for rmarkdown's pandoc

Usage

markdown_custom_options(
  add_to_format = c("+autolink_bare_uris", "+ascii_identifiers",
    "+tex_math_single_backslash", "-implicit_figures"),
  fragment.only = FALSE,
  section_divs = TRUE,
  break_on_error = FALSE,
  ...
)

Arguments

Value

An rmarkdown output_format object (from rmarkdown::html_document()/html_fragment()) with customised pandoc/knitr options.

github_markdown for rmarkdown

Description

Custom template with github-flavoured markdown based on the standard rmarkdown::html_document(). Adds +pipe_tables, +raw_html, +tex_math_single_backslash, +fenced_code_blocks, +auto_identifiers, +ascii_identifiers, +backtick_code_blocks, +autolink_bare_uris, +intraword_underscores, +strikeout, +hard_line_breaks over markdown_strict. A number of pandoc features are disabled (see markdown_custom_options()), but +yaml_metadata_block is re-enabled, so that it is possible to specify this output function using YAML.

Usage

markdown_github(fragment.only = FALSE, break_on_error = FALSE, ...)

Arguments

Value

An rmarkdown output_format object configured for GitHub-flavoured markdown.

hard line breaks

Description

Custom rmarkdown template based on the standard rmarkdown::html_document(), but with hard line breaks. Will add the pandoc '+hard_line_breaks' argument if the origin format is markdown.

Usage

markdown_hard_line_breaks(...)

Arguments

Value

An rmarkdown output_format object with hard line breaks enabled.

checks whether a new day has broken (date has increased by at least one day)

Description

a simple utility functions to avoid that looped Skip Backwards/Skip Forwards in formr are true repeatedly.

Usage

next_day(date = NULL)

Arguments

Value

A POSIXct: midnight at the start of the day after date.

Examples

next_day(Sys.time())

paste.knit_asis

Description

Helper function for knit_asis objects, useful when e.g. asis_knit_child() was used in a loop.

Usage

paste.knit_asis(..., sep = "\n\n\n", collapse = "\n\n\n")

Arguments

Details

Works like paste() with both the sep and the collapse argument set to two empty lines

Value

A length-1 character string of class knit_asis (the inputs concatenated).

Examples

paste.knit_asis("# Headline 1", "## Headline 2")

Print method for formr run structure

Description

Print method for formr run structure

Usage

## S3 method for class 'formr_api_run_structure'
print(x, ...)

Arguments

Value

Invisibly returns x; called for its side effect of printing a formatted table of the run's units.

Print new lines in knit_asis outputs

Description

Print new lines in knit_asis outputs

Usage

## S3 method for class 'knit_asis'
print(x, ...)

Arguments

Value

Invisibly NULL; called for its side effect of printing the knit_asis content.

Plot normed values as a barchart

Description

Pass in a data.frame with z-standardised values (x - Mean)/SD, and variable names, get a bar chart. Getting your data.frame into this shape probably will mean using tidyr and dplyr If the data.frame has an se column or ymax/ymin columns, these will be displayed on top of the bars and the bars will become transparent.

Usage

qplot_on_bar(
  normed_data,
  ylab = "Your value",
  xlab = "Trait",
  title = "",
  y_ticks = c("--", "-", "0", "+", "++")
)

Arguments

Value

A ggplot object: a bar chart, optionally with error bars.

Examples

normed_data = data.frame(variable = c("Extraversion","Openness",
"Agreeableness","Neuroticism","Conscientiousness"), 
value = c(-3,1,-1,0.5,2)) # standardise value
qplot_on_bar(normed_data, title = "Your personality")
normed_data = data.frame(variable = c("Extraversion","Openness",
"Agreeableness","Neuroticism","Conscientiousness"), 
value = c(-3,1,-1,0.5,2), se = c(0.2,0.3,0.2,0.25,0.4)) # standardise value
qplot_on_bar(normed_data, title = "Your personality")

Plot a normed value on the standard normal

Description

Pass in a z-standardised value (x - Mean)/SD, get a standard normal distribution.

Usage

qplot_on_normal(
  normed_value,
  ylab = "Percentage of other people with this value",
  xlab = "",
  colour = "blue",
  x_ticks = c("--", "-", "0", "+", "++")
)

Arguments

Value

A ggplot object showing the standard normal distribution with a reference line at normed_value.

Examples

normed_value = scale(x = 20, center = 14, scale = 5) # standardise value
qplot_on_normal(normed_value, xlab = "Extraversion")

Time-polar plot

Description

Pass in a data.frame with z-standardised values (x - Mean)/SD, and variable names, get a bar chart. Getting your data.frame into this shape probably will mean using tidyr + dplyr. If the data.frame has an se column or ymax/ymin columns, these will be displayed on top of the bars and the bars will become transparent.

Usage

qplot_on_polar(normed_data, ylab = "Your value", title = "")

Arguments

Value

A ggplot object drawn in polar coordinates.

Examples

weekdays = c("Monday","Tuesday","Wednesday","Thursday","Friday","Saturday","Sunday")
normed_data = data.frame(variable = factor(weekdays, weekdays), 
	value = c(0,1,0.2,0.5,1.5,2,1)) # standardise value
qplot_on_polar(normed_data, title = "Your alcohol consumption across the week")
normed_data = data.frame(variable = factor(1:24,1:24), 
	value = 3+rnorm(24), se = rep(0.2,24)) # standardise value
qplot_on_polar(normed_data, title = "Your mood around the clock")

Random date in range

Description

taken from Dirk Eddelbuettel's answer here https://stackoverflow.com/a/14721124/263054.

Usage

random_date_in_range(N, lower = "2012/01/01", upper = "2012/12/31")

Arguments

Value

A POSIXct vector of N random dates within the given range.

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

dplyr

%>%

render text

Description

Render text

Usage

render_text(text, ...)

Arguments

Value

A length-1 character string of rendered HTML.

Rescue lost attributes

Description

Taken from codebook You can use this function if some of your items have lost their attributes during wrangling Variables have to have the same name (Duh) and no attributes should be overwritten. But use with care. Similar to labelled::copy_labels().

Usage

rescue_attributes(df_no_attributes, df_with_attributes)

Arguments

Value

A data.frame: df with variable attributes restored from a reference data.frame.

Reverse labelled values

Description

Taken from codebook package reverse the underlying values for a numeric haven::labelled() vector while keeping the labels correct

Usage

reverse_labelled_values(x)

Arguments

Value

return the labelled vector with the underlying values having been reversed

Examples

x <- haven::labelled(rep(1:3, each = 3), c(Bad = 1, Good = 5))
x
reverse_labelled_values(x)

Summarize Processing History

Description

Prints a human-readable audit trail of all data cleaning steps.

Usage

## S3 method for class 'formr_results'
summary(object, ...)

Arguments

Value

Invisibly NULL; called for its side effect of printing the processing audit trail.

Send text message via Clickatell

Description

Connects to Clickatell using your token and sends a text message.

Usage

text_message_clickatell(To, Body, Token, return_result = FALSE)

Arguments

Value

Logical TRUE/FALSE indicating send success, or (when return_result = TRUE) the raw API response list.

Examples

## Not run: 
# Not run: sends a real SMS via the Clickatell gateway (needs an account).
text_message_clickatell(
	To = '492222', 
	Body = 'Hello friend', 
	Token = 'Tokentokentoken')
	
## End(Not run)

Send text message via Massenversand.de

Description

Connects to Massenversand.de using your token and sends a text message.

Usage

text_message_massenversand(
  To,
  From,
  Body,
  id,
  pw,
  time = "0",
  msgtype = "t",
  tarif = "OA",
  test = "0",
  return_result = FALSE
)

Arguments

Value

Logical TRUE/FALSE indicating send success (or the raw character API response).

Examples

## Not run: 
# Not run: sends a real SMS via the massenversand.de gateway (needs an account).
text_message_massenversand(
	To = '492222', 
	From = '15005000', 
	Body = 'Hello friend',
	id = 'ID', 
	pw = 'Tokentokentoken')
	
## End(Not run)

Send text message via Twilio

Description

Connects to Twilio using your token and sends a text message.

Usage

text_message_twilio(To, From, Body, Account, Token, return_result = FALSE)

Arguments

Value

Logical TRUE/FALSE indicating send success, or (when return_result = TRUE) the raw API response list.

Examples

## Not run: 
# Not run: sends a real SMS via the Twilio gateway (needs an account).
text_message_twilio(
	To = '492222',
	From = '15005000',
	Body = 'Hello friend',
	Account = 'ID', Token = 'Tokentokentoken')

## End(Not run)

checks how much time has passed relative to the user's last action

Description

checks how much time has passed. You can choose the unit. Implemented via lubridate::dseconds(), not periods, i.e. a minute has 60 seconds, an hour 60 minutes, a day 24 hours. Months and years are not well-defined durations, but we offer them anyway for convenience. Returns true or false.

Usage

time_passed(
  years = 0,
  months = 0,
  weeks = 0,
  days = 0,
  hours = 0,
  minutes = 0,
  seconds = 0,
  time = NULL
)

Arguments

Value

A length-1 logical: TRUE if at least the specified duration has elapsed since time.

Examples

time_passed(hours = 7, time = Sys.time())

word_document from rmarkdown, but has an added option not to break on error

Description

Exactly like rmarkdown::word_document(), but with one added argument

Usage

word_document(..., break_on_error = FALSE)

Arguments

Value

An rmarkdown output_format object like rmarkdown::word_document(), with an added option not to break on error.