| Title: | formr survey framework |
|---|---|
| Description: | The formr R package provides a few convenience functions that may be useful to the users of formr (formr.org), an online survey framework which heavily relies on R via openCPU. Some of the functions are for conveniently generating individual feedback graphics, some are just shorthands to make certain common operations in formr more palatable to R novices. |
| Authors: | Ruben Arslan [aut, cre] |
| Maintainer: | Ruben Arslan <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.10.2 |
| Built: | 2024-11-04 11:18:42 UTC |
| Source: | https://github.com/rubenarslan/formr |
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\%.
haystack %begins_with% needlehaystack %begins_with% needle
haystack |
string in which you search |
needle |
string to search for |
"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"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
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().
haystack %contains_word% needlehaystack %contains_word% needle
haystack |
string in which you search |
needle |
string to search for |
"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"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
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).
haystack %contains% needlehaystack %contains% needle
haystack |
string in which you search |
needle |
string to search for |
"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"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
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().
haystack %ends_with% needlehaystack %ends_with% needle
haystack |
string in which you search |
needle |
string to search for |
"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"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
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.
aggregate_and_document_scale(items, fun = rowMeans, stem = NULL)aggregate_and_document_scale(items, fun = rowMeans, stem = NULL)
items |
data.frame of the items that should be aggregated |
fun |
aggregation function, defaults to rowMeans with na.rm = FALSE |
stem |
common stem for the variables, specify if it should not be auto-detected as the longest common stem of the variable names |
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_neurotestdf <- 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
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.
## S3 method for class 'formr_item_list' as.data.frame(x, row.names, ...)## S3 method for class 'formr_item_list' as.data.frame(x, row.names, ...)
x |
a formr_item_list |
row.names |
not used |
... |
not used |
## Not run: formr_connect(email = '[email protected]', 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,]## Not run: formr_connect(email = '[email protected]', 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,]
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
asis_knit_child( input = NULL, text = NULL, ..., quiet = TRUE, options = NULL, envir = parent.frame() )asis_knit_child( input = NULL, text = NULL, ..., quiet = TRUE, options = NULL, envir = parent.frame() )
input |
if you specify a file path here, it will be read in before being passed to knitr (to avoid a working directory mess) |
text |
passed to |
... |
passed to |
quiet |
passed to |
options |
defaults to NULL. |
envir |
passed to |
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()
## Not run: # 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)## Not run: # 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)
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.
choice_labels_for_values(survey, item_name)choice_labels_for_values(survey, item_name)
survey |
survey with item_list attribute |
item_name |
item name |
example(formr_post_process_results) table(processed_results$BFIK_extra_4) table(choice_labels_for_values(processed_results, "BFIK_extra_4"))example(formr_post_process_results) table(processed_results$BFIK_extra_4) table(choice_labels_for_values(processed_results, "BFIK_extra_4"))
xtabs requires two arguments (na.action and exclude) to show missing values along with other values. This function defaults to including missings and has only one argument
crosstabs(x, ..., exclude = NULL)crosstabs(x, ..., exclude = NULL)
x |
passed to xtabs if it is a formula, transformed into a formula if it's a single object |
... |
passed to xtabs |
exclude |
defaults to NULL (i.e. includes NA) |
x = NA crosstabs(~ x)x = NA crosstabs(~ x)
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).
current(x)current(x)
x |
vector of which you want the current element |
current( c(1:10,NA) ) current( 1:10 )current( c(1:10,NA) ) current( 1:10 )
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.
email_image(x, ext = ".png")email_image(x, ext = ".png")
x |
image ID |
ext |
extension, defaults to .png |
## Not run: library(knitr); library(formr) opts_knit$set(upload.fun=formr::email_image) ## End(Not run)## Not run: library(knitr); library(formr) opts_knit$set(upload.fun=formr::email_image) ## End(Not run)
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.
expired(survey, variable = "expired")expired(survey, variable = "expired")
survey |
which survey are you asking about? |
variable |
which variable should be filled out, defaults to "ended" |
survey = data.frame(expired = c(NA, "2016-05-29 10:11:00", NA)) expired(survey = survey)survey = data.frame(expired = c(NA, "2016-05-29 10:11:00", NA)) expired(survey = survey)
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]
feedback_chunk(normed_value, chunks)feedback_chunk(normed_value, chunks)
normed_value |
a z-standardised value |
chunks |
a three or five element long character vector containing the text chunks for feedback |
feedback_chunk(normed_value = 0.7, chunks = c("You are rather introverted.", "You're approximately as extraverted as most people.","You are rather extraverted."))feedback_chunk(normed_value = 0.7, chunks = c("You are rather introverted.", "You're approximately as extraverted as most people.","You are rather extraverted."))
Just a simple to check how many times a survey (e.g. diary) was finished. It defaults to checking the "ended" variable for this.
finished(survey, variable = "ended")finished(survey, variable = "ended")
survey |
which survey are you asking about? |
variable |
which variable should be filled out, defaults to "ended" |
survey = data.frame(ended = c("2016-05-28 10:11:00", NA, "2016-05-30 11:18:28")) finished(survey = survey)survey = data.frame(ended = c("2016-05-28 10:11:00", NA, "2016-05-30 11:18:28")) finished(survey = survey)
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().
first(x, n = 1, na.rm = TRUE)first(x, n = 1, na.rm = TRUE)
x |
vector of which you want the first element |
n |
number of elements to take from the beginning |
na.rm |
whether to remove missings first, defaults to TRUE |
first( c(NA,1:10) ) first( c(NA, 1:10), 2, TRUE )first( c(NA,1:10) ) first( c(NA, 1:10), 2, TRUE )
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.
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, ... )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, ... )
survey_name |
case-sensitive name of a survey your account owns |
item_list |
an item_list, will be auto-retrieved based on survey_name if omitted |
results |
survey results, will be auto-retrieved based on survey_name if omitted |
host |
defaults to |
compute_alphas |
deprecated, functionality migrated to codebook package |
fallback_max |
defaults to 5 - if the item_list is set to null, we will use this to reverse |
plot_likert |
deprecated, functionality migrated to codebook package |
quiet |
defaults to FALSE - If set to true, likert plots and reliability computations are not echoed. |
aggregation_function |
defaults to rowMeans with na.rm = FALSE |
... |
passed to |
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')]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')]
Connects to formr using your client_id and client_secret (OAuth 2.0 grant type: client_credentials).
formr_api_access_token( client_id, client_secret, host = "https://api.formr.org/" )formr_api_access_token( client_id, client_secret, host = "https://api.formr.org/" )
client_id |
your client_id |
client_secret |
your client_secret |
host |
defaults to https://formr.org |
## Not run: formr_api_access_token(client_id = 'your_id', client_secret = 'your_secret' ) ## End(Not run)## Not run: formr_api_access_token(client_id = 'your_id', client_secret = 'your_secret' ) ## End(Not run)
After obtaining a token from formr, use this request
formr_api_results(request = NULL, token = NULL)formr_api_results(request = NULL, token = NULL)
request |
parameter (see example, API docs) |
token |
defaults to last used token |
## Not run: request <- list( "run[name]" = 'widgets', "run[sessions]" = 'PJ_nACjFQDEBhx7pMUfZQz3mV-OtetnpEdqT88aiY8eXE4-HegFI7Sri4yifxPXO', "surveys[all_widgets]" = "abode, yourstory, mc_god" ) formr_api_results(request) ## End(Not run)## Not run: request <- list( "run[name]" = 'widgets', "run[sessions]" = 'PJ_nACjFQDEBhx7pMUfZQz3mV-OtetnpEdqT88aiY8eXE4-HegFI7Sri4yifxPXO', "surveys[all_widgets]" = "abode, yourstory, mc_god" ) formr_api_results(request) ## End(Not run)
Get current API session Return or set URL in list form for formr API (if available)
formr_api_session()formr_api_session()
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://formr.org) in further formr_ function
calls. You can change this by calling formr_last_host()
formr_connect( email = NULL, password = NULL, host = formr_last_host(), keyring = NULL )formr_connect( email = NULL, password = NULL, host = formr_last_host(), keyring = NULL )
email |
your registered email address |
password |
your password |
host |
defaults to |
keyring |
a shorthand for the account you're using |
## Not run: formr_connect(keyring = "formr_diary_study_account" ) ## End(Not run)## Not run: formr_connect(keyring = "formr_diary_study_account" ) ## End(Not run)
Disconnects from formr if connected.
formr_disconnect(host = formr_last_host())formr_disconnect(host = formr_last_host())
host |
defaults to |
## Not run: formr_disconnect() ## End(Not run)## Not run: formr_disconnect() ## End(Not run)
Render text
formr_inline_render(text, self_contained = TRUE, ...)formr_inline_render(text, self_contained = TRUE, ...)
text |
that will be written to a tmp file and used as the input argument |
self_contained |
passed to markdown_custom_options |
... |
all other arguments passed to |
After connecting to formr using formr_connect()
you can download detailed times and display counts for each item using this command.
formr_item_displays(survey_name, host = formr_last_host())formr_item_displays(survey_name, host = formr_last_host())
survey_name |
case-sensitive name of a survey your account owns |
host |
defaults to |
## Not run: formr_connect(email = '[email protected]', password = 'zebrafinch' ) formr_item_displays(survey_name = 'training_diary' ) ## End(Not run)## Not run: formr_connect(email = '[email protected]', password = 'zebrafinch' ) formr_item_displays(survey_name = 'training_diary' ) ## End(Not run)
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.
formr_items(survey_name = NULL, host = formr_last_host(), path = NULL)formr_items(survey_name = NULL, host = formr_last_host(), path = NULL)
survey_name |
case-sensitive name of a survey your account owns |
host |
defaults to |
path |
path to local JSON copy of the item table |
## Not run: formr_connect(email = '[email protected]', 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]## Not run: formr_connect(email = '[email protected]', 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]
Render text
formr_knit(text)formr_knit(text)
text |
rmarkdown that will be knit |
This function returns the default or the last specified host if called without an argument. It changes the host when called with an argument.
formr_last_host(host = NULL)formr_last_host(host = NULL)
host |
defaults to https://formr.org |
the last specified host
formr_last_host("https://formr.org") formr_last_host()formr_last_host("https://formr.org") formr_last_host()
This function chains formr_recognise() and formr_aggregate()
in sequence. Useful if you want to post-process raw results before aggregating etc.
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 )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 )
item_list |
an item_list, defaults to NULL |
results |
survey results |
compute_alphas |
passed to formr_aggregate, defaults to TRUE |
fallback_max |
passed to formr_reverse, defaults to 5 |
plot_likert |
passed to formr_aggregate, defaults to TRUE |
quiet |
passed to formr_aggregate, defaults to FALSE |
item_displays |
an item display table, necessary to tag missings |
tag_missings |
should missings that result from an item not being shown be distinguished from missings due to skipped questions? |
remove_test_sessions |
by default, formr removes results resulting from test session (animal names and null session codes) |
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)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)
After connecting to formr using formr_connect()
you can download data using this command.
formr_raw_results(survey_name, host = formr_last_host())formr_raw_results(survey_name, host = formr_last_host())
survey_name |
case-sensitive name of a survey your account owns |
host |
defaults to |
## Not run: formr_raw_results(survey_name = 'training_diary' ) ## End(Not run)## Not run: formr_raw_results(survey_name = 'training_diary' ) ## End(Not run)
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).
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() )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() )
survey_name |
case-sensitive name of a survey your account owns |
item_list |
an item_list, will be auto-retrieved based on survey_name if omitted |
results |
survey results, will be auto-retrieved based on survey_name if omitted |
host |
defaults to |
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)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
formr_render(text, self_contained = FALSE, ...)formr_render(text, self_contained = FALSE, ...)
text |
that will be written to a tmp file and used as the input argument |
self_contained |
passed to markdown_custom_options |
... |
all other arguments passed to |
Render text
formr_render_commonmark(text)formr_render_commonmark(text)
text |
that will be passed to knitr |
formr_render_commonmark("There are only `r sample(2:3, 1)` types of people.")formr_render_commonmark("There are only `r sample(2:3, 1)` types of people.")
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)
formr_results(survey_name, host = formr_last_host(), ...)formr_results(survey_name, host = formr_last_host(), ...)
survey_name |
case-sensitive name of a survey your account owns |
host |
defaults to |
... |
passed to |
## Not run: formr_results(survey_name = 'training_diary' ) ## End(Not run)## Not run: formr_results(survey_name = 'training_diary' ) ## End(Not run)
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.
formr_reverse(results, item_list = NULL, fallback_max = 5)formr_reverse(results, item_list = NULL, fallback_max = 5)
results |
survey results |
item_list |
an item_list, defaults to NULL |
fallback_max |
defaults to 5 - if the item_list is set to null, we will use this to reverse |
## Not run: formr_connect(email = '[email protected]', 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)## Not run: formr_connect(email = '[email protected]', 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)
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.
formr_shuffled(run_name, host = formr_last_host())formr_shuffled(run_name, host = formr_last_host())
run_name |
case-sensitive name of the run in which you randomised participants |
host |
defaults to |
## Not run: formr_connect(email = '[email protected]', password = 'zebrafinch' ) formr_shuffled(run_name = 'different_drills' ) ## End(Not run)## Not run: formr_connect(email = '[email protected]', password = 'zebrafinch' ) formr_shuffled(run_name = 'different_drills' ) ## End(Not run)
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
formr_simulate_from_items(item_list, n = 300)formr_simulate_from_items(item_list, n = 300)
item_list |
the result of a call to |
n |
defaults to 300 |
## Not run: formr_connect(email = '[email protected]', 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,]## Not run: formr_connect(email = '[email protected]', 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 keys in the system keyring/keychain instead of plaintext.
formr_store_keys(account_name)formr_store_keys(account_name)
account_name |
a shorthand for the account you're using |
## Not run: formr_store_keys("formr_diary_study_account") ## End(Not run)## Not run: formr_store_keys("formr_diary_study_account") ## End(Not run)
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).
formr_upload_items(survey_file_path, host = formr_last_host())formr_upload_items(survey_file_path, host = formr_last_host())
survey_file_path |
the path to an item table in csv/json/xlsx etc. |
host |
defaults to |
## Not run: formr_connect(email = '[email protected]', password = 'zebrafinch' ) items <- system.file('extdata/gods_example_items.json', package = 'formr', mustWork = TRUE) formr_upload_items(items) ## End(Not run)## Not run: formr_connect(email = '[email protected]', password = 'zebrafinch' ) items <- system.file('extdata/gods_example_items.json', package = 'formr', mustWork = TRUE) formr_upload_items(items) ## End(Not run)
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.
formr_user_detail(run_name, host = formr_last_host())formr_user_detail(run_name, host = formr_last_host())
run_name |
case-sensitive name of the run in which you randomised participants |
host |
defaults to |
## Not run: formr_connect(email = '[email protected]', password = 'zebrafinch' ) formr_user_detail(run_name = 'different_drills' ) ## End(Not run)## Not run: formr_connect(email = '[email protected]', password = 'zebrafinch' ) formr_user_detail(run_name = 'different_drills' ) ## End(Not run)
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.
formr_user_overview(run_name, host = formr_last_host())formr_user_overview(run_name, host = formr_last_host())
run_name |
case-sensitive name of the run in which you randomised participants |
host |
defaults to |
## Not run: formr_connect(email = '[email protected]', password = 'zebrafinch' ) formr_user_overview(run_name = 'different_drills' ) ## End(Not run)## Not run: formr_connect(email = '[email protected]', password = 'zebrafinch' ) formr_user_overview(run_name = 'different_drills' ) ## End(Not run)
useful to programmatically access openCPU session object stored in character variables etc.
get_opencpu_rds(session_url, local = TRUE)get_opencpu_rds(session_url, local = TRUE)
session_url |
the session url, e.g. https://public.opencpu.org/ocpu/tmp/x02a93ec/R/.val/rds |
local |
defaults to FALSE, if true, will assume that the session is not on another server, and do some not-very-smart substitution to load it via the file system instead of HTTP/HTTPS |
## Not run: get_opencpu_rds('https://public.opencpu.org/ocpu/tmp/x02a93ec/R/.val/rds') ## End(Not run)## Not run: get_opencpu_rds('https://public.opencpu.org/ocpu/tmp/x02a93ec/R/.val/rds') ## End(Not run)
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)
if_na(x, missing)if_na(x, missing)
x |
the variable |
missing |
What to replace missing values with |
number_of_sex_partners <- c(1, 3, 5, 10, NA, 29) if_na(number_of_sex_partners, 0)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 formr.org, where a field in a survey may either not exist, be missing or have a value to check.
if_na_null(test, na = FALSE, null = FALSE)if_na_null(test, na = FALSE, null = FALSE)
test |
condition. can only have length 0 or length 1 |
na |
returned if the condition has a missing value |
null |
passed to ifelse |
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) })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) })
ifelse(), but allows you to assign a third value to missings.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.
ifelsena(test, yes, no, missing = no)ifelsena(test, yes, no, missing = no)
test |
passed to ifelse |
yes |
passed to ifelse |
no |
passed to ifelse |
missing |
defaults to the value for no |
## Not run: 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$hyperactive) ## End(Not run)## Not run: data(beavers) beaver1$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)
supply min,max as POSIXct
in_time_window(min, max)in_time_window(min, max)
min |
POSIXct < max |
max |
POSIXct > min |
in_time_window(Sys.time() - 1, Sys.time() + 1)in_time_window(Sys.time() - 1, Sys.time() + 1)
Shortcut for attributes(survey$item_name)$item. Fails with a warning.
item(survey, item_name)item(survey, item_name)
survey |
survey with item_list attribute |
item_name |
item name |
example(formr_post_process_results) item(processed_results, "BFIK_extra_4")example(formr_post_process_results) item(processed_results, "BFIK_extra_4")
get item list from survey attributes
items(survey)items(survey)
survey |
survey with item_list attribute |
example(formr_post_process_results) items(processed_results)[[1]]example(formr_post_process_results) items(processed_results)[[1]]
Knit using knitr, but prefix file name to figure and cache folder (to knit in parallel on e.g. a cluster)
knit_prefixed(input, ...)knit_prefixed(input, ...)
input |
input document |
... |
all arguments passed to |
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().
last(x, n = 1, na.rm = TRUE)last(x, n = 1, na.rm = TRUE)
x |
vector of which you want the last element |
n |
number of elements to take from the end |
na.rm |
whether to remove missings first, defaults to TRUE |
last( c(1:10,NA) ) last( c(1:10,NA), 2, TRUE )last( c(1:10,NA) ) last( c(1:10,NA), 2, TRUE )
saveRDS() saves an object to a file, so unlike save() and load() you can assign the loaded object to a new variable using readRDS().
However, sometimes it may be more convenient to assign the object in the RDS file to an object of the same name as the file. This is what loadRDS() does. It extracts the filename using basename() and tools::file_path_sans_ext()
loadRDS(file, refhook = NULL, overwrite = FALSE)loadRDS(file, refhook = NULL, overwrite = FALSE)
file |
path to file |
refhook |
passed to readRDS |
overwrite |
whether to overwrite an existing object of the same name. defaults to false. |
## Not run: loadRDS(file = '~/Models/Spouses.rds') # assigns object contained in file to variable 'Spouses' ## End(Not run)## Not run: loadRDS(file = '~/Models/Spouses.rds') # assigns object contained in file to variable 'Spouses' ## End(Not run)
get functions in the environment by their class. Useful to find e.g. all regression models you've stored in interactive programming.
ls_by_class(classes, envir = parent.frame(), top_class_only = FALSE, ...)ls_by_class(classes, envir = parent.frame(), top_class_only = FALSE, ...)
classes |
objects should have one of these classes |
envir |
defaults to looking in the calling environment of this function, passed to ls |
top_class_only |
defaults to FALSE. If false, also returns objects inheriting from one of the specified classes. |
... |
passed to ls |
data(ChickWeight) chickweight.m1 <- glm(weight ~ Time + Diet, family = gaussian, data = ChickWeight) ls_by_class('lm') c('chickweight.m1') %in% ls_by_class('lm') c('chickweight.m1') %in% ls_by_class('lm', top_class_only = TRUE)data(ChickWeight) chickweight.m1 <- glm(weight ~ Time + Diet, family = gaussian, data = ChickWeight) ls_by_class('lm') c('chickweight.m1') %in% ls_by_class('lm') c('chickweight.m1') %in% ls_by_class('lm', top_class_only = TRUE)
custom markdown options for rmarkdown's pandoc
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, ... )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, ... )
add_to_format |
add these arguments to the default specification |
fragment.only |
whether to get only a html fragment |
section_divs |
whether to disable –section-divs (headings generate section including everything up to the next same-or-higher-level heading) |
break_on_error |
should an error in the R code execution interrupt the rendering or should rendering continue, defaults to FALSE |
... |
all other arguments passed to Custom rmarkdown input format options based on the standard |
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.
markdown_github(fragment.only = FALSE, break_on_error = FALSE, ...)markdown_github(fragment.only = FALSE, break_on_error = FALSE, ...)
fragment.only |
whether to get only a html fragment |
break_on_error |
should an error in the R code execution interrupt the rendering or should rendering continue, defaults to FALSE |
... |
all other arguments passed to |
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.
markdown_hard_line_breaks(...)markdown_hard_line_breaks(...)
... |
all other arguments passed to |
This functions simply reports the number of missings as the percentage of the maximum number of rows. It also works on single variables.
miss_frac(df, vars = 1:NCOL(df))miss_frac(df, vars = 1:NCOL(df))
df |
data.frame or variable |
vars |
subset of variables, defaults to all |
fruits = c('apple', 'banana', NA, 'pear', 'pinapple', NA) pets = c('cat', 'dog', 'anteater', NA, NA, NA) favorites = data.frame(fruits, pets) miss_frac(favorites) miss_frac(favorites$fruits) miss_frac(favorites, 2)fruits = c('apple', 'banana', NA, 'pear', 'pinapple', NA) pets = c('cat', 'dog', 'anteater', NA, NA, NA) favorites = data.frame(fruits, pets) miss_frac(favorites) miss_frac(favorites$fruits) miss_frac(favorites, 2)
Returns the number of missings in a variable or dataset. If missings are an explicit level in a factor variable, this function defaults to reporting them anyway.
n_missing(x, exclude = NA)n_missing(x, exclude = NA)
x |
variable |
exclude |
only needed for factors. defaults to NA (count level=missing as missing), setting to 0 allows you to count level=missing as nonmissing |
data(beavers) beaver1$activ[1:10] = NA n_missing(beaver1$activ) beaver1$activ = factor(beaver1$activ, exclude = NULL) sum(is.na(beaver1$activ)) n_missing(beaver1$activ) n_missing(beaver1$activ, exclude = NULL)data(beavers) beaver1$activ[1:10] = NA n_missing(beaver1$activ) beaver1$activ = factor(beaver1$activ, exclude = NULL) sum(is.na(beaver1$activ)) n_missing(beaver1$activ) n_missing(beaver1$activ, exclude = NULL)
Returns the number of nonmissings in a variable or dataset. If missings are an explicit level in a factor variable, this function defaults to excluding them anyway.
n_nonmissing(x, exclude = NA)n_nonmissing(x, exclude = NA)
x |
variable |
exclude |
only needed for factors. defaults to NA (count level=missing as missing), setting to 0 allows you to count level=missing as nonmissing |
data(beavers) beaver1$activ[1:10] = NA n_nonmissing(beaver1$activ) beaver1$activ = factor(beaver1$activ, exclude = NULL) sum(!is.na(beaver1$activ)) n_nonmissing(beaver1$activ) n_nonmissing(beaver1$activ, exclude = NULL)data(beavers) beaver1$activ[1:10] = NA n_nonmissing(beaver1$activ) beaver1$activ = factor(beaver1$activ, exclude = NULL) sum(!is.na(beaver1$activ)) n_nonmissing(beaver1$activ) n_nonmissing(beaver1$activ, exclude = NULL)
a simple utility functions to avoid that looped Skip Backwards/Skip Forwards in formr are true repeatedly.
next_day(date = NULL)next_day(date = NULL)
date |
defaults to .formr$last_action_date, a hidden variable that is automatically set by formr.org. Will be coerced to POSIXct. |
next_day(Sys.time())next_day(Sys.time())
Helper function for knit_asis objects, useful when e.g. asis_knit_child() was used in a loop.
paste.knit_asis(..., sep = "\n\n\n", collapse = "\n\n\n")paste.knit_asis(..., sep = "\n\n\n", collapse = "\n\n\n")
... |
passed to |
sep |
defaults to two empty lines, passed to |
collapse |
defaults to two empty lines, passed to |
Works like paste() with both the sep and the collapse argument set to two empty lines
paste.knit_asis("# Headline 1", "## Headline 2")paste.knit_asis("# Headline 1", "## Headline 2")
Print new lines in knit_asis outputs
## S3 method for class 'knit_asis' print(x, ...)## S3 method for class 'knit_asis' print(x, ...)
x |
the knit_asis object |
... |
ignored |
quick and easy function to show proportions of values of a variable, defaults to including missings
props(..., exclude = NULL)props(..., exclude = NULL)
... |
passed to crosstabs |
exclude |
defaults to NULL (i.e. includes NA) |
x = NA props(~ x)x = NA props(~ x)
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.
qplot_on_bar( normed_data, ylab = "Your value", xlab = "Trait", title = "", y_ticks = c("--", "-", "0", "+", "++") )qplot_on_bar( normed_data, ylab = "Your value", xlab = "Trait", title = "", y_ticks = c("--", "-", "0", "+", "++") )
normed_data |
a dataset with a value column containing z-standardised value and a variable column containing labels for those values |
ylab |
Y-axis label, defaults to "Percentage of other people with this value" |
xlab |
X-axis label, empty by default, useful for labeling the plotted trait |
title |
Plot title |
y_ticks |
the ticks labels for -2,1,0,1 and 2 SDs around the mean, default to minuses, pluses and the average sign |
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")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")
Pass in a z-standardised value (x - Mean)/SD, get a standard normal distribution.
qplot_on_normal( normed_value, ylab = "Percentage of other people with this value", xlab = "", colour = "blue", x_ticks = c("--", "-", "0", "+", "++") )qplot_on_normal( normed_value, ylab = "Percentage of other people with this value", xlab = "", colour = "blue", x_ticks = c("--", "-", "0", "+", "++") )
normed_value |
a z-standardised value |
ylab |
Y-axis label, defaults to "Percentage of other people with this value" |
xlab |
X-axis label, empty by default, useful for labeling the plotted trait |
colour |
defaults to blue |
x_ticks |
the ticks labels for -2,1,0,1 and 2 SDs around the mean, default to minuses, pluses and the average sign |
normed_value = scale(x = 20, center = 14, scale = 5) # standardise value qplot_on_normal(normed_value, xlab = "Extraversion")normed_value = scale(x = 20, center = 14, scale = 5) # standardise value qplot_on_normal(normed_value, xlab = "Extraversion")
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.
qplot_on_polar(normed_data, ylab = "Your value", title = "")qplot_on_polar(normed_data, ylab = "Your value", title = "")
normed_data |
a dataset with a value column containing z-standardised value and a variable column containing labels for those values |
ylab |
Y-axis label, defaults to "Percentage of other people with this value" |
title |
Plot title |
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")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")
taken from Dirk Eddelbuettel's answer here http://stackoverflow.com/a/14721124/263054
random_date_in_range(N, lower = "2012/01/01", upper = "2012/12/31")random_date_in_range(N, lower = "2012/01/01", upper = "2012/12/31")
N |
desired number of random dates |
lower |
lower limit |
upper |
upper limit |
Render text
render_text(text, ...)render_text(text, ...)
text |
that will be written to a tmp file and used as the input argument |
... |
all other arguments passed to |
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().
rescue_attributes(df_no_attributes, df_with_attributes)rescue_attributes(df_no_attributes, df_with_attributes)
df_no_attributes |
the data frame with missing attributes |
df_with_attributes |
the data frame from which you want to restore attributes |
Taken from codebook package
reverse the underlying values for a numeric haven::labelled() vector while keeping the labels correct
reverse_labelled_values(x)reverse_labelled_values(x)
x |
a labelled vector |
return the labelled vector with the underlying values having been reversed
x <- haven::labelled(rep(1:3, each = 3), c(Bad = 1, Good = 5)) x reverse_labelled_values(x)x <- haven::labelled(rep(1:3, each = 3), c(Bad = 1, Good = 5)) x reverse_labelled_values(x)
Connects to Clickatell using your token and sends a text message.
text_message_clickatell(To, Body, Token, return_result = F)text_message_clickatell(To, Body, Token, return_result = F)
To |
the number you're texting to (usually without zeroes at the beginning) |
Body |
the text message body/text |
Token |
your Clickatell token |
return_result |
whether to return simply TRUE/FALSE on success/failure or the whole result |
## Not run: text_message_clickatell( To = '492222', Body = 'Hello friend', Token = 'Tokentokentoken') ## End(Not run)## Not run: text_message_clickatell( To = '492222', Body = 'Hello friend', Token = 'Tokentokentoken') ## End(Not run)
Connects to Massenversand.de using your token and sends a text message.
text_message_massenversand( To, From, Body, id, pw, time = "0", msgtype = "t", tarif = "OA", test = "0", return_result = F )text_message_massenversand( To, From, Body, id, pw, time = "0", msgtype = "t", tarif = "OA", test = "0", return_result = F )
To |
the number you're texting to (usually without zeroes at the beginning) |
From |
the number you're texting from |
Body |
the text message body/text |
id |
your Massenversand ID |
pw |
your Massenversand password |
time |
see provider API (defaults to immediate sending) |
msgtype |
see provider API |
tarif |
see provider API |
test |
see provider API |
return_result |
whether to return simply TRUE/FALSE on success/failure or the whole result |
## Not run: text_message_massenversand( To = '492222', From = '15005000', Body = 'Hello friend', id = 'ID', pw = 'Tokentokentoken') ## End(Not run)## Not run: text_message_massenversand( To = '492222', From = '15005000', Body = 'Hello friend', id = 'ID', pw = 'Tokentokentoken') ## End(Not run)
Connects to Twilio using your token and sends a text message.
text_message_twilio(To, From, Body, Account, Token, return_result = F)text_message_twilio(To, From, Body, Account, Token, return_result = F)
To |
the number you're texting to (usually without zeroes at the beginning) |
From |
the number you're texting from |
Body |
the text message body/text |
Account |
your Twilio account ID |
Token |
your Twili token |
return_result |
whether to return simply TRUE/FALSE on success/failure or the whole result |
text_message_twilio( To = '492222', From = '15005000', Body = 'Hello friend', Account = 'ID', Token = 'Tokentokentoken')text_message_twilio( To = '492222', From = '15005000', Body = 'Hello friend', Account = 'ID', Token = 'Tokentokentoken')
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.
time_passed( years = 0, months = 0, weeks = 0, days = 0, hours = 0, minutes = 0, seconds = 0, time = NULL )time_passed( years = 0, months = 0, weeks = 0, days = 0, hours = 0, minutes = 0, seconds = 0, time = NULL )
years |
365 days |
months |
30 days |
weeks |
7 days |
days |
24 hours |
hours |
60 minutes |
minutes |
60 seconds |
seconds |
argument to |
time |
defaults to .formr$last_action_time, a hidden variable that is automatically set by formr.org |
time_passed(hours = 7, time = Sys.time())time_passed(hours = 7, time = Sys.time())
Exactly like rmarkdown::word_document(), but with one added argument
word_document(..., break_on_error = FALSE)word_document(..., break_on_error = FALSE)
... |
all other arguments passed to |
break_on_error |
should an error in the R code execution interrupt the rendering or should rendering continue, defaults to FALSE |