Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
eval = FALSE,
comment = "#>"
)
data.table::setDTthreads(1)
## ----eval=FALSE---------------------------------------------------------------
# library(occupationMeasurement)
#
# # Start the interactive application with default settings
# app()
## ----app_flow, eval=FALSE, include=FALSE--------------------------------------
# # Since Graphviz is rendering the diagram via JS, it actually includes the
# # whole JS library in the resulting document. It is therefore much more efficient to just
# # render the document once and then copy-paste the resulting svg as a figure.
# # This code is still included in-case the diagram needs to be updated.
# DiagrammeR::grViz('
# digraph G {
# free_text_1 [
# label = "Get free text response describing occupation";
# shape = rect;
# ];
# free_text_1 -> generate_suggestions;
# generate_suggestions [
# label = "Generate a list of suggestions\nbased on the free text response";
# shape = rect;
# ];
#
# free_text_2 [
# label = "Ask again for a more detailed free text response";
# shape = rect;
# ];
# generate_suggestions -> check_generate_suggestions;
# check_generate_suggestions [
# label = "Was it possible\nto generate suggestions?";
# shape = diamond;
# ];
# check_suggestions [
# label = "Are we trying to generate\nsuggestions for the first time?";
# shape = diamond;
# ];
# check_suggestions -> free_text_2 [ label = "1st time"; style="dashed" ];
# free_text_2 -> generate_suggestions;
#
# check_generate_suggestions -> pick_suggestion [ label = "Yes"; style="dashed" ];
# check_generate_suggestions -> check_suggestions [ label = "No"; style="dashed" ];
# pick_suggestion [
# label = "The participant picks a suggestion from the list\n(or indicates that none of them match).";
# shape = rect;
# ];
# pick_suggestion -> check_pick_suggestions;
# check_pick_suggestions [
# label = "Did the participant pick\none of the suggestions?";
# shape = diamond;
# ];
#
# check_pick_suggestions -> free_text_manual [ label = "No (none match)"; style="dashed" ];
# check_suggestions -> free_text_manual [ label = "2nd time"; style="dashed" ];
# free_text_manual [
# label = "Ask for final free text response\nfor later manual coding of occupation";
# shape = oval;
# ];
#
# check_pick_suggestions -> check_followup_questions [ label = "Yes"; style="dashed" ];
# check_followup_questions [
# label = "Does this suggestion have followup questions?\n(Only applies when using the Auxiliary Classification)";
# shape = diamond;
# ];
# check_followup_questions -> coding_finished [ label = "No"; style="dashed" ];
# check_followup_questions -> next_followup_question [ label = "Yes"; style="dashed" ];
#
# next_followup_question [
# label = "Ask up to 2 followup questions to the participant\nto clarify the classification";
# shape = rect;
# ];
# next_followup_question -> coding_finished;
# coding_finished [
# label = "✅ Coding is finished";
# shape = oval;
# ];
# }
# ')
## ----eval=FALSE---------------------------------------------------------------
# library(occupationMeasurement)
#
# app(
# # Use the questionnaire for interviewer-administered interviews
# questionnaire = questionnaire_interviewer_administered(),
# app_settings = create_app_settings(
# # ... specify your custom settings here:
#
# # Collect an interview_id, so that you can merge data from your questionnaire
# # and from the app after data collection
# require_respondent_id = TRUE,
#
# # Skip follow-up questions related to ISCO skill level
# # or ISCO supervisory/managment occupations
# # (in case similar questions are already included in your questionnaire)
# skip_followup_types = c("anforderungsniveau", "aufsicht")
#
# # ...
# )
# )
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