Function reference • MoreyHoekstra2019

Access to data Functions and objects that give access the data in the paper.
`compile_complete_dataset()`	Complete data set after extracting samples and making some columns "neater"
`load_clean_dataset()`	Load the data set and apply all cleaning steps
`load_complete_dataset()`	Read the complete data set from the (compressed) CSV
`load_text_responses()`	Text responses for each participant from the cleaned data
`christmas_stats_participants`	Cleaned, participant-level data
`christmas_stats_samples`	Cleaned, event-level data
`text_coding`	Coding of the text responses
Figures Functions to create project figures.
`fig_confidence()`	Response confidence ratings
`fig_desc_evidence()`	Show distributions of the "evidence" as a function of sample size, evidence scale/power, and true effect size
`fig_desc_evidence_transform()`	Show the transformation between the z/p value and the visual location
`fig_dprime_effect_size()`	d prime as a function of effect size (model fits)
`fig_edu_training_edu()`	Distribution of attained educational level in the sample
`fig_edu_training_stats()`	Distribution of reported number of years of statistical training in the sample
`fig_error_rates()`	Error/correct response rates as a function of effect size
`fig_error_rates_dprime()`	Error/correct response rates as a function of effect size, with model fits
`fig_evidence_p_vals()`	Generate image to show the interface with p values overlaid
`fig_evidence_scale_boxes()`	Number of samples as a function of sample type, evidence scale and true effect size
`fig_evidence_scale_logistic_p()`	Probability of making a "difference" decision as a function of the most extreme p value and evidence scale
`fig_evidence_scale_logistic_x()`	Probability of making a "difference" decision as a function of the most extreme x value and evidence scale
`fig_field()`	Distribution of self-reported scientific field in the sample
`fig_field_scientific()`	Participants' reports of whether others would call their field scientific
`fig_how_use_stat()`	Distribution of self-reported ways participants use statistics
`fig_preferred_method()`	Distribution of self-reported preferences for statistical methods
`fig_recreate_display()`	Generate image to show a participant's sampling behaviour
`fig_response_evidence()`	Distribution of "evidence" in the display by response
`fig_sampling_behavior()`	Number of samples per participant for each activity, by effect size
`fig_shuffle_understanding()`	Distribution of self-reported understanding of what usefulness of random shuffle reports before experiment
`fig_sig_testing_opinion()`	Distribution of opinions about significance testing in sample
`fig_strategies()`	Frequencies of self-reported strategies
Interactivity Functions that provide interactive access to project materials
`explore_data()`	Run shiny app to explore the (cleaned) data set
`task_demo()`	Start a web server with a demo of the experimental task and open browser
HTML widget functions Functions that make the data exploration HTML widget work in shiny apps
`santaDisplayOutput()` `renderSantaDisplay()`	Shiny bindings for santaDisplay

Reference

Access to data

Figures

Interactivity

HTML widget functions