## ----message = FALSE----------------------------------------------------------
knit_by_pkgdown <- !is.null(knitr::opts_chunk$get("fig.retina"))
ggplot2::theme_set(ggplot2::theme_bw())
knitr::opts_chunk$set(warning = TRUE, message = TRUE, error = FALSE, echo = TRUE)
library(dplyr)
library(codebook)

## -----------------------------------------------------------------------------
darktriad <- rio::import("https://osf.io/j4fcb/download", format = "sav")
if (!knit_by_pkgdown) {
  darktriad <- darktriad %>%
  select(DG, sex, relStat, education, NPI_avg)
}

## -----------------------------------------------------------------------------
metadata(darktriad)$name <- "How alluring are dark personalities? The Dark Triad and attractiveness in speed dating"
metadata(darktriad)$description <- paste0("The data to this speed dating study comes in two different formats: Personwise (one record for each individual) and dyadic (pairwise; one record for each date). The respective SPSS files are named \"DarkTriadDate_person.sav\" and \"DarkTriadDate_dyad.sav\".

### Download link
[Open Science Framework](https://osf.io/j4fcb/download)

### Personwise datafile 
The personwise datafile contains individual differences variables and perceiver and target effects according to the social relations model. These are centered marginal means that were calculated according to the formulae provided by Kenny, Kashy, and Cook (2006). These effects are not (!) based on multilevel analyses.

### Preprocessing
All rating variables (i.e., actual choice, friendship, short-term relationship etc.) were corrected for prior acquaintance, which means that dates wih prior acquaintance were excluded (set to missing) on a dyadic basis.

Variables are labeled in SPSS. 

### A list of important abbreviations, prefixes and suffixes:

* _acq = acquaintance (i.e., variables with this suffix are controlled for prior * acquaintance)
* avg = average
* _rat = rating variable
* _z = z-standardized score
* BC = booty call
* DG = dating group (three groups in this study)
* FIPI = five item personality inventory
* FS = friendship
* FWB = friends-with-benefits
* Int = Intelligence
* Like = Likeability
* LTR = long-term relationship
* MACHIV = mach-iv machiavellianism questionnaire
* N, E, O, A, C = Big5
* NPI = narcissistic personality inventory
* ONS = one night stand
* P = perceiver
* PA = physical attractiveness
* PercEff = perceiver effect
* SD = speed dating
* SRM = social relations model
* SRP = self-report psychopathy scale
* STR = short-term relationship
* T = target
* TargEff = target effect


")
metadata(darktriad)$identifier <- "https://osf.io/jvk3u/"
metadata(darktriad)$datePublished <- "2015-10-07"
metadata(darktriad)$creator <- list(
      "@type" = "Person",
      givenName = "Emanuel", familyName = "Jauk",
      email = "emanuel.jauk@uni‐graz.at", 
      affiliation = list("@type" = "Organization",
        name = "Karl‐Franzens‐Universität Graz, Austria"))
metadata(darktriad)$citation <- "Jauk, E., Neubauer, A. C., Mairunteregger, T., Pemp, S., Sieber, K. P., & Rauthmann, J. F. (2016). How alluring are dark personalities? The Dark Triad and attractiveness in speed dating. European Journal of Personality, 30(2), 125-138."
metadata(darktriad)$url <- "https://osf.io/j4fcb/"
metadata(darktriad)$temporalCoverage <- "2015" 
metadata(darktriad)$spatialCoverage <- "Graz, Austria" 
metadata(darktriad)$distribution = list(
  list("@type" = "DataDownload",
       "requiresSubscription" = "https://schema.org/True",
       "encodingFormat" = "https://www.loc.gov/preservation/digital/formats/fdd/fdd000469.shtml",
       contentUrl = "https://osf.io/j4fcb/download")
)

## -----------------------------------------------------------------------------
# We don't want to look at the code in the codebook.
knitr::opts_chunk$set(warning = TRUE, message = TRUE, echo = FALSE)

## ----setup,eval=TRUE,echo=FALSE-----------------------------------------------
if (exists("testing")) {
	indent = '#' # ugly hack so _regression_summary can be "spun" (variables included via `r ` have to be available)
	results = data("bfi")
	metadata(results)$description <- data_description_default(bfi)
}

meta <- metadata(results)
description <- meta$description
meta <- recursive_escape(meta)

## ----results='asis'-----------------------------------------------------------
if (exists("name", meta)) {
  glue::glue_data(
    "__Dataset name__: {name}",
    .x = meta)
}

## ----results='asis'-----------------------------------------------------------
cat(description)

## ----results='asis', echo = FALSE---------------------------------------------
if (exists("temporalCoverage", meta)) {
  glue::glue_data(
    "- __Temporal Coverage__: {temporalCoverage}",
    .x = meta)
}

## ----results='asis', echo = FALSE---------------------------------------------
if (exists("spatialCoverage", meta)) {
  glue::glue_data(
    "- __Spatial Coverage__: {spatialCoverage}",
    .x = meta)
}

## ----results='asis', echo = FALSE---------------------------------------------
if (exists("citation", meta)) {
  glue::glue_data(
    "- __Citation__: {citation}",
    .x = meta)
}

## ----results='asis', echo = FALSE---------------------------------------------
if (exists("url", meta)) {
  glue::glue_data(
    "- __URL__: [{url}]({url})",
    .x = meta)
}

## ----results='asis', echo = FALSE---------------------------------------------
if (exists("identifier", meta)) {
  if (stringr::str_detect(meta$identifier, "^doi:")) {
    meta$identifier <- paste0('<a href="https://dx.doi.org/', 
      stringr::str_match(meta$identifier, "^doi:(.+)")[,2], '">', 
      meta$identifier, '</a>')
  }
  glue::glue_data(
    "- __Identifier__: {identifier}",
    .x = meta)
}

## ----results='asis', echo = FALSE---------------------------------------------
if (exists("datePublished", meta)) {
  glue::glue_data(
    "- __Date published__: {datePublished}",
    .x = meta)
}

## ----results='asis', echo = FALSE---------------------------------------------
if (exists("creator", meta)) {
  cat("- __Creator__:")
  knitr::kable(tibble::enframe(meta$creator))
}

## -----------------------------------------------------------------------------
meta <- meta[setdiff(names(meta),
                     c("creator", "datePublished", "identifier",
                       "url", "citation", "spatialCoverage", 
                       "temporalCoverage", "description", "name"))]
if(length(meta)) {
  knitr::kable(meta)
}

## ----setup,eval=TRUE,echo=FALSE-----------------------------------------------
if (!exists("indent")) {
	indent <- '#' # ugly hack so it can be "spun" (variables included via `r ` have to be available)
}
if (exists("testing")) {
	item <- 1:10
	item_name <- safe_name <- "yay"
	attributes(item) <- list(label = 'yayya')
}

item_attributes <- attributes(item)
item_attributes <- recursive_escape(item_attributes)
html_item_name <- recursive_escape(item_name)
item_label <- ifelse(is.null(item_attributes) || is.null(item_attributes$label), 
                     "", item_attributes$label)
item_info <- item_attributes$item
choices <- item_attributes$labels

## ----setup_missing_values-----------------------------------------------------
show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

fig_height_dist <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

	if (is.null(choices) || 
	    dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
		non_missing_choices <- unique(item_nomiss)
		names(non_missing_choices) <- non_missing_choices
	}
  if(!(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss))) {
    choice_multiplier <- fig_height_dist/6.5
  	fig_height_dist <- 2 + choice_multiplier * length(non_missing_choices)
  	fig_height_dist <- ifelse(fig_height_dist > 20, 20, fig_height_dist)
  	fig_height_dist <- ifelse(fig_height_dist < 1, 1, fig_height_dist)
  }
}


## ----distribution,fig.height=fig_height_dist,fig.cap=paste("Distribution of values for", html_item_name)----
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
	  cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

## ----summary------------------------------------------------------------------
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
cb_table <- codebook_table(df)
if(!is.null(choices)) {
  cb_table$value_labels <- NULL
}
escaped_table(cb_table)

## ----missing_values,fig.cap=paste("Plot of missing values for", html_item_name)----
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

## ----item_info----------------------------------------------------------------
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (is.null(choices)) {
    choices <- tibble::enframe(item_info$choices)
  }
  item_info$choices <- NULL
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  knitr::kable(purrr::flatten_df(item_info), caption = "Item options")
}

## ----choices------------------------------------------------------------------
if (!is.null(choices) && length(choices) && length(choices) < 30) {
  try({choices <- tibble::enframe(choices)}, silent = TRUE)
  knitr::kable(choices, caption = "Response choices")
}

## ----setup,eval=TRUE,echo=FALSE-----------------------------------------------
if (!exists("indent")) {
	indent <- '#' # ugly hack so it can be "spun" (variables included via `r ` have to be available)
}
if (exists("testing")) {
	item <- 1:10
	item_name <- safe_name <- "yay"
	attributes(item) <- list(label = 'yayya')
}

item_attributes <- attributes(item)
item_attributes <- recursive_escape(item_attributes)
html_item_name <- recursive_escape(item_name)
item_label <- ifelse(is.null(item_attributes) || is.null(item_attributes$label), 
                     "", item_attributes$label)
item_info <- item_attributes$item
choices <- item_attributes$labels

## ----setup_missing_values-----------------------------------------------------
show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

fig_height_dist <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

	if (is.null(choices) || 
	    dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
		non_missing_choices <- unique(item_nomiss)
		names(non_missing_choices) <- non_missing_choices
	}
  if(!(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss))) {
    choice_multiplier <- fig_height_dist/6.5
  	fig_height_dist <- 2 + choice_multiplier * length(non_missing_choices)
  	fig_height_dist <- ifelse(fig_height_dist > 20, 20, fig_height_dist)
  	fig_height_dist <- ifelse(fig_height_dist < 1, 1, fig_height_dist)
  }
}


## ----distribution,fig.height=fig_height_dist,fig.cap=paste("Distribution of values for", html_item_name)----
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
	  cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

## ----summary------------------------------------------------------------------
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
cb_table <- codebook_table(df)
if(!is.null(choices)) {
  cb_table$value_labels <- NULL
}
escaped_table(cb_table)

## ----missing_values,fig.cap=paste("Plot of missing values for", html_item_name)----
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

## ----item_info----------------------------------------------------------------
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (is.null(choices)) {
    choices <- tibble::enframe(item_info$choices)
  }
  item_info$choices <- NULL
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  knitr::kable(purrr::flatten_df(item_info), caption = "Item options")
}

## ----choices------------------------------------------------------------------
if (!is.null(choices) && length(choices) && length(choices) < 30) {
  try({choices <- tibble::enframe(choices)}, silent = TRUE)
  knitr::kable(choices, caption = "Response choices")
}

## ----setup,eval=TRUE,echo=FALSE-----------------------------------------------
if (!exists("indent")) {
	indent <- '#' # ugly hack so it can be "spun" (variables included via `r ` have to be available)
}
if (exists("testing")) {
	item <- 1:10
	item_name <- safe_name <- "yay"
	attributes(item) <- list(label = 'yayya')
}

item_attributes <- attributes(item)
item_attributes <- recursive_escape(item_attributes)
html_item_name <- recursive_escape(item_name)
item_label <- ifelse(is.null(item_attributes) || is.null(item_attributes$label), 
                     "", item_attributes$label)
item_info <- item_attributes$item
choices <- item_attributes$labels

## ----setup_missing_values-----------------------------------------------------
show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

fig_height_dist <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

	if (is.null(choices) || 
	    dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
		non_missing_choices <- unique(item_nomiss)
		names(non_missing_choices) <- non_missing_choices
	}
  if(!(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss))) {
    choice_multiplier <- fig_height_dist/6.5
  	fig_height_dist <- 2 + choice_multiplier * length(non_missing_choices)
  	fig_height_dist <- ifelse(fig_height_dist > 20, 20, fig_height_dist)
  	fig_height_dist <- ifelse(fig_height_dist < 1, 1, fig_height_dist)
  }
}


## ----distribution,fig.height=fig_height_dist,fig.cap=paste("Distribution of values for", html_item_name)----
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
	  cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

## ----summary------------------------------------------------------------------
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
cb_table <- codebook_table(df)
if(!is.null(choices)) {
  cb_table$value_labels <- NULL
}
escaped_table(cb_table)

## ----missing_values,fig.cap=paste("Plot of missing values for", html_item_name)----
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

## ----item_info----------------------------------------------------------------
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (is.null(choices)) {
    choices <- tibble::enframe(item_info$choices)
  }
  item_info$choices <- NULL
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  knitr::kable(purrr::flatten_df(item_info), caption = "Item options")
}

## ----choices------------------------------------------------------------------
if (!is.null(choices) && length(choices) && length(choices) < 30) {
  try({choices <- tibble::enframe(choices)}, silent = TRUE)
  knitr::kable(choices, caption = "Response choices")
}

## ----setup,eval=TRUE,echo=FALSE-----------------------------------------------
if (!exists("indent")) {
	indent <- '#' # ugly hack so it can be "spun" (variables included via `r ` have to be available)
}
if (exists("testing")) {
	item <- 1:10
	item_name <- safe_name <- "yay"
	attributes(item) <- list(label = 'yayya')
}

item_attributes <- attributes(item)
item_attributes <- recursive_escape(item_attributes)
html_item_name <- recursive_escape(item_name)
item_label <- ifelse(is.null(item_attributes) || is.null(item_attributes$label), 
                     "", item_attributes$label)
item_info <- item_attributes$item
choices <- item_attributes$labels

## ----setup_missing_values-----------------------------------------------------
show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

fig_height_dist <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

	if (is.null(choices) || 
	    dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
		non_missing_choices <- unique(item_nomiss)
		names(non_missing_choices) <- non_missing_choices
	}
  if(!(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss))) {
    choice_multiplier <- fig_height_dist/6.5
  	fig_height_dist <- 2 + choice_multiplier * length(non_missing_choices)
  	fig_height_dist <- ifelse(fig_height_dist > 20, 20, fig_height_dist)
  	fig_height_dist <- ifelse(fig_height_dist < 1, 1, fig_height_dist)
  }
}


## ----distribution,fig.height=fig_height_dist,fig.cap=paste("Distribution of values for", html_item_name)----
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
	  cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

## ----summary------------------------------------------------------------------
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
cb_table <- codebook_table(df)
if(!is.null(choices)) {
  cb_table$value_labels <- NULL
}
escaped_table(cb_table)

## ----missing_values,fig.cap=paste("Plot of missing values for", html_item_name)----
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

## ----item_info----------------------------------------------------------------
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (is.null(choices)) {
    choices <- tibble::enframe(item_info$choices)
  }
  item_info$choices <- NULL
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  knitr::kable(purrr::flatten_df(item_info), caption = "Item options")
}

## ----choices------------------------------------------------------------------
if (!is.null(choices) && length(choices) && length(choices) < 30) {
  try({choices <- tibble::enframe(choices)}, silent = TRUE)
  knitr::kable(choices, caption = "Response choices")
}

## ----setup,eval=TRUE,echo=FALSE-----------------------------------------------
if (!exists("indent")) {
	indent <- '#' # ugly hack so it can be "spun" (variables included via `r ` have to be available)
}
if (exists("testing")) {
	item <- 1:10
	item_name <- safe_name <- "yay"
	attributes(item) <- list(label = 'yayya')
}

item_attributes <- attributes(item)
item_attributes <- recursive_escape(item_attributes)
html_item_name <- recursive_escape(item_name)
item_label <- ifelse(is.null(item_attributes) || is.null(item_attributes$label), 
                     "", item_attributes$label)
item_info <- item_attributes$item
choices <- item_attributes$labels

## ----setup_missing_values-----------------------------------------------------
show_missing_values <- FALSE
if (has_labels(item)) {
  missing_values <- item[is.na(haven::zap_missing(item))]
  attributes(missing_values) <- attributes(item)
  if (!is.null(attributes(item)$labels)) {
    attributes(missing_values)$labels <- attributes(missing_values)$labels[is.na(attributes(missing_values)$labels)]
    attributes(item)$labels <- attributes(item)$labels[!is.na(attributes(item)$labels)]
  }
  if (is.double(item)) {
    show_missing_values <- length(unique(haven::na_tag(missing_values))) > 1
    item <- haven::zap_missing(item)
  }
  if (length(item_attributes$labels) == 0 && is.numeric(item)) {
    item <- haven::zap_labels(item)
  }
}
item_nomiss <- item[!is.na(item)]

# unnest mc_multiple and so on
if (
  is.character(item_nomiss) &&
  any(stringr::str_detect(item_nomiss, stringr::fixed(", "))) &&
  !is.null(item_info) &&
  (exists("type", item_info) && 
    any(stringr::str_detect(item_info$type, 
                            pattern = stringr::fixed("multiple"))))
  ) {
  item_nomiss <- unlist(stringr::str_split(item_nomiss, pattern = stringr::fixed(", ")))
}
attributes(item_nomiss) <- attributes(item)

fig_height_dist <- knitr::opts_chunk$get("fig.height")
non_missing_choices <- item_attributes[["labels"]]
many_labels <- length(non_missing_choices) > 7
go_vertical <- !is_numeric_or_time_var(item_nomiss) || many_labels
  
if ( go_vertical ) {
  # numeric items are plotted horizontally (because that's what usually expected)
  # categorical items are plotted vertically because we can use the screen real estate better this way

	if (is.null(choices) || 
	    dplyr::n_distinct(item_nomiss) > length(non_missing_choices)) {
		non_missing_choices <- unique(item_nomiss)
		names(non_missing_choices) <- non_missing_choices
	}
  if(!(could_disclose_unique_values(item_nomiss) && is.character(item_nomiss))) {
    choice_multiplier <- fig_height_dist/6.5
  	fig_height_dist <- 2 + choice_multiplier * length(non_missing_choices)
  	fig_height_dist <- ifelse(fig_height_dist > 20, 20, fig_height_dist)
  	fig_height_dist <- ifelse(fig_height_dist < 1, 1, fig_height_dist)
  }
}


## ----distribution,fig.height=fig_height_dist,fig.cap=paste("Distribution of values for", html_item_name)----
wrap_at <- knitr::opts_chunk$get("fig.width") * 10
# todo: if there are free-text choices mingled in with the pre-defined ones, don't show
# todo: show rare items if they are pre-defined
# todo: bin rare responses into "other category"
if (!length(item_nomiss)) {
  cat("No non-missing values to show.")
} else if (!could_disclose_unique_values(item_nomiss)) {
  plot_labelled(item_nomiss, item_name, wrap_at, go_vertical)
} else {
  if (is.character(item_nomiss)) {
      char_count <- stringr::str_count(item_nomiss)
      attributes(char_count)$label <- item_label
      plot_labelled(char_count, 
                    item_name, wrap_at, FALSE, trans = "log1p", "characters")
  } else {
	  cat(dplyr::n_distinct(item_nomiss), " unique, categorical values, so not shown.")
  }
}

## ----summary------------------------------------------------------------------
attributes(item) <- item_attributes
df = data.frame(item, stringsAsFactors = FALSE)
names(df) = html_item_name
cb_table <- codebook_table(df)
if(!is.null(choices)) {
  cb_table$value_labels <- NULL
}
escaped_table(cb_table)

## ----missing_values,fig.cap=paste("Plot of missing values for", html_item_name)----
if (show_missing_values) {
  plot_labelled(missing_values, item_name, wrap_at)
}

## ----item_info----------------------------------------------------------------
if (!is.null(item_info)) {
  # don't show choices again, if they're basically same thing as value labels
  if (is.null(choices)) {
    choices <- tibble::enframe(item_info$choices)
  }
  item_info$choices <- NULL
  item_info$label_parsed <- 
    item_info$choice_list <- item_info$study_id <- item_info$id <- NULL
  knitr::kable(purrr::flatten_df(item_info), caption = "Item options")
}

## ----choices------------------------------------------------------------------
if (!is.null(choices) && length(choices) && length(choices) < 30) {
  try({choices <- tibble::enframe(choices)}, silent = TRUE)
  knitr::kable(choices, caption = "Response choices")
}

## ----setup,eval=TRUE,echo=FALSE-----------------------------------------------
if (!exists("indent")) {
	indent <- '#' # ugly hack
}
if (exists("testing")) {
	results <- data.frame()
	survey_repetition <- 'single'
	reliabilities <- list()
	md_pattern <- data.frame()
}

## ----missingness_all_setup----------------------------------------------------
if (length(md_pattern)) {
  if (knitr::is_html_output() && requireNamespace("rmarkdown", quietly = TRUE)) {
    rmarkdown::paged_table(md_pattern, options = list(rows.print = 10))
  } else {
    knitr::kable(md_pattern)
  }
}

## ----setup,eval=TRUE,echo=FALSE-----------------------------------------------
if (!exists("indent")) {
	indent <- '#' # ugly hack
}
if (exists("testing")) {
	results <- data.frame()
	survey_repetition <- 'single'
	reliabilities <- list()
	missingness_report <- ''
	data_info <- '' 
	survey_overview <- '' 
	scales_items <- c()
	detailed_items <- TRUE
	detailed_scales <- TRUE
}

## -----------------------------------------------------------------------------
knitr::asis_output(data_info)

## -----------------------------------------------------------------------------
knitr::asis_output(survey_overview)

## ----scales-------------------------------------------------------------------
if (detailed_variables || detailed_scales) {
  knitr::asis_output(paste0(scales_items, sep = "\n\n\n", collapse = "\n\n\n"))
}

## -----------------------------------------------------------------------------
missingness_report

## -----------------------------------------------------------------------------
items

## -----------------------------------------------------------------------------
jsonld

## ----cb-----------------------------------------------------------------------
codebook(darktriad, survey_repetition = "single",
         metadata_table = knit_by_pkgdown, metadata_json = knit_by_pkgdown)

## -----------------------------------------------------------------------------
if (!knit_by_pkgdown) {
  codebook:::escaped_table(codebook_table(darktriad))
}