_02-10_emotion_adult.qmd

## Emotional/Behavioral/Personality {#sec-emotion}

{{< include _02-10_emotion_adult_text.qmd >}}

```{r}
#| label: setup-emotion
#| include: false

# Suppress warnings from being converted to errors
options(warn = 1) # Set warn to 1 to make warnings not halt execution

# domain
domains <- c(
  "Psychiatric Disorders", "Personality Disorders", "Substance Use",
  "Psychosocial Problems", "Behavioral/Emotional/Social", "Emotional/Behavioral/Personality"
)

# phenotype
pheno <- "emotion"
```

```{r}
#| label: export-emotion
#| include: false
#| eval: true

# Read the CSV file into a data frame
emotion <- vroom::vroom("neurobehav.csv")

# Filter the data frame based on certain conditions
# Keep only the rows where 'domain' equals 'domains' and 'z_mean_domain' is not NA
emotion <- emotion |>
  dplyr::filter(domain %in% domains)

# Select specific columns from the data frame
emotion <- emotion |>
  dplyr::select(
    test,
    test_name,
    scale,
    raw_score,
    score,
    ci_95,
    percentile,
    range,
    domain,
    subdomain,
    narrow,
    pass,
    verbal,
    timed,
    description,
    result,
    z,
    z_mean_domain,
    z_sd_domain,
    z_mean_subdomain,
    z_sd_subdomain,
    z_mean_narrow,
    z_sd_narrow
  )

# Write the 'emotion' data frame to a CSV file
# The file name is derived from the 'pheno' variable
readr::write_excel_csv(emotion, paste0(pheno, ".csv"), na = "", col_names = TRUE, append = FALSE)
```

```{r}
#| label: data-emotion
#| include: false

scales <- c(
  "Activity Level",
  "Affective (A)",
  "Affective (D)",
  "Affective Instability",
  "Aggression",
  "Aggressive Attitude",
  "ALC Estimated Score",
  "Alcohol Problems",
  "Antisocial Behaviors",
  "Antisocial Features",
  "Anxiety-Related Disorders",
  "Anxiety",
  "Borderline Features",
  "Cognitive (A)",
  "Cognitive (D)",
  "Conversion",
  "Depression",
  "Dominance",
  "DRG Estimated Score",
  "Drug Problems",
  "Egocentricity",
  "Grandiosity",
  "Health Concerns",
  "Hypervigilance",
  "Identity Problems",
  "Irritability",
  "Mania",
  "Negative Relationships",
  "Nonsupport",
  "Obsessive-Compulsive",
  "Paranoia",
  "Persecution",
  "Phobias",
  "Physical Aggression",
  "Physiological (A)",
  "Physiological (D)",
  "Psychotic Experiences",
  "Resentment",
  "Schizophrenia",
  "Self-Harm",
  "Social Detachment",
  "Somatic Complaints",
  "Somatization",
  "Stimulus-Seeking",
  "Stress",
  "Suicidal Ideation",
  "Thought Disorder",
  "Traumatic Stress",
  "Treatment Rejection",
  "Verbal Aggression",
  "Warmth",
  "BAI Total Score",
  "BDI-2 Total Score",
  "Activities of Daily Living",
  "Adaptability",
  "Adaptive Skills",
  "Aggression",
  "Anxiety",
  "Attention Problems",
  "Attitude to School",
  "Attitude to Teachers",
  "Atypicality",
  "Behavioral Symptoms Index",
  "Conduct Problems",
  "Depression",
  "Emotional Symptoms Index",
  "Externalizing Problems",
  "Functional Communication",
  "Hyperactivity",
  "Inattention/Hyperactivity",
  "Internalizing Problems",
  "Interpersonal Relations",
  "Leadership",
  "Locus of Control",
  "Personal Adjustment",
  "Relations with Parents",
  "School Problems",
  "Self-Esteem",
  "Self-Reliance",
  "Sensation Seeking",
  "Sense of Inadequacy",
  "Social Skills",
  "Social Stress",
  "Somatization",
  "Withdrawal"
)

# Filter the data using the filter_data function from the bwu library
# The domain is specified by the 'domains' variable
# The scale is specified by the 'scales' variable
data_emotion <- bwu::filter_data(
  data = emotion,
  domain = domains,
  scale = scales
)
```

```{r}
#| label: text-emotion
#| cache: true
#| include: false
#| eval: true

# Flatten the text
bwu::cat_neuropsych_results(
  data = data_emotion,
  file = "_02-10_emotion_adult_text.qmd"
)
```

```{r}
#| label: qtbl-emotion
#| dev: tikz
#| fig-process: pdf2png
#| eval: true
#| include: false
options(tikzDefaultEngine = "xetex")

# table arguments
table_name <- "table_emotion"
vertical_padding <- 0
multiline <- TRUE

# Source/footnote
source_note <- gt::md("Score = _T_ score (Mean = 50 [50th‰], SD ± 10 [16th‰, 84th‰])

_Note_: Higher scores reflect reduced functioning")

# groupings
grp_emotion <- list(
  t_score = c("PAI", "MMPI-3")
)

data_emotion <-
  dplyr::filter(data_emotion, domain %in% c(
    "Psychiatric Disorders",
    "Personality Disorders",
    "Psychosocial Problems",
    "Substance Use"
    # "Behavioral/Emotional/Social",
    # "Emotional/Behavioral/Personality"
  )) |>
  dplyr::arrange(factor(test_name, levels = c("PAI", "MMPI-3")), subdomain)

# Define the specific order of scales
scale_order <- c(
  "Somatic Complaints",
  "Anxiety",
  "Anxiety-Related Disorders",
  "Depression",
  "Mania",
  "Paranoia",
  "Social Detachment",
  "Schizophrenia",
  "Borderline Features",
  "Antisocial Features",
  "Alcohol Problems",
  "Drug Problems",
  "Stress",
  "Nonsupport"
)

# Filter and arrange the data in the specified order
data_emotion <- data_emotion |>
  dplyr::filter(scale %in% scale_order) |>
  dplyr::mutate(scale = factor(scale, levels = scale_order)) |>
  dplyr::arrange(scale)

# View the arranged data
print(data_emotion)

# make `gt` table
bwu::tbl_gt(
  data = data_emotion,
  pheno = pheno,
  table_name = table_name,
  source_note = source_note,
  vertical_padding = vertical_padding,
  multiline = multiline
)
```

```{r}
#| label: fig-emotion
#| fig-cap: "Mood/Self-Report"
#| include: false
#| fig-height: 6
#| out-height: 100%

colors <- NULL
return_plot <- Sys.getenv("RETURN_PLOT")
filename <- "fig_emotion.svg"

# arguments
x <- data_emotion$z_mean_subdomain
y <- data_emotion$subdomain

# Make dotplot
bwu::dotplot(
  colors = colors,
  data = data_emotion,
  filename = filename,
  na.rm = TRUE,
  return_plot = return_plot,
  x = x,
  y = y
)
```

```{=typst}
#let domain(title: none, file_qtbl, file_fig) = {
  let font = (font: "Roboto Slab", size: 0.7em)
  set text(..font)
    grid(
      columns: (50%, 50%),
      gutter: 8pt,
        figure([#image(file_qtbl)],
          caption: figure.caption(position: top, [#title]),
          kind: "qtbl",
          supplement: [*Table*],
          ),
        figure([#image(file_fig, width: auto)],
          caption: figure.caption(
            position: bottom,
            [
              Emotional, behavioral, and personality scores collapsed across broad domains of functioning.
              ]),
          placement: none,
          kind: "image",
          supplement: [*Figure*],
          gap: 0.5em,
        ),
      )
  }
```
```{=typst}
#let title = "Personality Assessment Scores"
#let file_qtbl = "table_emotion.png"
#let file_fig = "fig_emotion.svg"
#domain(
  title: [#title],
  file_qtbl,
  file_fig
  )
```