_02-08_social.qmd

## Social Cognition {#sec-social}

{{< include _02-08_social_text.qmd >}}

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

# domain
domains <- c("Social Cognition")

# phenotype
pheno <- "social"
```

```{r}
#| label: export-social
#| include: false

# Read the CSV file into a data frame
social1 <- vroom::vroom("neurocog.csv")
social2 <- vroom::vroom("neurobehav.csv")
social <- dplyr::bind_rows(social1, social2)

# Filter the data frame based on certain conditions
social <- social |>
  dplyr::filter(domain %in% domains)

# Select specific columns from the data frame
social <- social |>
  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,
    z_mean_pass,
    z_sd_pass,
    z_mean_verbal,
    z_sd_verbal,
    z_mean_timed,
    z_sd_timed
  )

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

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

# read and filter data
data <- social

scales <- c(
  "Affect Recognition",
  "Theory of Mind",
  "Theory of Mind Verbal",
  "Affect Recognition",
  "Affect Recognition Happy Errors",
  "Affect Recognition Sad Errors",
  "Affect Recognition Neutral Errors",
  "Affect Recognition Fear Errors",
  "Affect Recognition Angry Errors",
  "Affect Recognition Disgust Errors",
  "Social Perception",
  "Affect Naming",
  "Prosody-Face Matching",
  "Prosody-Pair Matching",
  "Faces I",
  "Faces II",
  "Names I",
  "Names II",
  "Restricted/Repetitive Behaviors",
  "Social Interaction",
  "Social Communication",
  "Emotional Responses",
  "Cognitive Style",
  "Maladaptive Speech",
  "Autism Index",
  # CARS-2
  "Relating to People",
  "Imitation",
  "Emotional Response",
  "Body Use",
  "Object Use",
  "Adaptation to Change",
  "Visual Response",
  "Listening Response",
  "Taste, Smell, and Touch Response and Use",
  "Fear or Nervousness",
  "Verbal Communication",
  "Nonverbal Communication",
  "Activity Level",
  "Level of Consistency of Intellectual Response",
  "General Impressions",
  "CARS-2 Total Score",
  # CARS-2 High Functioning Version
  "Social-Emotional Understanding",
  "Emotional Expression and Regulation of Emotions",
  "Relating to People",
  "Body Use",
  "Object Use in Play",
  "Adaptation to Change/Restricted Interests",
  "Visual Response",
  "Listening Response",
  "Taste, Smell, and Touch Response and Use",
  "Fear or Anxiety",
  "Verbal Communication",
  "Nonverbal Communication",
  "Thinking/Cognitive Integration Skills",
  "Level of Consistency of Intellectual Response",
  "General Impressions",
  "CARS 2-HF Total Score"
)

# 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_social <- bwu::filter_data(data, domain = domains, scale = scales)
```

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

# Generate the text for the social domain
bwu::cat_neuropsych_results(data = data_social, file = "_02-08_social_text.qmd")
```

```{r}
#| label: qtbl-social
#| dev: tikz
#| fig-process: pdf2png
#| include: false

# Set the default engine for tikz to "xetex"
options(tikzDefaultEngine = "xetex")

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

# footnotes
fn_standard_score <- gt::md("Index score: Mean = 100 [50th‰], SD ± 15 [16th‰, 84th‰]")
fn_scaled_score <- gt::md("Scaled score: Mean = 10 [50th‰], SD ± 3 [16th‰, 84th‰]")
fn_t_score <- gt::md("T-score: Mean = 50 [50th‰], SD ± 10 [16th‰, 84th‰]")
fn_z_score <- gt::md("z-score: Mean = 0 [50th‰], SD ± 1 [16th‰, 84th‰]")
fn_raw_score <- gt::md("Raw score: Range = 0-4")

# Groupings
grp_social <- list(
  # scaled_score = c("NEPSY-2", "ACS Social Cognition"),
  # t_score = c("CARS-2", "GARS-3", "CARS-2 HF", "CARS-2 High Functioning Version", "CARS 2-HF"),
  raw_score = c("CARS-2", "CARS-2 HF", "CARS 2-HF")
)

data_social <- data_social |> dplyr::select(c(test_name, scale, raw_score, score, percentile, range))

# tbl_gt2 <-
#   function(data,
#            pheno = NULL,
#            table_name = NULL,
#            source_note = NULL,
#            names = NULL,
#            title = NULL,
#            tab_stubhead = NULL,
#            caption = NULL,
#            process_md = FALSE,
#            fn_scaled_score = NULL,
#            fn_standard_score = NULL,
#            fn_t_score = NULL,
#            fn_z_score = NULL,
#            fn_raw_score = NULL,
#            grp_scaled_score = NULL,
#            grp_standard_score = NULL,
#            grp_t_score = NULL,
#            grp_z_score = NULL,
#            grp_raw_score = NULL,
#            dynamic_grp,
#            vertical_padding = NULL,
#            multiline = TRUE,
#            ...,
#            # Add a new argument to control displayed columns
#            table_vars = c("range")) {
#     # Create data counts
#     data_counts <- data |>
#       dplyr::select(test_name, scale, all_of(table_vars)) |>
#       dplyr::mutate(across(any_of(c("score", "percentile", "raw_score")), ~ tidyr::replace_na(., replace = 0)))

#     # Create table
#     table <- data_counts |>
#       dplyr::mutate(
#         score = if_else(score == 0, NA_integer_, score),
#         percentile = if_else(percentile == 0, NA_integer_, percentile),
#         test_name = as.character(paste0(test_name)),
#         scale = as.character(scale)
#       ) |>
#       # gt table formatting
#       gt::gt(
#         rowname_col = "scale",
#         groupname_col = "test_name",
#         process_md = process_md,
#         caption = caption,
#         rownames_to_stub = FALSE,
#         id = paste0("table_", pheno)
#       ) |>
#       cols_label(
#         test_name = md("**Test**"),
#         scale = md("**Scale**"),
#         raw_score = md("**Raw Score**"),
#         score = md("**Score**"),
#         percentile = md("**‰ Rank**"),
#         range = md("**Range**")
#       ) |>
#       tab_header(title = title) |>
#       tab_stubhead(label = tab_stubhead) |>
#       sub_missing(missing_text = "--") |>
#       tab_stub_indent(rows = scale, indent = 2) |>
#       cols_align(
#         align = "center",
#         # Use any_of to dynamically select columns
#         columns = any_of(c("raw_score", "score", "percentile", "range"))
#       )

#     # Extract unique row groups from the data to check against the groups we want to apply footnotes to
#     existing_row_groups <- unique(data$test_name)

#     # Filter out non-existent groups
#     grp_scaled_score <- intersect(grp_scaled_score, existing_row_groups)
#     grp_standard_score <- intersect(grp_standard_score, existing_row_groups)
#     grp_t_score <- intersect(grp_t_score, existing_row_groups)
#     grp_z_score <- intersect(grp_z_score, existing_row_groups)
#     grp_raw_score <- intersect(grp_raw_score, existing_row_groups)

#     # Concatenate footnotes
#     fn_combined <- list(
#       scaled_score = fn_scaled_score,
#       standard_score = fn_standard_score,
#       t_score = fn_t_score,
#       z_score = fn_z_score,
#       raw_score = fn_raw_score
#     )

#     # Add footnotes
#     for (score_type in names(fn_combined)) {
#       fn <- fn_combined[[score_type]]
#       groups <- get(paste0("grp_", score_type))
#       if (!is.null(fn) && any(groups %in% dynamic_grp[[score_type]])) {
#         table <- table |>
#           tab_footnote(
#             footnote = glue::glue_collapse(fn, sep = " "),
#             locations = cells_row_groups(groups = groups)
#           )
#       }
#     }

#     # Adding source note
#     table <- table |>
#       tab_style(style = cell_text(size = "small"), locations = cells_source_notes()) |>
#       tab_source_note(source_note = source_note) |>
#       gtExtras::gt_theme_538() |>
#       tab_options(
#         row_group.font.weight = "bold",
#         footnotes.multiline = multiline,
#         footnotes.font.size = "small"
#       ) |>
#       opt_vertical_padding(scale = vertical_padding)

#     gt::gtsave(table, glue::glue("table_{pheno}.pdf"))
#     gt::gtsave(table, glue::glue("table_{pheno}.png"))

#     return(table)
#   }

# make `gt` table
bwu::tbl_gt(
  data = data_social,
  pheno = pheno,
  table_name = table_name,
  # table_vars = c("raw_score", "range"),
  # fn_scaled_score = fn_scaled_score,
  # fn_t_score = fn_t_score,
  fn_raw_score = fn_raw_score,
  # grp_scaled_score = grp_social[["scaled_score"]],
  # grp_t_score = grp_social[["t_score"]],
  grp_raw_score = grp_social[["raw_score"]],
  dynamic_grp = grp_social,
  vertical_padding = vertical_padding,
  multiline = multiline
)
```

```{r}
#| label: fig-social
#| include: false
#| eval: true
#| fig-cap: "Social cognition measures are used to gauge aspects of social-emotional processing and social aptitude (sometimes referred to as _emotional intelligence_), which are important facets of how individuals process social information about other children, adults, groups, and social contexts."

# Filter the data frame to include only the specified scales
exclude <- c(
  "CARS-2 Total Score",
  "Theory of Mind Verbal",
  "Affect Recognition",
  "CARS 2-HF Total Score"
)

data_social <- data_social |>
  dplyr::filter(!(scale %in% exclude))

# Dotplot args
x <- data_social$raw_score
y <- data_social$scale
colors <- NULL
return_plot <- Sys.getenv("RETURN_PLOT")
filename <- "fig_social.svg"

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

# Plotting function call
bwu::dotplot(
  data = data_social,
  x = x,
  y = y,
  colors = colors,
  return_plot = return_plot,
  filename = filename,
  na.rm = TRUE
)

# Reset warning options to default if needed
options(warn = 0) # Reset to default behavior
```

```{=typst}
#let domain(title: none, file_qtbl, file_fig) = {
  let font = (font: "Roboto Slab", size: 0.5em)
  set text(..font)
  pad(top: 0.5em)[]  
    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, [
            Social cognition and autism spectrum disorder (ASD) measures are used to gauge aspects of social-emotional processing and social aptitude (sometimes referred to as _emotional intelligence_), which are important facets of how individuals process social information about other children, adults, groups, and social contexts.
            ]),
          placement: none,
          kind: "image",
          supplement: [Figure],
          gap: 0.5em,
        ),
      )
  }
```
```{=typst}
#let title = "Social Cognition"
#let file_qtbl = "table_social.png"
#let file_fig = "fig_social.svg"
#domain(
  title: [#title Scores],
  file_qtbl,
  file_fig
)
```