_02-03_verbal.qmd

## Verbal/Language {#sec-verbal}

{{< include _02-03_verbal_text.qmd >}}

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

# domain
domains <- c("Verbal/Language")

# phenotype
pheno <- "verbal"
```

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

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

# Filter the data frame to keep only rows where 'domain' equals 'domains'
verbal <- verbal |> dplyr::filter(domain %in% domains)

verbal <- verbal |>
  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 resulting data frame to a new CSV file
# The file name is created by concatenating the 'pheno' variable and ".csv"
# NA values are replaced with an empty string in the output file
# Column names are included in the output file
# If the file already exists, it is overwritten (not appended)
readr::write_excel_csv(verbal, paste0(pheno, ".csv"), na = "", col_names = TRUE, append = FALSE)
```

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

# Read data and filter
data <- verbal

scales <- c(
  "Academic Language Readiness Index",
  "Auditory Comprehension Colors",
  "Auditory Comprehension Colors/Shapes/Numbers",
  "Auditory Comprehension Shapes",
  "Auditory Comprehension",
  "Basic Concepts",
  "Bill Payment",
  "Body Part Identification",
  "Body Part Naming",
  "Category Fluency",
  "Comprehension of Instructions",
  "Comprehension",
  "Core Language Score",
  "D-KEFS Color Naming",
  "D-KEFS Word Reading",
  "Descriptive Pragmatics Profile",
  "Early Literacy Index",
  "Expressive Language Index",
  "Expressive Vocabulary",
  "Following Directions",
  "Information",
  "Language Content Index",
  "Language Domain",
  "Language Index",
  "Language Structure Index",
  "Letter Fluency",
  "NAB Language Index",
  "Naming Phonemic Cuing",
  "Naming Semantic Cuing",
  "Naming vs Identification",
  "Naming",
  "Oral Production",
  "Oromotor Sequences",
  "Phonological Awareness",
  "Picture Naming",
  "Preliteracy Rating Scale",
  "Reading Comprehension",
  "Recalling Sentences",
  "Receptive Language Index",
  "Receptive Vocabulary",
  "Semantic Fluency",
  "Sentence Comprehension",
  "Similarities",
  "Speeded Naming Correct",
  "Speeded Naming Errors",
  "Speeded Naming Time",
  "Speeded Naming",
  "Vocabulary",
  "Word Classes",
  "Word Generation Initial Letter",
  "Word Generation Semantic",
  "Semantic vs. Initial Letter",
  "Word Generation",
  "Word Structure",
  "Writing",
  "Word Generation-Semantic",
  "Word Generation-Initial Letter",
  "Semantic vs. Initial Letter Contrast"
)

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

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

# export text
bwu::cat_neuropsych_results(data = verbal, file = "_02-03_verbal_text.qmd")
```

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

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

# args
table_name <- "table_verbal"
vertical_padding <- 0
multiline <- TRUE

# footnotes
fn_scaled_score <- gt::md("Scaled score: Mean = 10 [50th‰], SD ± 3 [16th‰, 84th‰]")
fn_standard_score <- gt::md("Index score: Mean = 100 [50th‰], SD ± 15 [16th‰, 84th‰]")
fn_t_score <- gt::md("_T_-score: Mean = 50 [50th‰], SD ± 10 [16th‰, 84th‰]")

# Define the groups for the table
grp_verbal <- list(
  scaled_score = c("WAIS-IV", "WISC-5", "NEPSY-2", "D-KEFS", "WPPSI-IV", "CELF-5", "RBANS"),
  standard_score = c("NAB", "WPPSI-IV", "NAB-S", "CELF-5", "RBANS"),
  t_score = c("NAB", "NAB-S", "NIH EXAMINER", "WASI-2")
)

# make `gt` table
bwu::tbl_gt(
  data = data_verbal,
  pheno = pheno,
  table_name = table_name,
  fn_scaled_score = fn_scaled_score,
  fn_standard_score = fn_standard_score,
  fn_t_score = fn_t_score,
  grp_scaled_score = grp_verbal[["scaled_score"]],
  grp_standard_score = grp_verbal[["standard_score"]],
  grp_t_score = grp_verbal[["t_score"]],
  dynamic_grp = grp_verbal,
  vertical_padding = vertical_padding,
  multiline = multiline
)
```

```{r}
#| label: fig-verbal-subdomain
#| include: false
#| fig-cap: "Verbal and language functioning refers to the ability to access and apply acquired word knowledge, to verbalize meaningful concepts, to understand complex multistep instructions, to think about verbal information, and to express oneself using words."

# Arguments
colors <- NULL
return_plot <- Sys.getenv("RETURN_PLOT")
filename <- "fig_verbal_subdomain.svg"

# variables to plot
x <- data_verbal$z_mean_subdomain
y <- data_verbal$subdomain

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

# Make dotplot
bwu::dotplot(
  data = data_verbal,
  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
```

```{r}
#| label: fig-verbal-narrow
#| include: false
#| fig-cap: "Verbal and language functioning refers to the ability to access and apply acquired word knowledge, to verbalize meaningful concepts, to understand complex multistep instructions, to think about verbal information, and to express oneself using words."

# Arguments
colors <- NULL
return_plot <- Sys.getenv("RETURN_PLOT")
filename <- "fig_verbal_narrow.svg"

# variables to plot
x <- data_verbal$z_mean_narrow
y <- data_verbal$narrow

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

# Make dotplot
bwu::dotplot(
  data = data_verbal,
  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)],
          caption: figure.caption(position: bottom, [
            Verbal and language functioning refers to the ability to access and apply acquired word knowledge, to verbalize meaningful concepts, to understand complex multistep instructions, to think about verbal information, and to express oneself using words.
            ]),
          placement: none,
          kind: "image",
          supplement: [Figure],
          gap: 0.5em,
          ),
        )
    }
```

```{=typst}
#let title = "Verbal/Language"
#let file_qtbl = "table_verbal.png"
#let file_fig = "fig_verbal_subdomain.svg"
#domain(
  title: [#title Scores],
  file_qtbl,
  file_fig
)
```


```{=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)],
          caption: figure.caption(position: bottom, [
            Verbal and language functioning refers to the ability to access and apply acquired word knowledge, to verbalize meaningful concepts, to understand complex multistep instructions, to think about verbal information, and to express oneself using words.
            ]),
          placement: none,
          kind: "image",
          supplement: [Figure],
          gap: 0.5em,
          ),
        )
    }
```
```{=typst}
#let title = "Verbal/Language"
#let file_qtbl = "table_verbal.png"
#let file_fig = "fig_verbal_narrow.svg"
#domain(
  title: [#title Scores],
  file_qtbl,
  file_fig
)
```