_02-06_executive.qmd

## Attention/Executive {#sec-executive}

{{< include _02-06_executive_text.qmd >}}

```{r}
#| label: setup-executive
#| 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("Attention/Executive")

# Target phenotype
pheno <- "executive"
```

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

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

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

# Select specific columns from the data frame
executive <- executive |>
  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 'executive' data frame to a CSV file
# The file name is derived from the 'pheno' variable
readr::write_excel_csv(executive, paste0(pheno, ".csv"), na = "", col_names = TRUE, append = FALSE)
```

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

# read and filter data
data <- executive

scales <- c(
  "Animal Coding",
  "Arithmetic",
  "Attention Domain",
  "Attention Index",
  "Auditory Working Memory (AWMI)",
  "Bug Search",
  "Cancellation Random",
  "Cancellation Structured",
  "Cancellation",
  "Categories",
  "Category Fluency",
  "Clock Drawing",
  "Coding",
  "Cognitive Proficiency (CPI)",
  "Comprehension",
  "CVLT-3 Total Intrusions",
  "CVLT-3 Total Repetitions",
  "D-KEFS Color Naming",
  "D-KEFS Inhibition Total Errors",
  "D-KEFS Inhibition",
  "D-KEFS Switching Total Errors",
  "D-KEFS Switching",
  "D-KEFS Word Reading",
  "Digit Span Backward",
  "Digit Span Forward",
  "Digit Span Sequencing",
  "Digit Span",
  "Digits Backward Longest Span",
  "Digits Backward",
  "Digits Forward Longest Span",
  "Digits Forward",
  "Dots",
  "Driving Scenes",
  "Spatial Addition",
  "Executive Functions Domain",
  "Judgment",
  "Letter Fluency",
  "Letter-Number Sequencing",
  "List Memory Intrusions",
  "List Memory Repetitions",
  "Longest Digit Span Backward",
  "Longest Digit Span Forward",
  "Longest Digit Span Sequence",
  "Longest Letter-Number Sequence",
  "Mazes",
  "NAB Attention Index",
  "NAB Executive Functions Index",
  "Numbers & Letters Part A Efficiency",
  "Numbers & Letters Part A Errors",
  "Numbers & Letters Part A Speed",
  "Numbers & Letters Part B Efficiency",
  "Numbers & Letters Part C Efficiency",
  "Numbers & Letters Part D Disruption",
  "Numbers & Letters Part D Efficiency",
  "Orientation",
  "Picture Memory",
  "Picture Span",
  "Processing Speed (PSI)",
  "Processing Speed",
  "Psychomotor Speed",
  "ROCFT Copy",
  "Sentence Repetition",
  "Similarities",
  "Spatial Span",
  "Statue-Body Movement",
  "Statue-Eye Opening",
  "Statue-Vocalization",
  "Statue",
  "Symbol Search",
  "Symbol Span",
  "TMT, Part A",
  "TMT, Part B",
  "Total Deviation Score",
  "Unstructured Task",
  "Word Generation Perseverations",
  "Word Generation",
  "Working Memory (WMI)",
  "Working Memory",
  "Zoo Locations",
  "RBANS Digit Span",
  "RBANS Coding"
)

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

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

# Generate the text for the executive domain
bwu::cat_neuropsych_results(data = data_executive, file = "_02-06_executive_text.qmd")
```

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

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

# more filtering for exe tables
data_executive <-
  data_executive |>
  dplyr::filter(test_name != "CVLT-3 Brief") |>
  dplyr::filter(scale != "Orientation") |>
  dplyr::filter(scale %in% c(
    "Animal Coding",
    "Arithmetic",
    "Attention Domain",
    "Attention Index",
    "Auditory Working Memory (AWMI)",
    "Bug Search",
    # "Cancellation Random",
    # "Cancellation Structured",
    "Cancellation",
    "Categories",
    "Category Fluency",
    "Clock Drawing",
    "Coding",
    "Cognitive Proficiency (CPI)",
    "Comprehension",
    # "CVLT-3 Total Intrusions",
    # "CVLT-3 Total Repetitions",
    "D-KEFS Color Naming",
    "D-KEFS Inhibition Total Errors",
    "D-KEFS Inhibition",
    "D-KEFS Switching Total Errors",
    "D-KEFS Switching",
    "D-KEFS Word Reading",
    "Digit Span Backward",
    "Digit Span Forward",
    "Digit Span Sequencing",
    "Digit Span",
    "Digits Backward Longest Span",
    "Digits Backward",
    "Digits Forward Longest Span",
    "Digits Forward",
    "Dots",
    "Driving Scenes",
    "Spatial Addition",
    "Executive Functions Domain",
    "Judgment",
    "Letter Fluency",
    "Letter-Number Sequencing",
    "List Memory Intrusions",
    "List Memory Repetitions",
    "Longest Digit Span Backward",
    "Longest Digit Span Forward",
    "Longest Digit Span Sequence",
    "Longest Letter-Number Sequence",
    "Mazes",
    "NAB Attention Index",
    "NAB Executive Functions Index",
    "Numbers & Letters Part A Efficiency",
    # "Numbers & Letters Part A Errors",
    # "Numbers & Letters Part A Speed",
    "Numbers & Letters Part B Efficiency",
    "Numbers & Letters Part C Efficiency",
    "Numbers & Letters Part D Disruption",
    "Numbers & Letters Part D Efficiency",
    "Orientation",
    "Picture Memory",
    "Picture Span",
    "Processing Speed (PSI)",
    "Processing Speed",
    "Psychomotor Speed",
    "ROCFT Copy",
    "Sentence Repetition",
    "Similarities",
    "Spatial Span",
    "Statue",
    # "Statue-Body Movement",
    # "Statue-Eye Opening",
    # "Statue-Vocalization",
    "Symbol Search",
    "Symbol Span",
    "TMT, Part A",
    "TMT, Part B",
    "Total Deviation Score",
    "Unstructured Task",
    "Word Generation Perseverations",
    "Word Generation",
    "Working Memory (WMI)",
    "Working Memory",
    "Zoo Locations"
    # add RBANS
  ))

# table arguments
table_name <- "table_executive"
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("Standard score: Mean = 100 [50th‰], SD ± 15 [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‰]")

# groupings
grp_executive <- list(
  scaled_score = c("WAIS-IV", "D-KEFS", "NEPSY-2", "WISC-5", "WISC-V", "WPPSI-IV", "RBANS"),
  standard_score = c("NAB", "NAB-S", "WISC-5", "WISC-V", "WAIS-IV", "WPPSI-IV", "WASI-II", "RBANS"),
  t_score = c("NAB", "NAB-S", "NIH EXAMINER", "Trail Making Test")
)

# make `gt` table
bwu::tbl_gt(
  data = data_executive,
  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_executive[["scaled_score"]],
  grp_standard_score = grp_executive[["standard_score"]],
  grp_t_score = grp_executive[["t_score"]],
  dynamic_grp = grp_executive,
  vertical_padding = vertical_padding,
  multiline = multiline
)
```

```{r}
#| label: fig-executive-subdomain
#| include: false
#| fig-cap: "Attention and executive functions are essential for successful cognitive functioning, enabling us to perform everyday tasks, handle academic challenges, solve problems, manage our emotions, and interact effectively with others and our environment."

# dotplot arguments
filename <- "fig_executive_subdomain.svg"
colors <- NULL
return_plot <- Sys.getenv("RETURN_PLOT")

# dotplot variables to plot (x, y)
x <- data_executive$z_mean_subdomain
y <- data_executive$subdomain

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

bwu::dotplot(
  data = data_executive,
  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-executive-narrow
#| include: false
#| fig-cap: "Attention and executive functions are essential for successful cognitive functioning, enabling us to perform everyday tasks, handle academic challenges, solve problems, manage our emotions, and interact effectively with others and our environment."

# dotplot arguments
filename <- "fig_executive_narrow.svg"
colors <- NULL
return_plot <- Sys.getenv("RETURN_PLOT")

# dotplot variables to plot (x, y)
x <- data_executive$z_mean_narrow
y <- data_executive$narrow

bwu::dotplot(
  data = data_executive,
  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, [
            Attention and executive functions are essential for successful
            cognitive functioning, enabling us to perform everyday tasks, handle
            academic challenges, solve problems, manage our emotions, and
            interact effectively with others and our environment.
            ]),
          placement: none,
          kind: "image",
          supplement: [Figure],
          gap: 0.5em,
        ),
      )
  }
```
```{=typst}
#let title = "Attention/Executive"
#let file_qtbl = "table_executive.png"
#let file_fig = "fig_executive_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)
    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, [
            Attention and executive functions are essential for successful
            cognitive functioning, enabling us to perform everyday tasks, handle
            academic challenges, solve problems, manage our emotions, and
            interact effectively with others and our environment.
            ]),
          placement: none,
          kind: "image",
          supplement: [Figure],
          gap: 0.5em,
        ),
      )
  }
```
```{=typst}
#let title = "Attention/Executive"
#let file_qtbl = "table_executive.png"
#let file_fig = "fig_executive_narrow.svg"
#domain(
  title: [#title Scores],
  file_qtbl,
  file_fig
  )
```