_02-09_adhd_child.qmd

---
format: 
  typst:
    keep_md: true
---

## ADHD Symptoms and Executive Functioning {#sec-adhd}

```{r}
#| label: setup-adhd
#| 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("ADHD")

# target phenotype
pheno <- "adhd"
```

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

# Read the CSV file into a data frame
adhd <- 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
adhd <- adhd |>
  dplyr::filter(domain %in% domains)

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

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

# read and filter data
data <- adhd

# scales in this domain
scales <- c(
  "Activation",
  "Focus",
  "Effort",
  "Emotion",
  "Memory",
  "Action",
  "Total Composite",
  "Inattention/Memory Problems",
  "Hyperactivity/Restlessness",
  "Impulsivity/Emotional Lability",
  "Problems with Self-Concept",
  "DSM-5 Inattentive Symptoms",
  "DSM-5 Hyperactive-Impulsive Symptoms",
  "DSM-5 ADHD Symptoms Total",
  "ADHD Index",
  "Full Scale",
  "Attention",
  "Emotion Regulation",
  "Flexibility",
  "Inhibitory Control",
  "Initiation",
  "Organization",
  "Planning",
  "Self-Monitoring",
  "Working Memory",
  "CEFI-SR Full Scale",
  "CEFI-SR Attention",
  "CEFI-SR Emotion Regulation",
  "CEFI-SR Flexibility",
  "CEFI-SR Inhibitory Control",
  "CEFI-SR Initiation",
  "CEFI-SR Organization",
  "CEFI-SR Planning",
  "CEFI-SR Self-Monitoring",
  "CEFI-SR Working Memory",
  "CEFI-OR Full Scale",
  "CEFI-OR Attention",
  "CEFI-OR Emotion Regulation",
  "CEFI-OR Flexibility",
  "CEFI-OR Inhibitory Control",
  "CEFI-OR Initiation",
  "CEFI-OR Organization",
  "CEFI-OR Planning",
  "CEFI-OR Self-Monitoring",
  "CEFI-OR Working Memory",
  # Conners 4
  "Conners-4 Parent Inattention/Executive Dysfunction",
  "Conners-4 Parent Hyperactivity",
  "Conners-4 Parent Impulsivity",
  "Conners-4 Parent Emotional Dysregulation",
  "Conners-4 Parent Negative Self-Concept",
  "Conners-4 Parent ADHD Inattentive Symptoms",
  "Conners-4 Parent ADHD Hyperactive/Impulsive Symptoms",
  "Conners-4 Parent Total ADHD Symptoms",
  "Conners-4 Parent ADHD Index",
  "Conners-4 Self Inattention/Executive Dysfunction",
  "Conners-4 Self Hyperactivity",
  "Conners-4 Self Impulsivity",
  "Conners-4 Self Emotional Dysregulation",
  "Conners-4 Self Negative Self-Concept",
  "Conners-4 Self ADHD Inattentive Symptoms",
  "Conners-4 Self ADHD Hyperactive/Impulsive Symptoms",
  "Conners-4 Self Total ADHD Symptoms",
  "Conners-4 Self ADHD Index",
  "Inattention/Executive Dysfunction",
  "Hyperactivity",
  "Impulsivity",
  "Emotional Dysregulation",
  "Depressed Mood",
  "Anxious Thoughts",
  "Schoolwork",
  "Peer Interactions",
  "Family Life",
  "ADHD Inattentive Symptoms",
  "ADHD Hyperactive/Impulsive Symptoms",
  "Total ADHD Symptoms",
  "Oppositional Defiant Disorder Symptoms",
  "Conduct Disorder Symptoms",
  "ADHD Index"
)

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

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

# Self-report
data_adhd_self <-
  data_adhd |>
  dplyr::filter(test == "conners4_self" | test == "cefi_sr" | test == "brown_efa_self") |>
  dplyr::arrange(dplyr::desc(percentile)) |>
  dplyr::distinct(.keep_all = FALSE)

# Flatten the text
bwu::cat_neuropsych_results(data = data_adhd_self, file = "_02-09_adhd_child_text_self.qmd")
```

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

# Parent report
data_adhd_parent <-
  data_adhd |>
  dplyr::filter(test == "conners4_parent" | test == "cefi_or" | test == "brown_efa_parent") |>
  dplyr::distinct(.keep_all = FALSE)

# Flatten the text
bwu::cat_neuropsych_results(data = data_adhd_parent, file = "_02-09_adhd_child_text_parent.qmd")
```

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

# Parent report
data_adhd_teacher <-
  data_adhd |>
  dplyr::filter(test == "conners4_teacher" | test == "cefi_or" | test == "brown_efa_teacher") |>
  dplyr::distinct(.keep_all = FALSE)

# Flatten the text
bwu::cat_neuropsych_results(data = data_adhd_teacher, file = "_02-09_adhd_child_text_teacher.qmd")
```

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

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

# footnotes
fn_standard_score <- gt::md("CEFI Standard score: Mean = 100 [50th‰], SD ± 15
[16th‰, 84th‰].

Lower scores reflect reduced functioning.")

fn_t_score <- gt::md("T-score: Mean = 50 [50th‰], SD ± 10 [16th‰, 84th‰].

Higher scores reflect reduced functioning.")

# groupings
grp_adhd <- list(
  standard_score = c("CEFI Self-Report", "CEFI Observer-Report"),
  t_score = c("Conners-4 Self", "Conners-4 Parent", "Conners-4 Teacher")
)
grp_conners4 <- list(
  t_score = c("Conners-4 Self", "Conners-4 Parent", "Conners-4 Teacher")
)
grp_cefi <- list(
  standard_score = c("CEFI Self-Report", "CEFI Observer-Report")
)
grp_self <- list(
  standard_score = c("CEFI Self-Report"),
  t_score = c("Conners-4 Self", "Brown EF/A Self")
)

# redo this bc got sorted by pct
data_adhd_self <-
  data_adhd |>
  dplyr::filter(test == "conners4_self" | test == "cefi_sr" | test == "brown_efa_self") |>
  dplyr::distinct(.keep_all = FALSE)

pheno <- "adhd_self"

# make `gt` table
bwu::tbl_gt(
  data = data_adhd_self,
  pheno = pheno,
  table_name = table_name,
  fn_standard_score = fn_standard_score,
  fn_t_score = fn_t_score,
  grp_standard_score = grp_self[["standard_score"]],
  grp_t_score = grp_self[["t_score"]],
  dynamic_grp = grp_self,
  vertical_padding = vertical_padding,
  multiline = multiline
)
```


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

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

# footnotes
fn_standard_score <- gt::md("CEFI Standard score: Mean = 100 [50th‰], SD ± 15
[16th‰, 84th‰].

Lower scores reflect reduced functioning.")

fn_t_score <- gt::md("T-score: Mean = 50 [50th‰], SD ± 10 [16th‰, 84th‰].

Higher scores reflect reduced functioning.")

# groupings
grp_adhd <- list(
  standard_score = c("CEFI Self-Report", "CEFI Observer-Report"),
  t_score = c("Conners-4 Self", "Conners-4 Parent", "Conners-4 Teacher")
)
grp_conners4 <- list(
  t_score = c("Conners-4 Self", "Conners-4 Parent", "Conners-4 Teacher")
)
grp_cefi <- list(
  standard_score = c("CEFI Self-Report", "CEFI Observer-Report")
)

grp_parent <- list(
  standard_score = c("CEFI Parent-Report"),
  t_score = c("Conners-4 Parent", "Brown EF/A Parent")
)

# redo this bc got sorted by pct
# data_adhd_parent <-
#   data_adhd |>
#   dplyr::filter(test == "conners4_parent" | test == "cefi_or" | test == "brown_efa_parent") |>
#   dplyr::distinct(.keep_all = FALSE)

pheno <- "adhd_parent"

# make `gt` table
bwu::tbl_gt(
  data = data_adhd_parent,
  pheno = pheno,
  table_name = table_name,
  fn_standard_score = fn_standard_score,
  fn_t_score = fn_t_score,
  grp_standard_score = grp_parent[["standard_score"]],
  grp_t_score = grp_parent[["t_score"]],
  dynamic_grp = grp_parent,
  vertical_padding = vertical_padding,
  multiline = multiline
)
```

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

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

# footnotes
fn_standard_score <- gt::md("CEFI Standard score: Mean = 100 [50th‰], SD ± 15
[16th‰, 84th‰].

Lower scores reflect reduced functioning.")

fn_t_score <- gt::md("T-score: Mean = 50 [50th‰], SD ± 10 [16th‰, 84th‰].

Higher scores reflect reduced functioning.")

# groupings
grp_adhd <- list(
  standard_score = c("CEFI Self-Report", "CEFI Observer-Report"),
  t_score = c("Conners-4 Self", "Conners-4 Parent", "Conners-4 Teacher")
)
grp_cefi <- list(
  standard_score = c("CEFI Self-Report", "CEFI Observer-Report")
)
grp_conners4 <- list(
  t_score = c("Conners-4 Self", "Conners-4 Parent", "Conners-4 Teacher")
)

grp_teacher <- list(
  standard_score = c("CEFI Teacher-Report"),
  t_score = c("Conners-4 Teacher", "Brown EF/A Teacher")
)

# redo this bc got sorted by pct
data_adhd_teacher <-
  data_adhd |>
  dplyr::filter(test == "conners4_teacher" | test == "cefi_tr" | test == "brown_efa_teacher") |>
  dplyr::distinct(.keep_all = FALSE)

pheno <- "adhd_teacher"

# make `gt` table
bwu::tbl_gt(
  data = data_adhd_teacher,
  pheno = pheno,
  table_name = table_name,
  fn_standard_score = fn_standard_score,
  fn_t_score = fn_t_score,
  grp_standard_score = grp_teacher[["standard_score"]],
  grp_t_score = grp_teacher[["t_score"]],
  dynamic_grp = grp_teacher,
  vertical_padding = vertical_padding,
  multiline = multiline
)
```

```{r}
#| label: fig-adhd-self
#| eval: false
#| include: false
#| fig-height: 6
#| out-height: 100%
#| fig-cap: "Attention and executive functions are multidimensional concepts that contain several related processes. Both concepts require self-regulatory skills and have some common subprocesses; therefore, it is common to treat them together, or even to refer to both processes when talking about one or the other."

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

# dotplot variables to plot (x, y)
x <- data_adhd_self$z
y <- data_adhd_self$scale

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

bwu::dotplot(
  data = data_adhd_self,
  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-adhd-parent
#| eval: true
#| include: false
#| fig-height: 6
#| out-height: 100%
#| fig-cap: "Attention and executive functions are multidimensional concepts that contain several related processes. Both concepts require self-regulatory skills and have some common subprocesses; therefore, it is common to treat them together, or even to refer to both processes when talking about one or the other."

# dotplot arguments
filename <- "fig_adhd_parent.svg"
colors <- NULL
return_plot <- TRUE

# dotplot variables to plot (x, y)
x <- data_adhd_parent$z
y <- data_adhd_parent$scale

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

bwu::dotplot(
  data = data_adhd_parent,
  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-adhd-teacher
#| eval: false
#| include: false
#| fig-height: 6
#| out-height: 100%
#| fig-cap: "Attention and executive functions are multidimensional concepts that contain several related processes. Both concepts require self-regulatory skills and have some common subprocesses; therefore, it is common to treat them together, or even to refer to both processes when talking about one or the other."

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

# dotplot variables to plot (x, y)
x <- data_adhd_teacher$z
y <- data_adhd_teacher$scale

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

bwu::dotplot(
  data = data_adhd_teacher,
  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
```

### SELF-REPORT

{{< include _02-09_adhd_child_text_self.qmd >}}

```{=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,
        [
        Attention and executive functions are multidimensional concepts that contain several related processes. Both concepts require self-regulatory skills and have some common subprocesses; therefore, it is common to treat them together, or even to refer to both processes when talking about one or the other.
          ],
      ),
      placement: none,
      kind: "image",
      supplement: [*Figure*],
      gap: 0.5em,
    ),
  )
}

#let title = "ADHD Self-Report"
#let file_qtbl = "table_adhd_self.png"
#let file_fig = "fig_adhd_self.svg"
#domain(title: [#title], file_qtbl, file_fig)
```

### PARENT REPORT

{{< include _02-09_adhd_child_text_self.qmd >}}

```{=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,
        [
        Attention and executive functions are multidimensional concepts that contain several related processes. Both concepts require self-regulatory skills and have some common subprocesses; therefore, it is common to treat them together, or even to refer to both processes when talking about one or the other.
          ],
      ),
      placement: none,
      kind: "image",
      supplement: [*Figure*],
      gap: 0.5em,
    ),
  )
}

#let title = "ADHD Parent Report"
#let file_qtbl = "table_adhd_parent.png"
#let file_fig = "fig_adhd_parent.svg"
#domain(title: [#title], file_qtbl, file_fig)
```

### TEACHER REPORT

{{< include _02-09_adhd_child_text_teacher.qmd >}}

```{=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,
        [
        Attention and executive functions are multidimensional concepts that contain several related processes. Both concepts require self-regulatory skills and have some common subprocesses; therefore, it is common to treat them together, or even to refer to both processes when talking about one or the other.
          ],
      ),
      placement: none,
      kind: "image",
      supplement: [*Figure*],
      gap: 0.5em,
    ),
  )
}

#let title = "ADHD Teacher Report"
#let file_qtbl = "table_adhd_teacher.png"
#let file_fig = "fig_adhd_teacher.svg"
#domain(title: [#title], file_qtbl, file_fig)
```