_02-02_academics.qmd

## Academic Skills {#sec-academics}

{{< include _02-02_academics_text.qmd >}}

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

# domain
domains <- c("Academic Skills")

# Target phenotype
pheno <- "academics"

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

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

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

# Select specific columns from the data frame
academics <- academics |>
  dplyr::select(
    test,
    test_name,
    scale,
    raw_score,
    # age_equiv,
    score,
    ci_95,
    percentile,
    range,
    domain,
    subdomain,
    narrow,
    pass,
    verbal,
    timed,
    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(academics, here::here("data", paste0(pheno, ".csv")), na = "", col_names = TRUE, append = FALSE)
```

```{r}
#| label: data-academics
#| include: false
#| eval: true

# Read the data using the read_data function from the bwu library
# The phenotype is specified by the 'pheno' variable
data <- academics

scales <- c(
  "Academic Fluency Composite",
  "Academic Language Readiness Index",
  "Academic Skills Battery (ASB) Composite",
  "Alphabet Writing Fluency",
  "Associational Fluency",
  "Basic Reading",
  "Comprehension Composite",
  "Decoding Composite",
  "Decoding Fluency",
  "Decoding",
  "Dyslexia Index",
  "Essay Composition",
  "Expression Composite",
  "Expressive Vocabulary",
  "Letter & Word Recognition",
  "Letter Naming Facility",
  "Listening Comprehension",
  "Math Composite",
  "Math Computation",
  "Math Concepts & Applications",
  "Math Fluency-Addition",
  "Math Fluency-Multiplication",
  "Math Fluency-Subtraction",
  "Math Fluency",
  "Math Problem Solving",
  "Mathematics",
  "Nonsense Word Decoding",
  "Numerical Operations",
  "Object Naming Facility",
  "Oral Discourse Comprehension",
  "Oral Expression",
  "Oral Fluency Composite",
  "Oral Language Composite",
  "Oral Reading Fluency",
  "Oral Word Fluency",
  "Orthographic Choice",
  "Orthographic Fluency",
  "Orthographic Processing Composite",
  "Orthographic Processing Extended",
  "Orthographic Processing",
  "Phonemic Proficiency",
  "Phonological Processing",
  "Pseudoword Decoding",
  "Reading Composite",
  "Reading Comprehension",
  "Reading Comprehension",
  "Reading Fluency Composite",
  "Reading Fluency",
  "Reading Index",
  "Reading Understanding Composite",
  "Reading Vocabulary",
  "Reading",
  "Receptive Vocabulary",
  "Sentence Composition",
  "Sentence Comprehension",
  "Sentence Repetition",
  "Sentence Writing Fluency",
  "Silent Reading Fluency",
  "Sound-Symbol Composite",
  "Spelling",
  "Total Achievement",
  "Word Reading",
  "Word Recognition Fluency",
  "Writing Fluency",
  "Written Expression",
  "Written Language Composite"
)

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

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

# export text
bwu::cat_neuropsych_results(data = data_academics, file = "_02-02_academics_text.qmd")
```

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

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

# arguments
table_name <- "table_academics"
vertical_padding <- 0
multiline <- TRUE

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

# Define the groups for the table
grp_academics <- list(
  standard_score = c("KTEA-3", "WIAT-4", "WRAT-5")
)

# Create the table using the tbl_gt function from the bwu library
# source("table_gt.R")
bwu::tbl_gt(
  data = data_academics,
  pheno = pheno,
  table_name = table_name,
  fn_standard_score = fn_standard_score,
  grp_standard_score = grp_academics[["standard_score"]],
  dynamic_grp = grp_academics,
  vertical_padding = vertical_padding,
  multiline = multiline
)
```

```{r}
#| label: fig-academics-subdomain
#| include: false
#| fig-height: 6
#| out-height: 100%
#| fig-cap: "_Reading ability_ consists of three interrelated abilities: decoding, comprehension, and fluency. _Writing ability_ can be described in terms of spelling, grammar, expression of ideas, and writing fluency. _Math ability_ can be described in terms of calculation skills, applied problem solving, and math fluency."

# data_academics <- data_academics |>
#   dplyr::filter(
#     scale == "School Readiness Composite (SRC)"
#   )

# Arguments
filename <- "fig_academics_subdomain.svg"
colors <- NULL

# Define the x and y variables for the dotplot
x <- data_academics$z_mean_subdomain
y <- data_academics$subdomain

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

```{r}
#| label: fig-academics-narrow
#| include: false
#| fig-height: 6
#| out-height: 100%
#| fig-cap: "_Reading ability_ consists of three interrelated abilities: decoding, comprehension, and fluency. _Writing ability_ can be described in terms of spelling, grammar, expression of ideas, and writing fluency. _Math ability_ can be described in terms of calculation skills, applied problem solving, and math fluency."

# Define the filename for the plot
filename <- "fig_academics_narrow.svg"

# Define the x and y variables for the dotplot
x <- data_academics$z_mean_narrow
y <- data_academics$narrow

# plot args
colors <- NULL

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

```{=typst}
#let domain(title: none, file_qtbl, file_fig) = {
  let font = (font: "Roboto Slab", size: 0.7em)
  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,
        [
          Reading, writing, and math are the three main academic skills assessed on exam. _Reading ability_ consists of three interrelated abilities: decoding, comprehension, and fluency. _Writing ability_ can be described in terms of spelling, grammar, expression of ideas, and writing fluency. _Math ability_ can be described in terms of calculation skills, applied problem solving, and math fluency.
          ],
      ),
      placement: none,
      kind: "image",
      supplement: [*Figure*],
      gap: 0.5em,
    ),
  )
}
```

```{=typst}
#let title = "Academic Skills"
#let file_qtbl = "table_academics.png"
#let file_fig = "fig_academics_subdomain.svg"
#domain(
  title: [#title Scores],
  file_qtbl,
  file_fig,
  )
```


```{=typst}
#let title = "Academic Skills"
#let file_qtbl = "table_academics.png"
#let file_fig = "fig_academics_narrow.svg"
#domain(
  title: [#title Scores],
  file_qtbl,
  file_fig,
  )
```