_02-04_spatial.qmd

## Visual Perception/Construction {#sec-spatial}

{{< include _02-04_spatial_text.qmd >}}

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

# domain
domains <- c("Visual Perception/Construction")

# phenotype
pheno <- "spatial"
```

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

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

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

spatial <- spatial |>
  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(spatial, paste0(pheno, ".csv"), na = "", col_names = TRUE, append = FALSE)
```

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

# Read data and filter
data <- spatial

scales <- c(
  "Arrows",
  "Bicycle Drawing",
  "Block Design No Time Bonus",
  "Block Design Partial Score",
  "Block Design",
  "Clock Drawing",
  "Clocks",
  "Design Construction",
  "Design Copying General",
  "Design Copying Motor",
  "Design Copying Process",
  "Design Copying",
  "Figure Copy",
  "Figure Drawing Copy",
  "Figure Weights",
  "Figure Weights (Double-Time)",
  "Geometric Puzzles",
  "Line Orientation",
  "Map Reading",
  "Matrix Reasoning",
  "NAB Spatial Index",
  "Object Assembly",
  "Picture Concepts",
  "ROCF Copy",
  "ROCFT Copy",
  "Spatial Domain",
  "Visual Discrimination",
  "Visual Puzzles",
  "Visuospatial/Constructional 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_spatial <-
  bwu::filter_data(
    data,
    domain = domains,
    scale = scales
  )
```

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

# export text
bwu::cat_neuropsych_results(data = data_spatial, file = "_02-04_spatial_text.qmd")
```

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

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

# args
table_name <- "table_spatial"
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‰]")
fn_z_score <- gt::md("_z_-score: Mean = 0 [50th‰], SD ± 1 [16th‰, 84th‰]")

grp_spatial <- list(
  scaled_score = c("WAIS-IV", "WISC-5", "NEPSY-2", "WPPSI-IV", "WISC-V"),
  standard_score = c("NAB", "WPPSI-IV", "NAB-S"),
  t_score = c("NAB", "Rey Complex Figure", "WASI-2", "NAB-S")
)

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

```{r}
#| label: fig-spatial
#| include: false
#| fig-cap: "Perception, construction, and visuospatial processing refer to abilities such as mentally visualizing how objects should look from different angles, visualizing how to put objects together so that they fit correctly, and being able to accurately and efficiently copy and/or reproduce visual-spatial information onto paper."

# filename for plot
filename <- "fig_spatial.svg"

# x and y axis variables
x <- data_spatial$z_mean_subdomain
y <- data_spatial$subdomain

# plot args
colors <- NULL
return_plot <- Sys.getenv("RETURN_PLOT")

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

```{=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, [
            Perception, construction, and visuospatial processing refer to
            abilities such as mentally visualizing how objects should look from
            different angles, visualizing how to put objects together so that
            they fit correctly, and being able to accurately and efficiently
            copy and/or reproduce visual-spatial information onto paper.
            ]),
          placement: none,
          kind: "image",
          supplement: [Figure],
          gap: 0.5em,
          ),
        )
    }
```
```{=typst}
#let title = "Visual Perception/Construction"
#let file_qtbl = "table_spatial.png"
#let file_fig = "fig_spatial.svg"
#domain(
  title: [#title Scores],
  file_qtbl,
  file_fig
)
```