_02-05_memory.qmd

## Memory {#sec-memory}

{{< include _02-05_memory_text.qmd >}}

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

# domain
domains <- c("Memory")

# phenotype
pheno <- "memory"
```

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

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

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

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

# read and filter data
data <- memory

# Memory scales
scales <- c(
  "CVLT-3 Forced-Choice Recognition Hits",
  "CVLT-3 Total Intrusions",
  "CVLT-3 Total Repetitions",
  "Daily Living Memory Delayed Recall",
  "Daily Living Memory Immediate Recall",
  "Delayed Memory Index",
  "Designs I",
  "Designs II",
  "Figure Recall",
  "Immediate Memory Index",
  "List B Correct",
  "List B Free Recall Correct",
  "List Learning Immediate Recall",
  "List Learning Long Delayed Recall",
  "List Learning Short Delayed Recall",
  "List Learning",
  "List Memory Delay Effect",
  "List Memory Interference Effect",
  "List Memory Intrusions",
  "List Memory Learning Effect",
  "List Memory Repetitions",
  "List Memory Total and Delayed Recall",
  "List Memory Total Trials 1-5",
  "List Recall",
  "List Recognition",
  "Logical Memory I",
  "Logical Memory II",
  "Long Delay Cued Recall",
  "Long Delay Free Recall",
  "Long-Delay Cued Recall",
  "Long-Delay Free Recall",
  "Long-Delay Recognition Discriminability",
  "Long-Delay Recognition Response Bias",
  "Memory Domain",
  "Memory for Designs Content",
  "Memory for Designs Delayed Content",
  "Memory for Designs Delayed Spatial",
  "Memory for Designs Delayed",
  "Memory for Designs Spatial",
  "Memory for Designs",
  "Memory for Faces Delayed",
  "Memory for Faces",
  "NAB Memory Index",
  "Narrative Memory Free and Cued Recall",
  "Narrative Memory Free Recall",
  "Narrative Memory Recall",
  "Narrative Memory Recognition",
  "Recognition Discriminability (d')",
  "Recognition Discriminability Nonparametric",
  "ROCFT Delayed Recall",
  "Sentence Repetition",
  "Shape Learning Delayed Recognition",
  "Shape Learning Immediate Recognition",
  "Shape Learning Percent Retention",
  "Short Delay Cued Recall",
  "Short Delay Free Recall",
  "Short-Delay Cued Recall",
  "Short-Delay Free Recall",
  "Story Learning Delayed Recall",
  "Story Learning Immediate Recall",
  "Story Learning Percent Retention",
  "Story Memory",
  "Story Recall",
  "Total False Positives",
  "Total Hits",
  "Total Intrusions",
  "Total Repetitions",
  "Trial 1 Correct",
  "Trial 1 Free Recall Correct",
  "Trial 2 Correct",
  "Trial 3 Correct",
  "Trial 4 Correct",
  "Trial 5 Correct",
  "Trial 5 Free Recall Correct",
  "Trials 1-4 Correct",
  "Trials 1-5 Correct",
  "Trials 1-5 Free Recall Correct",
  "Visual Reproduction I",
  "Visual Reproduction II",
  "Word List Interference-Recall",
  "Word List Interference-Repetition"
)

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

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

# Generate the text for the memory domain
bwu::cat_neuropsych_results(data = data_memory, file = "_02-05_memory_text.qmd")
```

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

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

# more filtering for exe tables
data_memory <-
  data_memory |>
  dplyr::filter(scale %in% c(
    "CVLT-3 Forced-Choice Recognition Hits",
    "CVLT-3 Total Intrusions",
    "CVLT-3 Total Repetitions",
    "Daily Living Memory Delayed Recall",
    "Daily Living Memory Immediate Recall",
    "Delayed Memory Index",
    "Designs I",
    "Designs II",
    "Figure Recall",
    "Immediate Memory Index",
    # "List B Correct",
    # "List B Free Recall Correct",
    "List Learning Immediate Recall",
    "List Learning Long Delayed Recall",
    "List Learning Short Delayed Recall",
    "List Learning",
    "List Memory Delay Effect",
    "List Memory Interference Effect",
    "List Memory Intrusions",
    "List Memory Learning Effect",
    "List Memory Repetitions",
    "List Memory Total and Delayed Recall",
    "List Memory Total Trials 1-5",
    "List Recall",
    "List Recognition",
    "Logical Memory I",
    "Logical Memory II",
    "Long Delay Cued Recall",
    "Long Delay Free Recall",
    "Long-Delay Cued Recall",
    "Long-Delay Free Recall",
    "Long-Delay Recognition Discriminability",
    "Long-Delay Recognition Response Bias",
    "Memory Domain",
    "Memory for Designs Content",
    "Memory for Designs Delayed Content",
    "Memory for Designs Delayed Spatial",
    "Memory for Designs Delayed",
    "Memory for Designs Spatial",
    "Memory for Designs",
    "Memory for Faces Delayed",
    "Memory for Faces",
    "NAB Memory Index",
    "Narrative Memory Free and Cued Recall",
    "Narrative Memory Free Recall",
    "Narrative Memory Recall",
    "Narrative Memory Recognition",
    "Recognition Discriminability (d')",
    # "Recognition Discriminability Nonparametric",
    "ROCFT Delayed Recall",
    "Sentence Repetition",
    "Shape Learning Delayed Recognition",
    "Shape Learning Immediate Recognition",
    "Shape Learning Percent Retention",
    "Short Delay Cued Recall",
    "Short Delay Free Recall",
    "Short-Delay Cued Recall",
    "Short-Delay Free Recall",
    "Story Learning Delayed Recall",
    "Story Learning Immediate Recall",
    "Story Learning Percent Retention",
    "Story Memory",
    "Story Recall",
    "Total False Positives",
    "Total Hits",
    "Total Intrusions",
    "Total Repetitions",
    "Trial 1 Correct",
    "Trial 1 Free Recall Correct",
    "Trial 2 Correct",
    "Trial 3 Correct",
    "Trial 4 Correct",
    "Trial 5 Correct",
    "Trial 5 Free Recall Correct",
    "Trials 1-4 Correct",
    "Trials 1-5 Correct",
    "Trials 1-5 Free Recall Correct",
    "Visual Reproduction I",
    "Visual Reproduction II",
    "Word List Interference-Recall",
    "Word List Interference-Repetition"
  ))

# table arguments
table_name <- "table_memory"
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‰]")

# groupings
grp_memory <- list(
  standard_score = c("CVLT-3", "CVLT-3 Brief", "NAB", "NAB-S", "CVLT-C"), # Also NAB if decide to keep here
  t_score = c("NAB", "Rey Complex Figure", "NAB-S"),
  scaled_score = c("CVLT-3 Brief", "NEPSY-2", "CVLT-C", "CVLT-3")
)

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

```{r}
#| label: fig-memory
#| include: false
#| fig-cap: "Learning and memory refer to the rate and ease with which new information (e.g., facts, stories, lists, faces, names) can be encoded, stored, and later recalled from long-term memory."

# dotplot arguments
x <- data_memory$z_mean_narrow
y <- data_memory$narrow
colors <- NULL
return_plot <- Sys.getenv("RETURN_PLOT")
filename <- "fig_memory.svg"

# 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_memory,
  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,

          [Learning and memory refer to the rate and ease with which new
          information (e.g., facts, stories, lists, faces, names) can be
          encoded, stored, and later recalled from long-term memory.]),

          placement: none,
          kind: "image",
          supplement: [Figure],
          gap: 0.5em,
        ),
      )
  }
```
```{=typst}
#let title = "Learning and Memory"
#let file_qtbl = "table_memory.png"
#let file_fig = "fig_memory.svg"
#domain(
  title: [#title Scores],
  file_qtbl,
  file_fig
)
```