In jtrampush/npsych.data: BrainWorkup Neuropsychological Report Writing Package
Data
- Export raw CSV file from Q-interactive using "download files" or whatever its callled
Setup
# Set JAVA environment
Sys.setenv(JAVA_HOME = "/Library/Java/JavaVirtualMachines/graalvm-community-openjdk-22.0.1+8.1/Contents/Home")
# options(java.parameters = "-Xmx16000m")
knitr::opts_chunk$set(
root.dir = normalizePath("./"),
echo = FALSE,
eval = TRUE,
include = TRUE,
message = FALSE,
warning = FALSE,
error = TRUE
)
library(tidyverse)
library(tabulapdf)
library(rJava)
library(shiny)
library(here)
library(pdftools)
library(fs)
library(magrittr)
library(hablar)
library(googledrive)
library(bwu)
Patient
patient <- params$patient
Test
Name of neuropsych test or rating scale.
test <- params$test
test_name <- params$test_name
Load text/csv file and save as file.path
input_file_path <- file.path(file.choose())
# input_file_path <- file.path(params$file)
RAW SCORES
library(tidyverse)
input_file_path <- "data/0099999_5_11_2024Biggie_S_FollowUp_ADHD-LD_Age14_scores.csv"
test_name_prefix <- "RBANS Update Form A "
output_file_path <- "data/processed_rbans_raw_data.csv"
df <- read_csv(input_file_path,
col_names = FALSE,
show_col_types = FALSE,
locale = locale(encoding = "UTF-16LE")
)
# function
pluck_rbans_raw <- function(input_file_path,
test_name_prefix,
output_file_path = NULL) {
df <- read_csv(
input_file_path,
col_names = FALSE,
show_col_types = FALSE,
locale = locale(encoding = "UTF-16LE")
)
# Rename the columns
names(df) <- c("Subtest", "NA", "Raw score")
# Remove the second column
df <- df |> select(Subtest, `Raw score`)
# Find the start of the "Raw Score" section
start_line <- which(df == "RAW SCORES") + 1
# Find the stop of the "Raw Score" section
stop_line <- which(df == "SCALED SCORES") - 1
# Read from the "Raw Score" section
df_raw <- df |>
dplyr::slice(start_line:stop_line)
# Keep only rows with the specified prefix in the first column
df_raw <- df_raw |> filter(str_starts(Subtest, test_name_prefix))
# Your new names stored in a character vector (ensure it matches the number of columns in `df`)
vars <- c("scale", "raw_score")
# Use `set_names()` to rename the columns
df_raw <- df_raw |> set_names(vars)
df_raw$scale <- as.character(df_raw$scale)
df_raw$raw_score <- as.numeric(df_raw$raw_score)
# Write the combined data to a CSV file
write_csv(df_raw, output_file_path)
return(df_raw)
}
rbans_raw <- pluck_rbans_raw(input_file_path, output_file_path = output_file_path, test_name_prefix)
SCALED SCORES
# input_file_path <- "data/004004907_3_10_2024.csv"
# test_name_prefix <- "RBANS Update Form A "
output_file_path <- "data/processed_rbans_scaled_data.csv"
# function
pluck_rbans_score <- function(input_file_path,
test_name_prefix,
output_file_path = NULL) {
df <- read_csv(
input_file_path,
col_names = FALSE,
show_col_types = FALSE,
locale = locale(encoding = "UTF-16LE")
)
# Rename the columns
names(df) <- c("Subtest", "NA", "Scaled score")
# Remove the second column
df <- df |> select(Subtest, `Scaled score`)
# Find the start of the "Raw Score" section
start_line <- which(df == "SCALED SCORES") + 1
# Find the stop of the "Raw Score" section
stop_line <- which(df == "CONTEXTUAL EVENTS") - 1
# Read from the "score" section
df_score <- df |>
dplyr::slice(start_line:stop_line)
# Keep only rows with the specified prefix in the first column
df_score <- df_score |> filter(str_starts(Subtest, test_name_prefix))
# Your new names stored in a character vector (ensure it matches the number of columns in `df`)
vars <- c("scale", "score")
# Use `set_names()` to rename the columns
df_score <- df_score |> set_names(vars)
df_score$scale <- as.character(df_score$scale)
df_score$score <- as.numeric(df_score$score)
# Write the combined data to a CSV file
write_csv(df_score, output_file_path)
return(df_score)
}
rbans_score <- pluck_rbans_score(input_file_path, test_name_prefix, output_file_path = output_file_path)
SUBTEST COMPLETION TIMES
# input_file_path <- "data/004004907_3_10_2024.csv"
# test_name_prefix <- "RBANS Update Form A "
output_file_path <- "data/processed_rbans_completion_time_data.csv"
# function
pluck_rbans_completion_times <- function(input_file_path,
test_name_prefix,
output_file_path = NULL) {
df <- read_csv(
input_file_path,
col_names = FALSE,
show_col_types = FALSE,
locale = locale(encoding = "UTF-16LE")
)
# Rename the columns
names(df) <- c("Subtest", "NA", "Completion Time (seconds)")
# Remove the second column
df <- df |> select(Subtest, `Completion Time (seconds)`)
# Find the start of the section
start_line <- which(df == "SUBTEST COMPLETION TIMES") + 1
# Find the stop of the section
stop_line <- which(df == "RULES TRIGGERED") - 1
# Read from the "Raw Score" section
df_times <- df |>
dplyr::slice(start_line:stop_line)
# Keep only rows with the specified prefix in the first column
df_times <- df_times |> filter(str_starts(Subtest, test_name_prefix))
# Your new names stored in a character vector (ensure it matches the number of columns in `df`)
vars <- c("scale", "completion_time_seconds")
# Use `set_names()` to rename the columns
df_times <- df_times |> set_names(vars)
df_times$scale <- as.character(df_times$scale)
df_times$completion_time_seconds <- as.numeric(df_times$completion_time_seconds)
# Write the combined data to a CSV file
write_csv(df_times, output_file_path)
return(df_times)
}
rbans_time <- pluck_rbans_completion_times(input_file_path, test_name_prefix, output_file_path = output_file_path)
COMPOSITE SCORES
# input_file_path <- "data/004004907_3_10_2024.csv"
# test_name_prefix <- "RBANS Update Form A "
output_file_path <- "data/processed_rbans_composite_data.csv"
# function
pluck_rbans_composite <- function(input_file_path,
test_name_prefix,
output_file_path = NULL) {
df <- read_csv(
input_file_path,
col_names = FALSE,
show_col_types = FALSE,
locale = locale(encoding = "UTF-16LE")
)
# Assume the first row after "Composite Score" has the column names
start_line <- which(df$X1 == "Composite Score")
# Assuming there's no specific end line, use the end of the file
stop_line <- nrow(df)
# Extracting the relevant section
df_composite <- df |>
slice((start_line + 1):stop_line) |>
tidyr::separate(
X3,
sep = ",",
into = c(
"percentile",
"ci_90_lo",
"ci_90_up",
"ci_95_lower",
"ci_95_upper"
)
) |>
slice(-1) |>
rename(scale = X1, score = X2) |>
# Filter based on the prefix
filter(str_starts(scale, test_name_prefix)) |>
select(-c(ci_90_lo, ci_90_up)) |>
mutate(
scale = as.character(scale),
score = as.numeric(score),
percentile = as.numeric(percentile),
ci_95_lower = as.numeric(ci_95_lower),
ci_95_upper = as.numeric(ci_95_upper)
)
# Optionally write to a CSV file
if (!is.null(output_file_path)) {
write_csv(df_composite, output_file_path)
}
return(df_composite)
}
rbans_composite <- pluck_rbans_composite(input_file_path, test_name_prefix, output_file_path = output_file_path)
MERGE
#' Process and Save RBANS Data
#'
#' This function processes RBANS raw, score, and composite data frames by joining them,
#' updating specific values, and saving the combined data to a CSV file.
#'
#' @param rbans_raw A data frame containing raw RBANS data.
#' @param rbans_score A data frame containing RBANS scores.
#' @param rbans_time A df containing completion times.
#' @param rbans_composite A data frame containing RBANS composite scores.
#' @param test_name_prefix A string representing the prefix to be removed from the scale names.
#' @param output_file_path A string specifying the path to save the combined CSV file.
#' @import dplyr
#' @import stringr
#' @export
process_and_save_rbans_data <- function(rbans_raw,
rbans_score,
rbans_time,
rbans_composite,
test_name_prefix,
output_file_path) {
library(dplyr)
library(stringr)
# Join the data into one dataframe by the test name
df <- left_join(rbans_raw, rbans_score, by = "scale") |>
mutate(percentile = as.numeric(""), range = as.character("")) |>
left_join(rbans_time, by = "scale")
# Update specific percentile values
df$percentile[df$scale == "RBANS Update Form A Line Orientation"] <- (params$pct1)
df$percentile[df$scale == "RBANS Update Form A Picture Naming"] <- (params$pct2)
df$percentile[df$scale == "RBANS Update Form A List Recall"] <- (params$pct3)
df$percentile[df$scale == "RBANS Update Form A List Recognition"] <- (params$pct4)
# Recalculate percentiles based on score
df <- df |>
mutate(z = ifelse(!is.na(score), (score - 10) / 3, NA)) |>
mutate(percentile = ifelse(is.na(percentile), trunc(pnorm(z) * 100), percentile)) |>
select(-z)
# Merge with composite scores
df <- bind_rows(df, rbans_composite) |>
relocate(completion_time_seconds, .after = ci_95_upper)
# Test score ranges (assuming bwu::gpluck_make_score_ranges is a predefined function)
df <- bwu::gpluck_make_score_ranges(table = df, test_type = "npsych_test")
# Remove prefix from scale names
df <- df |>
mutate(scale = str_remove(scale, test_name_prefix))
scales_to_rename <- c(
"Immediate Memory Index (IMI)" = "Immediate Memory Index",
"Visuospatial/ Constructional Index (VCI)" = "Visuospatial/Constructional Index",
"Language Index (LGI)" = "Language Index",
"Attention Index (ATI)" = "Attention Index",
"Delayed Memory Index (DRI)" = "Delayed Memory Index",
"Total Scale (TOT)" = "RBANS Total Index"
)
df$scale <- map_chr(df$scale, ~ if_else(.x %in% names(scales_to_rename), scales_to_rename[.x], .x))
# Write the combined data to a CSV file
return(write_csv(df, output_file_path))
}
df <- process_and_save_rbans_data(
rbans_raw = rbans_raw,
rbans_score = rbans_score,
rbans_time = rbans_time,
rbans_composite = rbans_composite,
test_name_prefix = test_name_prefix,
output_file_path = output_file_path
)
# Write the combined data to a CSV file
output_file_path <- "data/rbans.csv"
write_csv(df, output_file_path)
MUTATE
rbans <- df
rbans <- bwu::gpluck_make_columns(
data = rbans,
test = params$test,
test_name = params$test_name,
ci_95 = paste0(rbans$ci_95_lower, "-", rbans$ci_95_upper),
domain = "",
subdomain = "",
narrow = "",
pass = "",
verbal = "",
timed = "",
test_type = "npsych_test",
score_type = "",
description = "",
result = ""
)
Domain
rbans <-
rbans |>
dplyr::mutate(
domain = dplyr::case_when(
scale == "RBANS Total Index" ~ "General Cognitive Ability",
scale == "Immediate Memory Index" ~ "Memory",
scale == "List Learning" ~ "Memory",
scale == "Story Memory" ~ "Memory",
scale == "Visuospatial/Constructional Index" ~ "Visual Perception/Construction",
scale == "Figure Copy" ~ "Visual Perception/Construction",
scale == "Line Orientation" ~ "Visual Perception/Construction",
scale == "Language Index" ~ "Verbal/Language",
scale == "Picture Naming" ~ "Verbal/Language",
scale == "Semantic Fluency" ~ "Verbal/Language",
scale == "Attention Index" ~ "Attention/Executive",
scale == "Digit Span" ~ "Attention/Executive",
scale == "Coding" ~ "Attention/Executive",
scale == "Delayed Memory Index" ~ "Memory",
scale == "List Recall" ~ "Memory",
scale == "List Recognition" ~ "Memory",
scale == "Story Recall" ~ "Memory",
scale == "Figure Recall" ~ "Memory",
TRUE ~ domain
)
)
Subdomain
rbans <-
rbans |>
dplyr::mutate(
subdomain = dplyr::case_when(
scale == "RBANS Total Index" ~ "Neuropsychological Functioning",
scale == "Immediate Memory Index" ~ "Neuropsychological Functioning",
scale == "List Learning" ~ "Learning Efficiency",
scale == "Story Memory" ~ "Learning Efficiency",
scale == "Visuospatial/Constructional Index" ~ "Neuropsychological Functioning",
scale == "Figure Copy" ~ "Organization",
scale == "Line Orientation" ~ "Perception",
scale == "Language Index" ~ "Neuropsychological Functioning",
scale == "Picture Naming" ~ "Retrieval",
scale == "Semantic Fluency" ~ "Fluency",
scale == "Attention Index" ~ "Neuropsychological Functioning",
scale == "Digit Span" ~ "Attention",
scale == "Coding" ~ "Processing Speed",
scale == "Delayed Memory Index" ~ "Neuropsychological Functioning",
scale == "List Recall" ~ "Delayed Recall",
scale == "List Recognition" ~ "Recognition Memory",
scale == "Story Recall" ~ "Delayed Recall",
scale == "Figure Recall" ~ "Delayed Recall",
TRUE ~ subdomain
)
)
Narrow
rbans <-
rbans |>
mutate(
narrow = case_when(
scale == "RBANS Total Index" ~ "RBANS Total Index",
scale == "Immediate Memory Index" ~ "RBANS Memory Index",
scale == "List Learning" ~ "Word-List Learning",
scale == "Story Memory" ~ "Story Memory",
scale == "Visuospatial/Constructional Index" ~ "RBANS Visuospatial/Constructional Index",
scale == "Figure Copy" ~ "Figure Copy",
scale == "Line Orientation" ~ "Visual Perception",
scale == "Language Index" ~ "RBANS Language Index",
scale == "Picture Naming" ~ "Naming",
scale == "Semantic Fluency" ~ "Semantic Fluency",
scale == "Attention Index" ~ "RBANS Attention Index",
scale == "Digit Span" ~ "Attention Span",
scale == "Coding" ~ "Cognitive Efficiency",
scale == "Delayed Memory Index" ~ "RBANS Memory Index",
scale == "List Recall" ~ "Word-List Learning",
scale == "List Recognition" ~ "Recognition Memory",
scale == "Story Recall" ~ "Story Memory",
scale == "Figure Recall" ~ "Visual Memory",
TRUE ~ narrow
)
)
Timed/Untimed
rbans <-
rbans |>
mutate(
timed = case_when(
scale == "RBANS Total Index" ~ "",
scale == "Immediate Memory Index" ~ "Untimed",
scale == "List Learning" ~ "Untimed",
scale == "Story Memory" ~ "Untimed",
scale == "Visuospatial/Constructional Index" ~ "Untimed",
scale == "Figure Copy" ~ "Untimed",
scale == "Line Orientation" ~ "Untimed",
scale == "Language Index" ~ "",
scale == "Picture Naming" ~ "Untimed",
scale == "Semantic Fluency" ~ "Timed",
scale == "Attention Index" ~ "",
scale == "Digit Span" ~ "Untimed",
scale == "Coding" ~ "Timed",
scale == "Delayed Memory Index" ~ "Untimed",
scale == "List Recall" ~ "Untimed",
scale == "List Recognition" ~ "Untimed",
scale == "Story Recall" ~ "Untimed",
scale == "Figure Recall" ~ "Untimed",
TRUE ~ timed
)
)
Verbal/Nonverbal
rbans <-
rbans |>
mutate(
verbal = case_when(
scale == "RBANS Total Index" ~ "",
scale == "Immediate Memory Index" ~ "Verbal",
scale == "List Learning" ~ "Verbal",
scale == "Story Memory" ~ "Verbal",
scale == "Visuospatial/Constructional Index" ~ "Nonverbal",
scale == "Figure Copy" ~ "Nonverbal",
scale == "Line Orientation" ~ "Nonverbal",
scale == "Language Index" ~ "Verbal",
scale == "Picture Naming" ~ "Verbal",
scale == "Semantic Fluency" ~ "Verbal",
scale == "Attention Index" ~ "",
scale == "Digit Span" ~ "Verbal",
scale == "Coding" ~ "Nonverbal",
scale == "Delayed Memory Index" ~ "",
scale == "List Recall" ~ "Verbal",
scale == "List Recognition" ~ "Verbal",
scale == "Story Recall" ~ "Verbal",
scale == "Figure Recall" ~ "Nonverbal",
TRUE ~ verbal
)
)
PASS
rbans <-
rbans |>
mutate(
pass = case_when(
scale == "RBANS Total Index" ~ "",
scale == "Immediate Memory Index" ~ "Sequential",
scale == "List Learning" ~ "Sequential",
scale == "Story Memory" ~ "Sequential",
scale == "Visuospatial/Constructional Index" ~ "Simultaneous",
scale == "Figure Copy" ~ "Simultaneous",
scale == "Line Orientation" ~ "Simultaneous",
scale == "Language Index" ~ "Sequential",
scale == "Picture Naming" ~ "Knowledge",
scale == "Semantic Fluency" ~ "Sequential",
scale == "Attention Index" ~ "Attention",
scale == "Digit Span" ~ "Attention",
scale == "Coding" ~ "Planning",
scale == "Delayed Memory Index" ~ "",
scale == "List Recall" ~ "Sequential",
scale == "List Recognition" ~ "Sequential",
scale == "Story Recall" ~ "Sequential",
scale == "Figure Recall" ~ "Simultaneous",
TRUE ~ as.character(pass)
)
)
Score type
rbans <-
rbans |>
mutate(
score_type = case_when(
scale == "RBANS Total Index" ~ "standard_score",
scale == "Immediate Memory Index" ~ "standard_score",
scale == "List Learning" ~ "scaled_score",
scale == "Story Memory" ~ "scaled_score",
scale == "Visuospatial/Constructional Index" ~ "standard_score",
scale == "Figure Copy" ~ "scaled_score",
scale == "Line Orientation" ~ "percentile",
scale == "Language Index" ~ "standard_score",
scale == "Picture Naming" ~ "percentile",
scale == "Semantic Fluency" ~ "scaled_score",
scale == "Attention Index" ~ "standard_score",
scale == "Digit Span" ~ "scaled_score",
scale == "Coding" ~ "scaled_score",
scale == "Delayed Memory Index" ~ "standard_score",
scale == "List Recall" ~ "percentile",
scale == "List Recognition" ~ "percentile",
scale == "Story Recall" ~ "scaled_score",
scale == "Figure Recall" ~ "scaled_score",
TRUE ~ as.character(score_type)
)
)
Descriptions
rbans <-
rbans |>
mutate(
description = case_when(
scale == "RBANS Total Index" ~ "composite indicator of general cognitive functioning",
scale == "Immediate Memory Index" ~ "composite verbal learning of a word list and a logical story",
scale == "List Learning" ~ "word list learning",
scale == "Story Memory" ~ "expository story learning",
scale == "Visuospatial/Constructional Index" ~ "broad visuospatial processing",
scale == "Figure Copy" ~ "copy of a complex abstract figure",
scale == "Line Orientation" ~ "basic perception of visual stimuli",
scale == "Language Index" ~ "general language processing",
scale == "Picture Naming" ~ "confrontation naming/expressive vocabulary",
scale == "Semantic Fluency" ~ "semantic word fluency/generativity",
scale == "Attention Index" ~ "general attentional and executive functioning",
scale == "Digit Span" ~ "attention span and auditory attention",
scale == "Coding" ~ "speed of information processing",
scale == "Delayed Memory Index" ~ "long-term recall of verbal information",
scale == "List Recall" ~ "long-term recall of a word list",
scale == "List Recognition" ~ "delayed recognition of a word list",
scale == "Story Recall" ~ "long-term recall of a detailed story",
scale == "Figure Recall" ~ "long-term recall and reconstruction of a complex abstract figure",
TRUE ~ as.character(description)
)
)
Glue results
rbans <-
rbans |>
dplyr::mutate(
result = glue::glue(
"{patient}'s score on {.data$scale} ({.data$description}) was {.data$range}."
)
)
EXPORT
readr::write_csv(rbans, here::here("data", "rbans.csv"), col_names = TRUE, na = "")
cat("Finished!")
jtrampush/npsych.data documentation built on Feb. 25, 2025, 12:30 a.m.
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Data
- Export raw CSV file from Q-interactive using "download files" or whatever its callled
Setup
# Set JAVA environment Sys.setenv(JAVA_HOME = "/Library/Java/JavaVirtualMachines/graalvm-community-openjdk-22.0.1+8.1/Contents/Home") # options(java.parameters = "-Xmx16000m") knitr::opts_chunk$set( root.dir = normalizePath("./"), echo = FALSE, eval = TRUE, include = TRUE, message = FALSE, warning = FALSE, error = TRUE ) library(tidyverse) library(tabulapdf) library(rJava) library(shiny) library(here) library(pdftools) library(fs) library(magrittr) library(hablar) library(googledrive) library(bwu)
Patient
patient <- params$patient
Test
Name of neuropsych test or rating scale.
test <- params$test test_name <- params$test_name
Load text/csv file and save as file.path
input_file_path <- file.path(file.choose()) # input_file_path <- file.path(params$file)
RAW SCORES
library(tidyverse) input_file_path <- "data/0099999_5_11_2024Biggie_S_FollowUp_ADHD-LD_Age14_scores.csv" test_name_prefix <- "RBANS Update Form A " output_file_path <- "data/processed_rbans_raw_data.csv" df <- read_csv(input_file_path, col_names = FALSE, show_col_types = FALSE, locale = locale(encoding = "UTF-16LE") ) # function pluck_rbans_raw <- function(input_file_path, test_name_prefix, output_file_path = NULL) { df <- read_csv( input_file_path, col_names = FALSE, show_col_types = FALSE, locale = locale(encoding = "UTF-16LE") ) # Rename the columns names(df) <- c("Subtest", "NA", "Raw score") # Remove the second column df <- df |> select(Subtest, `Raw score`) # Find the start of the "Raw Score" section start_line <- which(df == "RAW SCORES") + 1 # Find the stop of the "Raw Score" section stop_line <- which(df == "SCALED SCORES") - 1 # Read from the "Raw Score" section df_raw <- df |> dplyr::slice(start_line:stop_line) # Keep only rows with the specified prefix in the first column df_raw <- df_raw |> filter(str_starts(Subtest, test_name_prefix)) # Your new names stored in a character vector (ensure it matches the number of columns in `df`) vars <- c("scale", "raw_score") # Use `set_names()` to rename the columns df_raw <- df_raw |> set_names(vars) df_raw$scale <- as.character(df_raw$scale) df_raw$raw_score <- as.numeric(df_raw$raw_score) # Write the combined data to a CSV file write_csv(df_raw, output_file_path) return(df_raw) } rbans_raw <- pluck_rbans_raw(input_file_path, output_file_path = output_file_path, test_name_prefix)
SCALED SCORES
# input_file_path <- "data/004004907_3_10_2024.csv" # test_name_prefix <- "RBANS Update Form A " output_file_path <- "data/processed_rbans_scaled_data.csv" # function pluck_rbans_score <- function(input_file_path, test_name_prefix, output_file_path = NULL) { df <- read_csv( input_file_path, col_names = FALSE, show_col_types = FALSE, locale = locale(encoding = "UTF-16LE") ) # Rename the columns names(df) <- c("Subtest", "NA", "Scaled score") # Remove the second column df <- df |> select(Subtest, `Scaled score`) # Find the start of the "Raw Score" section start_line <- which(df == "SCALED SCORES") + 1 # Find the stop of the "Raw Score" section stop_line <- which(df == "CONTEXTUAL EVENTS") - 1 # Read from the "score" section df_score <- df |> dplyr::slice(start_line:stop_line) # Keep only rows with the specified prefix in the first column df_score <- df_score |> filter(str_starts(Subtest, test_name_prefix)) # Your new names stored in a character vector (ensure it matches the number of columns in `df`) vars <- c("scale", "score") # Use `set_names()` to rename the columns df_score <- df_score |> set_names(vars) df_score$scale <- as.character(df_score$scale) df_score$score <- as.numeric(df_score$score) # Write the combined data to a CSV file write_csv(df_score, output_file_path) return(df_score) } rbans_score <- pluck_rbans_score(input_file_path, test_name_prefix, output_file_path = output_file_path)
SUBTEST COMPLETION TIMES
# input_file_path <- "data/004004907_3_10_2024.csv" # test_name_prefix <- "RBANS Update Form A " output_file_path <- "data/processed_rbans_completion_time_data.csv" # function pluck_rbans_completion_times <- function(input_file_path, test_name_prefix, output_file_path = NULL) { df <- read_csv( input_file_path, col_names = FALSE, show_col_types = FALSE, locale = locale(encoding = "UTF-16LE") ) # Rename the columns names(df) <- c("Subtest", "NA", "Completion Time (seconds)") # Remove the second column df <- df |> select(Subtest, `Completion Time (seconds)`) # Find the start of the section start_line <- which(df == "SUBTEST COMPLETION TIMES") + 1 # Find the stop of the section stop_line <- which(df == "RULES TRIGGERED") - 1 # Read from the "Raw Score" section df_times <- df |> dplyr::slice(start_line:stop_line) # Keep only rows with the specified prefix in the first column df_times <- df_times |> filter(str_starts(Subtest, test_name_prefix)) # Your new names stored in a character vector (ensure it matches the number of columns in `df`) vars <- c("scale", "completion_time_seconds") # Use `set_names()` to rename the columns df_times <- df_times |> set_names(vars) df_times$scale <- as.character(df_times$scale) df_times$completion_time_seconds <- as.numeric(df_times$completion_time_seconds) # Write the combined data to a CSV file write_csv(df_times, output_file_path) return(df_times) } rbans_time <- pluck_rbans_completion_times(input_file_path, test_name_prefix, output_file_path = output_file_path)
COMPOSITE SCORES
# input_file_path <- "data/004004907_3_10_2024.csv" # test_name_prefix <- "RBANS Update Form A " output_file_path <- "data/processed_rbans_composite_data.csv" # function pluck_rbans_composite <- function(input_file_path, test_name_prefix, output_file_path = NULL) { df <- read_csv( input_file_path, col_names = FALSE, show_col_types = FALSE, locale = locale(encoding = "UTF-16LE") ) # Assume the first row after "Composite Score" has the column names start_line <- which(df$X1 == "Composite Score") # Assuming there's no specific end line, use the end of the file stop_line <- nrow(df) # Extracting the relevant section df_composite <- df |> slice((start_line + 1):stop_line) |> tidyr::separate( X3, sep = ",", into = c( "percentile", "ci_90_lo", "ci_90_up", "ci_95_lower", "ci_95_upper" ) ) |> slice(-1) |> rename(scale = X1, score = X2) |> # Filter based on the prefix filter(str_starts(scale, test_name_prefix)) |> select(-c(ci_90_lo, ci_90_up)) |> mutate( scale = as.character(scale), score = as.numeric(score), percentile = as.numeric(percentile), ci_95_lower = as.numeric(ci_95_lower), ci_95_upper = as.numeric(ci_95_upper) ) # Optionally write to a CSV file if (!is.null(output_file_path)) { write_csv(df_composite, output_file_path) } return(df_composite) } rbans_composite <- pluck_rbans_composite(input_file_path, test_name_prefix, output_file_path = output_file_path)
MERGE
#' Process and Save RBANS Data #' #' This function processes RBANS raw, score, and composite data frames by joining them, #' updating specific values, and saving the combined data to a CSV file. #' #' @param rbans_raw A data frame containing raw RBANS data. #' @param rbans_score A data frame containing RBANS scores. #' @param rbans_time A df containing completion times. #' @param rbans_composite A data frame containing RBANS composite scores. #' @param test_name_prefix A string representing the prefix to be removed from the scale names. #' @param output_file_path A string specifying the path to save the combined CSV file. #' @import dplyr #' @import stringr #' @export process_and_save_rbans_data <- function(rbans_raw, rbans_score, rbans_time, rbans_composite, test_name_prefix, output_file_path) { library(dplyr) library(stringr) # Join the data into one dataframe by the test name df <- left_join(rbans_raw, rbans_score, by = "scale") |> mutate(percentile = as.numeric(""), range = as.character("")) |> left_join(rbans_time, by = "scale") # Update specific percentile values df$percentile[df$scale == "RBANS Update Form A Line Orientation"] <- (params$pct1) df$percentile[df$scale == "RBANS Update Form A Picture Naming"] <- (params$pct2) df$percentile[df$scale == "RBANS Update Form A List Recall"] <- (params$pct3) df$percentile[df$scale == "RBANS Update Form A List Recognition"] <- (params$pct4) # Recalculate percentiles based on score df <- df |> mutate(z = ifelse(!is.na(score), (score - 10) / 3, NA)) |> mutate(percentile = ifelse(is.na(percentile), trunc(pnorm(z) * 100), percentile)) |> select(-z) # Merge with composite scores df <- bind_rows(df, rbans_composite) |> relocate(completion_time_seconds, .after = ci_95_upper) # Test score ranges (assuming bwu::gpluck_make_score_ranges is a predefined function) df <- bwu::gpluck_make_score_ranges(table = df, test_type = "npsych_test") # Remove prefix from scale names df <- df |> mutate(scale = str_remove(scale, test_name_prefix)) scales_to_rename <- c( "Immediate Memory Index (IMI)" = "Immediate Memory Index", "Visuospatial/ Constructional Index (VCI)" = "Visuospatial/Constructional Index", "Language Index (LGI)" = "Language Index", "Attention Index (ATI)" = "Attention Index", "Delayed Memory Index (DRI)" = "Delayed Memory Index", "Total Scale (TOT)" = "RBANS Total Index" ) df$scale <- map_chr(df$scale, ~ if_else(.x %in% names(scales_to_rename), scales_to_rename[.x], .x)) # Write the combined data to a CSV file return(write_csv(df, output_file_path)) } df <- process_and_save_rbans_data( rbans_raw = rbans_raw, rbans_score = rbans_score, rbans_time = rbans_time, rbans_composite = rbans_composite, test_name_prefix = test_name_prefix, output_file_path = output_file_path ) # Write the combined data to a CSV file output_file_path <- "data/rbans.csv" write_csv(df, output_file_path)
MUTATE
rbans <- df rbans <- bwu::gpluck_make_columns( data = rbans, test = params$test, test_name = params$test_name, ci_95 = paste0(rbans$ci_95_lower, "-", rbans$ci_95_upper), domain = "", subdomain = "", narrow = "", pass = "", verbal = "", timed = "", test_type = "npsych_test", score_type = "", description = "", result = "" )
Domain
rbans <- rbans |> dplyr::mutate( domain = dplyr::case_when( scale == "RBANS Total Index" ~ "General Cognitive Ability", scale == "Immediate Memory Index" ~ "Memory", scale == "List Learning" ~ "Memory", scale == "Story Memory" ~ "Memory", scale == "Visuospatial/Constructional Index" ~ "Visual Perception/Construction", scale == "Figure Copy" ~ "Visual Perception/Construction", scale == "Line Orientation" ~ "Visual Perception/Construction", scale == "Language Index" ~ "Verbal/Language", scale == "Picture Naming" ~ "Verbal/Language", scale == "Semantic Fluency" ~ "Verbal/Language", scale == "Attention Index" ~ "Attention/Executive", scale == "Digit Span" ~ "Attention/Executive", scale == "Coding" ~ "Attention/Executive", scale == "Delayed Memory Index" ~ "Memory", scale == "List Recall" ~ "Memory", scale == "List Recognition" ~ "Memory", scale == "Story Recall" ~ "Memory", scale == "Figure Recall" ~ "Memory", TRUE ~ domain ) )
Subdomain
rbans <- rbans |> dplyr::mutate( subdomain = dplyr::case_when( scale == "RBANS Total Index" ~ "Neuropsychological Functioning", scale == "Immediate Memory Index" ~ "Neuropsychological Functioning", scale == "List Learning" ~ "Learning Efficiency", scale == "Story Memory" ~ "Learning Efficiency", scale == "Visuospatial/Constructional Index" ~ "Neuropsychological Functioning", scale == "Figure Copy" ~ "Organization", scale == "Line Orientation" ~ "Perception", scale == "Language Index" ~ "Neuropsychological Functioning", scale == "Picture Naming" ~ "Retrieval", scale == "Semantic Fluency" ~ "Fluency", scale == "Attention Index" ~ "Neuropsychological Functioning", scale == "Digit Span" ~ "Attention", scale == "Coding" ~ "Processing Speed", scale == "Delayed Memory Index" ~ "Neuropsychological Functioning", scale == "List Recall" ~ "Delayed Recall", scale == "List Recognition" ~ "Recognition Memory", scale == "Story Recall" ~ "Delayed Recall", scale == "Figure Recall" ~ "Delayed Recall", TRUE ~ subdomain ) )
Narrow
rbans <- rbans |> mutate( narrow = case_when( scale == "RBANS Total Index" ~ "RBANS Total Index", scale == "Immediate Memory Index" ~ "RBANS Memory Index", scale == "List Learning" ~ "Word-List Learning", scale == "Story Memory" ~ "Story Memory", scale == "Visuospatial/Constructional Index" ~ "RBANS Visuospatial/Constructional Index", scale == "Figure Copy" ~ "Figure Copy", scale == "Line Orientation" ~ "Visual Perception", scale == "Language Index" ~ "RBANS Language Index", scale == "Picture Naming" ~ "Naming", scale == "Semantic Fluency" ~ "Semantic Fluency", scale == "Attention Index" ~ "RBANS Attention Index", scale == "Digit Span" ~ "Attention Span", scale == "Coding" ~ "Cognitive Efficiency", scale == "Delayed Memory Index" ~ "RBANS Memory Index", scale == "List Recall" ~ "Word-List Learning", scale == "List Recognition" ~ "Recognition Memory", scale == "Story Recall" ~ "Story Memory", scale == "Figure Recall" ~ "Visual Memory", TRUE ~ narrow ) )
Timed/Untimed
rbans <- rbans |> mutate( timed = case_when( scale == "RBANS Total Index" ~ "", scale == "Immediate Memory Index" ~ "Untimed", scale == "List Learning" ~ "Untimed", scale == "Story Memory" ~ "Untimed", scale == "Visuospatial/Constructional Index" ~ "Untimed", scale == "Figure Copy" ~ "Untimed", scale == "Line Orientation" ~ "Untimed", scale == "Language Index" ~ "", scale == "Picture Naming" ~ "Untimed", scale == "Semantic Fluency" ~ "Timed", scale == "Attention Index" ~ "", scale == "Digit Span" ~ "Untimed", scale == "Coding" ~ "Timed", scale == "Delayed Memory Index" ~ "Untimed", scale == "List Recall" ~ "Untimed", scale == "List Recognition" ~ "Untimed", scale == "Story Recall" ~ "Untimed", scale == "Figure Recall" ~ "Untimed", TRUE ~ timed ) )
Verbal/Nonverbal
rbans <- rbans |> mutate( verbal = case_when( scale == "RBANS Total Index" ~ "", scale == "Immediate Memory Index" ~ "Verbal", scale == "List Learning" ~ "Verbal", scale == "Story Memory" ~ "Verbal", scale == "Visuospatial/Constructional Index" ~ "Nonverbal", scale == "Figure Copy" ~ "Nonverbal", scale == "Line Orientation" ~ "Nonverbal", scale == "Language Index" ~ "Verbal", scale == "Picture Naming" ~ "Verbal", scale == "Semantic Fluency" ~ "Verbal", scale == "Attention Index" ~ "", scale == "Digit Span" ~ "Verbal", scale == "Coding" ~ "Nonverbal", scale == "Delayed Memory Index" ~ "", scale == "List Recall" ~ "Verbal", scale == "List Recognition" ~ "Verbal", scale == "Story Recall" ~ "Verbal", scale == "Figure Recall" ~ "Nonverbal", TRUE ~ verbal ) )
PASS
rbans <- rbans |> mutate( pass = case_when( scale == "RBANS Total Index" ~ "", scale == "Immediate Memory Index" ~ "Sequential", scale == "List Learning" ~ "Sequential", scale == "Story Memory" ~ "Sequential", scale == "Visuospatial/Constructional Index" ~ "Simultaneous", scale == "Figure Copy" ~ "Simultaneous", scale == "Line Orientation" ~ "Simultaneous", scale == "Language Index" ~ "Sequential", scale == "Picture Naming" ~ "Knowledge", scale == "Semantic Fluency" ~ "Sequential", scale == "Attention Index" ~ "Attention", scale == "Digit Span" ~ "Attention", scale == "Coding" ~ "Planning", scale == "Delayed Memory Index" ~ "", scale == "List Recall" ~ "Sequential", scale == "List Recognition" ~ "Sequential", scale == "Story Recall" ~ "Sequential", scale == "Figure Recall" ~ "Simultaneous", TRUE ~ as.character(pass) ) )
Score type
rbans <- rbans |> mutate( score_type = case_when( scale == "RBANS Total Index" ~ "standard_score", scale == "Immediate Memory Index" ~ "standard_score", scale == "List Learning" ~ "scaled_score", scale == "Story Memory" ~ "scaled_score", scale == "Visuospatial/Constructional Index" ~ "standard_score", scale == "Figure Copy" ~ "scaled_score", scale == "Line Orientation" ~ "percentile", scale == "Language Index" ~ "standard_score", scale == "Picture Naming" ~ "percentile", scale == "Semantic Fluency" ~ "scaled_score", scale == "Attention Index" ~ "standard_score", scale == "Digit Span" ~ "scaled_score", scale == "Coding" ~ "scaled_score", scale == "Delayed Memory Index" ~ "standard_score", scale == "List Recall" ~ "percentile", scale == "List Recognition" ~ "percentile", scale == "Story Recall" ~ "scaled_score", scale == "Figure Recall" ~ "scaled_score", TRUE ~ as.character(score_type) ) )
Descriptions
rbans <- rbans |> mutate( description = case_when( scale == "RBANS Total Index" ~ "composite indicator of general cognitive functioning", scale == "Immediate Memory Index" ~ "composite verbal learning of a word list and a logical story", scale == "List Learning" ~ "word list learning", scale == "Story Memory" ~ "expository story learning", scale == "Visuospatial/Constructional Index" ~ "broad visuospatial processing", scale == "Figure Copy" ~ "copy of a complex abstract figure", scale == "Line Orientation" ~ "basic perception of visual stimuli", scale == "Language Index" ~ "general language processing", scale == "Picture Naming" ~ "confrontation naming/expressive vocabulary", scale == "Semantic Fluency" ~ "semantic word fluency/generativity", scale == "Attention Index" ~ "general attentional and executive functioning", scale == "Digit Span" ~ "attention span and auditory attention", scale == "Coding" ~ "speed of information processing", scale == "Delayed Memory Index" ~ "long-term recall of verbal information", scale == "List Recall" ~ "long-term recall of a word list", scale == "List Recognition" ~ "delayed recognition of a word list", scale == "Story Recall" ~ "long-term recall of a detailed story", scale == "Figure Recall" ~ "long-term recall and reconstruction of a complex abstract figure", TRUE ~ as.character(description) ) )
Glue results
rbans <- rbans |> dplyr::mutate( result = glue::glue( "{patient}'s score on {.data$scale} ({.data$description}) was {.data$range}." ) )
EXPORT
readr::write_csv(rbans, here::here("data", "rbans.csv"), col_names = TRUE, na = "")
cat("Finished!")
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