R Markdown to Check for Correspondence between Differently Worded Versions of the Same Scale Item.Rmd

---
title: "Harmony Prelim Review of Item Correspondence for Github"
author: "Deanna Varley"
date: "2024-11-20"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

#-------------------------Description of This Script----------------------------

#This script uses the harmonydata package to check for semantic similarity/
#correspondence between different wordings of the same item from the same scale
#where different wordings were used across different surveys/datasets.

#----------------------Preparation and Loading Packages-------------------------
#If necessary, uncomment and install the below packages:
#install.packages("devtools")
#devtools::install_github("harmonydata/harmony_r")
#install.packages(tidyverse)
#install.packages(dplyr)
#install.packages(tidyr)
#install.packages(knitr)
#install.packages(kableExtra)
#install.packages(flextable)
#install.packages(readxl)
#library(devtools)

#Load the required packages:
library(harmonydata)
library(tidyverse)
library(dplyr)
library(tidyr)
library(knitr)
library(kableExtra)
library(flextable)
library(readxl)

harmonydata::set_url()

#----------------------Set Working Drive, Load Data-----------------------------
#Set working drive:
setwd("~/Data Harmonisation Project/Content Analysis")

#Load data
dat1 <- read_xlsx("Survey Items.xlsx") #The xlsx file is a list of survey items.

#Add an ID column to the dataframe
dat1$id<-as.character(seq(1,nrow(dat1)))
```


```{r Checking Item Correspondence, echo=FALSE}

#----------------------Checking Item Correspondence-----------------------------

#The code below creates a function that checks for correspondence between 
#differently worded versions of the same item and then returns the mean and 
#range of the cosine similarity indices for all the pairs of differently worded
#versions of the same survey item.

#Creating function:
check_correspondence <- function(scale_name, item_number) {
  # Filter data for the given scale and item number
  nested_list <- dat1 %>%
    filter(Scale == scale_name & `Item Number` == item_number) %>%
    group_by(Name) %>%
    summarise(
      instrument_name = first(Name),
      questions = list(
        lapply(1:n(), function(i) {
          list(
            question_no = id[i],
            question_text = Items[i]
          )
        })
      ),
      .groups = 'drop'
    ) %>%
    # Convert to list of lists
    mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
    pull(nested)
  
  # Perform the match using harmony_r package
  match <- match_instruments(nested_list)
  
  # Extract the matrix of similarity values
  df <- data.frame(match$matches[[1]])
  for (x in 1:length(match$matches)) {
    df[x, ] <- match$matches[[x]]
  }
  
  # Compute correlation values
  matrix_df <- as.matrix(df)
  cor_values <- matrix_df[lower.tri(matrix_df, diag = FALSE)]
  
  # Calculate mean and range
  item_mean <- mean(cor_values)
  item_range <- range(cor_values)
  
  return(list(mean = item_mean, range = item_range))
}

#Creating function to process all items of a scale and return results in a table
#Note: This function won't work for scales that need to pull corresponding 
#items from multiple scales (e.g., the K10 as it has items that correspond to 
#K6 and K5 items).
process_scale <- function(scale_name, total_items) {
  results <- data.frame(
    Item = 1:total_items,
    Mean = NA,
    `Range (Min)` = NA,
    `Range (Max)` = NA
  )
  
  for (item_number in 1:total_items) {
    res <- check_correspondence(scale_name, item_number)
    results$Mean[item_number] <- res$mean
    results$`Range (Min)`[item_number] <- res$range[1]
    results$`Range (Max)`[item_number] <- res$range[2]
  }
  
  return(results)
}

process_scale <- function(scale_name, total_items) {
  results <- data.frame(
    Item = 1:total_items,
    Mean = NA,
    `Range (Min)` = NA,
    `Range (Max)` = NA,
    check.names = FALSE
  )
  
  for (item_number in 1:total_items) {
    res <- check_correspondence(scale_name, item_number)
    results$Mean[item_number] <- res$mean
    results$`Range (Min)`[item_number] <- res$range[1]
    results$`Range (Max)`[item_number] <- res$range[2]
  }
  
  #Construct the object name by concatenating scale_name and "_results"
  object_name <- paste0(scale_name, "_results")
  
  #Assign the results data frame to an object in the environment
  assign(object_name, results, envir = .GlobalEnv)
  
  return(results)
}

#The code below creates a list of scales and their item counts (i.e., the number
#of items in each scale), then creates a loop that runs the 'process_scale'
#function for each scale in the list.

#Define the scales and their item counts (i.e., the number of items in each scale):
#Note that the list doesn't include scales that have multiple versions 
#(e.g., K10, K6 and K5) as these require different code to pull items from 
#multiple scales. These are coded separately below in separate code chunks.
scales <- list(
  "GHQ-12" = 12, 
  "SDQ" = 25,
  "CES-D" = 20,
  "GADS" = 9 
)

#Loop over each scale in the scales list:
for (scale_name in names(scales)) {
  total_items <- scales[[scale_name]]  #Get the number of items for the scale
  process_scale(scale_name, total_items)  #Call the function for each scale
}

#Create and display the results as tables:
set_flextable_defaults(
  font.family = "Times New Roman", font.size = 12, 
  border.color = "black", big.mark = "", border.width = .5)

table1 <- flextable(`GHQ-12_results`) %>% 
  bold(part = "header") %>% autofit() %>%
  set_table_properties(width = 1.0, layout = "autofit") %>%
  set_caption(caption = "Mean and Range of Item Correspondence Values for the GHQ-12")
table1

table2 <- flextable(`PHQ-9_results`) %>% 
  bold(part = "header") %>% autofit() %>%
  set_table_properties(width = 1.0, layout = "autofit") %>%
  set_caption(caption = "Mean and Range of Item Correspondence Values for the PHQ-9")
table2

table3 <- flextable(`PHQ-9 Modified (PHQ-9M)_results`) %>% 
  bold(part = "header") %>% autofit() %>%
  set_table_properties(width = 1.0, layout = "autofit") %>%
  set_caption(caption = "Mean and Range of Item Correspondence Values for the PHQ-9 Modified")
table3

table4 <- flextable(`SDQ_results`) %>% 
  bold(part = "header") %>% autofit() %>%
  set_table_properties(width = 1.0, layout = "autofit") %>%
  set_caption(caption = "Mean and Range of Item Correspondence Values for the SDQ")
table4

table5 <- flextable(`CES-D_results`) %>% 
  bold(part = "header") %>% autofit() %>%
  set_table_properties(width = 1.0, layout = "autofit") %>%
  set_caption(caption = "Mean and Range of Item Correspondence Values for the CES-D")
table5

table6 <- flextable(`GADS_results`) %>% 
  bold(part = "header") %>% autofit() %>%
  set_table_properties(width = 1.0, layout = "autofit") %>%
  set_caption(caption = "Mean and Range of Item Correspondence Values for the GADS")
table6

```

```{r Checking Item Correspondence for K10, K6 and K5, echo = FALSE}

#-------------Checking Item Correspondence for K10, K6 and K5-------------------

#The below code chunk will check the item correspondence for the K10 items,
#along with corresponding items from the K6 and the K5. 

#Creating an empty dataframe that will later be used to store the results:
K10_results <- data.frame(
    Item = 1:10,
    Mean = NA,
    `Range (Min)` = NA,
    `Range (Max)` = NA,
    check.names = FALSE
  )

#Checking item correspondence for K10 item 1:
#First create a nested list of all K10 item 1 items:
nested_list_k101 <- dat1 %>%
  filter(Scale=="K10" & `Item Number`==1) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)


match = match_instruments(nested_list_k101)


df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
K10_results[1,"Mean"] <- mean(cor_values)
range<-range(cor_values)
K10_results[1,"Range (Min)"] <- range[1]
K10_results[1,"Range (Max)"] <- range[2]

#K10 item 2
nested_list_k102 <- dat1 %>%
  filter((Scale=="K10" & `Item Number`==2) | (Scale=="K6" & `Item Number`==1) | (Scale=="K5" & `Item Number`==1)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)


match = match_instruments(nested_list_k102)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
K10_results[2,"Mean"] <- mean(cor_values)
range<-range(cor_values)
K10_results[2,"Range (Min)"] <- range[1]
K10_results[2,"Range (Max)"] <- range[2]

#K10 item 3
nested_list_k103 <- dat1 %>%
  filter(Scale=="K10" & `Item Number`==3) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)


match = match_instruments(nested_list_k103)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
K10_results[3,"Mean"] <- mean(cor_values)
range<-range(cor_values)
K10_results[3,"Range (Min)"] <- range[1]
K10_results[3,"Range (Max)"] <- range[2]

#K10 item 4
nested_list_k104 <- dat1 %>%
  filter((Scale=="K10" & `Item Number`==4) | (Scale=="K6" & `Item Number`==2) | (Scale=="K5" & `Item Number`==2)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)


match = match_instruments(nested_list_k104)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
K10_results[4,"Mean"] <- mean(cor_values)
range<-range(cor_values)
K10_results[4,"Range (Min)"] <- range[1]
K10_results[4,"Range (Max)"] <- range[2]

#K10 item 5
nested_list_k105 <- dat1 %>%
  filter((Scale=="K10" & `Item Number`==5) | (Scale=="K6" & `Item Number`==3) | (Scale=="K5" & `Item Number`==3)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)


match = match_instruments(nested_list_k105)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
K10_results[5,"Mean"] <- mean(cor_values)
range<-range(cor_values)
K10_results[5,"Range (Min)"] <- range[1]
K10_results[5,"Range (Max)"] <- range[2]

#K10 item 6
nested_list_k106 <- dat1 %>%
  filter(Scale=="K10" & `Item Number`==6) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)


match = match_instruments(nested_list_k106)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
K10_results[6,"Mean"] <- mean(cor_values)
range<-range(cor_values)
K10_results[6,"Range (Min)"] <- range[1]
K10_results[6,"Range (Max)"] <- range[2]

## Nested list of just the Kessler 10 item 7
nested_list_k107 <- dat1 %>%
  filter(Scale=="K10" & `Item Number`==7) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)


match = match_instruments(nested_list_k107)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
K10_results[7,"Mean"] <- mean(cor_values)
range<-range(cor_values)
K10_results[7,"Range (Min)"] <- range[1]
K10_results[7,"Range (Max)"] <- range[2]

#K10 item 8
nested_list_k108 <- dat1 %>%
  filter((Scale=="K10" & `Item Number`==8) | (Scale=="K6" & `Item Number`==4) | (Scale=="K5" & `Item Number`==4)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_k108)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
K10_results[8,"Mean"] <- mean(cor_values)
range<-range(cor_values)
K10_results[8,"Range (Min)"] <- range[1]
K10_results[8,"Range (Max)"] <- range[2]

#K10 Item 9
nested_list_k109 <- dat1 %>%
  filter((Scale=="K10" & `Item Number`==9) | (Scale=="K6" & `Item Number`==5) | (Scale=="K5" & `Item Number`==5)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)


match = match_instruments(nested_list_k109)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
K10_results[9,"Mean"] <- mean(cor_values)
range<-range(cor_values)
K10_results[9,"Range (Min)"] <- range[1]
K10_results[9,"Range (Max)"] <- range[2]

#K10 item 10
nested_list_k1010 <- dat1 %>%
  filter((Scale=="K10" & `Item Number`==10) | (Scale=="K6" & `Item Number`==6)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_k1010)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
K10_results[10,"Mean"] <- mean(cor_values)
range<-range(cor_values)
K10_results[10,"Range (Min)"] <- range[1]
K10_results[10,"Range (Max)"] <- range[2]

#Create and display the results as a table:
set_flextable_defaults(
  font.family = "Times New Roman", font.size = 12, 
  border.color = "black", big.mark = "", border.width = .5)

table7 <- flextable(`K10_results`) %>% 
  bold(part = "header") %>% autofit() %>%
  set_table_properties(width = 1.0, layout = "autofit") %>%
  set_caption(caption = "Mean and Range of Item Correspondence Values for the K10, K6 and K5")
table7

```

``` {r Checking Item Correspondence for SF-36 Mental Health Focused Items, echo = FALSE}

#---------Checking Item Correspondence for SF-36, SF-12 and SF-8----------------

#The below code chunk will check the item correspondence for the SF-36 items
#that are focused on mental health but excluding items focused on physical
#health. This code also includes items from the SF-12 and SF-8 that match items
#from the SF-36 when checking for item correspondence.
#Items from the SF-36 that will be included: 17, 18, 19, 20, 23, 24, 25, 26,
# 27, 28, 29, 30, 31, 32.
#Note: The MHI-5 is comprised of items 24, 25, 26, 28 and 30 of the SF-36.

#Creating an empty dataframe that will later be used to store the results:
SF36_results <- data.frame(
    Item = c(17,18,19,20,23,24,25,26,27,28,29,30,31,32),
    Mean = NA,
    `Range (Min)` = NA,
    `Range (Max)` = NA,
    check.names = FALSE
  )

#Checking item correspondence for SF-36 item 17:
#First create a nested list of all versions of SF-36 item 17:
nested_list_sf3617 <- dat1 %>%
  filter(Scale=="SF-36" & `Item Number`==17 ) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3617)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[1,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[1,"Range (Min)"] <- range[1]
SF36_results[1,"Range (Max)"] <- range[2]

#SF-36 item 18 and matching SF-12 item 6:
nested_list_sf3618 <- dat1 %>%
  filter((Scale=="SF-36" & `Item Number`==18) | (Scale=="SF-12" & `Item Number`==6)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3618)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[2,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[2,"Range (Min)"] <- range[1]
SF36_results[2,"Range (Max)"] <- range[2]

#SF-36 item 19 and matching SF-12 item 7:
nested_list_sf3619 <- dat1 %>%
  filter((Scale=="SF-36" & `Item Number`==19) | (Scale=="SF-12" & `Item Number`==7)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3619)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[3,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[3,"Range (Min)"] <- range[1]
SF36_results[3,"Range (Max)"] <- range[2]

#SF-36 item 20 and corresponding SF-12 item 12 and SF-8 item 6:
nested_list_sf3620 <- dat1 %>%
  filter((Scale=="SF-36" & `Item Number`==20) | (Scale=="SF-12" & `Item Number`==12) | (Scale=="SF-8" & `Item Number`==6)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3620)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[4,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[4,"Range (Min)"] <- range[1]
SF36_results[4,"Range (Max)"] <- range[2]

#SF-36 item 23:
nested_list_sf3623 <- dat1 %>%
  filter(Scale=="SF-36" & `Item Number`==23) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3623)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[5,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[5,"Range (Min)"] <- range[1]
SF36_results[5,"Range (Max)"] <- range[2]

#SF-36 item 24:
nested_list_sf3624 <- dat1 %>%
  filter(Scale=="SF-36" & `Item Number`==24) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3624)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[6,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[6,"Range (Min)"] <- range[1]
SF36_results[6,"Range (Max)"] <- range[2]

#SF-36 item 25:
nested_list_sf3625 <- dat1 %>%
  filter(Scale=="SF-36" & `Item Number`==25) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3625)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[7,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[7,"Range (Min)"] <- range[1]
SF36_results[7,"Range (Max)"] <- range[2]

#SF-36 item 26 and corresponding SF-12 item 9:
nested_list_sf3626 <- dat1 %>%
  filter((Scale=="SF-36" & `Item Number`==26) | (Scale=="SF-12" & `Item Number`==9)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3626)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[8,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[8,"Range (Min)"] <- range[1]
SF36_results[8,"Range (Max)"] <- range[2]

#SF-36 item 27 and corresponding SF-12 item 10:
nested_list_sf3627 <- dat1 %>%
  filter((Scale=="SF-36" & `Item Number`==27) | (Scale=="SF-12" & `Item Number`==10)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3627)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[9,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[9,"Range (Min)"] <- range[1]
SF36_results[9,"Range (Max)"] <- range[2]

#SF-36 item 28 and corresponding SF-12 item 11:
nested_list_sf3628 <- dat1 %>%
  filter((Scale=="SF-36" & `Item Number`==28) | (Scale=="SF-12" & `Item Number`==11)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3628)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[10,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[10,"Range (Min)"] <- range[1]
SF36_results[10,"Range (Max)"] <- range[2]

#SF-36 item 29:
nested_list_sf3629 <- dat1 %>%
  filter(Scale=="SF-36" & `Item Number`==29) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3629)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[11,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[11,"Range (Min)"] <- range[1]
SF36_results[11,"Range (Max)"] <- range[2]

#SF-36 item 30:
nested_list_sf3630 <- dat1 %>%
  filter(Scale=="SF-36" & `Item Number`==30) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3630)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[12,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[12,"Range (Min)"] <- range[1]
SF36_results[12,"Range (Max)"] <- range[2]

#SF-36 item 31:
nested_list_sf3631 <- dat1 %>%
  filter(Scale=="SF-36" & `Item Number`==31) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3631)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[13,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[13,"Range (Min)"] <- range[1]
SF36_results[13,"Range (Max)"] <- range[2]

#SF-36 item 32 and corresponding SF-12 item 12:
nested_list_sf3632 <- dat1 %>%
  filter((Scale=="SF-36" & `Item Number`==32) | (Scale=="SF-12" & `Item Number`==12)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_sf3632)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SF36_results[14,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SF36_results[14,"Range (Min)"] <- range[1]
SF36_results[14,"Range (Max)"] <- range[2]

#Create and display the results as a table:
set_flextable_defaults(
  font.family = "Times New Roman", font.size = 12, 
  border.color = "black", big.mark = "", border.width = .5)

table8 <- flextable(`SF36_results`) %>% 
  bold(part = "header") %>% autofit() %>%
  set_table_properties(width = 1.0, layout = "autofit") %>%
  set_caption(caption = "Mean and Range of Item Correspondence Values for the SF-36, SF-12 and SF-8")
table8

```

``` {r Checking for Item Correspondence for PHQ-9 and PHQ-9M, echo = FALSE}

#------------Checking Item Correspondence for PHQ-9 and PHQ-9M------------------

#The below code chunk will check the item correspondence for the PHQ9 items,
#along with corresponding items from the PHQ-9M (Modified for Teens). 

#Creating an empty dataframe that will later be used to store the results:
PHQ9_results <- data.frame(
    Item = 1:10,
    Mean = NA,
    `Range (Min)` = NA,
    `Range (Max)` = NA,
    check.names = FALSE
  )

#Checking item correspondence for PHQ-9 item 1 and corresponding PHQ-9M item 2:
#First create a nested list of all relevant items:
nested_list_phq91 <- dat1 %>%
  filter((Scale=="PHQ-9" & `Item Number`==1) | (Scale=="PHQ-9 Modified (PHQ-9M)" & `Item Number`==2)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_phq91)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
PHQ9_results[1,"Mean"] <- mean(cor_values)
range<-range(cor_values)
PHQ9_results[1,"Range (Min)"] <- range[1]
PHQ9_results[1,"Range (Max)"] <- range[2]

#PHQ9 item 2 and corresponding PHQ9M item 1:
nested_list_phq92 <- dat1 %>%
  filter((Scale=="PHQ-9" & `Item Number`==2) | (Scale=="PHQ-9 Modified (PHQ-9M)" & `Item Number`==1)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_phq92)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
PHQ9_results[2,"Mean"] <- mean(cor_values)
range<-range(cor_values)
PHQ9_results[2,"Range (Min)"] <- range[1]
PHQ9_results[2,"Range (Max)"] <- range[2]

#PHQ9 item 3 and corresponding PHQ9M item 3:
nested_list_phq93 <- dat1 %>%
  filter((Scale=="PHQ-9" & `Item Number`==3) | (Scale=="PHQ-9 Modified (PHQ-9M)" & `Item Number`==3)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_phq93)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
PHQ9_results[3,"Mean"] <- mean(cor_values)
range<-range(cor_values)
PHQ9_results[3,"Range (Min)"] <- range[1]
PHQ9_results[3,"Range (Max)"] <- range[2]

#PHQ9 item 4 and corresponding PHQ9M item 5:
nested_list_phq94 <- dat1 %>%
  filter((Scale=="PHQ-9" & `Item Number`==4) | (Scale=="PHQ-9 Modified (PHQ-9M)" & `Item Number`==5)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_phq94)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
PHQ9_results[4,"Mean"] <- mean(cor_values)
range<-range(cor_values)
PHQ9_results[4,"Range (Min)"] <- range[1]
PHQ9_results[4,"Range (Max)"] <- range[2]

#PHQ9 item 5 and corresponding PHQ9M item 4:
nested_list_phq95 <- dat1 %>%
  filter((Scale=="PHQ-9" & `Item Number`==5) | (Scale=="PHQ-9 Modified (PHQ-9M)" & `Item Number`==4)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_phq95)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
PHQ9_results[5,"Mean"] <- mean(cor_values)
range<-range(cor_values)
PHQ9_results[5,"Range (Min)"] <- range[1]
PHQ9_results[5,"Range (Max)"] <- range[2]

#PHQ9 item 6 and corresponding PHQ9M item 6:
nested_list_phq96 <- dat1 %>%
  filter((Scale=="PHQ-9" & `Item Number`==6) | (Scale=="PHQ-9 Modified (PHQ-9M)" & `Item Number`==6)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_phq96)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
PHQ9_results[6,"Mean"] <- mean(cor_values)
range<-range(cor_values)
PHQ9_results[6,"Range (Min)"] <- range[1]
PHQ9_results[6,"Range (Max)"] <- range[2]

#PHQ9 item 7 and corresponding PHQ9M item 7:
nested_list_phq97 <- dat1 %>%
  filter((Scale=="PHQ-9" & `Item Number`==7) | (Scale=="PHQ-9 Modified (PHQ-9M)" & `Item Number`==7)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_phq97)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
PHQ9_results[7,"Mean"] <- mean(cor_values)
range<-range(cor_values)
PHQ9_results[7,"Range (Min)"] <- range[1]
PHQ9_results[7,"Range (Max)"] <- range[2]

#PHQ9 item 8 and corresponding PHQ9M item 8:
nested_list_phq98 <- dat1 %>%
  filter((Scale=="PHQ-9" & `Item Number`==8) | (Scale=="PHQ-9 Modified (PHQ-9M)" & `Item Number`==8)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_phq98)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
PHQ9_results[8,"Mean"] <- mean(cor_values)
range<-range(cor_values)
PHQ9_results[8,"Range (Min)"] <- range[1]
PHQ9_results[8,"Range (Max)"] <- range[2]

#PHQ9 item 9 and corresponding PHQ9M item 9:
nested_list_phq99 <- dat1 %>%
  filter((Scale=="PHQ-9" & `Item Number`==9) | (Scale=="PHQ-9 Modified (PHQ-9M)" & `Item Number`==9)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_phq99)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
PHQ9_results[9,"Mean"] <- mean(cor_values)
range<-range(cor_values)
PHQ9_results[9,"Range (Min)"] <- range[1]
PHQ9_results[9,"Range (Max)"] <- range[2]

#PHQ9 item 10 and corresponding PHQ9M item 10:
nested_list_phq910 <- dat1 %>%
  filter((Scale=="PHQ-9" & `Item Number`==10) | (Scale=="PHQ-9 Modified (PHQ-9M)" & `Item Number`==10)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_phq910)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
PHQ9_results[10,"Mean"] <- mean(cor_values)
range<-range(cor_values)
PHQ9_results[10,"Range (Min)"] <- range[1]
PHQ9_results[10,"Range (Max)"] <- range[2]

#Create and display the results as a table:
set_flextable_defaults(
  font.family = "Times New Roman", font.size = 12, 
  border.color = "black", big.mark = "", border.width = .5)

table9 <- flextable(`PHQ9_results`) %>% 
  bold(part = "header") %>% autofit() %>%
  set_table_properties(width = 1.0, layout = "autofit") %>%
  set_caption(caption = "Mean and Range of Item Correspondence Values for the PHQ-9 and PHQ-9M")
table9

```

``` {r Checking Item Correspondence for SCAS 10 to 14 yrs and 15 to 17 yrs, echo = FALSE}

#-------Checking Item Correspondence for SCAS 10-14yrs and SCAS 15-17yrs--------

#The below code chunk will check the item correspondence for the SCAS items
#from both versions of the SCAS (10-14 years and 15-17 years)
#Note that it will only be possible to calculate correspondence for items that
#match between the two scales, as the other items do not have more than one
#version that occurs across all the surveys/datasets we identified, so the
#correspondence for those items would be impossible to calculate.
#This is the case for 4 items from the SCAS (15-17 years) that are not present
#in the SCAS (10-14 years).

#Creating an empty dataframe that will later be used to store the results:
SCAS_results <- data.frame(
    Item = 1:6,
    Mean = NA,
    `Range (Min)` = NA,
    `Range (Max)` = NA,
    check.names = FALSE
  )

#Checking item correspondence for SCAS 10-14 item 1 and SCAS 15-17 item 1:
#First create a nested list of all relevant items:
nested_list_scas1 <- dat1 %>%
  filter((Scale=="SCAS - 10-14 years" & `Item Number`==1) | (Scale=="SCAS - 15-17 years" & `Item Number`==1)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_scas1)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SCAS_results[1,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SCAS_results[1,"Range (Min)"] <- range[1]
SCAS_results[1,"Range (Max)"] <- range[2]

#SCAS 10-14 item 2 and SCAS 15-17 item 2:
nested_list_scas2 <- dat1 %>%
  filter((Scale=="SCAS - 10-14 years" & `Item Number`==2) | (Scale=="SCAS - 15-17 years" & `Item Number`==2)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_scas2)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SCAS_results[2,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SCAS_results[2,"Range (Min)"] <- range[1]
SCAS_results[2,"Range (Max)"] <- range[2]

#SCAS 10-14 item 3 and SCAS 15-17 item 3:
nested_list_scas3 <- dat1 %>%
  filter((Scale=="SCAS - 10-14 years" & `Item Number`==3) | (Scale=="SCAS - 15-17 years" & `Item Number`==3)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_scas3)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SCAS_results[3,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SCAS_results[3,"Range (Min)"] <- range[1]
SCAS_results[3,"Range (Max)"] <- range[2]

#SCAS 10-14 item 4 and SCAS 15-17 item 8:
nested_list_scas4 <- dat1 %>%
  filter((Scale=="SCAS - 10-14 years" & `Item Number`==4) | (Scale=="SCAS - 15-17 years" & `Item Number`==8)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_scas4)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SCAS_results[4,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SCAS_results[4,"Range (Min)"] <- range[1]
SCAS_results[4,"Range (Max)"] <- range[2]

#SCAS 10-14 item 5 and SCAS 15-17 item 9:
nested_list_scas5 <- dat1 %>%
  filter((Scale=="SCAS - 10-14 years" & `Item Number`==5) | (Scale=="SCAS - 15-17 years" & `Item Number`==9)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_scas5)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SCAS_results[5,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SCAS_results[5,"Range (Min)"] <- range[1]
SCAS_results[5,"Range (Max)"] <- range[2]

#SCAS 10-14 item 6 and SCAS 15-17 item 10:
nested_list_scas6 <- dat1 %>%
  filter((Scale=="SCAS - 10-14 years" & `Item Number`==6) | (Scale=="SCAS - 15-17 years" & `Item Number`==10)) %>%
  group_by(Name) %>%
  summarise(
    instrument_name = first(Name),  # Get the study name as instrument_name
    questions = list(
      lapply(1:n(), function(i) {
        list(
          question_no = id[i],
          question_text = Items[i]
        )
      })
    ),
    .groups = 'drop'
  ) %>%
  # Convert to list of lists
  mutate(nested = map2(instrument_name, questions, ~ list(instrument_name = .x, questions = .y))) %>%
  pull(nested)

match = match_instruments(nested_list_scas6)

df <- data.frame(match$matches[[1]])
for (x in 1:length(match$matches)) {
  df[x, ] = match$matches[[x]]
}
colnames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )
rownames(df) <- lapply(match$questions, function(x) paste(x$question_no,  x$question_text , sep=" ") )

matrix_df <-as.matrix(df)
cor_values<-matrix_df[lower.tri(matrix_df, diag=FALSE)]
SCAS_results[6,"Mean"] <- mean(cor_values)
range<-range(cor_values)
SCAS_results[6,"Range (Min)"] <- range[1]
SCAS_results[6,"Range (Max)"] <- range[2]

#Create and display the results as a table:
set_flextable_defaults(
  font.family = "Times New Roman", font.size = 12, 
  border.color = "black", big.mark = "", border.width = .5)

table10 <- flextable(`SCAS_results`) %>% 
  bold(part = "header") %>% autofit() %>%
  set_table_properties(width = 1.0, layout = "autofit") %>%
  set_caption(caption = "Mean and Range of Item Correspondence Values for the SCAS (10-14 years) and the SCAS (15-17 years)")
table10

```