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ddop_data.R
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############################################################################
# DATA FILTER & MANIPULATION ###############################################
############################################################################
############################################################################
# euro_analysis ############################################################
############################################################################
#' Perform an analysis on 'euro' variables in the dataset
#'
#' This function filters the dataset to include only rows with specified IDs and then counts the number of rows
#' where more than one 'euro' variable has values of -1, -2, or is NA.
#'
#' @param data A data frame containing the dataset, which should include a column named 'mergeid' and several columns
#' starting with 'euro'.
#' @param ids A vector of IDs to filter the dataset (`mergeid` values).
#'
#' @return An integer representing the number of rows that have more than one 'euro' variable with values -1, -2, or NA.
#'
#' @examples
#' # Example usage:
#' count <- euro_analysis(data, ids = c(1, 2, 3))
euro_analysis <- function(data, ids) {
# Filter the dataset to include only rows with specified IDs
filtered_data <- data %>%
filter(mergeid %in% ids)
# Count rows with more than one 'euro' variable having values -1, -2, or NA
count_rows <- filtered_data %>%
rowwise() %>%
mutate(euro_conditions = sum(across(starts_with("euro"), ~ .x %in% c(-1, -2) | is.na(.x)))) %>%
ungroup() %>%
filter(euro_conditions > 1) %>% # Keep rows where more than one 'euro' variable meets the condition
nrow() # Count the number of such rows
return(count_rows) # Return the count
}
############################################################################
# old_score_process_wave ###################################################
############################################################################
#' Process 'euro' variables and calculate a summary score
#'
#' This function filters the dataset to include only rows with specified IDs and then calculates two new variables:
#' the number of anomalous values (NA, -1, -2) and the count of values equal to 1 in columns starting with "euro".
#' Based on these counts, it creates a new variable (`euro_d`) that summarizes the data using specific conditions.
#'
#' @param data A data frame containing the dataset, which should include a column named 'mergeid' and several columns
#' starting with 'euro'.
#' @param ids A vector of IDs to filter the dataset (`mergeid` values).
#'
#' @return A data frame with columns `mergeid` and `euro_d`, where `euro_d` is calculated based on the conditions.
#'
#' @examples
#' # Example usage:
#' processed_data <- old_score_process_wave(data, ids = c(1, 2, 3))
old_score_process_wave <- function(data, ids) {
# Filter the dataset to include only rows with specified IDs
filtered_data <- data %>%
filter(mergeid %in% ids) %>%
rowwise() %>%
# Calculate the number of anomalous values and count of ones in 'euro' variables
mutate(
num_anomalous_values = sum(across(starts_with("euro"), ~ .x %in% c(NA, -1, -2))),
num_ones = sum(across(starts_with("euro"), ~ .x == 1))
) %>%
ungroup()
# Create 'euro_d' based on conditions involving 'num_ones' and 'num_anomalous_values'
filtered_data <- filtered_data %>%
select(mergeid, starts_with("euro"), num_anomalous_values, num_ones) %>%
mutate(
euro_d = case_when(
num_ones >= 4 & num_anomalous_values < 4 ~ num_ones, # If there are 4 or more ones and fewer than 4 anomalous values
num_ones < 4 & num_anomalous_values < 4 ~ num_ones, # If there are fewer than 4 ones and fewer than 4 anomalous values
TRUE ~ NA # Set to NA in all other cases
)
)
# Select only 'mergeid' and 'euro_d' columns for the final output
filtered_data <- filtered_data %>%
select(mergeid, euro_d)
return(filtered_data) # Return the processed data frame
}
############################################################################
# old_process_wave #########################################################
############################################################################
#' Process 'euro' variables and create a depression label
#'
#' This function filters the dataset to include only rows with specified IDs and then calculates two new variables:
#' the number of anomalous values (NA, -1, -2) and the count of values equal to 1 in columns starting with "euro".
#' Based on these counts, it creates a new labeled variable (`euro_d`) that indicates whether the row is classified
#' as "depressed" or "not depressed".
#'
#' @param data A data frame containing the dataset, which should include a column named 'mergeid' and several columns
#' starting with 'euro'.
#' @param ids A vector of IDs to filter the dataset (`mergeid` values).
#'
#' @return A data frame with columns `mergeid` and `euro_d`, where `euro_d` is a labeled factor indicating "depressed"
#' or "not depressed".
#'
#' @examples
#' # Example usage:
#' processed_data <- old_process_wave(data, ids = c(1, 2, 3))
old_process_wave <- function(data, ids) {
# Filter the dataset to include only rows with specified IDs
filtered_data <- data %>%
filter(mergeid %in% ids) %>%
rowwise() %>%
# Calculate the number of anomalous values and count of ones in 'euro' variables
mutate(
num_anomalous_values = sum(across(starts_with("euro"), ~ .x %in% c(NA, -1, -2))), # Count anomalous values
num_ones = sum(across(starts_with("euro"), ~ .x == 1)) # Count values equal to 1
) %>%
ungroup()
# Create 'euro_d' based on conditions and label it
filtered_data <- filtered_data %>%
select(mergeid, starts_with("euro"), num_anomalous_values, num_ones) %>%
mutate(
euro_d = case_when(
num_ones >= 4 & num_anomalous_values < 4 ~ "yes", # Set to "yes" if conditions are met
num_ones < 4 & num_anomalous_values < 4 ~ "no", # Set to "no" if conditions are met
TRUE ~ NA_character_ # Set to NA in all other cases
),
# Label 'euro_d' as a factor with levels "depressed" and "not depressed"
euro_d = labelled(euro_d, labels = c("depressed" = "yes", "not depressed" = "no"), label = "depression")
)
# Select only 'mergeid' and 'euro_d' columns for the final output
filtered_data <- filtered_data %>%
select(mergeid, euro_d)
return(filtered_data) # Return the processed data frame
}
############################################################################
# old_combine_wave_dataset #################################################
############################################################################
#' Combine and process datasets across multiple waves
#'
#' This function combines and processes data from different waves, applying filters, merging data, and selecting patients
#' based on various conditions, such as those diagnosed with cancer. The function can handle data filtering, scoring,
#' and specific wave-to-wave processing to prepare a final combined dataset for analysis.
#'
#' @param only_onco Logical, whether to include only cancer patients in the analysis (default: TRUE).
#' @param only_onco_fu Logical, whether to include only cancer patients who were followed up in the analysis (default: TRUE).
#' @param score Logical, whether to use scoring with the `old_score_process_wave` function (default: TRUE).
#' @param filters A list of column filters for various datasets, used to select specific columns in each dataset.
#' @param data_names A character vector specifying the dataset names to be included in the analysis.
#'
#' @return A combined data frame containing processed data from the specified waves, filtered, scored, and labeled as needed.
#'
#' @examples
#' # Example usage:
#' combined_data <- old_combine_wave_dataset()
old_combine_wave_dataset <- function(
only_onco = TRUE,
only_onco_fu = TRUE,
score = TRUE,
filters = list(
cv_r_filter = c("age_int", "hhsize"),
dn_filter = c("dn042_", "dn503_", "dn014_", "dn034_"),
iv_filter = c("iv009_"),
hh_filter = c("hh022_", "hh025_", "hh017e"),
sp_filter = c("sp002_", "sp008_"),
ch_filter = c("ch001_", "ch021_"),
ep_filter = c("ep005_"),
co_filter = c("co007_"),
ac_filter = c("ac035d1", "ac035d4", "ac035d5", "ac035d7", "ac035d8", "ac035d9", "ac035d10","ac035dno", "ac012_", "ac014_", "ac015_", "ac016_", "ac017_", "ac018_", "ac019_", "ac020_", "ac021_", "ac022_", "ac022_", "ac023_", "ac024_", "ac025_"),
it_filter = c("it003_"),
gv_health_filter = c("euro1", "euro2", "euro3", "euro4", "euro5", "euro6", "euro7", "euro8", "euro9", "euro10", "euro11", "euro12", "bmi2", "phactiv"),
ph_filter = c("ph011d10", "ph006d16", "ph006d1", "ph072_1", "ph006d2", "ph006d4", "ph072_2", "ph006d5", "ph006d6", "ph006d12", "ph006d14", "ph072_3", "ph072_4", "ph011d1", "ph011d11", "ph011d13", "ph011d14", "ph011d15", "ph011d2", "ph011d3", "ph011d4", "ph011d6", "ph011d7", "ph011d8", "ph011d9", "ph011dno", "ph011dot", "ph011d15", "ph003_", "ph006d3", "ph046_", "ph043_", "ph044_", "ph092_", "ph065_", "ph048d1", "ph048d2", "ph048d3", "ph048d4", "ph048d5", "ph048d6", "ph048d7", "ph048d8", "ph048d9", "ph048d10", "ph048dno", "ph049d1", "ph049d2", "ph049d3", "ph049d4", "ph049d5", "ph049d6", "ph049d7", "ph049d8", "ph049d9", "ph049d10", "ph049d11", "ph049d12", "ph049d13", "ph049dno", "ph089d1", "ph089d2", "ph089d3", "ph084_", "ph089d4", "ph006d10", "ph071_3"),
mh_filter = c("mh037_"),
hc_filter = c( "hc012_", "hc029_", "hc114_", "hc115_", "hc125_"),
br_filter = c("br015_", "br016_"),
gv_isced_filter = c("isced1997_r")),
data_names = c("cv_r", "dn", "iv", "hh", "sp", "ch", "ep", "co", "ac", "it", "gv_health", "ph", "mh", "hc", "br", "gv_isced")
)
{
# Initialize lists to store the data for each wave, IDs of patients diagnosed with cancer, and subsets of data for cancer patients
waves <- list()
id_cancer <- list()
cancer_data <- list()
# Loop through each wave (5 to 8) to process the data
for (wave in 5:8) {
waves_ds <- list() # Initialize a list to store sub-datasets for the current wave
# Determine which data_names to use for the current wave
if (wave == 8) {
current_data_names <- c("gv_health", "ph")
} else {
current_data_names <- data_names
}
# Read and filter datasets for the current wave
for (name in current_data_names) {
ds_path <- paste0("share data/sharew", wave, "_rel8-0-0_ALL_datasets_spss/sharew", wave, "_rel8-0-0_", name, ".sav")
if (file.exists(ds_path)) {
dataset <- read_sav(ds_path)
filter_name <- paste0(name, "_filter")
if (name == "ph" || (wave == 8 && name == "gv_health")) {
waves_ds[[name]] <- dataset
} else {
if (filter_name %in% names(filters)) {
columns_to_select <- c("mergeid", filters[[filter_name]])
filtered_ds <- dataset %>% select(all_of(columns_to_select))
waves_ds[[name]] <- filtered_ds
}
}
}
}
combined_wave <- reduce(waves_ds, full_join, by = "mergeid")
combined_wave <- combined_wave %>% mutate(wave = wave)
cancer_filter <- combined_wave %>% filter(ph006d10 == 1 | ph071_3 > 0 | ph072_3 == 1)
cancer_ids <- cancer_filter %>% pull(mergeid)
cancer_subset <- combined_wave %>% filter(mergeid %in% cancer_ids)
waves[[paste0("wave", wave)]] <- combined_wave
id_cancer[[paste0("w", wave, "_id_cancer")]] <- cancer_ids
cancer_data[[paste0("w", wave, "_cancer")]] <- cancer_subset
}
if(only_onco == TRUE){
#w4_id_cancer <- intersect(waves$wave4$mergeid, id_cancer$w4_id_cancer)
w5_id_cancer <- intersect(waves$wave5$mergeid, id_cancer$w5_id_cancer)
w6_id_cancer <- intersect(waves$wave6$mergeid, id_cancer$w6_id_cancer)
w7_id_cancer <- intersect(waves$wave7$mergeid, id_cancer$w7_id_cancer)
w8_id_cancer <- intersect(waves$wave8$mergeid, id_cancer$w8_id_cancer)
if(only_onco_fu == TRUE){
#ids_wave_4_5 <- intersect(w4_id_cancer, waves$wave5$mergeid)
ids_wave_5_6 <- intersect(w5_id_cancer, w6_id_cancer)
ids_wave_not5_6_7 <- setdiff(intersect(w6_id_cancer, w7_id_cancer), ids_wave_5_6)
ids_wave_not5_not6_7_8 <- setdiff(intersect(w7_id_cancer, w8_id_cancer), union(ids_wave_5_6, ids_wave_not5_6_7))
}
else{
#ids_wave_4_5 <- intersect(w4_id_cancer, waves$wave5$mergeid)
ids_wave_5_6 <- intersect(w5_id_cancer, waves$wave6$mergeid)
ids_wave_not5_6_7 <- setdiff(intersect(w6_id_cancer, waves$wave7$mergeid), ids_wave_5_6)
ids_wave_not5_not6_7_8 <- setdiff(intersect(w7_id_cancer, waves$wave8$mergeid), union(ids_wave_5_6, ids_wave_not5_6_7))
}
}else{
all_cancer_ids <- unique(unlist(id_cancer))
w5_id_cancer <- setdiff(waves$wave5$mergeid, all_cancer_ids)
w6_id_cancer <- setdiff(waves$wave6$mergeid, all_cancer_ids)
w7_id_cancer <- setdiff(waves$wave7$mergeid, all_cancer_ids)
w8_id_cancer <- setdiff(waves$wave8$mergeid, all_cancer_ids)
#ids_wave_4_5 <- intersect(w4_id_cancer, waves$wave5$mergeid)
ids_wave_5_6 <- intersect(w5_id_cancer, waves$wave6$mergeid)
ids_wave_not5_6_7 <- setdiff(intersect(w6_id_cancer, waves$wave7$mergeid), ids_wave_5_6)
ids_wave_not5_not6_7_8 <- setdiff(intersect(w7_id_cancer, waves$wave8$mergeid), union(ids_wave_5_6, ids_wave_not5_6_7))
}
#data_wave_4 <- process_wave(waves$wave4, ids_wave_4_5, initial = TRUE)
#euro_sum_wave_5 <- process_wave(waves$wave6, ids_wave_4_5, sum_only = TRUE)
data_wave_5 <- waves$wave5 %>%
filter(mergeid %in% ids_wave_5_6)
euro_sum_wave_6 <- if(score == TRUE) old_score_process_wave(waves$wave6, ids_wave_5_6) else process_wave(waves$wave6, ids_wave_5_6)
data_wave_6 <- waves$wave6 %>%
filter(mergeid %in% ids_wave_not5_6_7)
euro_sum_wave_7 <- if(score == TRUE) old_score_process_wave(waves$wave7, ids_wave_not5_6_7) else process_wave(waves$wave7, ids_wave_not5_6_7)
data_wave_7 <- waves$wave7 %>%
filter(mergeid %in% ids_wave_not5_not6_7_8)
euro_sum_wave_8 <- if(score == TRUE) old_score_process_wave(waves$wave8, ids_wave_not5_not6_7_8) else process_wave(waves$wave8, ids_wave_not5_not6_7_8)
#data_wave_4_5 <- left_join(data_wave_4, euro_sum_wave_5, by = "mergeid")
data_wave_5_6 <- left_join(data_wave_5, euro_sum_wave_6, by = "mergeid")
data_wave_6_7 <- left_join(data_wave_6, euro_sum_wave_7, by = "mergeid")
data_wave_7_8 <- left_join(data_wave_7, euro_sum_wave_8, by = "mergeid")
if(only_onco == TRUE && only_onco_fu == FALSE){
data_wave_5_6 <- data_wave_5_6 %>%
mutate( flag_cfu = if_else(mergeid %in% w6_id_cancer, 0, 1))
data_wave_5_6$flag_cfu <- labelled(data_wave_5_6$flag_cfu,
labels = c( "yes" = 0,"no" = 1),
label = "have cancer in follow up wave?")
data_wave_6_7 <- data_wave_6_7 %>%
mutate( flag_cfu = if_else(mergeid %in% w7_id_cancer, 0, 1))
data_wave_6_7$flag_cfu <- labelled(data_wave_6_7$flag_cfu,
labels = c( "yes" = 0,"no" = 1),
label = "have cancer in follow up wave?")
data_wave_7_8 <- data_wave_7_8 %>%
mutate( flag_cfu = if_else(mergeid %in% w8_id_cancer, 0, 1))
data_wave_7_8$flag_cfu <- labelled(data_wave_7_8$flag_cfu,
labels = c( "yes" = 0,"no" = 1),
label = "have cancer in follow up wave?")
}
combined_data <- bind_rows(data_wave_5_6, data_wave_6_7, data_wave_7_8)
combined_data <- combined_data %>% filter(!is.na(euro_d))
return(combined_data)
}
############################################################################
# score_process_wave #######################################################
############################################################################
#' Process 'euro' variables and calculate scores
#'
#' This function processes the dataset for specified IDs, calculates the number of anomalous values (NA, -1)
#' and the count of values equal to 1 for columns starting with "euro". It then creates a new variable (`euro_d` or `initial_euro_d`)
#' based on these counts.
#'
#' @param data A data frame containing the dataset, which should include a column named 'mergeid' and several columns starting with "euro".
#' @param ids A vector of IDs to filter the dataset (`mergeid` values).
#' @param initial Logical, if TRUE, the function creates an `initial_euro_d` column for the initial wave. If FALSE, it creates `euro_d`.
#'
#' @return A data frame with columns `mergeid` and either `euro_d` or `initial_euro_d` based on the `initial` parameter.
#'
#' @examples
#' # Example usage:
#' processed_data <- score_process_wave(data, ids = c(1, 2, 3), initial = TRUE)
score_process_wave <- function(data, ids, initial = FALSE) {
# Filter the dataset to include only rows with specified IDs
filtered_data <- data %>%
filter(mergeid %in% ids) %>%
rowwise() %>%
# Calculate the number of anomalous values and count of ones in 'euro' variables
mutate(
num_anomalous_values = sum(across(starts_with("euro"), ~ .x %in% c(NA, -1, -2))), # Count anomalous values
num_ones = sum(across(starts_with("euro"), ~ .x == 1)) # Count values equal to 1
) %>%
ungroup()
# Create 'initial_euro_d' for the initial wave or 'euro_d' for other waves
if (initial) {
filtered_data <- filtered_data %>%
mutate(
initial_euro_d = case_when(
num_ones >= 4 & num_anomalous_values < 4 ~ num_ones, # Set initial_euro_d if conditions are met
num_ones < 4 & num_anomalous_values < 4 ~ num_ones, # Set initial_euro_d if conditions are met
TRUE ~ NA # Set to NA in all other cases
)
) %>%
select(-num_anomalous_values, -num_ones) # Remove auxiliary columns
} else {
filtered_data <- filtered_data %>%
select(mergeid, starts_with("euro"), num_anomalous_values, num_ones) %>%
mutate(
euro_d = case_when(
num_ones >= 4 & num_anomalous_values < 4 ~ num_ones, # Set euro_d if conditions are met
num_ones < 4 & num_anomalous_values < 4 ~ num_ones, # Set euro_d if conditions are met
TRUE ~ NA # Set to NA in all other cases
)
) %>%
select(mergeid, euro_d) # Select relevant columns for the output
}
return(filtered_data) # Return the processed data frame
}
############################################################################
# process_wave #############################################################
############################################################################
#' Process 'euro' variables and assign depression labels
#'
#' This function processes the dataset for specified IDs, calculates the number of anomalous values (NA, -1, -2)
#' and the count of values equal to 1 in columns starting with "euro". Based on these counts, it creates a new labeled
#' variable (`euro_d` or `initial_euro_d`) to classify the entries as "depressed" or "not depressed".
#'
#' @param data A data frame containing the dataset, which should include a column named 'mergeid' and several columns
#' starting with "euro".
#' @param ids A vector of IDs to filter the dataset (`mergeid` values).
#' @param initial Logical, if TRUE, the function creates an `initial_euro_d` column for the initial wave. If FALSE, it creates `euro_d`.
#'
#' @return A data frame with columns `mergeid` and either `euro_d` or `initial_euro_d`, with labels indicating "depressed"
#' or "not depressed".
#'
#' @examples
#' # Example usage:
#' processed_data <- process_wave(data, ids = c(1, 2, 3), initial = TRUE)
process_wave <- function(data, ids, initial = FALSE) {
# Filter the dataset to include only rows with specified IDs
filtered_data <- data %>%
filter(mergeid %in% ids) %>%
rowwise() %>%
# Calculate the number of anomalous values and count of ones in 'euro' variables
mutate(
num_anomalous_values = sum(across(starts_with("euro"), ~ .x %in% c(NA, -1, -2))), # Count anomalous values
num_ones = sum(across(starts_with("euro"), ~ .x == 1)) # Count values equal to 1
) %>%
ungroup()
# Create 'initial_euro_d' for the initial wave or 'euro_d' for other waves
if (initial) {
filtered_data <- filtered_data %>%
mutate(
initial_euro_d = case_when(
num_ones >= 4 & num_anomalous_values < 4 ~ "yes", # Set to "yes" if conditions are met
num_ones < 4 & num_anomalous_values < 4 ~ "no", # Set to "no" if conditions are met
TRUE ~ NA_character_ # Set to NA in all other cases
))%>%
mutate(
initial_euro_d = labelled(
initial_euro_d,
labels = c("depressed" = "yes", "not depressed" = "no"),
label = "Depression Status"
)
) %>%
select(-num_anomalous_values, -num_ones)
var_label(filtered_data$initial_euro_d) <- "Depression at BaseLine"
} else {
filtered_data <- filtered_data %>%
select(mergeid, starts_with("euro"), num_anomalous_values, num_ones) %>%
mutate(
euro_d = case_when(
num_ones >= 4 & num_anomalous_values < 4 ~ "yes", # Set to "yes" if conditions are met
num_ones < 4 & num_anomalous_values < 4 ~ "no", # Set to "no" if conditions are met
TRUE ~ NA_character_ # Set to NA in all other cases
),
# Label 'euro_d' as "depressed" or "not depressed"
euro_d = labelled(euro_d, labels = c("depressed" = "yes", "not depressed" = "no"), label = "depression")
) %>%
select(mergeid, euro_d) # Select relevant columns for the output
}
return(filtered_data) # Return the processed data frame
}
############################################################################
# combine_wave_dataset #####################################################
############################################################################
#' Combine and process datasets across multiple waves
#'
#' This function combines and processes data from different waves, applying filters, merging data, and selecting patients
#' based on various conditions, such as those diagnosed with cancer. It can handle data filtering, scoring, and wave-to-wave
#' processing to prepare a final combined dataset for analysis.
#'
#' @param only_onco Logical, whether to include only cancer patients in the analysis (default: TRUE).
#' @param only_onco_fu Logical, whether to include only cancer patients who were followed up in the analysis (default: TRUE).
#' @param score Logical, whether to use scoring with the `score_process_wave` function (default: TRUE).
#' @param filters A list of column filters for various datasets, used to select specific columns in each dataset.
#' @param data_names A character vector specifying the dataset names to be included in the analysis.
#'
#' @return A combined data frame containing processed data from the specified waves, filtered, scored, and labeled as needed.
#'
#' @examples
#' # Example usage:
#' combined_data <- combine_wave_dataset()
combine_wave_dataset <- function(
only_onco = TRUE,
only_onco_fu = TRUE,
score = TRUE,
filters = list(
cv_r_filter = c("age_int", "hhsize"),
dn_filter = c("dn042_", "dn503_", "dn014_", "dn034_"),
iv_filter = c("iv009_"),
hh_filter = c("hh022_", "hh025_", "hh017e"),
sp_filter = c("sp002_", "sp008_"),
ch_filter = c("ch001_", "ch021_"),
ep_filter = c("ep005_"),
co_filter = c("co007_"),
ac_filter = c("ac035d1", "ac035d4", "ac035d5", "ac035d7", "ac035d8", "ac035d9", "ac035d10","ac035dno", "ac012_", "ac014_", "ac015_", "ac016_", "ac017_", "ac018_", "ac019_", "ac020_", "ac021_", "ac022_", "ac022_", "ac023_", "ac024_", "ac025_"),
it_filter = c("it003_"),
gv_health_filter = c("euro1", "euro2", "euro3", "euro4", "euro5", "euro6", "euro7", "euro8", "euro9", "euro10", "euro11", "euro12", "bmi2", "phactiv"),
ph_filter = c("ph011d10", "ph006d16", "ph006d1", "ph072_1", "ph006d2", "ph006d4", "ph072_2", "ph006d5", "ph006d6", "ph006d12", "ph006d14", "ph072_3", "ph072_4", "ph011d1", "ph011d11", "ph011d13", "ph011d14", "ph011d15", "ph011d2", "ph011d3", "ph011d4", "ph011d6", "ph011d7", "ph011d8", "ph011d9", "ph011dno", "ph011dot", "ph011d15", "ph003_", "ph006d3", "ph046_", "ph043_", "ph044_", "ph092_", "ph065_", "ph048d1", "ph048d2", "ph048d3", "ph048d4", "ph048d5", "ph048d6", "ph048d7", "ph048d8", "ph048d9", "ph048d10", "ph048dno", "ph049d1", "ph049d2", "ph049d3", "ph049d4", "ph049d5", "ph049d6", "ph049d7", "ph049d8", "ph049d9", "ph049d10", "ph049d11", "ph049d12", "ph049d13", "ph049dno", "ph089d1", "ph089d2", "ph089d3", "ph084_", "ph089d4", "ph006d10", "ph071_3"),
mh_filter = c("mh037_"),
hc_filter = c( "hc012_", "hc029_", "hc114_", "hc115_", "hc125_"),
br_filter = c("br015_", "br016_"),
gv_isced_filter = c("isced1997_r") ),
data_names = c("cv_r", "dn", "iv", "hh", "sp", "ch", "ep", "co", "ac", "it", "gv_health", "ph", "mh", "hc", "br", "gv_isced")
)
{
# Initialize lists to store the data for each wave, IDs of patients diagnosed with cancer, and subsets of data for cancer patients
waves <- list()
id_cancer <- list()
cancer_data <- list()
# Loop through each wave (5 to 8) to process the data
for (wave in 5:8) {
waves_ds <- list() # Initialize a list to store sub-datasets for the current wave
# Determine which data_names to use for the current wave
if (wave == 8) {
current_data_names <- c("gv_health", "ph")
} else {
current_data_names <- data_names
}
# Read and filter datasets for the current wave
for (name in current_data_names) {
ds_path <- paste0("share data/sharew", wave, "_rel8-0-0_ALL_datasets_spss/sharew", wave, "_rel8-0-0_", name, ".sav")
if (file.exists(ds_path)) {
dataset <- read_sav(ds_path)
filter_name <- paste0(name, "_filter")
if (name == "ph" || (wave == 8 && name == "gv_health")) {
waves_ds[[name]] <- dataset
} else {
if (filter_name %in% names(filters)) {
columns_to_select <- c("mergeid", filters[[filter_name]])
filtered_ds <- dataset %>% select(all_of(columns_to_select))
waves_ds[[name]] <- filtered_ds
}
}
}
}
combined_wave <- reduce(waves_ds, full_join, by = "mergeid")
combined_wave <- combined_wave %>% mutate(wave = wave)
#ph006d10:Cancer: ever diagnosed/currently havin == 1
#ph071_3: How often diagnosed with cancer since last interview > 0
#ph072_3: Been diagnosed with cancer since last interview == 1
cancer_filter <- combined_wave %>% filter(ph006d10 == 1 | ph071_3 > 0 | ph072_3 == 1)
cancer_ids <- cancer_filter %>% pull(mergeid)
cancer_subset <- combined_wave %>% filter(mergeid %in% cancer_ids)
waves[[paste0("wave", wave)]] <- combined_wave
id_cancer[[paste0("w", wave, "_id_cancer")]] <- cancer_ids
cancer_data[[paste0("w", wave, "_cancer")]] <- cancer_subset
}
if(only_onco == TRUE){
#w4_id_cancer <- intersect(waves$wave4$mergeid, id_cancer$w4_id_cancer)
w5_id_cancer <- intersect(waves$wave5$mergeid, id_cancer$w5_id_cancer)
w6_id_cancer <- intersect(waves$wave6$mergeid, id_cancer$w6_id_cancer)
w7_id_cancer <- intersect(waves$wave7$mergeid, id_cancer$w7_id_cancer)
w8_id_cancer <- intersect(waves$wave8$mergeid, id_cancer$w8_id_cancer)
if(only_onco_fu == TRUE){
#ids_wave_4_5 <- intersect(w4_id_cancer, waves$wave5$mergeid)
ids_wave_5_6 <- intersect(w5_id_cancer, w6_id_cancer)
ids_wave_not5_6_7 <- setdiff(intersect(w6_id_cancer, w7_id_cancer), ids_wave_5_6)
ids_wave_not5_not6_7_8 <- setdiff(intersect(w7_id_cancer, w8_id_cancer), union(ids_wave_5_6, ids_wave_not5_6_7))
}
else{
#ids_wave_4_5 <- intersect(w4_id_cancer, waves$wave5$mergeid)
ids_wave_5_6 <- intersect(w5_id_cancer, waves$wave6$mergeid)
ids_wave_not5_6_7 <- setdiff(intersect(w6_id_cancer, waves$wave7$mergeid), ids_wave_5_6)
ids_wave_not5_not6_7_8 <- setdiff(intersect(w7_id_cancer, waves$wave8$mergeid), union(ids_wave_5_6, ids_wave_not5_6_7))
}
}else{
all_cancer_ids <- unique(unlist(id_cancer))
w5_id_cancer <- setdiff(waves$wave5$mergeid, all_cancer_ids)
w6_id_cancer <- setdiff(waves$wave6$mergeid, all_cancer_ids)
w7_id_cancer <- setdiff(waves$wave7$mergeid, all_cancer_ids)
w8_id_cancer <- setdiff(waves$wave8$mergeid, all_cancer_ids)
#ids_wave_4_5 <- intersect(w4_id_cancer, waves$wave5$mergeid)
ids_wave_5_6 <- intersect(w5_id_cancer, waves$wave6$mergeid)
ids_wave_not5_6_7 <- setdiff(intersect(w6_id_cancer, waves$wave7$mergeid), ids_wave_5_6)
ids_wave_not5_not6_7_8 <- setdiff(intersect(w7_id_cancer, waves$wave8$mergeid), union(ids_wave_5_6, ids_wave_not5_6_7))
}
#data_wave_4 <- process_wave(waves$wave4, ids_wave_4_5, initial = TRUE)
#euro_sum_wave_5 <- process_wave(waves$wave6, ids_wave_4_5, sum_only = TRUE)
data_wave_5 <- if(score == TRUE) score_process_wave(waves$wave5, ids_wave_5_6, initial = TRUE) else process_wave(waves$wave5, ids_wave_5_6, initial = TRUE)
euro_sum_wave_6 <- if(score == TRUE) score_process_wave(waves$wave6, ids_wave_5_6, initial = FALSE) else process_wave(waves$wave6, ids_wave_5_6, initial = FALSE)
data_wave_6 <- if(score == TRUE) score_process_wave(waves$wave6, ids_wave_not5_6_7, initial = TRUE) else process_wave(waves$wave6, ids_wave_not5_6_7, initial = TRUE)
euro_sum_wave_7 <- if(score == TRUE) score_process_wave(waves$wave7, ids_wave_not5_6_7, initial = FALSE) else process_wave(waves$wave7, ids_wave_not5_6_7, initial = FALSE)
data_wave_7 <- if(score == TRUE) score_process_wave(waves$wave7, ids_wave_not5_not6_7_8, initial = TRUE) else process_wave(waves$wave7, ids_wave_not5_not6_7_8, initial = TRUE)
euro_sum_wave_8 <- if(score == TRUE) score_process_wave(waves$wave8, ids_wave_not5_not6_7_8, initial = FALSE) else process_wave(waves$wave8, ids_wave_not5_not6_7_8, initial = FALSE)
#data_wave_4_5 <- left_join(data_wave_4, euro_sum_wave_5, by = "mergeid")
data_wave_5_6 <- left_join(data_wave_5, euro_sum_wave_6, by = "mergeid")
data_wave_6_7 <- left_join(data_wave_6, euro_sum_wave_7, by = "mergeid")
data_wave_7_8 <- left_join(data_wave_7, euro_sum_wave_8, by = "mergeid")
if(only_onco == TRUE){
data_wave_5_6 <- data_wave_5_6 %>%
mutate( flag_cfu = if_else(mergeid %in% w6_id_cancer, 0, 1))
data_wave_5_6$flag_cfu <- labelled(data_wave_5_6$flag_cfu,
labels = c( "yes" = 0,"no" = 1),
label = "have cancer in follow up wave?")
data_wave_6_7 <- data_wave_6_7 %>%
mutate( flag_cfu = if_else(mergeid %in% w7_id_cancer, 0, 1))
data_wave_6_7$flag_cfu <- labelled(data_wave_6_7$flag_cfu,
labels = c( "yes" = 0,"no" = 1),
label = "have cancer in follow up wave?")
data_wave_7_8 <- data_wave_7_8 %>%
mutate( flag_cfu = if_else(mergeid %in% w8_id_cancer, 0, 1))
data_wave_7_8$flag_cfu <- labelled(data_wave_7_8$flag_cfu,
labels = c( "yes" = 0,"no" = 1),
label = "have cancer in follow up wave?")
}
combined_data <- bind_rows(data_wave_5_6, data_wave_6_7, data_wave_7_8)
combined_data <- combined_data %>% filter(!is.na(euro_d))
#combined_data <- combined_data %>% filter(!is.na(euro_d), !is.na(initial_euro_d))
return(combined_data)
}
############################################################################
# load_death_dataset #######################################################
############################################################################
#' Load and process cancer patient data across waves with death information
#'
#' This function reads datasets from waves 4 to 8, filters the data based on specified columns, and identifies cancer patients.
#' It then checks for deaths in follow-up waves and creates labels indicating the patient's status (alive, dead, or missing)
#' in the next wave. The resulting dataset includes a combined data frame with all the processed information.
#'
#' @param filters A list of column filters for various datasets, used to select specific columns in each dataset.
#' @param data_names A character vector specifying the dataset names to be included in the analysis.
#'
#' @return A combined data frame containing processed data from waves 5 to 7 with cancer patient status (alive, death, missing) in follow-up waves.
#'
#' @examples
#' # Example usage:
#' combined_data <- load_death_dataset()
load_death_dataset <- function(
filters = list(
cv_r_filter = c("age_int", "hhsize"),
dn_filter = c("dn042_", "dn503_", "dn014_", "dn034_"),
iv_filter = c("iv009_"),
hh_filter = c("hh022_", "hh025_"),
sp_filter = c("sp002_", "sp008_"),
ch_filter = c("ch001_", "ch021_"),
ep_filter = c("ep005_"),
co_filter = c("co007_"),
ac_filter = c("ac035d1", "ac035d4", "ac035d5", "ac035d7", "ac035d8", "ac035d9", "ac035d10","ac035dno", "ac012_", "ac014_", "ac015_", "ac016_", "ac017_", "ac018_", "ac019_", "ac020_", "ac021_", "ac022_", "ac022_", "ac023_", "ac024_", "ac025_"),
it_filter = c("it003_"),
gv_health_filter = c("euro1", "euro2", "euro3", "euro4", "euro5", "euro6", "euro7", "euro8", "euro9", "euro10", "euro11", "euro12", "bmi2", "phactiv"),
ph_filter = c("ph011d10", "ph006d16", "ph006d1", "ph072_1", "ph006d2", "ph006d4", "ph072_2", "ph006d5", "ph006d6", "ph006d12", "ph006d14", "ph072_3", "ph072_4", "ph011d1", "ph011d11", "ph011d13", "ph011d14", "ph011d15", "ph011d2", "ph011d3", "ph011d4", "ph011d6", "ph011d7", "ph011d8", "ph011d9", "ph011dno", "ph011dot", "ph011d15", "ph003_", "ph006d3", "ph046_", "ph043_", "ph044_", "ph092_", "ph065_", "ph048d1", "ph048d2", "ph048d3", "ph048d4", "ph048d5", "ph048d6", "ph048d7", "ph048d8", "ph048d9", "ph048d10", "ph048dno", "ph049d1", "ph049d2", "ph049d3", "ph049d4", "ph049d5", "ph049d6", "ph049d7", "ph049d8", "ph049d9", "ph049d10", "ph049d11", "ph049d12", "ph049d13", "ph049dno", "ph089d1", "ph089d2", "ph089d3", "ph084_", "ph089d4", "ph006d10", "ph071_3"),
mh_filter = c("mh037_"),
hc_filter = c( "hc012_", "hc029_", "hc114_", "hc115_", "hc125_"),
br_filter = c("br015_", "br016_"),
gv_isced_filter = c("isced1997_r")),
data_names = c("cv_r", "dn", "iv", "hh", "sp", "ch", "ep", "co", "ac", "it", "gv_health", "ph", "mh", "hc", "br", "gv_isced")
){
waves <- list()
id_cancer <- list()
cancer_data <- list()
# Loop through each wave (5 to 8) to process the data
for (wave in 4:8) {
waves_ds <- list() # Initialize a list to store sub-datasets for the current wave
# Determine which data_names to use for the current wave
if (wave == 8) {
current_data_names <- c("gv_health", "ph")
}else if(wave == 4){
current_data_names <- c("ph")
}else{
current_data_names <- data_names
}
# Read and filter datasets for the current wave
for (name in current_data_names) {
ds_path <- paste0("share data/sharew", wave, "_rel8-0-0_ALL_datasets_spss/sharew", wave, "_rel8-0-0_", name, ".sav")
if (file.exists(ds_path)) {
dataset <- read_sav(ds_path)
filter_name <- paste0(name, "_filter")
if (name == "ph" || (wave == 8 && name == "gv_health")) {
waves_ds[[name]] <- dataset
} else {
if (filter_name %in% names(filters)) {
columns_to_select <- c("mergeid", filters[[filter_name]])
filtered_ds <- dataset %>% select(all_of(columns_to_select))
waves_ds[[name]] <- filtered_ds
}
}
}
}
combined_wave <- reduce(waves_ds, full_join, by = "mergeid")
combined_wave <- combined_wave %>% mutate(wave = wave)
cancer_filter <- combined_wave %>% filter(ph006d10 == 1 | ph071_3 > 0 | ph072_3 == 1)
cancer_ids <- cancer_filter %>% pull(mergeid)
cancer_subset <- combined_wave %>% filter(mergeid %in% cancer_ids)
waves[[paste0("wave", wave)]] <- combined_wave
id_cancer[[paste0("w", wave, "_id_cancer")]] <- cancer_ids
cancer_data[[paste0("w", wave, "_cancer")]] <- cancer_subset
}
# ALL WAVE DEATH DATASET
death_w4 <- read_sav("share data/sharew4_rel8-0-0_ALL_datasets_spss/sharew4_rel8-0-0_xt.sav")
death_w5 <- read_sav("share data/sharew5_rel8-0-0_ALL_datasets_spss/sharew5_rel8-0-0_xt.sav")
death_w6 <- read_sav("share data/sharew6_rel8-0-0_ALL_datasets_spss/sharew6_rel8-0-0_xt.sav")
death_w7 <- read_sav("share data/sharew7_rel8-0-0_ALL_datasets_spss/sharew7_rel8-0-0_xt.sav")
death_w8 <- read_sav("share data/sharew8_rel8-0-0_ALL_datasets_spss/sharew8_rel8-0-0_xt.sav")
# CANCER ID
w4_id_cancer <- intersect(waves$wave4$mergeid, id_cancer$w4_id_cancer)
w5_id_cancer <- intersect(waves$wave5$mergeid, id_cancer$w5_id_cancer)
w6_id_cancer <- intersect(waves$wave6$mergeid, id_cancer$w6_id_cancer)
w7_id_cancer <- intersect(waves$wave7$mergeid, id_cancer$w7_id_cancer)
w8_id_cancer <- intersect(waves$wave8$mergeid, id_cancer$w8_id_cancer)
# DEATH ID FOR ONCO PATIENT IN FU
ids_death_w4_to_w5_with_cancer <- intersect(w4_id_cancer, death_w5$mergeid)
ids_death_w5_to_w6_with_cancer <- intersect(w5_id_cancer, death_w6$mergeid)
ids_death_w6_to_w7_with_cancer <- intersect(w6_id_cancer, death_w7$mergeid)
ids_death_w7_to_w8_with_cancer <- intersect(w7_id_cancer, death_w8$mergeid)
# FIND MISSING PATINET
ids_wave_5_not6 <- setdiff(w5_id_cancer, waves$wave6$mergeid)
ids_wave_6_not7 <- setdiff(w6_id_cancer, waves$wave7$mergeid)
ids_wave_7_not8 <- setdiff(w7_id_cancer, waves$wave8$mergeid)
# REMOVING DEATH IN BL
waves$wave5 <- waves$wave5 %>% filter(!mergeid %in% ids_death_w4_to_w5_with_cancer)
waves$wave6 <- waves$wave6 %>% filter(!mergeid %in% ids_death_w5_to_w6_with_cancer)
waves$wave7 <- waves$wave7 %>% filter(!mergeid %in% ids_death_w6_to_w7_with_cancer)
# DATASET FOR DEATH ONCO PATIENT
#death_w5_to_w6 <- waves$wave5 %>% filter(mergeid %in% ids_death_w5_to_w6_with_cancer)
#death_w6_to_w7 <- waves$wave6 %>% filter(mergeid %in% ids_death_w6_to_w7_with_cancer)
#death_w7_to_w8 <- waves$wave7 %>% filter(mergeid %in% ids_death_w7_to_w8_with_cancer)
# FILTER WAVE FOR ID IN DEATH OR MISSING
#waves$wave5 <- waves$wave5 %>% filter(mergeid %in% union(ids_wave_5_not6, ids_death_w5_to_w6_with_cancer))
#waves$wave6 <- waves$wave6 %>% filter(mergeid %in% union(ids_wave_6_not7, ids_death_w6_to_w7_with_cancer))
#waves$wave7 <- waves$wave7 %>% filter(mergeid %in% union(ids_wave_7_not8, ids_death_w7_to_w8_with_cancer))
waves$wave5 <- waves$wave5 %>%
mutate(condition_fu = if_else(mergeid %in% ids_death_w5_to_w6_with_cancer, 1,
if_else(mergeid %in% ids_wave_5_not6, 2, 0)))
waves$wave5$condition_fu <- labelled(waves$wave5$condition_fu,
labels = c( "alive" = 0, "death" = 1,"missing" = 2),
label = "death or missing in follow up wave?")
waves$wave6 <- waves$wave6 %>%
mutate(condition_fu = if_else(mergeid %in% ids_death_w6_to_w7_with_cancer, 1,
if_else(mergeid %in% ids_wave_6_not7, 2, 0)))
waves$wave6$condition_fu <- labelled(waves$wave6$condition_fu,
labels = c("alive" = 0, "death" = 1,"missing" = 2),
label = "death or missing in follow up wave?")
waves$wave7 <- waves$wave7 %>%
mutate(condition_fu = if_else(mergeid %in% ids_death_w7_to_w8_with_cancer, 1,
if_else(mergeid %in% ids_wave_7_not8, 2, 0)))
waves$wave7$condition_fu <- labelled(waves$wave7$condition_fu,
labels = c("alive" = 0, "death" = 1,"missing" = 2),
label = "death or missing in follow up wave?")
waves$wave5 <- waves$wave5 %>%
mutate( flag_cfu = if_else(mergeid %in% w6_id_cancer, 0, 1))
waves$wave5$flag_cfu <- labelled(waves$wave5$flag_cfu,
labels = c( "yes" = 0,"no" = 1),
label = "have cancer in follow up wave?")
waves$wave6 <- waves$wave6 %>%
mutate( flag_cfu = if_else(mergeid %in% w7_id_cancer, 0, 1))
waves$wave6$flag_cfu <- labelled(waves$wave6$flag_cfu,
labels = c( "yes" = 0,"no" = 1),
label = "have cancer in follow up wave?")
waves$wave7 <- waves$wave7 %>%
mutate( flag_cfu = if_else(mergeid %in% w8_id_cancer, 0, 1))
waves$wave7$flag_cfu <- labelled(waves$wave7$flag_cfu,
labels = c( "yes" = 0,"no" = 1),
label = "have cancer in follow up wave?")
waves$wave5 <- waves$wave5 %>% filter(mergeid %in% w5_id_cancer)
waves$wave6 <- waves$wave6 %>% filter(mergeid %in% w6_id_cancer)
waves$wave7 <- waves$wave7 %>% filter(mergeid %in% w7_id_cancer)
combined_data <- bind_rows(waves$wave5, waves$wave6, waves$wave7)
return(combined_data)
}
############################################################################
# filtering_dataset ########################################################
############################################################################
# Function to filter and preprocess the dataset
# This function performs several preprocessing steps on the input dataset, such as replacing specific values, recoding variables, adding new variables, and adjusting variable domains.
# Arguments:
# - combined_dataset: The input dataset containing the variables to process.
# - var_euro: A vector of variable names related to depression (defaults to a predefined set).
# - score: A logical flag to determine whether to label the initial depression variable.
# Returns:
# - The modified dataset after applying the filtering and preprocessing steps.
filtering_dataset <- function(
combined_dataset,
var_euro = c("euro1", "euro2", "euro3", "euro4", "euro5", "euro6", "euro7", "euro8", "euro9", "euro10", "euro11", "euro12"),
score = FALSE
){
# Negative value to NA
combined_dataset <- combined_dataset %>%
mutate(across(where(~ is.numeric(.) || is.labelled(.)), ~ replace(., . %in% c(-9999992, -9999991, -1, -2, -3, -9), NA)))
# NA to -1
combined_dataset <- combined_dataset %>%
mutate(across(where(is.integer), ~if_else(is.na(.), as.integer(-1), .)),
across(where(is.double), ~if_else(is.na(.), as.double(-1), .)),
across(where(is.character), ~if_else(is.na(.), "-1", .)))
if(score == FALSE){
combined_dataset <- combined_dataset %>%
mutate(initial_euro_d = labelled(initial_euro_d, labels = c(
"missing" = -1,
"depressed" = "yes",
"not depressed" = "no"
)))
}
# Find variable domain without 'mergeid' and 'wave'
domains <- sapply(combined_dataset[setdiff(names(combined_dataset), c("mergeid", "wave"))], get_domain)
# Create a dataframe mapping variables to their respective domains
domain_mapping <- tibble(
variable = names(domains),
domain = domains
)
# new variable 2 more years with cancer
combined_dataset <- combined_dataset %>%
mutate(`more2y_c` = case_when(
ph072_3 == 5 ~ 0,
ph072_3 == 1 ~ 1,
TRUE ~ -1
)) %>% mutate(more2y_c = labelled(more2y_c, labels = c(
"missing" = -1,
"Less than 2 years with cancer" = 0,
"2 or more years with cancer" = 1
)))
var_label(combined_dataset$more2y_c) <- "Two or more year with cancer"
adapt_variable <- domain_mapping %>%
filter(domain == "-2/-1/1/2/3/4/5/97") %>%
pull(variable)
for(var in adapt_variable) {
combined_dataset[[var]] <- recode( combined_dataset[[var]], `97` = 6)
current_labels <- attr(combined_dataset[[var]], "labels")
new_labels <- current_labels[names(current_labels) != 97]
new_labels[names(current_labels[8])] <- 6
attr(combined_dataset[[var]], "labels") <- new_labels
}
adapt_variable <- domain_mapping %>%
filter(domain == "-2/-1/0/1/2/3/4/5/6/95/97") %>%
pull(variable)
for(var in adapt_variable) {
combined_dataset[[var]] <- recode( combined_dataset[[var]], `95` = 7, `97` = 8)
current_labels <- attr(combined_dataset[[var]], "labels")
new_labels <- current_labels[!names(current_labels) %in% c(95,97)]
new_labels[names(current_labels[10])] <- 7
new_labels[names(current_labels[11])] <- 8
attr(combined_dataset[[var]], "labels") <- new_labels
}
adapt_variable <- domain_mapping %>%
filter(domain == "-2/-1/1/3/5") %>%
pull(variable)
for(var in adapt_variable) {
combined_dataset[[var]] <- recode( combined_dataset[[var]], `1` = 1, `3` = 2, `5` = 3)
current_labels <- attr(combined_dataset[[var]], "labels")
new_labels <- current_labels[!names(current_labels) %in% c(1,3,5)]
new_labels[names(current_labels[3])] <- 1
new_labels[names(current_labels[4])] <- 2
new_labels[names(current_labels[5])] <- 3
attr(combined_dataset[[var]], "labels") <- new_labels
}
adapt_variable <- domain_mapping %>%
filter(domain == "-2/-1/1/5") %>%
pull(variable)
for(var in adapt_variable) {
combined_dataset[[var]] <- recode( combined_dataset[[var]], `1` = 0, `5` = 1)
current_labels <- attr(combined_dataset[[var]], "labels")
new_labels <- current_labels[!names(current_labels) %in% c(1,5)]
new_labels[names(current_labels[3])] <- 0
new_labels[names(current_labels[4])] <- 1
attr(combined_dataset[[var]], "labels") <- new_labels
}
adapt_variable <- domain_mapping %>%
filter(domain == "-2/-1/1/2") %>%
pull(variable)
for(var in adapt_variable) {
combined_dataset[[var]] <- recode( combined_dataset[[var]], `1` = 0, `2` = 1)
current_labels <- attr(combined_dataset[[var]], "labels")
new_labels <- current_labels[!names(current_labels) %in% c(1,5)]
new_labels[names(current_labels[3])] <- 0
new_labels[names(current_labels[4])] <- 1
attr(combined_dataset[[var]], "labels") <- new_labels
}
adapt_variable <- domain_mapping %>%
filter(domain == "-2/-1/1/2/3/97") %>%
pull(variable)
for(var in adapt_variable) {
combined_dataset[[var]] <- recode( combined_dataset[[var]], `97` = 4)
current_labels <- attr(combined_dataset[[var]], "labels")
new_labels <- current_labels[names(current_labels) != 97]
new_labels[names(current_labels[6])] <- 4
attr(combined_dataset[[var]], "labels") <- new_labels
}
adapt_variable <- domain_mapping %>%
filter(domain == "-2/-1/1/3") %>%
pull(variable)
for(var in adapt_variable) {
combined_dataset[[var]] <- recode( combined_dataset[[var]], `1` = 0, `3` = 1)
current_labels <- attr(combined_dataset[[var]], "labels")
new_labels <- current_labels[!names(current_labels) %in% c(1,3)]
new_labels[names(current_labels[3])] <- 0
new_labels[names(current_labels[4])] <- 1
attr(combined_dataset[[var]], "labels") <- new_labels
}
adapt_variable <- c("ph071_3", "ph071_1", "ph071_2")
for(var in adapt_variable) {
current_labels<- attr(combined_dataset[[var]], "label")
values <- as.numeric(combined_dataset[[var]])
combined_dataset[[var]] <- labelled(values)
attr(combined_dataset[[var]], "labels") <- c("Refusal" = -2, "Don't know" = -1, "1" = 1, "2" = 2, "3 or more" = 3)
attr(combined_dataset[[var]], "label") <- current_labels
}
combined_dataset <- combined_dataset %>%
mutate(
ch001_ = if_else(ch001_ > 10, -2, ch001_),
ch021_ = if_else(ch021_ > 10, -2, ch021_),
ph072_3 = if_else(ph072_3 == 10, -2, ph072_3),
ph012_ = ifelse(ph012_ >= 0 & ph012_ < 35, -1, ph012_),
ph013_ = ifelse(ph013_ >= 0 & ph013_ < 135, -1, ph013_)
)
combined_dataset <- combined_dataset %>%
mutate(
bmi = ifelse(ph012_ == -1 | ph013_ == -1, -1, ph012_ / (ph013_ / 100)^2),
bmi = ifelse(bmi == -1, -1, round(bmi, 2))
)%>%
mutate(bmi = labelled(bmi, labels = c(
"missing" = -1
)))
var_label(combined_dataset$bmi) <- "bmi"
# remove cancer variable
combined_dataset <- combined_dataset %>%
select(-ph006d10)
return(combined_dataset)
}
############################################################################
# categorization_variable ##################################################
############################################################################
# Function to categorize variables based on specific conditions
# This function processes multiple variables in the dataset, creating new categorized variables based on their respective conditions.
# It handles variables that might have multiple categories, recodes values, and adds appropriate labels.
# Arguments:
# - dataset: The input dataset to be processed.
# - variables: A vector of variable prefixes to be categorized.
# Returns:
# - The modified dataset with newly created categorized variables.
categorization_variable <- function(dataset, variables){
for (var in variables){
prefix_cols <- grep(var, names(dataset), value = TRUE)
vars_with_dno <- grep("dno$", prefix_cols, value = TRUE)
if (length(vars_with_dno) > 0) {
prefix_cols <- setdiff(prefix_cols, vars_with_dno)