## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
options(rmarkdown.html_vignette.check_title = FALSE)


## ----checkIDs-----------------------------------------------------------------
library(BGmisc)
# Create a sample dataset
df <- ped2fam(potter, famID = "newFamID", personID = "personID")

# Call the checkIDs function
result <- checkIDs(df, repair = FALSE)
print(result)

#> $all_unique_ids
#> [1] TRUE
#> 
#> $total_non_unique_ids
#> [1] 0
#> 
#> $total_own_father
#> [1] 0
#> 
#> $total_own_mother
#> [1] 0
#> 
#> $total_duplicated_parents
#> [1] 0
#> 
#> $total_within_row_duplicates
#> [1] 0
#> 
#> $within_row_duplicates
#> [1] FALSE

## ----repair-------------------------------------------------------------------
# Create a sample dataset with duplicates
df <- ped2fam(potter, famID = "newFamID", personID = "personID")

# Sibling overwrite
df$personID[df$name == "Vernon Dursley"] <- df$personID[df$name == "Marjorie Dursley"]

# Add a copy of Dudley Dursley
df <- rbind(df, df[df$name == "Dudley Dursley",])

## -----------------------------------------------------------------------------
library(tidyverse)

summarizeFamilies(df, famID = "newFamID", personID = "personID")$family_summary %>% glimpse()

## -----------------------------------------------------------------------------
# Call the checkIDs
result <- checkIDs(df)

print(result)

## -----------------------------------------------------------------------------

df %>% filter(personID %in% result$non_unique_ids) %>%
  arrange(personID)


## -----------------------------------------------------------------------------
df_repair <- checkIDs(df, repair = TRUE)

df_repair %>% filter(ID %in% result$non_unique_ids) %>%
  arrange(ID)

result <- checkIDs(df_repair)

print(result)

## ----within-------------------------------------------------------------------
# Create a sample dataset with within-person duplicate parent IDs

df <- ped2fam(potter, famID = "newFamID", personID = "personID")

df$momID[df$name == "Vernon Dursley"] <- df$personID[df$name == "Vernon Dursley"]

# Check for within-row duplicates
result <- checkIDs(df, repair = FALSE)
print(result)

## -----------------------------------------------------------------------------
# Validate sex coding
results <- checkSex(potter, code_male = 1, code_female = 0, verbose = TRUE, repair = FALSE)
print(results)

## -----------------------------------------------------------------------------
# Repair sex coding
df_fix <- checkSex(potter, code_male = 1, code_female = 0, verbose = TRUE, repair = TRUE)
print(df_fix)

## ----eval = FALSE-------------------------------------------------------------
#  # note, is broken right now
#  # Load necessary libraries and datasets
#  library(tidyverse)
#  library(BGmisc)
#  set.seed(123)
#  # Create a sample dataset similar to the one used in Mason's approach
#  sample_data <- data.frame(
#    ID = 1:10,
#    name = c("Person1", "Person2", "Person3", "Person4", "Person5", "Person6", "Person7", "Person8", "Person9", "Person10"),
#    dadID = c(NA, NA, 1, 1, 3, 3, 5, 5, 7, 7),
#    momID = c(NA, NA, 2, 2, 4, 4, 6, 6, 7, 8),
#    sex = c(1, 0, 1, 0, 1, 0, 1, 0, 1, 0),
#    byr = runif(10, 1900, 2000),
#    dyr = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)
#  )
#  
#  
#  
#  summarizePedigrees(sample_data)
#  
#  
#  # Clean the sample dataset
#  cleaned_data <- sample_data %>%
#    janitor::remove_empty(c("rows", "cols")) %>%
#    mutate(
#      sex_factor = as.factor(case_when(sex == 1 ~ "male", sex == 0 ~ "female"))
#    )
#  
#  # Check for duplicate IDs
#  temp_check <- checkIDs(cleaned_data, verbose = TRUE, repair = FALSE)
#   all_duplicated_ids   <- cbind(temp_check$non_unique_ids, temp_check$duplicated_parents_ids)
#  
#  cleaned_data <- cleaned_data %>%
#    mutate(
#      duplicated = case_when(ID %in% temp_check$non_unique_ids ~ 1, TRUE ~ 0),
#      duplicated_parent = case_when(dadID %in% all_duplicated_ids  | momID %in% all_duplicated_ids  ~ 1, TRUE ~ 0),
#      duplicated_source_ID = case_when(ID %in% all_duplicated_ids ~ ID, dadID %in% all_duplicated_ids  ~ dadID, momID %in% all_duplicated_ids  ~ momID, TRUE ~ NA_integer_),
#      alteredlinks = 0
#    )
#  
#  # Display and manually correct specific errors
#  cleaned_data %>%
#    filter(duplicated == 1 | duplicated_parent == 1) %>%
#    arrange(duplicated_source_ID, ID) %>%
#    print(n = Inf)
#  
#  # Perform specific corrections
#  cleaned_data <- cleaned_data %>%
#    mutate(
#      alteredlinks = case_when(ID == 9  ~ 1, TRUE ~ alteredlinks),
#      ID = case_when(ID == 7 & round(byr, digits = 0) == 2020 ~ ID + 1e6, TRUE ~ ID)
#    )
#  
#  # Final check for remaining duplicates
#  final_check <- checkIDs(cleaned_data, verbose = TRUE, repair = FALSE)
#  print(final_check)
#