192 lines
8.3 KiB
R
192 lines
8.3 KiB
R
# Script to combine and recode Likert scale items in eohi2.csv
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# Combines 01 and 02 versions of items, then recodes text to numeric values
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# Load necessary library
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library(dplyr)
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setwd("C:/Users/irina/Documents/DND/EOHI/eohi2")
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# Read the data
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df <- read.csv("eohi2.csv", stringsAsFactors = FALSE)
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# Define the mapping function
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recode_likert <- function(x) {
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case_when(
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tolower(x) == "strongly disagree" ~ -3,
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tolower(x) == "disagree" ~ -2,
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tolower(x) == "somewhat disagree" ~ -1,
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tolower(x) == "neither agree nor disagree" ~ 0,
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tolower(x) == "somewhat agree" ~ 1,
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tolower(x) == "agree" ~ 2,
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tolower(x) == "strongly agree" ~ 3,
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TRUE ~ NA_real_
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)
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}
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# Define source column pairs (Set A and Set B)
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source_cols_A <- c(
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"01past5PrefItem_1", "01past5PrefItem_2", "01past5PrefItem_3", "01past5PrefItem_4", "01past5PrefItem_5",
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"01past5PersItem_1", "01past5PersItem_2", "01past5PersItem_3", "01past5PersItem_4", "01past5PersItem_5",
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"01past5ValItem_1", "01past5ValItem_2", "01past5ValItem_3", "01past5ValItem_4", "01past5ValItem_5",
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"01past10PrefItem_1", "01past10PrefItem_2", "01past10PrefItem_3", "01past10PrefItem_4", "01past10PrefItem_5",
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"01past10PersItem_1", "01past10PersItem_2", "01past10PersItem_3", "01past10PersItem_4", "01past10PersItem_5",
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"01past10ValItem_1", "01past10ValItem_2", "01past10ValItem_3", "01past10ValItem_4", "01past10ValItem_5",
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"01fut5PrefItem_1", "01fut5PrefItem_2", "01fut5PrefItem_3", "01fut5PrefItem_4", "01fut5PrefItem_5",
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"01fut5PersItem_1", "01fut5PersItem_2", "01fut5PersItem_3", "01fut5PersItem_4", "01fut5PersItem_5",
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"01fut5ValItem_1", "01fut5ValItem_2", "01fut5ValItem_3", "01fut5ValItem_4", "01fut5ValItem_5",
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"01fut10PrefItem_1", "01fut10PrefItem_2", "01fut10PrefItem_3", "01fut10PrefItem_4", "01fut10PrefItem_5",
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"01fut10PersItem_1", "01fut10PersItem_2", "01fut10PersItem_3", "01fut10PersItem_4", "01fut10PersItem_5",
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"01fut10ValItem_1", "01fut10ValItem_2", "01fut10ValItem_3", "01fut10ValItem_4", "01fut10ValItem_5"
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)
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source_cols_B <- c(
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"02past5PrefItem_1", "02past5PrefItem_2", "02past5PrefItem_3", "02past5PrefItem_4", "02past5PrefItem_5",
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"02past5PersItem_1", "02past5PersItem_2", "02past5PersItem_3", "02past5PersItem_4", "02past5PersItem_5",
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"02past5ValItem_1", "02past5ValItem_2", "02past5ValItem_3", "02past5ValItem_4", "02past5ValItem_5",
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"02past10PrefItem_1", "02past10PrefItem_2", "02past10PrefItem_3", "02past10PrefItem_4", "02past10PrefItem_5",
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"02past10PersItem_1", "02past10PersItem_2", "02past10PersItem_3", "02past10PersItem_4", "02past10PersItem_5",
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"02past10ValItem_1", "02past10ValItem_2", "02past10ValItem_3", "02past10ValItem_4", "02past10ValItem_5",
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"02fut5PrefItem_1", "02fut5PrefItem_2", "02fut5PrefItem_3", "02fut5PrefItem_4", "02fut5PrefItem_5",
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"02fut5PersItem_1", "02fut5PersItem_2", "02fut5PersItem_3", "02fut5PersItem_4", "02fut5PersItem_5",
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"02fut5ValItem_1", "02fut5ValItem_2", "02fut5ValItem_3", "02fut5ValItem_4", "02fut5ValItem_5",
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"02fut10PrefItem_1", "02fut10PrefItem_2", "02fut10PrefItem_3", "02fut10PrefItem_4", "02fut10PrefItem_5",
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"02fut10PersItem_1", "02fut10PersItem_2", "02fut10PersItem_3", "02fut10PersItem_4", "02fut10PersItem_5",
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"02fut10ValItem_1", "02fut10ValItem_2", "02fut10ValItem_3", "02fut10ValItem_4", "02fut10ValItem_5"
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)
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# Define target column names
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target_cols <- c(
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"past_5_pref_read", "past_5_pref_music", "past_5_pref_TV", "past_5_pref_nap", "past_5_pref_travel",
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"past_5_pers_extravert", "past_5_pers_critical", "past_5_pers_dependable", "past_5_pers_anxious", "past_5_pers_complex",
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"past_5_val_obey", "past_5_val_trad", "past_5_val_opinion", "past_5_val_performance", "past_5_val_justice",
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"past_10_pref_read", "past_10_pref_music", "past_10_pref_TV", "past_10_pref_nap", "past_10_pref_travel",
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"past_10_pers_extravert", "past_10_pers_critical", "past_10_pers_dependable", "past_10_pers_anxious", "past_10_pers_complex",
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"past_10_val_obey", "past_10_val_trad", "past_10_val_opinion", "past_10_val_performance", "past_10_val_justice",
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"fut_5_pref_read", "fut_5_pref_music", "fut_5_pref_TV", "fut_5_pref_nap", "fut_5_pref_travel",
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"fut_5_pers_extravert", "fut_5_pers_critical", "fut_5_pers_dependable", "fut_5_pers_anxious", "fut_5_pers_complex",
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"fut_5_val_obey", "fut_5_val_trad", "fut_5_val_opinion", "fut_5_val_performance", "fut_5_val_justice",
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"fut_10_pref_read", "fut_10_pref_music", "fut_10_pref_TV", "fut_10_pref_nap", "fut_10_pref_travel",
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"fut_10_pers_extravert", "fut_10_pers_critical", "fut_10_pers_dependable", "fut_10_pers_anxious", "fut_10_pers_complex",
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"fut_10_val_obey", "fut_10_val_trad", "fut_10_val_opinion", "fut_10_val_performance", "fut_10_val_justice"
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)
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# ============= TROUBLESHOOTING: CHECK COLUMN EXISTENCE =============
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cat("\n=== COLUMN EXISTENCE CHECK ===\n\n")
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# Get actual column names from dataframe (trimmed)
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df_cols <- trimws(names(df))
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# Check Source A columns
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missing_A <- source_cols_A[!source_cols_A %in% df_cols]
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existing_A <- source_cols_A[source_cols_A %in% df_cols]
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cat("Source Set A:\n")
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cat(" Expected: 60 columns\n")
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cat(" Found:", length(existing_A), "columns\n")
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cat(" Missing:", length(missing_A), "columns\n")
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if (length(missing_A) > 0) {
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cat("\n Missing columns from Set A:\n")
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for (col in missing_A) {
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cat(" -", col, "\n")
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}
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}
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# Check Source B columns
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missing_B <- source_cols_B[!source_cols_B %in% df_cols]
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existing_B <- source_cols_B[source_cols_B %in% df_cols]
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cat("\nSource Set B:\n")
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cat(" Expected: 60 columns\n")
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cat(" Found:", length(existing_B), "columns\n")
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cat(" Missing:", length(missing_B), "columns\n")
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if (length(missing_B) > 0) {
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cat("\n Missing columns from Set B:\n")
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for (col in missing_B) {
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cat(" -", col, "\n")
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}
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}
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# Check for columns with similar names (potential typos/spaces)
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if (length(missing_A) > 0 || length(missing_B) > 0) {
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cat("\n\n=== CHECKING FOR SIMILAR COLUMN NAMES ===\n")
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all_missing <- c(missing_A, missing_B)
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for (miss_col in all_missing) {
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# Find columns that start with similar pattern
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pattern <- substr(miss_col, 1, 10)
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similar <- grep(pattern, df_cols, value = TRUE, ignore.case = TRUE)
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if (length(similar) > 0) {
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cat("\nLooking for:", miss_col)
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cat("\n Similar columns found:\n")
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for (sim in similar) {
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cat(" - '", sim, "' (length:", nchar(sim), ")\n", sep = "")
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}
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}
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}
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}
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cat("\n=== END CHECK ===\n\n")
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# Stop if critical columns are missing
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if (length(missing_A) > 30 || length(missing_B) > 30) {
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stop("ERROR: Too many columns missing! Please check column names in CSV file.")
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}
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cat("Proceeding with processing...\n\n")
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# Process each pair of columns
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for (i in 1:60) {
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col_A <- source_cols_A[i]
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col_B <- source_cols_B[i]
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target_col <- target_cols[i]
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# Get values from columns, handling missing columns
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vals_A <- if (col_A %in% names(df)) df[[col_A]] else rep(NA, nrow(df))
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vals_B <- if (col_B %in% names(df)) df[[col_B]] else rep(NA, nrow(df))
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# Coalesce: take value from vals_A if present, otherwise from vals_B
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combined <- ifelse(!is.na(vals_A) & vals_A != "",
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vals_A,
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vals_B)
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# Recode to numeric
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df[[target_col]] <- recode_likert(combined)
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# Print progress
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cat("Processed:", target_col, "\n")
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}
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# ============= VERIFY TARGET COLUMNS WERE CREATED =============
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cat("\n\n=== VERIFYING TARGET COLUMNS ===\n\n")
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# Get updated column names
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df_cols_after <- trimws(names(df))
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# Check which target columns exist
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existing_targets <- target_cols[target_cols %in% df_cols_after]
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missing_targets <- target_cols[!target_cols %in% df_cols_after]
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cat("Target Columns:\n")
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cat(" Expected: 60 columns\n")
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cat(" Created:", length(existing_targets), "columns\n")
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cat(" Missing:", length(missing_targets), "columns\n")
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if (length(missing_targets) > 0) {
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cat("\n WARNING: The following target columns were NOT created:\n")
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for (col in missing_targets) {
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cat(" -", col, "\n")
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}
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stop("\nERROR: Not all target columns were created successfully!")
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} else {
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cat("\n SUCCESS: All 60 target columns created successfully!\n")
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}
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cat("\n=== END VERIFICATION ===\n\n")
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# Save the modified dataframe back to CSV
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write.csv(df, "eohi3.csv", row.names = FALSE)
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cat("\nProcessing complete! 60 new columns added to eohi3.csv\n")
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