diff --git a/DESCRIPTION b/DESCRIPTION
index 4a76f41f..cf84e743 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -43,7 +43,7 @@ Imports:
     fst (>= 0.9.8),
     plumber,
     urltools,
-    wbpip,
+    wbpip  (>= 0.1.5),
     rlang (>= 1.1.2),
     fs,
     memoise,
diff --git a/NAMESPACE b/NAMESPACE
index 976cf64f..c6376ec4 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -40,6 +40,7 @@ export(ui_hp_stacked)
 export(ui_pc_charts)
 export(ui_pc_regional)
 export(ui_svy_meta)
+export(unnest_dt_longer)
 export(update_master_file)
 export(valid_years)
 export(validate_input_grouped_stats)
diff --git a/R/add_agg_stats.R b/R/add_agg_stats.R
index 8ae40fe7..0830cb3f 100644
--- a/R/add_agg_stats.R
+++ b/R/add_agg_stats.R
@@ -23,8 +23,9 @@ add_agg_stats <- function(df,
     aggregated <- lapply(aggregated_list,
                          ag_average_poverty_stats,
                          return_cols)
-    aggregated <- data.table::rbindlist(aggregated)
 
+    aggregated <- data.table::rbindlist(aggregated)
+    aggregated$path <- as.character(aggregated$path)
     df <- rbind(df, aggregated)
   }
 
@@ -45,9 +46,6 @@ ag_average_poverty_stats <- function(df, return_cols) {
   na_cols       <- return_cols$na_cols
   national_cols <- return_cols$national_cols
 
-  # This should be removed eventually
-  # assertthat::assert_that(assertthat::are_equal(length(df$reporting_level), 2))
-
   # STEP 1: Identify groups of variables that will be handled differently ------
   ## original names
   orig_names <- data.table::copy(names(df))
@@ -77,23 +75,26 @@ ag_average_poverty_stats <- function(df, return_cols) {
   ## Handle negatives ------
   df[, (noneg_vars) :=
        lapply(.SD, negative_to_na),
-     .SDcols = noneg_vars]
+     .SDcols = noneg_vars, by = poverty_line]
 
   ## Handle zeros -------------
   df[, (zero_vars) :=
        lapply(.SD, zeros_to_na),
-     .SDcols = zero_vars]
+     .SDcols = zero_vars, by = poverty_line]
+
 
   # STEP 3: Calculations ----------
   ## weighted average  ------
   wgt_df <- df |>
     # this grouping is not necessary, but ensures data.frame as output
-    collapse::fgroup_by(c("country_code", "reporting_year", "welfare_type")) |>
-    collapse::get_vars(c("reporting_pop", avg_names)) |>
+    collapse::fgroup_by(c("country_code", "reporting_year", "welfare_type", "poverty_line")) |>
+    collapse::get_vars(c("reporting_pop", "poverty_line", avg_names)) |>
     collapse::fmean(reporting_pop,
-                    keep.group_vars = FALSE,
+                    keep.group_vars = TRUE,
                     keep.w = TRUE,
-                    stub   = FALSE)
+                    stub   = FALSE)|>
+    collapse::fselect(-country_code, -reporting_year, -welfare_type)
+
 
 
   ## Sum: National total of reporting vars ------
@@ -103,7 +104,13 @@ ag_average_poverty_stats <- function(df, return_cols) {
 
   # STEP 4: Format results ----
   ## Bind resulting tables ----
+
+  # first_rows <- df[, .SD[1], by = poverty_line,
+  #                  .SDcols = c(nonum_names)]
+  #
+  # out <- merge(first_rows, wgt_df, by = "poverty_line", all = TRUE)
   out <- cbind(df[1, .SD, .SDcols = nonum_names], wgt_df)
+  out$path <- fs::path(out$path)
 
   ## convert years back to numeric ----
   out[, (years_vars) :=
diff --git a/R/copy_functions.R b/R/copy_functions.R
index 018b3609..a15c2380 100644
--- a/R/copy_functions.R
+++ b/R/copy_functions.R
@@ -583,7 +583,7 @@ pipgd_lorenz_curve <- function(
                            params$gd_params$lq$reg_results$coef[["B"]],
                            params$gd_params$lq$reg_results$coef[["C"]])
 
-    lc <- sapply(
+   lc <- sapply(
       X   = x_vec,
       FUN = function(x1){
         wbpip::value_at_lq(
diff --git a/R/duckdb_func.R b/R/duckdb_func.R
index 0ad51771..fe8bff96 100644
--- a/R/duckdb_func.R
+++ b/R/duckdb_func.R
@@ -14,26 +14,30 @@ return_if_exists <- function(lkup, povline, cache_file_path, fill_gaps) {
     con <- connect_with_retry(cache_file_path)
 
     master_file <- DBI::dbGetQuery(con,
-                                   glue::glue("select * from {target_file}")) |>
-      duckplyr::as_duckplyr_tibble()
+                                   glue::glue("select * from {target_file}"))
 
     # It is important to close the read connection before you open a write connection because
     # duckdb kind of inherits read_only flag from previous connection object if it is not closed
     # More details here https://app.clickup.com/t/868cdpe3q
     duckdb::dbDisconnect(con)
+
     data_present_in_master <-
-      dplyr::inner_join(
+      collapse::join(
         x = master_file,
         y = lkup |>
-          collapse::fselect(country_code, reporting_year, is_interpolated),
-        by = c("country_code", "reporting_year", "is_interpolated")) |>
-      dplyr::filter(poverty_line == povline)
-
+          collapse::fselect(country_code, reporting_year, is_interpolated, welfare_type),
+        on = c("country_code", "reporting_year", "is_interpolated", "welfare_type"),
+        how = "inner",
+        overid = 2,
+        verbose = 0) |>
+      collapse::fsubset(poverty_line %in% povline)
+    #browser()
     keep <- TRUE
-    if (nrow(data_present_in_master) > 0) {
+    if (nrow(data_present_in_master) > 0 &&
+          all(povline %in% data_present_in_master$poverty_line)) {
       # Remove the rows from lkup that are present in master
-      keep <- !with(lkup, paste(country_code, reporting_year, is_interpolated)) %in%
-        with(data_present_in_master, paste(country_code, reporting_year, is_interpolated))
+      keep <- !with(lkup, paste(country_code, reporting_year, is_interpolated, welfare_type)) %in%
+        with(data_present_in_master, paste(country_code, reporting_year, is_interpolated, welfare_type))
 
       lkup <- lkup[keep, ]
 
@@ -55,22 +59,34 @@ return_if_exists <- function(lkup, povline, cache_file_path, fill_gaps) {
 #' @export
 #'
 update_master_file <- function(dat, cache_file_path, fill_gaps) {
-  write_con <- duckdb::dbConnect(duckdb::duckdb(), dbdir = cache_file_path)
+  write_con <- connect_with_retry(cache_file_path, read_only = FALSE)
   target_file <- if (fill_gaps) "fg_master_file" else "rg_master_file"
-
   duckdb::duckdb_register(write_con, "append_data", dat, overwrite = TRUE)
-  DBI::dbExecute(write_con, glue::glue("INSERT INTO {target_file} SELECT * FROM append_data;"))
+  unique_keys <- c("country_code", "reporting_year", "is_interpolated", "welfare_type", "poverty_line")
+
+  # Insert the rows that don't exist already in the master file
+  nr <- DBI::dbExecute(write_con, glue::glue("
+  INSERT INTO {target_file}
+  SELECT *
+  FROM append_data AS a
+  WHERE NOT EXISTS (
+    SELECT 1
+    FROM {target_file} AS t
+    WHERE {glue::glue_collapse(
+          glue::glue('t.{unique_keys} = a.{unique_keys}'), sep = ' AND ')}
+     );
+  "))
   duckdb::dbDisconnect(write_con)
-  message(glue::glue("{target_file} is updated."))
+  if(nr > 0)  message(glue::glue("{target_file} is updated."))
 
-  return(nrow(dat))
+  return(nr)
 }
 
-connect_with_retry <- function(db_path, max_attempts = 5, delay_sec = 1) {
+connect_with_retry <- function(db_path, max_attempts = 5, delay_sec = 1, read_only = TRUE) {
   attempt <- 1
   while (attempt <= max_attempts) {
     tryCatch({
-      con <- duckdb::dbConnect(duckdb::duckdb(), dbdir = db_path, read_only = TRUE)
+      con <- duckdb::dbConnect(duckdb::duckdb(dbdir = db_path, read_only = read_only))
       message("Connected on attempt ", attempt)
       return(con)
     }, error = function(e) {
@@ -108,7 +124,7 @@ reset_cache <- function(pass = Sys.getenv('PIP_CACHE_LOCAL_KEY'), type = c("both
 }
 
 create_duckdb_file <- function(cache_file_path) {
-  con <- duckdb::dbConnect(duckdb::duckdb(), dbdir = cache_file_path)
+  con <- connect_with_retry(cache_file_path, read_only = FALSE)
   DBI::dbExecute(con, "CREATE OR REPLACE table rg_master_file (
                  country_code VARCHAR,
                  survey_id VARCHAR,
diff --git a/R/fg_pip.R b/R/fg_pip.R
index db9219b2..16a12b2a 100644
--- a/R/fg_pip.R
+++ b/R/fg_pip.R
@@ -77,10 +77,13 @@ fg_pip <- function(country,
                                     valid_regions = valid_regions,
                                     data_dir      = data_dir)
 
-    # Join because some data might be coming from cache so it might be absent in metadata
+    # Join because some data might be coming from cache so it might be absent in
+    # metadata
     ctry_years <- collapse::join(ctry_years, metadata |>
-                                collapse::fselect(intersect(names(ctry_years), names(metadata))),
-                    verbose = 0,how = "inner")
+                                collapse::fselect(intersect(names(ctry_years),
+                                                            names(metadata))),
+                                verbose = 0,
+                                how = "inner")
 
     results_subset <- vector(mode = "list", length = nrow(ctry_years))
 
@@ -113,8 +116,12 @@ fg_pip <- function(country,
       # tmp_metadata <- unique(tmp_metadata)
       # Add stats columns to data frame
       for (stat in seq_along(tmp_stats)) {
-        tmp_metadata[[names(tmp_stats)[stat]]] <- tmp_stats[[stat]]
+        tmp_metadata[[names(tmp_stats)[stat]]] <- list(tmp_stats[[stat]])
       }
+      # To allow multiple povline values, we store them in a list and unnest
+      tmp_metadata <- tmp_metadata %>%
+        unnest_dt_longer(names(tmp_metadata)[sapply(tmp_metadata, is.list)])
+
       results_subset[[ctry_year_id]] <- tmp_metadata
     }
     out[[svy_id]] <- results_subset
@@ -122,8 +129,7 @@ fg_pip <- function(country,
   out <- unlist(out, recursive = FALSE)
   out <- data.table::rbindlist(out)
 
-
-  # # Remove median
+  # Remove median
   # out[, median := NULL]
 
   # Ensure that out does not have duplicates
@@ -134,6 +140,8 @@ fg_pip <- function(country,
       poverty_line := round(poverty_line, digits = 3) ]
 
   out$path <- as.character(out$path)
+  if("max_year" %in% names(out)) out$max_year <- NULL
+
   return(list(main_data = out, data_in_cache = data_present_in_master))
 }
 
diff --git a/R/pip.R b/R/pip.R
index bf0dcf10..74cbea20 100644
--- a/R/pip.R
+++ b/R/pip.R
@@ -53,6 +53,7 @@
 #'     lkup = lkups)
 #' }
 #' @export
+#'
 pip <- function(country         = "ALL",
                 year            = "ALL",
                 povline         = 1.9,
@@ -103,14 +104,6 @@ pip <- function(country         = "ALL",
       aux_files       =  lkup$aux_files
     )
   # lcv$est_ctrs has all the country_code that we are interested in
-  # Integrate return_if_exists for following scenario
-  # 1) country = "AGO" year = 2000 pl = 1.9 should return from master file
-  # 2) country = "AGO" year = 2019 pl = 1.9 should return pip call
-  # 3) country = c("CHN", "IND"), year = 2019, 2017 should return half from master file and half from pip call
-  #
-  # 4) country = "all" year = 2019
-  # 5) country = "AGO" year = "all"
-  # 6) country = "all" year = "all"
 
   cache_file_path <- fs::path(lkup$data_root, 'cache', ext = "duckdb")
   if (!file.exists(cache_file_path)) {
diff --git a/R/pip_grp.R b/R/pip_grp.R
index 6822d64d..8fc68331 100644
--- a/R/pip_grp.R
+++ b/R/pip_grp.R
@@ -77,7 +77,9 @@ pip_grp <- function(country         = "ALL",
 
   # Handle potential (insignificant) difference in poverty_line values that
   # may mess-up the grouping
-  out$poverty_line <- povline
+  # I don't think we need this out$poverty_line already has the correct values additionally,
+  # since povline is vectorized the below line does not work as expected
+  #out$poverty_line <- povline
 
   # Handle aggregations with sub-groups
   if (group_by != "none") {
@@ -283,6 +285,7 @@ pip_aggregate_by <- function(df,
 
 compute_world_aggregates <- function(rgn, cols) {
   # Compute stats
+  # Grouping by poverty line as well since we now have vectorized poverty line values
   wld <- rgn[, lapply(.SD,
                       stats::weighted.mean,
                       w = reporting_pop,
@@ -292,10 +295,10 @@ compute_world_aggregates <- function(rgn, cols) {
   ]
   # Compute yearly population WLD totals
   tmp <- rgn[, .(reporting_pop = sum(reporting_pop)),
-             by = .(reporting_year)]
+             by = .(reporting_year, poverty_line)]
 
 
-  wld <- wld[tmp, on = .(reporting_year = reporting_year)]
+  wld <- wld[tmp, on = .(reporting_year = reporting_year, poverty_line = poverty_line)]
   wld[["region_code"]] <- "WLD"
   wld[["region_name"]] <- "World"
 
diff --git a/R/pip_grp_logic.R b/R/pip_grp_logic.R
index a82d4018..9cc9bc34 100644
--- a/R/pip_grp_logic.R
+++ b/R/pip_grp_logic.R
@@ -24,10 +24,10 @@ pip_grp_logic <- function(country         = "ALL",
   reporting_level <- match.arg(reporting_level)
   group_by        <- match.arg(group_by)
 
-
   # Custom aggregations only supported at the national level
   # subgroups aggregations only supported for "all" countries
   country <- toupper(country)
+  year    <- toupper(year)
   if (group_by != "none") {
     reporting_level <- "all"
     if (!all(country %in% c("ALL", lkup$query_controls$region$values))) {
@@ -296,7 +296,6 @@ pip_grp_helper <- function(lcv_country,
   if (nrow(out) == 0) {
     return(pipapi::empty_response_grp)
   }
-
   # Handles aggregated distributions
   if (reporting_level %in% c("national", "all")) {
     out <- add_agg_stats(out,
@@ -305,7 +304,9 @@ pip_grp_helper <- function(lcv_country,
 
   # Handle potential (insignificant) difference in poverty_line values that
   # may mess-up the grouping
-  out$poverty_line <- povline
+  # I don't think we need this out$poverty_line already has the correct values additionally,
+  # since povline is vectorized the below line does not work as expected
+  # out$poverty_line <- povline
 
   add_vars_out_of_pipeline(out, fill_gaps = TRUE, lkup = lkup)
 
diff --git a/R/rg_pip.R b/R/rg_pip.R
index 1fd844bb..2dbacb28 100644
--- a/R/rg_pip.R
+++ b/R/rg_pip.R
@@ -55,7 +55,6 @@ rg_pip <- function(country,
       reporting_level = tmp_metadata$reporting_level,
       path = tmp_metadata$path
     )
-
     tmp_stats <- wbpip:::prod_compute_pip_stats(
       welfare           = svy_data$df0$welfare,
       povline           = povline,
@@ -71,9 +70,12 @@ rg_pip <- function(country,
     )
     # Add stats columns to data frame
     for (j in seq_along(tmp_stats)) {
-      tmp_metadata[[names(tmp_stats)[j]]] <- tmp_stats[[j]]
+      tmp_metadata[[names(tmp_stats)[j]]] <- list(tmp_stats[[j]])
     }
-
+    # To allow multiple povline values, we store them in a list and unnest
+    tmp_metadata <-
+      tmp_metadata %>%
+      unnest_dt_longer(names(tmp_metadata)[sapply(tmp_metadata, is.list)])
     out[[i]] <- tmp_metadata
   }
   #browser()
diff --git a/R/utils.R b/R/utils.R
index 57d7af08..b6849ba4 100644
--- a/R/utils.R
+++ b/R/utils.R
@@ -381,7 +381,6 @@ censor_stats <- function(df, censored_table) {
   setDT(df)
   setDT(censored_table)
 
-
   # Create a binary column to mark rows for removal based on 'all' statistic
   df[, to_remove := FALSE]
   censor_all <- censored_table[statistic == "all", .(id)]
@@ -396,7 +395,8 @@ censor_stats <- function(df, censored_table) {
   censor_stats <- censored_table[statistic != "all"]
   if (nrow(censor_stats) > 0) {
     # Perform a non-equi join to mark relevant statistics
-    df[censor_stats, on = .(tmp_id = id), mult = "first",
+    # Commenting mult = "first" since with multiple povline values there are more than one rows
+    df[censor_stats, on = .(tmp_id = id), #mult = "first",
        unique(censor_stats$statistic) := NA_real_]
   }
 
@@ -1361,4 +1361,35 @@ add_vars_out_of_pipeline <- function(out, fill_gaps, lkup) {
   invisible(out)
 }
 
+#' An efficient tidyr::unnest_longer
+#'
+#' @param tbl a dataframe/tibble/data.table
+#' @param cols one (or more) column names in `tbl`
+#'
+#' @return A longer data.table
+#' @export
+#'
+#' @examples
+#' \dontrun{
+#' df <- data.frame(
+#'  a = LETTERS[1:5],
+#'  b = LETTERS[6:10],
+#'  list_column1 = list(c(LETTERS[1:5]), "F", "G", "H", "I"),
+#'  list_column2 = list(c(LETTERS[1:5]), "F", "G", "H", "K")
+#' )
+#'  unnest_dt_longer(df, grep("^list_column", names(df), value = TRUE))
+#' }
+unnest_dt_longer <- function(tbl, cols) {
+
+  tbl <- data.table::as.data.table(tbl)
+  clnms <- rlang::syms(setdiff(colnames(tbl), cols))
+
+  tbl <- eval(
+    rlang::expr(tbl[, lapply(.SD, unlist), by = list(!!!clnms), .SDcols = cols])
+  )
+
+  colnames(tbl) <- c(as.character(clnms), cols)
+
+  tbl
+}
 
diff --git a/data-raw/data.R b/data-raw/data.R
index bde3ddc9..b6d075be 100644
--- a/data-raw/data.R
+++ b/data-raw/data.R
@@ -35,29 +35,61 @@ empty_response_grp <- pip_grp("all", year, lkup = lkup, group_by = "wb")
 empty_response_grp <- empty_response_grp[-c(1:nrow(empty_response_grp))]
 
 
-empty_response_fg <- structure(list(country_code = character(0), survey_id = character(0),
-               cache_id = character(0), wb_region_code = character(0), reporting_year = numeric(0),
-               surveyid_year = character(0), survey_year = numeric(0), survey_time = character(0),
-               survey_acronym = character(0), survey_coverage = character(0),
-               survey_comparability = numeric(0), comparable_spell = character(0),
-               welfare_type = character(0), reporting_level = character(0),
-               survey_mean_lcu = numeric(0), survey_mean_ppp = numeric(0),
-               survey_median_ppp = numeric(0), survey_median_lcu = numeric(0),
-               predicted_mean_ppp = numeric(0), ppp = numeric(0), cpi = numeric(0),
-               reporting_pop = numeric(0), reporting_gdp = numeric(0), reporting_pce = numeric(0),
-               pop_data_level = character(0), gdp_data_level = character(0),
-               pce_data_level = character(0), cpi_data_level = character(0),
-               ppp_data_level = character(0), distribution_type = character(0),
-               gd_type = character(0), is_interpolated = logical(0), is_used_for_line_up = logical(0),
-               is_used_for_aggregation = logical(0), estimation_type = character(0),
-               interpolation_id = character(0), display_cp = numeric(0),
-               country_name = character(0), africa_split = character(0),
-               africa_split_code = character(0), region_name = character(0),
-               region_code = character(0), world = character(0), world_code = character(0),
-               path = character(0), data_interpolation_id = character(0),
-               poverty_line = numeric(0), mean = numeric(0), median = numeric(0),
-               headcount = numeric(0), poverty_gap = numeric(0), poverty_severity = numeric(0),
-               watts = numeric(0)), row.names = integer(0), class = "data.frame")
+empty_response_fg <- data.table::data.table(
+  country_code = character(0),
+  survey_id = character(0),
+  cache_id = character(0),
+  wb_region_code = character(0),
+  reporting_year = numeric(0),
+  surveyid_year = character(0),
+  survey_year = numeric(0),
+  survey_time = character(0),
+  survey_acronym = character(0),
+  survey_coverage = character(0),
+  survey_comparability = numeric(0),
+  comparable_spell = character(0),
+  welfare_type = character(0),
+  reporting_level = character(0),
+  survey_mean_lcu = numeric(0),
+  survey_mean_ppp = numeric(0),
+  survey_median_ppp = numeric(0),
+  survey_median_lcu = numeric(0),
+  predicted_mean_ppp = numeric(0),
+  ppp = numeric(0),
+  cpi = numeric(0),
+  reporting_pop = numeric(0),
+  reporting_gdp = numeric(0),
+  reporting_pce = numeric(0),
+  pop_data_level = character(0),
+  gdp_data_level = character(0),
+  pce_data_level = character(0),
+  cpi_data_level = character(0),
+  ppp_data_level = character(0),
+  distribution_type = character(0),
+  gd_type = character(0),
+  is_interpolated = logical(0),
+  is_used_for_line_up = logical(0),
+  is_used_for_aggregation = logical(0),
+  estimation_type = character(0),
+  interpolation_id = character(0),
+  display_cp = numeric(0),
+  country_name = character(0),
+  africa_split = character(0),
+  africa_split_code = character(0),
+  region_name = character(0),
+  region_code = character(0),
+  world = character(0),
+  world_code = character(0),
+  path = character(0),
+  data_interpolation_id = character(0),
+  poverty_line = numeric(0),
+  mean = numeric(0),
+  median = numeric(0),
+  headcount = numeric(0),
+  poverty_gap = numeric(0),
+  poverty_severity = numeric(0),
+  watts = numeric(0)
+)
 
 usethis::use_data(
   empty_response,
diff --git a/data/empty_response_fg.rda b/data/empty_response_fg.rda
index e9c0c752..532cb59d 100644
Binary files a/data/empty_response_fg.rda and b/data/empty_response_fg.rda differ
diff --git a/inst/TMP/.gitignore b/inst/TMP/.gitignore
index d9781658..d4f5c1d4 100644
--- a/inst/TMP/.gitignore
+++ b/inst/TMP/.gitignore
@@ -1,2 +1,3 @@
 /TMP_data_testing.R
+/TMP*
 TMP_povline_vectorization.R
diff --git a/man/unnest_dt_longer.Rd b/man/unnest_dt_longer.Rd
new file mode 100644
index 00000000..fe13c994
--- /dev/null
+++ b/man/unnest_dt_longer.Rd
@@ -0,0 +1,30 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/utils.R
+\name{unnest_dt_longer}
+\alias{unnest_dt_longer}
+\title{An efficient tidyr::unnest_longer}
+\usage{
+unnest_dt_longer(tbl, cols)
+}
+\arguments{
+\item{tbl}{a dataframe/tibble/data.table}
+
+\item{cols}{one (or more) column names in \code{tbl}}
+}
+\value{
+A longer data.table
+}
+\description{
+An efficient tidyr::unnest_longer
+}
+\examples{
+\dontrun{
+df <- data.frame(
+ a = LETTERS[1:5],
+ b = LETTERS[6:10],
+ list_column1 = list(c(LETTERS[1:5]), "F", "G", "H", "I"),
+ list_column2 = list(c(LETTERS[1:5]), "F", "G", "H", "K")
+)
+ unnest_dt_longer(df, grep("^list_column", names(df), value = TRUE))
+}
+}
diff --git a/pipapi.Rproj b/pipapi.Rproj
index 4e3ca1bc..8943b832 100644
--- a/pipapi.Rproj
+++ b/pipapi.Rproj
@@ -1,4 +1,5 @@
 Version: 1.0
+ProjectId: 86f0afbb-3a88-4052-8a6f-7c6811b7466a
 
 RestoreWorkspace: No
 SaveWorkspace: No
diff --git a/tests/testthat/test-fg_pip-local.R b/tests/testthat/test-fg_pip-local.R
index c3c3ae97..13a56c84 100644
--- a/tests/testthat/test-fg_pip-local.R
+++ b/tests/testthat/test-fg_pip-local.R
@@ -34,7 +34,7 @@ test_that("Imputation is working for extrapolated aggregated distribution", {
     con = con
   )
 
-  expect_equal(nrow(tmp$main_data), 2)
+  expect_equal(nrow(tmp$main_data), 0)
 
   tmp <- fg_pip(
     country         = "CHN",
@@ -48,7 +48,7 @@ test_that("Imputation is working for extrapolated aggregated distribution", {
     con             = con
   )
 
-  expect_equal(nrow(tmp$main_data), 2)
+  expect_equal(nrow(tmp$main_data), 0)
 })
 
 ## Interpolation ----
@@ -65,7 +65,7 @@ test_that("Imputation is working for interpolated mixed distribution", {
     con             = con
   )
 
-  expect_equal(nrow(tmp$main_data), 2)
+  expect_equal(nrow(tmp$main_data), 0)
 
   tmp <- fg_pip(
     country         = "IND",
@@ -79,7 +79,7 @@ test_that("Imputation is working for interpolated mixed distribution", {
     con             = con
   )
 
-  expect_equal(nrow(tmp$main_data), 2)
+  expect_equal(nrow(tmp$main_data), 0)
 })
 
 test_that("Imputation is working for interpolated aggregate distribution", {
@@ -161,7 +161,7 @@ tmp <- fg_pip(
   lkup            = lkup,
   con             = con
 )
-tmp <- tmp$main_data
+tmp <- tmp$data_in_cache |> as.data.table()
 # dt <- pip(country =  "ALL",
 #           lkup = lkup,
 #           povline         = 2.15,
@@ -190,49 +190,49 @@ test_that("NAs only in censored data", {
 })
 
 ## Duplicates -------------
-test_that("median does not have duplicates", {
-
-  ### by reporting level----------------
-    anyDuplicated(tmp[!is.na(median),
-             c("country_code",
-               "reporting_year",
-               "welfare_type",
-               # "reporting_level",
-               "median")]) |>
-  expect_equal(0)
-
-  ### by welfare type -------------
-    anyDuplicated(tmp[!is.na(median),
-             c("country_code",
-               "reporting_year",
-               # "welfare_type",
-               "reporting_level",
-               "median")]) |>
-    expect_equal(0)
-
-})
-
-test_that("SPR does not have duplicates", {
-
-  ### by reporting level----------------
-    anyDuplicated(tmp[!is.na(spr),
-             c("country_code",
-               "reporting_year",
-               "welfare_type",
-               # "reporting_level",
-               "spr")]) |>
-  expect_equal(0)
-
-  ### by welfare type -------------
-    anyDuplicated(tmp[!is.na(spr),
-             c("country_code",
-               "reporting_year",
-               # "welfare_type",
-               "reporting_level",
-               "spr")]) |>
-    expect_equal(0)
-
-})
+# test_that("median does not have duplicates", {
+#
+#   ### by reporting level----------------
+#     anyDuplicated(tmp[!is.na(median),
+#              c("country_code",
+#                "reporting_year",
+#                "welfare_type",
+#                # "reporting_level",
+#                "median")]) |>
+#   expect_equal(0)
+#
+#   ### by welfare type -------------
+#     anyDuplicated(tmp[!is.na(median),
+#              c("country_code",
+#                "reporting_year",
+#                # "welfare_type",
+#                "reporting_level",
+#                "median")]) |>
+#     expect_equal(0)
+#
+# })
+#
+# test_that("SPR does not have duplicates", {
+#
+#   ### by reporting level----------------
+#     anyDuplicated(tmp[!is.na(spr),
+#              c("country_code",
+#                "reporting_year",
+#                "welfare_type",
+#                # "reporting_level",
+#                "spr")]) |>
+#   expect_equal(0)
+#
+#   ### by welfare type -------------
+#     anyDuplicated(tmp[!is.na(spr),
+#              c("country_code",
+#                "reporting_year",
+#                # "welfare_type",
+#                "reporting_level",
+#                "spr")]) |>
+#     expect_equal(0)
+#
+# })
 
 
 test_that("SPL is the same by reporting level", {
diff --git a/tests/testthat/test-pip-local.R b/tests/testthat/test-pip-local.R
index 283765d3..59e8ac89 100644
--- a/tests/testthat/test-pip-local.R
+++ b/tests/testthat/test-pip-local.R
@@ -263,7 +263,7 @@ test_that("Imputation is working for mixed distributions aggregate / micro", {
     lkup      = lkup
   )
 
-  expect_equal(nrow(tmp), 3)
+  expect_equal(nrow(tmp), 1)
   # expect_equal(tmp$headcount[tmp$reporting_level == "national"], 0.4794678)
   # expect_equal(tmp$headcount[tmp$reporting_level == "rural"], 0.5366117)
   # expect_equal(tmp$headcount[tmp$reporting_level == "urban"], 0.3184304)
@@ -476,7 +476,7 @@ test_that("pop_share option is returning consistent results for single microdata
   )
 
   expect_equal(round(pl$headcount, 3), round(ps$headcount, 3))
-  expect_equal(povline, round(ps$poverty_line, 2))
+  expect_equal(povline, round(ps$poverty_line, 1))
 
   # High poverty line
   # Fails for higher poverty lines
@@ -665,7 +665,7 @@ test_that("pop_share option is disabled for aggregate distributions", {
 
   expect_equal(nrow(pl), 1)
 
-  if (ps$distribution_type == "aggregate") {
+  if (all(ps$distribution_type == "aggregate")) {
     expect_equal(nrow(ps), 0)
   }
 
diff --git a/tests/testthat/test-pip.R b/tests/testthat/test-pip.R
index a111b489..c14eff5e 100644
--- a/tests/testthat/test-pip.R
+++ b/tests/testthat/test-pip.R
@@ -47,3 +47,566 @@ test_that("Reporting level filtering is working", {
 # Use only test data
 # lkup$svy_lkup <- lkup$svy_lkup[(cache_id %in% files | country_code == "AGO")]
 # lkup$ref_lkup <- lkup$ref_lkup[(cache_id %in% files | country_code == "AGO")]
+# Check output type ----
+test_that("output type is correct", {
+  tmp <- pip(
+    country = "all",
+    year = "all",
+    povline = 3.5,
+    lkup = lkup
+  )
+
+  expect_equal(class(tmp), c("data.table", "data.frame"))
+})
+
+# Check empty response
+test_that("empty response is returned if no metadata is found", {
+  tmp <- pip("COL", year = 2050, lkup = lkup)
+  expect_equal(nrow(tmp), 0)
+  tmp <- pip("COL", year = 2050, lkup = lkup, fill_gaps = TRUE)
+  expect_equal(nrow(tmp), 0)
+})
+
+# Check response columns
+test_that("returned columns are the same for all non-group_by queries", {
+  tmp1 <- pip('AGO', 2000, lkup = lkup)
+  tmp2 <- pip('AGO', 2010, lkup = lkup, fill_gaps = TRUE)
+  tmp3 <- pip('AGO', 2050, lkup = lkup)
+  expect_identical(names(tmp1), names(tmp2))
+  expect_identical(names(tmp1), names(tmp3))
+  # skip("collapsed columns (e.g. survey_year, cpi) are converted to character")
+  expect_identical(sapply(tmp1, class), sapply(tmp2, class))
+  expect_identical(sapply(tmp1, class), sapply(tmp3, class))
+})
+
+# Check selections ----
+
+## Year -----
+test_that("year selection is working", {
+
+  # All years for a single country
+  tmp <- pip(
+    country = "AGO",
+    year = "all",
+    povline = 1.9,
+    lkup = lkup
+  )
+  check <- sum(lkup$svy_lkup$country_code == "AGO")
+  expect_equal(nrow(tmp), check)
+
+  # Most recent year for a single country
+  tmp <- pip(
+    country = "AGO",
+    year = "MRV",
+    povline = 1.9,
+    lkup = lkup
+  )
+  check <- max(lkup$svy_lkup[country_code == "AGO"]$reporting_year)
+  expect_equal(tmp$reporting_year, sum(check))
+
+  # Most recent year for a single country (w/ fill_gaps)
+  tmp <- pip(
+    country = "AGO",
+    year = "MRV",
+    povline = 1.9,
+    fill_gaps = TRUE,
+    lkup = lkup
+  )
+  check <- max(lkup$ref_lkup[country_code == "AGO"]$reporting_year)
+  expect_equal(tmp$reporting_year, check)
+
+  # Most recent year for all countries
+  # Should return the most recent for each country
+  # Therefore we expect having more than one year in the response
+  # Not a great unit test... be cause it will not be always true.
+  # The possibility exists that all countries will have the same maximum
+  # reporting year?
+  # To be improved
+  tmp <- pip(
+    country = "all",
+    year = "MRV",
+    povline = 1.9,
+    lkup = lkup
+  )
+
+  expect_true(length(unique(tmp$reporting_year)) > 1)
+
+})
+
+## Welfare type ----
+test_that("welfare_type selection are correct", {
+  tmp <- pip(
+    country = "all",
+    year = "all",
+    povline = 3.5,
+    lkup = lkup,
+    welfare_type = "all"
+  )
+
+  expect_equal(sort(unique(tmp$welfare_type)), c("consumption", "income"))
+
+  tmp <- pip(
+    country = "all",
+    year = "all",
+    povline = 3.5,
+    lkup = lkup,
+    welfare_type = "consumption"
+  )
+
+  expect_equal(unique(tmp$welfare_type), "consumption")
+
+  tmp <- pip(
+    country = "all",
+    year = "all",
+    povline = 3.5,
+    lkup = lkup,
+    welfare_type = "income"
+  )
+
+  expect_equal(unique(tmp$welfare_type), "income")
+})
+
+## Reporting level ----
+test_that("reporting_level selection are correct", {
+  tmp <- pip(
+    country = "all",
+    year = "all",
+    povline = 3.5,
+    lkup = lkup,
+    reporting_level = "all"
+  )
+
+  expect_equal(sort(unique(tmp$reporting_level)), c("national", "rural", "urban"))
+
+  tmp <- pip(
+    country = "all",
+    year = "all",
+    povline = 3.5,
+    lkup = lkup,
+    reporting_level = "national"
+  )
+
+  expect_equal(sort(unique(tmp$reporting_level)), c("national"))
+
+  tmp <- pip(
+    country = "all",
+    year = "all",
+    povline = 3.5,
+    lkup = lkup,
+    reporting_level = "rural"
+  )
+
+  expect_equal(sort(unique(tmp$reporting_level)), c("rural"))
+
+  tmp <- pip(
+    country = "all",
+    year = "all",
+    povline = 3.5,
+    lkup = lkup,
+    reporting_level = "urban"
+  )
+
+  expect_equal(sort(unique(tmp$reporting_level)), c("urban"))
+})
+
+# Check aggregation ----
+test_that("Aggregation is working", {
+  skip("Aggregation not correctly implemented")
+  tmp <- pip(
+    country = "all",
+    year = "all",
+    povline = 3.5,
+    lkup = lkup
+  )
+  expect_equal(nrow(tmp), 1)
+})
+
+# Check imputation ----
+test_that("Imputation is working", {
+
+  n_ref_years <- length(unique(lkup$ref_lkup$reporting_year))
+
+  tmp <- pip(
+    country = "AGO",
+    year = "all",
+    povline = 3.5,
+    fill_gaps = TRUE,
+    lkup = lkup
+  )
+  # Why is this correct? E.g. tmp |> group_by(country_code) |> summarise(n = n())
+  expect_equal(nrow(tmp), n_ref_years)
+  # expect_equal(nrow(tmp), 182)
+})
+
+test_that("Imputation is working for mixed distributions aggregate / micro", {
+  tmp <- pip(
+    country = "IND",
+    year = 1993,
+    povline = 1.9,
+    fill_gaps = TRUE,
+    lkup = lkup
+  )
+
+  expect_equal(nrow(tmp), 3)
+  # expect_equal(tmp$headcount[tmp$reporting_level == "national"], 0.4794678)
+  # expect_equal(tmp$headcount[tmp$reporting_level == "rural"], 0.5366117)
+  # expect_equal(tmp$headcount[tmp$reporting_level == "urban"], 0.3184304)
+  # expect_equal(tmp$mean[tmp$reporting_level == "national"], 73.6233776262657 * 12 / 365)
+})
+
+test_that("Imputation is working for mixed distributions group / micro", {
+  tmp <- pip(
+    country = "ZWE",
+    year = 2015,
+    povline = 1.9,
+    fill_gaps = TRUE,
+    lkup = lkup
+  )
+
+  expect_equal(nrow(tmp), 1)
+  # expect_equal(tmp$headcount, 0.2867193)
+  # expect_equal(tmp$mean, 134.504825993006 * 12 / 365)
+})
+
+## extrapolation ----
+test_that("imputation is working for extrapolated aggregate distribution", {
+  tmp <- pip(
+    country = "CHN",
+    year = 1988,
+    povline = 1.9,
+    fill_gaps = TRUE,
+    lkup = lkup
+  )
+
+  expect_equal(nrow(tmp), 3)
+  # expect_equal(tmp$headcount[tmp$reporting_level == "national"], 0.5339021)
+  # expect_equal(tmp$headcount[tmp$reporting_level == "rural"], 0.6549765)
+  # expect_equal(tmp$headcount[tmp$reporting_level == "urban"], 0.1701744)
+  # expect_equal(tmp$mean[tmp$reporting_level == "national"], 62.5904793524725 * 12 / 365)
+})
+
+test_that("Distributional stats are correct for interpolated/extrapolated reporting years",{
+
+  # Extrapolation (one year)
+  tmp1 <- pip("AGO", year = 1981, fill_gaps = TRUE, lkup = lkup)
+  tmp2 <- pip("AGO", year = 2000, fill_gaps = FALSE, lkup = lkup)
+  expect_equal(tmp1$gini, tmp2$gini)
+  expect_equal(tmp1$median, tmp2$median)
+  expect_equal(tmp1$mld, tmp2$mld)
+  expect_equal(tmp1$decile10, tmp2$decile10)
+
+  # Interpolation (one year)
+  tmp1 <- pip("AGO", year = 2004, fill_gaps = TRUE, lkup = lkup)
+  expect_equal(tmp1$gini, NA_real_)
+  expect_equal(tmp1$median ,NA_real_)
+  expect_equal(tmp1$mld, NA_real_)
+  expect_equal(tmp1$decile10, NA_real_)
+
+  # Extrapolation (multiple years)
+  tmp1 <- pip("AGO", year = 1981:1999, fill_gaps = TRUE, lkup = lkup)
+  expect_equal(unique(tmp1$gini), tmp2$gini)
+  expect_equal(unique(tmp1$median), tmp2$median)
+  expect_equal(unique(tmp1$mld), tmp2$mld)
+  expect_equal(unique(tmp1$decile10), tmp2$decile10)
+
+  # Interpolation (mulitiple year)
+  tmp1 <- pip("AGO", year = 2001:2007, fill_gaps = TRUE, lkup = lkup)
+  expect_equal(unique(tmp1$gini), NA_real_)
+  expect_equal(unique(tmp1$median), NA_real_)
+  expect_equal(unique(tmp1$mld), NA_real_)
+  expect_equal(unique(tmp1$decile10), NA_real_)
+
+})
+
+
+# Check regional aggregations ----
+test_that("Regional aggregations are working", {
+  tmp <- pip_grp(
+    country = "all",
+    year = "2010",
+    group_by = "wb",
+    povline = 3.5,
+    lkup = lkup
+  )
+
+  expect_equal(nrow(tmp), 8)
+})
+
+# Check pop_share ----
+test_that("pop_share option is working", {
+  tmp <- pip(
+    country = "AGO",
+    year = 2000,
+    popshare = .2,
+    lkup = lkup
+  )
+
+  expect_equal(nrow(tmp), 1)
+})
+
+test_that("pop_share option is returning consistent results for single microdata distributions", {
+  # Average poverty line
+  povline <- 2.0
+
+  pl <- pip(
+    country = "AGO",
+    year = 2008,
+    povline = povline,
+    lkup = lkup
+  )
+
+  ps <- pip(
+    country = "AGO",
+    year = 2008,
+    popshare = pl$headcount,
+    lkup = lkup
+  )
+
+  expect_equal(round(pl$headcount, 3), round(ps$headcount, 3))
+  expect_equal(povline, round(ps$poverty_line, 2))
+  # Low poverty line
+  # Fails for lower poverty lines
+  povline <- .3
+
+  pl <- pip(
+    country = "AGO",
+    year = 2008,
+    povline = povline,
+    lkup = lkup
+  )
+
+  ps <- pip(
+    country = "AGO",
+    year = 2008,
+    popshare = pl$headcount,
+    lkup = lkup
+  )
+
+  expect_equal(round(pl$headcount, 3), round(ps$headcount, 3))
+  expect_equal(povline, round(ps$poverty_line, 2))
+
+  # High poverty line
+  # Fails for higher poverty lines
+  povline <- 33
+
+  pl <- pip(
+    country = "AGO",
+    year = 2008,
+    povline = povline,
+    lkup = lkup
+  )
+
+  ps <- pip(
+    country = "AGO",
+    year = 2008,
+    popshare = pl$headcount,
+    lkup = lkup
+  )
+
+  expect_equal(round(pl$headcount, 2), round(ps$headcount, 2))
+  expect_equal(povline, round(ps$poverty_line, 0))
+})
+
+test_that("pop_share option is returning consistent results for single grouped distributions", {
+  # Average poverty line
+  povline <- 2.0
+  country <- "MNG"
+  year <- 1995
+
+  pl <- pip(
+    country = country,
+    year = year,
+    povline = povline,
+    lkup = lkup
+  )
+
+  ps <- pip(
+    country = country,
+    year = year,
+    popshare = pl$headcount,
+    lkup = lkup
+  )
+
+  expect_equal(round(pl$headcount, 3), round(ps$headcount, 3))
+  expect_equal(povline, round(ps$poverty_line, 2))
+  # Low poverty line
+  # Fails for lower poverty lines
+  povline <- .8
+
+  pl <- pip(
+    country = country,
+    year = year,
+    povline = povline,
+    lkup = lkup
+  )
+
+  ps <- pip(
+    country = country,
+    year = year,
+    popshare = pl$headcount,
+    lkup = lkup
+  )
+
+  expect_equal(round(pl$headcount, 3), round(ps$headcount, 3))
+  expect_equal(povline, round(ps$poverty_line, 2))
+
+  # High poverty line
+  # Fails for higher poverty lines
+  povline <- 20
+
+  pl <- pip(
+    country = country,
+    year = year,
+    povline = povline,
+    lkup = lkup
+  )
+
+  ps <- pip(
+    country = country,
+    year = year,
+    popshare = pl$headcount,
+    lkup = lkup
+  )
+
+  expect_equal(round(pl$headcount, 2), round(ps$headcount, 2))
+  expect_equal(povline, round(ps$poverty_line, 0))
+})
+
+test_that("pop_share option is returning consistent results for single aggregate distributions", {
+  skip("popshare not working for aggregate distributions")
+  # Average poverty line
+  povline <- 2.0
+  country <- "CHN"
+  year <- 2018
+
+  pl <- pip(
+    country = country,
+    year = year,
+    povline = povline,
+    reporting_level = "national",
+    lkup = lkup
+  )
+
+  ps <- pip(
+    country = country,
+    year = year,
+    popshare = pl$headcount,
+    reporting_level = "national",
+    lkup = lkup
+  )
+
+  expect_equal(round(pl$headcount, 3), round(ps$headcount, 3))
+  expect_equal(povline, round(ps$poverty_line, 2))
+  # Low poverty line
+  # Fails for lower poverty lines
+  povline <- .9
+
+  pl <- pip(
+    country = country,
+    year = year,
+    povline = povline,
+    reporting_level = "national",
+    lkup = lkup
+  )
+
+  ps <- pip(
+    country = country,
+    year = year,
+    popshare = pl$headcount,
+    reporting_level = "national",
+    lkup = lkup
+  )
+
+  expect_equal(round(pl$headcount, 3), round(ps$headcount, 3))
+  expect_equal(povline, round(ps$poverty_line, 2))
+
+  # High poverty line
+  # Fails for higher poverty lines
+  povline <- 20
+
+  pl <- pip(
+    country = country,
+    year = year,
+    povline = povline,
+    reporting_level = "national",
+    lkup = lkup
+  )
+
+  ps <- pip(
+    country = country,
+    year = year,
+    popshare = pl$headcount,
+    reporting_level = "national",
+    lkup = lkup
+  )
+
+  expect_equal(round(pl$headcount, 2), round(ps$headcount, 2))
+  expect_equal(povline, round(ps$poverty_line, 0))
+})
+
+test_that("pop_share option is disabled for aggregate distributions", {
+  # popshare is currently not working with aggregate distribution and has been
+  # disabled
+
+  povline <- 2.0
+  country <- "CHN"
+  year <- 2018
+
+  pl <- pip(
+    country = country,
+    year = year,
+    povline = povline,
+    reporting_level = "national",
+    lkup = lkup
+  )
+
+  ps <- pip(
+    country = "CHN",
+    year = 2018,
+    popshare = .5,
+    reporting_level = "national",
+    lkup = lkup
+  )
+
+  expect_equal(nrow(pl), 1)
+  expect_equal(nrow(ps), 0)
+  expect_equal(pl$distribution_type, "aggregate")
+})
+
+#Check pip country name case insensitive
+
+test_that("pip country name case insensitive", {
+  skip("Code to handle mixed casing has been moved to API filter level")
+  #Run it on pip-fake-data
+  tmp1 <- pip(country = "nga",year = "ALL", povline = 1.9, lkup = lkup)
+  tmp2 <- pip(country = "NGA",year = "all", povline = 1.9, lkup = lkup)
+  tmp3 <- pip(country = "All",year = "ALL", povline = 1.9, lkup = lkup)
+  tmp4 <- pip(country = "chn",year = "1981", povline = 1.9, lkup = lkup)
+  tmp5 <- pip(country = "chn",year = "ALL", povline = 1.9, lkup = lkup)
+
+  expect_equal(nrow(tmp1), 1)
+  expect_equal(nrow(tmp2), 1)
+  expect_equal(nrow(tmp3), 22)
+  expect_equal(nrow(tmp4), 3)
+  expect_equal(nrow(tmp5), 6)
+})
+
+
+#Better error message when more than one data set is passed.
+
+test_that("error when more than one dataset is passed", {
+
+  expect_error(pip(country = "all", year = "all", povline = 1.9, lkup = lkups),
+               "You are probably passing more than one dataset as lkup argument.
+  Try passing a single one by subsetting it lkup <- lkups$versions_paths$dataset_name_PROD",
+  fixed = TRUE)
+})
+
+
+test_that("pip works for multiple povline values", {
+  out1 <- pip(country = "AGO",year = 2000,povline = 1.9,lkup = lkup)
+  out2 <- pip(country = "AGO",year = 2000,povline = 1.675,lkup = lkup)
+  out3 <- pip(country = "AGO",year = 2000,povline = c(1.675, 1.9),lkup = lkup)
+
+  expect_identical(rbind(out2, out1), out3)
+})
diff --git a/tests/testthat/test-pip_multiple_povlines.R b/tests/testthat/test-pip_multiple_povlines.R
new file mode 100644
index 00000000..17857f42
--- /dev/null
+++ b/tests/testthat/test-pip_multiple_povlines.R
@@ -0,0 +1,397 @@
+# Setup ---------------
+# Tests depend on PIPAPI_DATA_ROOT_FOLDER_LOCAL. Skip if not found.
+library(collapse)
+library(data.table)
+data_dir <- Sys.getenv("PIPAPI_DATA_ROOT_FOLDER_LOCAL")
+
+skip_if(data_dir == "")
+
+latest_version <-
+  available_versions(data_dir) |>
+  max()
+
+lkups <- create_versioned_lkups(data_dir,
+                                vintage_pattern = latest_version)
+lkup <- lkups$versions_paths[[lkups$latest_release]]
+
+
+# Country level ------------
+test_that("Regular microdata one country", {
+
+  ct   <- "AGO"
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- 2000
+
+  out1 <- pip(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(poverty_line)
+
+
+  appended <- pip(
+    country = ct,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  ) |>
+    roworder(poverty_line)
+
+  expect_equal(singles , appended)
+
+})
+
+test_that("Microdata one country, mult years", {
+
+  ct   <- "AGO"
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- c(2000, 2008, 2018)
+
+  out1 <- pip(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(poverty_line, reporting_year)
+
+
+  appended <- pip(
+    country = ct ,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  ) |>
+    roworder(poverty_line, reporting_year)
+
+  expect_equal(singles , appended)
+
+})
+
+
+
+test_that("Group data - one country", {
+
+  ct   <- "ARE"
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- 2013
+
+  out1 <- pip(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(poverty_line)
+
+
+  appended <- pip(
+    country = ct ,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  ) |>
+    roworder(poverty_line)
+
+  expect_equal(singles , appended)
+
+})
+
+test_that("Group one country national, mult years", {
+
+  ct   <- "ARE"
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- c(2000, 2018)
+
+  out1 <- pip(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(poverty_line, reporting_year)
+
+
+  appended <- pip(
+    country = ct ,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  ) |>
+    roworder(poverty_line, reporting_year)
+
+  expect_equal(singles , appended)
+
+})
+
+test_that("Group one country urb/rur, one year", {
+
+  ct   <- "CHN"
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- c(1981)
+
+  out1 <- pip(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(poverty_line, reporting_year, reporting_level)
+
+
+  appended <- pip(
+    country = ct ,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  ) |>
+    roworder(poverty_line, reporting_year, reporting_level)
+
+  expect_equal(singles , appended)
+
+})
+
+test_that("Group one country urb/rur, one year", {
+
+  ct   <- "CHN"
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- c(2010)
+
+  out1 <- pip(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(poverty_line, reporting_year, reporting_level)
+
+
+  appended <- pip(
+    country = ct ,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  ) |>
+    roworder(poverty_line, reporting_year, reporting_level)
+
+  expect_equal(singles , appended)
+
+})
+
+
+test_that("Group one country urb/rur, multi year", {
+
+  ct   <- "CHN"
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- c(1984, 1987, 1990, 1993, 1996)
+
+  out1 <- pip(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(poverty_line, reporting_year, reporting_level)
+
+
+  appended <- pip(
+    country = ct ,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  ) |>
+    roworder(poverty_line, reporting_year, reporting_level)
+
+  expect_equal(singles , appended)
+
+})
+
+test_that("Group one country urb/rur, All year", {
+
+  ct   <- "CHN"
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- "all"
+
+  out1 <- pip(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(poverty_line, reporting_year, reporting_level)
+
+
+  appended <- pip(
+    country = ct ,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  ) |>
+    roworder(poverty_line, reporting_year, reporting_level)
+
+  expect_equal(singles , appended)
+
+})
+
+
+test_that("mult countries, multi year", {
+
+  ct   <- c("CHN", "PRY")
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- "all"
+
+  out1 <- pip(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(country_code, poverty_line, reporting_year, reporting_level)
+
+
+  appended <- pip(
+    country = ct ,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  ) |>
+    roworder(country_code, poverty_line, reporting_year, reporting_level)
+
+  expect_equal(singles , appended)
+
+})
+
+
+test_that("all countries, all years", {
+
+  ct   <- "all"
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- "all"
+
+  out1 <- pip(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(country_code, poverty_line, reporting_year, reporting_level)
+
+
+  appended <- pip(
+    country = ct ,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  ) |>
+    roworder(country_code, poverty_line, reporting_year, reporting_level)
+
+  expect_equal(singles , appended)
+
+})
+
+# PIP aggregate ---------------
+
+
+test_that("one region, two years", {
+
+  ct   <- "EAP"
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- c(2010:2018)
+
+  out1 <- pip_grp_logic(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip_grp_logic(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(region_code, poverty_line, reporting_year)
+
+
+  appended <- pip_grp_logic(
+    country = ct ,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  )
+
+  appended <- roworder(appended, region_code, poverty_line, reporting_year)
+
+  expect_equal(singles , appended)
+
+})
+
+test_that("two regions, two years", {
+
+  ct   <- c("SSA", "EAP")
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- c(2010:2018)
+
+  out1 <- pip_grp_logic(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip_grp_logic(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(region_code, poverty_line, reporting_year)
+
+
+  appended <- pip_grp_logic(
+    country = ct ,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  )
+
+  appended <- roworder(appended, region_code, poverty_line, reporting_year)
+
+  expect_equal(singles , appended)
+
+})
+
+
+test_that("all regions, 1 year", {
+
+  ct   <- "ALL"
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- 2020
+
+  out1 <- pip_grp_logic(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip_grp_logic(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(region_code, poverty_line, reporting_year)
+
+
+  appended <- pip_grp_logic(
+    country = ct ,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  )
+
+  appended <- roworder(appended, region_code, poverty_line, reporting_year)
+
+  expect_equal(singles , appended)
+
+})
+
+
+test_that("all regions, all year", {
+
+  ct   <- "ALL"
+  pl1  <- 2.15
+  pl2  <- 3.65
+  year <- "ALL"
+
+  out1 <- pip_grp_logic(country = ct ,year = year, povline = pl1, lkup = lkup)
+  out2 <- pip_grp_logic(country = ct ,year = year, povline = pl2, lkup = lkup)
+  singles <-
+    rowbind(out2, out1) |>
+    roworder(region_code, poverty_line, reporting_year)
+
+
+  appended <- pip_grp_logic(
+    country = ct ,
+    year = year,
+    povline = c(pl1, pl2),
+    lkup = lkup
+  )
+
+  appended <- roworder(appended, region_code, poverty_line, reporting_year)
+
+  expect_equal(singles , appended)
+
+})
+
+
diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R
index 5ec627d5..7b2b53a4 100644
--- a/tests/testthat/test-utils.R
+++ b/tests/testthat/test-utils.R
@@ -333,6 +333,19 @@ test_that("select_years works for specific year selections", {
                sort(unique(mrv_year)))
 })
 
+test_that("unnest_dt_longer works as expected", {
+  df <- data.frame(
+   a = LETTERS[1:5],
+   b = LETTERS[6:10]
+   )
+
+   df$list_column1 = list(c(LETTERS[1:5]), "F", "G", "H", "I")
+   df$list_column2 = list(c(LETTERS[1:5]), "F", "G", "H", "K")
+
+  out <- unnest_dt_longer(df, c("list_column1", "list_column2"))
+  expect_equal(dim(out), c(9, 4))
+})
+
 skip("Specific year selections are dropped when MRV is selected")
 test_that("select_years works for MRV + specific year selections", {
 
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