diff --git a/DESCRIPTION b/DESCRIPTION
index a696d37..97bf00d 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -53,6 +53,7 @@ Imports:
     tibble,
     tidyselect,
     units,
+    vctrs,
     robCompositions,
     ggplot2,
     ks,
diff --git a/R/archchem_basic.R b/R/archchem_basic.R
index 9641eb1..22d4582 100644
--- a/R/archchem_basic.R
+++ b/R/archchem_basic.R
@@ -93,11 +93,11 @@
 #' conc <- get_concentration_columns(arch) # see also other get_..._columns functions
 #'
 #' # unit-aware arithmetics on archchem columns thanks to the units package
-#' conc$Sb_ppm + conc$Ag_ppb # works
-#' \dontrun{conc$Sb_ppm + conc$`Sn_µg/ml`} # fails with: cannot convert µg/ml into ppm
+#' conc$Sb + conc$Ag # works
+#' \dontrun{conc$Sb + conc$Sn} # fails with: cannot convert µg/ml into ppm
 #'
 #' # converting units
-#' conc$Sb_ppb <- units::set_units(arch$Sb_ppm, "ppb") %>%
+#' conc$Sb <- units::set_units(arch$Sb, "ppb") %>%
 #'   magrittr::set_attr("archchem_class", "archchem_concentration")
 #'
 #' # removing all units from archchem tables
@@ -106,14 +106,13 @@
 #' # applying tidyverse data manipulation on archchem tables
 #' arch %>%
 #'   dplyr::group_by(Site) %>%
-#'   dplyr::summarise(mean_Na2O = mean(`Na2O_wt%`))
+#'   dplyr::summarise(mean_Na2O = mean(Na2O))
 #' conc_subset <- conc %>%
-#'   dplyr::select(-`Sn_µg/ml`, -`Sb_ppm`) %>%
-#'   dplyr::filter(`Na2O_wt%` > units::set_units(1, "%"))
+#'   dplyr::select(-Sn, -Sb) %>%
+#'   dplyr::filter(Na2O > units::set_units(4, "wtP"))
 #'
 #' # unify all concentration units
-#' unify_concentration_unit(conc_subset, "ppm")
-#' # note that the column names are inaccurate now
+#' unify_concentration_unit(conc_subset, "ppb")
 #'
 #' @export
 as_archchem <- function(
@@ -142,36 +141,39 @@ as_archchem <- function(
   }
   context <- append(context, id_column)
   # add ID column to context for the following operation
-  df <- df %>%
+  df1 <- df %>%
     dplyr::mutate(ID = .data[[id_column]]) %>%
     dplyr::relocate("ID", .before = 1)
   # handle ID duplicates
-  if (length(unique(df$ID)) != nrow(df)) {
+  if (length(unique(df1$ID)) != nrow(df1)) {
     warning(
       "Detected multiple data rows with the same ID. They will be renamed ",
       "consecutively using the following convention: _1, _2, ... _n"
     )
   }
-  df <- df %>%
+  df2 <- df1 %>%
     dplyr::group_by(.data[["ID"]]) %>%
     dplyr::mutate(
-      ID = dplyr::case_when(
-        dplyr::n() > 1 ~ paste0(.data[["ID"]], "_", as.character(dplyr::row_number())),
-        .default = .data[["ID"]]
-      )
+      ID = if (dplyr::n() > 1) {
+        paste0(.data[["ID"]], "_", as.character(dplyr::row_number()))
+      } else {
+        .data[["ID"]]
+      }
     ) %>%
     dplyr::ungroup()
   # determine and apply column types
-  constructors <- colnames_to_constructors(
-    df, context, bdl, bdl_strategy, guess_context_type, na, drop_columns
-  )
-  df <- purrr::map2(df, constructors, function(col, f) f(col)) %>%
+  column_table <- parse_colnames(df2, context, drop_columns)
+  constructors <- build_constructors(column_table, bdl, bdl_strategy, guess_context_type, na)
+  col_list <- purrr::map2(df2, constructors, function(col, f) f(col)) %>%
     purrr::discard(is.null)
+  df3 <- as.data.frame(col_list, check.names = FALSE)
+  # remove unit names from columns if they got a unit
+  df4 <- remove_unit_substrings(df3)
   # turn into tibble-derived object
-  df <- tibble::new_tibble(df, nrow = nrow(df), class = "archchem")
+  df5 <- tibble::new_tibble(df4, nrow = nrow(df4), class = "archchem")
   # post-reading validation
   if (validate) {
-    validation_output <- validate(df, quiet = FALSE)
+    validation_output <- validate(df5, quiet = FALSE)
     if (nrow(validation_output) > 0) {
       warning(
         "See the full list of validation output with: ",
@@ -179,7 +181,22 @@ as_archchem <- function(
       )
     }
   }
-  return(df)
+  return(df5)
+}
+
+# helper function to rename column names
+remove_unit_substrings <- function(x, ...) {
+  dplyr::rename_with(
+    x,
+    remove_suffix,
+    tidyselect::where(function(y) {
+      class(y) == "units" && !is_archchem_class(y, "archchem_error")
+    })
+  )
+}
+
+remove_suffix <- function(colname) {
+  sub("_.*$", "", colname, perl = TRUE)
 }
 
 #' @param path path to the file that should be read
@@ -359,20 +376,20 @@ print.archchem <- function(x, ...) {
 
 # carry over archchem_class column attribute
 preserve_archchem_attrs <- function(modified, original) {
-  purrr::map2(modified, original, function(new_col, old_col) {
-    arch_attr <- attr(old_col, "archchem_class")
-    if (!is.null(arch_attr)) attr(new_col, "archchem_class") <- arch_attr
-    new_col
-  }) %>%
-    magrittr::set_names(names(modified)) %>%
-    tibble::new_tibble(nrow = nrow(modified), class = class(original))
+  for (nm in intersect(names(modified), names(original))) {
+    arch_attr <- attr(original[[nm]], "archchem_class")
+    if (!is.null(arch_attr)) {
+      attr(modified[[nm]], "archchem_class") <- arch_attr
+    }
+  }
+  tibble::new_tibble(modified, class = class(original))
 }
 
 # row-slice method
 #' @exportS3Method dplyr::dplyr_row_slice
 dplyr_row_slice.archchem <- function(data, i, ...) {
-  sliced <- purrr::map(data, function(x) x[i])
-  sliced_tbl <- tibble::new_tibble(sliced, nrow = length(i), class = class(data))
+  sliced <- purrr::map(data, vctrs::vec_slice, i = i)
+  sliced_tbl <- tibble::new_tibble(sliced, class = class(data))
   preserve_archchem_attrs(sliced_tbl, data)
 }
 
@@ -380,12 +397,13 @@ dplyr_row_slice.archchem <- function(data, i, ...) {
 #' @exportS3Method dplyr::dplyr_col_modify
 dplyr_col_modify.archchem <- function(data, cols) {
   modified_list <- utils::modifyList(as.list(data), cols)
-  modified_tbl <- tibble::new_tibble(modified_list, nrow = nrow(data), class = class(data))
+  modified_tbl <- tibble::new_tibble(modified_list, class = class(data))
   preserve_archchem_attrs(modified_tbl, data)
 }
 
+
 # final reconstruction
 #' @exportS3Method dplyr::dplyr_reconstruct
 dplyr_reconstruct.archchem <- function(data, template) {
-  tibble::new_tibble(data, nrow = nrow(data), class = class(template))
+  preserve_archchem_attrs(data, template)
 }
diff --git a/R/archchem_colname_parser.R b/R/archchem_colname_parser.R
index b5e3ff4..9c3f723 100644
--- a/R/archchem_colname_parser.R
+++ b/R/archchem_colname_parser.R
@@ -5,14 +5,10 @@
 # SI-unit column types are defined with the units package
 # https://cran.r-project.org/web/packages/units/index.html
 # (so the udunits library)
-colnames_to_constructors <- function(
-  x,
-  context,
-  bdl, bdl_strategy,
-  guess_context_type, na,
-  drop_columns
-) {
-  purrr::imap(
+
+# 1. evaluate column names
+parse_colnames <- function(x, context, drop_columns) {
+  column_table <- purrr::imap_dfr(
     colnames(x),
     function(colname, idx) {
       # use while for hacky switch statement
@@ -20,91 +16,147 @@ colnames_to_constructors <- function(
         # ID column
         if (colname == "ID") {
           return(
-            function(x) {
-              x <- add_archchem_class(x, "archchem_id")
-              return(x)
-            }
+            tibble::tibble(
+              drop = FALSE,
+              idx,
+              colname,
+              consider_bdl = FALSE,
+              type = "as input",
+              unit = "none",
+              class = list("archchem_id"),
+            )
           )
           break
         }
         # contextual columns
         if (idx %in% context || colname %in% context) {
           return(
-            function(x) {
-              if (guess_context_type && is.character(x)) {
-                x <- readr::parse_guess(x, na = na)
-              }
-              x <- add_archchem_class(x, "archchem_context")
-              return(x)
-            }
+            tibble::tibble(
+              drop = FALSE,
+              idx,
+              colname,
+              consider_bdl = FALSE,
+              type = "guess",
+              unit = "none",
+              class = list("archchem_context")
+            )
           )
           break
         }
         # error columns
-        if (is_err(colname)) {
+        if (is_err_percent(colname)) { # check for percent err must be first
+          return(
+            tibble::tibble(
+              drop = FALSE,
+              idx,
+              colname,
+              consider_bdl = TRUE,
+              type = "numeric",
+              unit = "%",
+              class = list("archchem_error"),
+            )
+          )
+          break
+        }
+        if (is_err_abs(colname)) {
           return(
-            function(x) {
-              x <- apply_bdl_strategy(x, colname, bdl, bdl_strategy)
-              x <- as_numeric_info(x, colname)
-              x <- add_archchem_class(x, c("archchem_error"))
-            }
+            tibble::tibble(
+              drop = FALSE,
+              idx,
+              colname,
+              consider_bdl = TRUE,
+              type = "numeric",
+              unit = "from main field",
+              class = list("archchem_error")
+            )
           )
+          break
         }
         # ratios
         if (is_isotope_ratio(colname)) {
           return(
-            function(x) {
-              x <- as_numeric_info(x, colname)
-              x <- add_archchem_class(x, c("archchem_isotope", "archchem_ratio"))
-              return(x)
-            }
+            tibble::tibble(
+              drop = FALSE,
+              idx,
+              colname,
+              consider_bdl = FALSE,
+              type = "numeric",
+              unit = "none",
+              class = list(c("archchem_isotope", "archchem_ratio")),
+            )
           )
           break
         }
         if (is_isotope_delta_epsilon(colname)) {
-          # delta_epsilon <- extract_delta_epsilon_string(colname)
           return(
-            function(x) {
-              # if (delta_epsilon == "d") {
-              #   x / 10 # per mille -> percent
-              # } else if (delta_epsilon == "e") {
-              #   x / 100 # parts per 10000 -> percent
-              # }
-              x <- as_numeric_info(x, colname)
-              x <- add_archchem_class(x, c("archchem_isotope", "archchem_ratio"))
-              return(x)
-            }
+            tibble::tibble(
+              drop = FALSE,
+              idx,
+              colname,
+              consider_bdl = FALSE,
+              type = "numeric",
+              unit = "none",
+              class = list(c("archchem_isotope", "archchem_ratio"))
+            )
           )
           break
         }
         if (is_elemental_ratio(colname)) {
           return(
-            function(x) {
-              x <- as_numeric_info(x, colname)
-              x <- add_archchem_class(x, c("archchem_element", "archchem_ratio"))
-              return(x)
-            }
+            tibble::tibble(
+              drop = FALSE,
+              idx,
+              colname,
+              consider_bdl = FALSE,
+              type = "numeric",
+              unit = "none",
+              class = list(c("archchem_element", "archchem_ratio"))
+            )
           )
           break
         }
         # concentrations
         if (is_concentration(colname)) {
-          unit_from_col <- extract_unit_string(colname)
-          # handle special cases
-          unit_from_col <- dplyr::case_match(
-            unit_from_col,
-            c("at%", "wt%") ~ "%",
+          unit_from_name <- extract_unit_string(colname)
+          # handle relative units and special cases
+          unit_from_name <- dplyr::recode_values(
+            unit_from_name,
+            c("at%", "atP") ~ "atP",
+            c("wt%", "wtP",
+              "w/w%", "m/m%", "%w/w", "%m/m",
+              "(w/w)%", "(m/m)%", "%(w/w)", "%(m/m)",
+              "pph", "pph(m/m)", "pph(w/w)"
+            ) ~ "wtP",
+            c("%w/v", "%m/v", "%(w/v)", "%(m/v)",
+              "w/v%", "m/v%", "(w/v)%", "(m/v)%",
+              "pph(m/v)"
+            ) ~ "0.01 g/L",
+            c("%v/v", "v/v%", "%(v/v)", "(v/v)%", "pph(v/v)") ~ "0.01 L/L",
+            c("ppm(w/w)", "ppm(m/m)", "ppm") ~ "mg/kg",
+            c("ppm(m/v)") ~ "mg/L",
+            c("ppm(v/v)") ~ "ml/L",
+            c("ppb(m/m)", "ppb(w/w)", "ppb") ~ "ug/kg",
+            c("ppb(m/v)", "ppb(w/v)") ~ "ug/L",
+            c("ppb(v/v)") ~ "ul/L",
+            c("ppt(m/m)", "ppt(w/w)", "ppt") ~ "ng/kg",
+            c("ppt(m/v)", "ppt(w/v)") ~ "ng/L",
+            c("ppt(v/v)") ~ "nl/L",
+            c("ppq(m/m)", "ppq(w/w)", "ppq") ~ "pg/kg",
+            c("ppq(m/v)", "ppq(w/v)") ~ "pg/L",
+            c("ppq(v/v)") ~ "pl/L",
             c("cps") ~ "count/s",
-            .default = unit_from_col
+            default = unit_from_name
           )
           return(
-            function(x) {
-              x <- apply_bdl_strategy(x, colname, bdl, bdl_strategy)
-              x <- as_numeric_info(x, colname)
-              x <- units::set_units(x, value = unit_from_col, mode = "standard")
-              x <- add_archchem_class(x, c("archchem_concentration"))
-              return(x)
-            }
+            tibble::tibble(
+              drop = FALSE,
+              idx,
+              colname,
+              consider_bdl = TRUE,
+              type = "numeric",
+              unit = unit_from_name,
+              class = list("archchem_concentration"),
+            )
           )
           break
         }
@@ -116,17 +168,81 @@ colnames_to_constructors <- function(
           "Either analytical columns do not conform to ASTR conventions or ",
           "contextual columns are not specified as such."
         )
+        warning(m)
         if (drop_columns) {
-          warning(m)
-          return(function(x) {
-            NULL
-          })
+          return(
+            tibble::tibble(
+              drop = TRUE,
+              idx,
+              colname,
+              unit = "none"
+            )
+          )
         } else {
           stop(m)
         }
       }
     }
   )
+  # get units for columns that depend on a main column,
+  # so overwrite unit "from main field" with the unit of
+  # said main field
+  is_dependent <- column_table$unit == "from main field"
+  dependent_cols <- column_table[is_dependent, ]
+  dependent_bases <- remove_suffix(dependent_cols$colname)
+  independent_cols <- column_table[!is_dependent, ]
+  independent_bases <- remove_suffix(independent_cols$colname)
+  for (i in seq_along(dependent_bases)) {
+    j <- which(dependent_bases[i] == independent_bases)
+    if (length(j) != 1) {
+      stop("Column ", dependent_cols$colname[i], " cannot be uniquely matched to a non-error column.")
+    } else {
+      column_table[is_dependent, ]$unit[i] <- independent_cols$unit[j]
+    }
+  }
+  return(column_table)
+}
+
+# 2. build constructor functions
+build_constructors <- function(
+  column_table,
+  bdl, bdl_strategy,
+  guess_context_type, na
+) {
+  purrr::pmap(
+    column_table, function(drop, idx, colname, consider_bdl, type, unit, class) {
+      # delete fields that should be dropped (NULL-constructor)
+      if (drop) {
+        return(
+          function(x) {
+            NULL
+          }
+        )
+      }
+      # assemble normal constructor function based on column properties
+      return(
+        function(x) {
+          # bdl
+          if (consider_bdl) {
+            x <- apply_bdl_strategy(x, colname, bdl, bdl_strategy)
+          }
+          # type
+          if (type == "numeric") {
+            x <- as_numeric_info(x, colname)
+          } else if (type == "guess" && guess_context_type && is.character(x)) {
+            x <- readr::parse_guess(x, na = na)
+          }
+          # unit
+          if (unit != "none" && unit != "from main field") {
+            x <- units::set_units(x, value = unit, mode = "standard")
+          }
+          # class
+          x <- add_archchem_class(x, class)
+          return(x)
+        }
+      )
+    }
+  )
 }
 
 add_archchem_class <- function(x, class) {
@@ -162,8 +278,11 @@ apply_bdl_strategy <- function(x, colname, bdl, bdl_strategy) {
 
 #### regex validators ####
 
-is_err <- function(colname) {
-  grepl(err(), colname, perl = TRUE)
+is_err_percent <- function(colname) {
+  grepl(err_percent(), colname, perl = TRUE)
+}
+is_err_abs <- function(colname) {
+  grepl(err_abs(), colname, perl = TRUE)
 }
 is_isotope_ratio <- function(colname) {
   grepl(isotope_ratio(), colname, perl = TRUE)
@@ -215,7 +334,7 @@ isotope_ratio <- function() {
   paste0("(", isotopes_list(), ")/(", isotopes_list(), ")")
 }
 
-# define regex pattern for delta and espilon notation:
+# define regex pattern for delta and epsilon notation:
 # letter d OR e followed by any isotope
 isotope_delta_epsilon <- function() {
   paste0(
@@ -225,17 +344,22 @@ isotope_delta_epsilon <- function() {
 }
 
 # error states
-err <- function() {
+err_percent <- function() {
+  paste0(c(
+    err_2sd_percent(), err_sd_percent()
+  ), collapse = "|")
+}
+err_2sd_percent <- function() "\\_err2SD%"
+err_sd_percent <- function() "\\_errSD%"
+
+err_abs <- function() {
   paste0(c(
     err_2sd(), err_sd(),
-    err_2sd_percent(), err_sd_percent(),
     err_2se(), err_se()
   ), collapse = "|")
 }
 err_2sd <- function() "\\_err2SD"
 err_sd <- function() "\\_errSD"
-err_2sd_percent <- function() "\\_err2SD%"
-err_sd_percent <- function() "\\_errSD%"
 err_2se <- function() "\\_err2SE"
 err_se <- function() "\\_errSE"
 
diff --git a/R/archchem_column_define.R b/R/archchem_column_define.R
new file mode 100644
index 0000000..41ed462
--- /dev/null
+++ b/R/archchem_column_define.R
@@ -0,0 +1,11 @@
+# functions to define classes for columns in archchem (e.g. for columns newly defined in functions)
+
+#' @rdname archchem
+#' @export
+as_contextual_column <- function(x, ...) {
+  UseMethod("as_contextual_column")
+}
+#' @export
+as_contextual_column.archchem <- function(x, ...) {
+  sapply(x, structure, class = "archchem_context")
+}
diff --git a/R/archchem_unit_manipulation.R b/R/archchem_unit_manipulation.R
index ffecdd6..e90be16 100644
--- a/R/archchem_unit_manipulation.R
+++ b/R/archchem_unit_manipulation.R
@@ -4,7 +4,36 @@ remove_units <- function(x, ...) {
   UseMethod("remove_units")
 }
 #' @export
-remove_units.archchem <- function(x, ...) {
+remove_units.archchem <- function(x, recover_unit_names = FALSE, ...) {
+  # rename columns: recover units in column names
+  if (recover_unit_names) {
+    x <- dplyr::rename_with(
+      x,
+      function(column_names) {
+        unit_names <- purrr::map_chr(
+          column_names,
+          function(column_name) {
+            # render individual units
+            unit <- units(x[[column_name]])
+            rendered_unit <- as.character(unit, neg_power = FALSE, prod_sep = "*")
+            # handle special cases
+            dplyr::case_match(
+              rendered_unit,
+              "atP" ~ "at%",
+              "wtP" ~ "wt%",
+              "count/s" ~ "cps",
+              .default = rendered_unit
+            )
+          }
+        )
+        paste0(column_names, "_", unit_names)
+      },
+      tidyselect::where(function(y) {
+        class(y) == "units" && !is_archchem_class(y, "archchem_error")
+      })
+    )
+  }
+  # drop units
   dplyr::mutate(
     x,
     dplyr::across(
@@ -25,14 +54,42 @@ unify_concentration_unit <- function(x, unit, ...) {
 }
 #' @export
 unify_concentration_unit <- function(x, unit, ...) {
-  dplyr::mutate(
-    x,
-    dplyr::across(
-      tidyselect::where(function(y) {
-        class(y) == "units" &&
-          is_archchem_class(y, "archchem_concentration")
-      }),
-      function(z) units::set_units(z, unit, mode = "standard")
-    )
+  switch(unit,
+    # Conversion to atP
+    atP = {
+      # identify all oxides and present, convert to wt%
+
+      # convert all other concentrations to wt%
+
+      # convert to at%
+
+    },
+    # Conversion to oxP
+    oxP = {
+      # identify all elements that are not oxides and convert to wt%
+
+      # convert all elements to ox%
+
+    },
+    # conversion to all other units
+    {
+      # something here to convert from atP and oxP to wt% first
+
+      dplyr::mutate(
+        x,
+        dplyr::across(
+          tidyselect::where(function(y) {
+            class(y) == "units" &&
+              units::ud_are_convertible(units::deparse_unit(y), "mg/kg")
+          }),
+          function(z) units::set_units(z, unit, mode = "standard")
+        )
+      )
+    }
   )
 }
+
+# units_to_convert <- unlist(
+#   sapply(df[elements], function(x) class(x) == "units" & units(x)[1] != unit)
+# )
+# df_convert <- cbind(df$ID, subset(df[elements], select = units_to_convert))
diff --git a/R/data.R b/R/data.R
index ba7d33a..fe773c3 100644
--- a/R/data.R
+++ b/R/data.R
@@ -57,16 +57,6 @@
 #' @name conversion_oxides
 "conversion_oxides"
 
-#' Example input data for ASTR
-#'
-#' @docType data
-#' @keywords datasets
-#' @name archchem_example_input
-#' @usage data(archchem_example_input)
-#' @format A data frame with 15 observations and 53 variables.
-#'
-"archchem_example_input"
-
 #' ArgentinaDatabase
 #'
 #' Lead isotope data from ore deposits in Argentina prepared for TerraLID database.
diff --git a/R/zzz.R b/R/zzz.R
index 209e50d..d575db4 100644
--- a/R/zzz.R
+++ b/R/zzz.R
@@ -1,10 +1,49 @@
 # defining global variables
-# ugly solution to avoid magrittr NOTE
-# see http://stackoverflow.com/questions/9439256/
-# how-can-i-handle-r-cmd-check-no-visible-binding-for-global-variable-notes-when
-globalVariables(".")
+utils::globalVariables(
+  c(
+    # ugly solution to avoid magrittr NOTE
+    # see http://stackoverflow.com/questions/9439256/
+    # how-can-i-handle-r-cmd-check-no-visible-binding-for-global-variable-notes-when
+    ".",
+    # register data objects to make lintr aware of them
+    "isotopes_data",
+    "elements_data",
+    "oxides_data",
+    "special_oxide_states",
+    "conversion_oxides"
+  )
+)
 
 #' @importFrom magrittr "%>%"
 #' @importFrom rlang .data :=
 #'
 NULL
+
+# registers the dimensionless units at% (atom percent) and wt% (weight percent)
+# when the package is loaded
+.onLoad <- function(libname, pkgname) {
+  safe_install <- function(...) {
+    # tryCatch to prevent an error when these units are already defined in
+    # the current session
+    tryCatch(
+      units::install_unit(...),
+      error = function(e) NULL
+    )
+  }
+  # define dummy base units (purely semantic)
+  # see ?units::install_unit for details on this mechanism
+  safe_install("atomic_basis")
+  safe_install("mass_basis", "unitless")
+  # create percent-like units
+  # the %-sign can not be used in custom unit names, so we use P
+  safe_install(
+    symbol = "atP",
+    def    = "0.01 atomic_basis",
+    name   = "atom percent"
+  )
+  safe_install(
+    symbol = "wtP",
+    def    = "0.01 mass_basis",
+    name   = "weight percent"
+  )
+}
diff --git a/data-raw/.DS_Store b/data-raw/.DS_Store
deleted file mode 100644
index 5008ddf..0000000
Binary files a/data-raw/.DS_Store and /dev/null differ
diff --git a/data-raw/data_prep.R b/data-raw/data_prep.R
index 3d1e287..6b36035 100644
--- a/data-raw/data_prep.R
+++ b/data-raw/data_prep.R
@@ -72,11 +72,3 @@ usethis::use_data(
 # )
 #
 # usethis::use_data(isotopes, overwrite = T)
-
-#### archem data input table ####
-
-archchem_example_input <- readr::read_csv("data-raw/test_data_input_good.csv")
-
-usethis::use_data(archchem_example_input,
-  overwrite = TRUE
-)
diff --git a/data-raw/test_data_input_good.csv b/data-raw/test_data_input_good.csv
deleted file mode 100644
index 5322c4e..0000000
--- a/data-raw/test_data_input_good.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-Sample,Lab no.,Site,latitude,longitude,Type,method_comp,143Nd/144Nd,d65Cu,d65Cu_err2SD,Na2O_wt%,BaO_wt%,Pb_wt%,MgO_wt%,Al2O3_wt%,SiO2_wt%,SiO2_errSD%,P2O5_wt%,S_at%,CaO_wt%,TiO2_wt%,MnO_wt%,FeOtot_wt%,FeOtot_err2SD,ZnO_%,K2O_wt%,Cu_wt%,As_wt%,LOI_wt%,Ag_ppb,Sn_µg/ml,Sb_ppm,Te_ppm,Bi_ppm,U_ppm,V_ppm,Cr_ppm,Co_ppm,Ni_ppm,Sr_ppm,Se_ppm,FeOtot/SiO2,(Na2O+K2O)/SiO2,206Pb/204Pb,206Pb/204Pb_err2SD,207Pb/204Pb,207Pb/204Pb_err2SD,208Pb/204Pb,208Pb/204Pb_err2SD,207Pb/206Pb,207Pb/206Pb_err2SD,208Pb/206Pb,208Pb/206Pb_err2SD
-TR-001,3421/19,Bochum,51.48165,7.21648,1,ICP-MS,0.513014,1.24,0.124,3.1,0.07,6.34,0.77,3.92,31.63,4.3,0.15,2.57,2.11,0.52,0.2,43.83,4.12,5.64,1.34,0.11,0.02,1.89,500,85,370,2,18,3,60,160,60,60,130,<5,1.386,0.14037,18.61147,0.01082,15.65405,0.00915,38.7563,0.0227,0.8411,0.00006,2.08239,0.00017
-TR-002_1,3423/19,Oviedo,43.36029,-5.84476,2,ICP-MS,0.512994,0.88,0.06,2.2,0.07,3.52,0.55,3.46,29.06,2.88,-,NA,2.34,0.49,0.54,51.02,3.89,4.07,1.26,0.22,0.01,-,200,85,210,2,20,2,60,140,70,100,120,<5,1.756,0.11906,18.61617,0.01629,15.65682,0.01341,38.76404,0.0345,0.84103,0.00013,2.08229,0.00033
-TR-002_2,3435/19,Oviedo,43.36029,-5.84476,2,ICP-MS,0.512996,0.85,0.05,5,0.054,3.6,0.33,5.87,30.5,5.71,0.11,3.68,2,0.55,0.1,30.59,2.94,6.87,1.54,0.74,bdl,-,210,98,256,<1.5,47,-,63,99,55,87,121,15,1.003,0.21443,18.61615,0.01698,15.6574,0.01441,38.76411,0.03452,0.84107,0.00014,2.08228,0.00033
-TR-003,3422/19,佛山,23.02677,113.13148,3,ICP-MS,0.513008,2.76,0.276,6.9,0.04,5.07,0.76,4.33,25.73,2.22,0.23,2.76,3.91,0.27,0.7,46.09,3.5,5.87,1.66,0.13,0.01,NA,350,45,400,2,bdl,2,45,30,30,20,250,<5,1.791,0.33269,18.61272,0.00926,15.65677,0.00747,38.76097,0.0182,0.84119,0.0001,2.08251,0.00025
-TR-004,3429/19,Băiuț,43.234895,23.124983,4,ICP-MS,,-8.21,-0.821,3,0.03,9.15,0.52,4.17,43.5,4.65,0.4,0.57,1.79,0.24,1,31.91,2.87,2.26,2.15,0.14,0.03,3.57,400,14,410,2,6,3,55,75,n.a.,10,160,<5,0.734,0.11839,18.63562,0.01189,15.64765,0.00989,38.73583,0.02561,0.83966,0.00009,2.07858,0.00023
-TR-005,3430/19,Şuior,41.445566,24.935661,5,ICP-MS,,0.06,0.004,4.1,0.07,12.61,0.58,3.58,33.83,3.67,0.29,0.61,2.06,0.18,0.49,36.57,5.486,1.66,1.21,0.09,0.11,5.66,250,181,453,6,9,3,54,41,8,47,258,-,1.081,0.15696,18.63952,0.01802,15.65502,0.01513,38.75729,0.04157,0.83989,0.00012,2.07923,0.00032
-TR-006.1,3431/19,Blagodat,41.453133,25.505011,5,ICP-MS,,1.48,0.222,3.4,0.05,12.68,0.64,5.6,33.81,3,0.43,0.77,,NA,1.52,35.52,2.66,2.46,2.26,0.21,0.23,0.12,120,566,701,8,9,bdl,75,106,22,19,122,3,1.051,0.16741,18.64465,0.01468,15.65836,0.0121,38.76615,0.02716,0.83984,0.00009,2.07928,0.00023
-TR-006.2,3432/19,Pezinok,45.92125,15.919219,1,ICP-MS,,-0.42,-0.042,1.8,0.04,5.31,0.59,4.47,26.39,2.87,0.62,3.04,4.28,NA,0.4,41.2,3.89,11.65,1.86,0.43,0.02,0.89,430,203,231,15,3,b.d.l.,84,91,47,34,280,3,1.561,0.13869,18.83274,0.01608,15.82161,0.0135,39.20161,0.03356,0.84852,0.00007,2.10238,0.00017
-TR-006.3,3433/19,Free State Geduld Mines,50.35621322,7.651313679,4,ICP-MS,,0.3,0.02,5.1,0.09,2.36,0.53,3.73,21.18,3.44,0.24,3.93,4.08,0.15,0.46,48.26,2.268,6.8,2.01,0.59,0.08,4.75,300,5594,107,6,5,5,32,63,17,37,310,<5,2.279,0.33569,18.83312,0.01875,15.82042,0.01619,39.20067,0.04173,0.84846,0.00007,2.10235,0.00018
-smn348,3424/19,Aggenys,50.99159628,8.02757263,3,ICP-MS,0.512977,0.24,0.024,4.1,0.34,47.49,0.7,6.27,31.73,6.12,0.28,0.21,7.14,NA,0.24,5.26,0.305,0.03,1.34,0.07,0.06,n.a.,2420,32,3310,9,150,NA,61,45,-,4,287,5,0.166,0.17145,18.80371,0.03129,15.81646,0.026,39.15557,0.06458,0.84955,0.00007,2.10313,0.00026
-smn349,3425/19,Chiprovtsi,50.6890264,6.406379006,3,ICP-MS,0.513004,0.54,0.01,3.4,0.04,2.53,0.46,2.91,16.91,3.73,0.22,3.57,2.5,0.14,0.21,56.36,6.763,4.89,1.34,0.15,0.01,4.22,140,23,95,5,1,3,37,36,69,39,155,<5,3.333,0.28031,18.8076,0.0155,15.82128,0.01309,39.17608,0.03091,0.84963,0.00012,2.10388,0.00022
-smn350,3426/19,Krusov Dol; Krushev Dol,40.695753,24.591976,5,ICP-MS,0.512991,2.04,0.8,4.5,0.07,1.56,0.58,3.6,28.49,4.89,0.19,1.95,1.39,0.17,0.13,47.64,4.235,6.56,0.98,0.16,0.02,6.01,1610,45,78,5,4,3,40,33,43,39,165,<5,1.672,0.19235,18.81356,0.01557,15.82045,0.01305,39.17244,0.03263,0.84931,0.00007,2.10297,0.00022
-smn351,3427/19,Masua,37.726179,24.012951,1,ICP-MS,,-0.24,-0.0504,3.2,0.04,3.64,0.88,5.24,38.04,10.12,0.3,1.35,2.1,0.34,1.32,43.21,2.161,1.52,1.48,0.27,0.05,3.22,100,15,610,2,1,2,80,190,20,50,100,<5,1.136,0.12303,18.81501,0.01535,15.80824,0.0135,39.13346,0.03109,0.84859,0.00007,2.10076,0.00018
-8896,3428/19,Σπαρτη,37.07446,22.43009,1,ICP-MS,,-0.42,-0.042,3.7,0.06,7.51,0.84,3.97,31.67,4.3,0.18,0.83,3.51,0.24,0.34,45.9,2.387,0.85,1.32,0.09,0.14,n.a.,100,146,390,<1.5,<0.3,1,40,100,10,65,120,<5,1.449,0.15851,18.82037,0.01483,15.81119,0.01252,39.13715,0.03182,0.84851,0.00007,2.1003,0.00024
diff --git a/data/archchem_example_input.rda b/data/archchem_example_input.rda
deleted file mode 100644
index 5a16b8e..0000000
Binary files a/data/archchem_example_input.rda and /dev/null differ
diff --git a/inst/extdata/input_format.xlsx b/inst/extdata/input_format.xlsx
index 68df68b..5986850 100644
Binary files a/inst/extdata/input_format.xlsx and b/inst/extdata/input_format.xlsx differ
diff --git a/man/archchem.Rd b/man/archchem.Rd
index 255382a..88304ae 100644
--- a/man/archchem.Rd
+++ b/man/archchem.Rd
@@ -189,11 +189,11 @@ validate(arch)
 conc <- get_concentration_columns(arch) # see also other get_..._columns functions
 
 # unit-aware arithmetics on archchem columns thanks to the units package
-conc$Sb_ppm + conc$Ag_ppb # works
-\dontrun{conc$Sb_ppm + conc$`Sn_µg/ml`} # fails with: cannot convert µg/ml into ppm
+conc$Sb + conc$Ag # works
+\dontrun{conc$Sb + conc$Sn} # fails with: cannot convert µg/ml into ppm
 
 # converting units
-conc$Sb_ppb <- units::set_units(arch$Sb_ppm, "ppb") \%>\%
+conc$Sb <- units::set_units(arch$Sb, "ppb") \%>\%
   magrittr::set_attr("archchem_class", "archchem_concentration")
 
 # removing all units from archchem tables
@@ -202,13 +202,12 @@ remove_units(arch)
 # applying tidyverse data manipulation on archchem tables
 arch \%>\%
   dplyr::group_by(Site) \%>\%
-  dplyr::summarise(mean_Na2O = mean(`Na2O_wt\%`))
+  dplyr::summarise(mean_Na2O = mean(Na2O))
 conc_subset <- conc \%>\%
-  dplyr::select(-`Sn_µg/ml`, -`Sb_ppm`) \%>\%
-  dplyr::filter(`Na2O_wt\%` > units::set_units(1, "\%"))
+  dplyr::select(-Sn, -Sb) \%>\%
+  dplyr::filter(Na2O > units::set_units(4, "wtP"))
 
 # unify all concentration units
-unify_concentration_unit(conc_subset, "ppm")
-# note that the column names are inaccurate now
+unify_concentration_unit(conc_subset, "ppb")
 
 }
diff --git a/man/archchem_example_input.Rd b/man/archchem_example_input.Rd
deleted file mode 100644
index b86abb1..0000000
--- a/man/archchem_example_input.Rd
+++ /dev/null
@@ -1,16 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/data.R
-\docType{data}
-\name{archchem_example_input}
-\alias{archchem_example_input}
-\title{Example input data for ASTR}
-\format{
-A data frame with 15 observations and 53 variables.
-}
-\usage{
-data(archchem_example_input)
-}
-\description{
-Example input data for ASTR
-}
-\keyword{datasets}
diff --git a/tests/testthat.R b/tests/testthat.R
index a26f0d9..6be89e9 100644
--- a/tests/testthat.R
+++ b/tests/testthat.R
@@ -8,8 +8,5 @@
 
 library(testthat)
 library(ASTR)
-library(tibble)
-library(ggplot2)
-library(vdiffr)
 
 test_check("ASTR")
diff --git a/tests/testthat/_snaps/archchem_basic.md b/tests/testthat/_snaps/archchem_basic.md
index 3b8c409..de8d737 100644
--- a/tests/testthat/_snaps/archchem_basic.md
+++ b/tests/testthat/_snaps/archchem_basic.md
@@ -3,10 +3,181 @@
     Code
       as.data.frame(test_input)
     Output
-           ID 206Pb/204Pb   Al2O3_cps SiO2+Al2O3_ppt 204Pb_ppm other other2 K2O_wt%
-      1 troet         0.5 3 [count/s]       20 [ppt]   7 [ppm] troet     27  23 [%]
-         d18O SiO2/FeO   Mn_at%    Zn_ppm SiO2/(FeO+MnO) Sb/As   Sn_µg/g
-      1 -5.32      0.5 2.45 [%] 240 [ppm]           0.32  5.69 56 [µg/g]
-        K2O+MgO+Na2O_wt%
-      1           54 [%]
+           ID 206Pb/204Pb       Al2O3 SiO2+Al2O3     204Pb other other2      K2O
+      1 troet         0.5 3 [count/s] 20 [ng/kg] 7 [mg/kg] troet     27 23 [wtP]
+         d18O SiO2/FeO         Mn          Zn SiO2/(FeO+MnO) Sb/As        Sn
+      1 -5.32      0.5 2.45 [atP] 240 [mg/kg]           0.32  5.69 56 [µg/g]
+        K2O+MgO+Na2O
+      1     54 [wtP]
+
+---
+
+    Code
+      as.data.frame(test_input2)
+    Output
+               ID   Sample Lab no.                    Site latitude  longitude Type
+      1    TR-001   TR-001 3421/19                  Bochum 51.48165   7.216480    1
+      2  TR-002_1 TR-002_1 3423/19                  Oviedo 43.36029  -5.844760    2
+      3  TR-002_2 TR-002_2 3435/19                  Oviedo 43.36029  -5.844760    2
+      4    TR-003   TR-003 3422/19                    佛山 23.02677 113.131480    3
+      5    TR-004   TR-004 3429/19                   Băiuț 43.23490  23.124983    4
+      6    TR-005   TR-005 3430/19                   Şuior 41.44557  24.935661    5
+      7  TR-006.1 TR-006.1 3431/19                Blagodat 41.45313  25.505011    5
+      8  TR-006.2 TR-006.2 3432/19                 Pezinok 45.92125  15.919219    1
+      9  TR-006.3 TR-006.3 3433/19 Free State Geduld Mines 50.35621   7.651314    4
+      10   smn348   smn348 3424/19                 Aggenys 50.99160   8.027573    3
+      11   smn349   smn349 3425/19              Chiprovtsi 50.68903   6.406379    3
+      12   smn350   smn350 3426/19 Krusov Dol; Krushev Dol 40.69575  24.591976    5
+      13   smn351   smn351 3427/19                   Masua 37.72618  24.012951    1
+      14     8896     8896 3428/19                  Σπαρτη 37.07446  22.430090    1
+         method_comp 143Nd/144Nd d65Cu d65Cu_err2SD      Na2O         BaO          Pb
+      1       ICP-MS    0.513014  1.24       0.1240 3.1 [wtP] 0.070 [wtP]  6.34 [wtP]
+      2       ICP-MS    0.512994  0.88       0.0600 2.2 [wtP] 0.070 [wtP]  3.52 [wtP]
+      3       ICP-MS    0.512996  0.85       0.0500 5.0 [wtP] 0.054 [wtP]  3.60 [wtP]
+      4       ICP-MS    0.513008  2.76       0.2760 6.9 [wtP] 0.040 [wtP]  5.07 [wtP]
+      5       ICP-MS          NA -8.21      -0.8210 3.0 [wtP] 0.030 [wtP]  9.15 [wtP]
+      6       ICP-MS          NA  0.06       0.0040 4.1 [wtP] 0.070 [wtP] 12.61 [wtP]
+      7       ICP-MS          NA  1.48       0.2220 3.4 [wtP] 0.050 [wtP] 12.68 [wtP]
+      8       ICP-MS          NA -0.42      -0.0420 1.8 [wtP] 0.040 [wtP]  5.31 [wtP]
+      9       ICP-MS          NA  0.30       0.0200 5.1 [wtP] 0.090 [wtP]  2.36 [wtP]
+      10      ICP-MS    0.512977  0.24       0.0240 4.1 [wtP] 0.340 [wtP] 47.49 [wtP]
+      11      ICP-MS    0.513004  0.54       0.0100 3.4 [wtP] 0.040 [wtP]  2.53 [wtP]
+      12      ICP-MS    0.512991  2.04       0.8000 4.5 [wtP] 0.070 [wtP]  1.56 [wtP]
+      13      ICP-MS          NA -0.24      -0.0504 3.2 [wtP] 0.040 [wtP]  3.64 [wtP]
+      14      ICP-MS          NA -0.42      -0.0420 3.7 [wtP] 0.060 [wtP]  7.51 [wtP]
+                MgO      Al2O3        SiO2 SiO2_errSD%       P2O5          S
+      1  0.77 [wtP] 3.92 [wtP] 31.63 [wtP]    4.30 [%] 0.15 [wtP] 2.57 [atP]
+      2  0.55 [wtP] 3.46 [wtP] 29.06 [wtP]    2.88 [%]   NA [wtP]   NA [atP]
+      3  0.33 [wtP] 5.87 [wtP] 30.50 [wtP]    5.71 [%] 0.11 [wtP] 3.68 [atP]
+      4  0.76 [wtP] 4.33 [wtP] 25.73 [wtP]    2.22 [%] 0.23 [wtP] 2.76 [atP]
+      5  0.52 [wtP] 4.17 [wtP] 43.50 [wtP]    4.65 [%] 0.40 [wtP] 0.57 [atP]
+      6  0.58 [wtP] 3.58 [wtP] 33.83 [wtP]    3.67 [%] 0.29 [wtP] 0.61 [atP]
+      7  0.64 [wtP] 5.60 [wtP] 33.81 [wtP]    3.00 [%] 0.43 [wtP] 0.77 [atP]
+      8  0.59 [wtP] 4.47 [wtP] 26.39 [wtP]    2.87 [%] 0.62 [wtP] 3.04 [atP]
+      9  0.53 [wtP] 3.73 [wtP] 21.18 [wtP]    3.44 [%] 0.24 [wtP] 3.93 [atP]
+      10 0.70 [wtP] 6.27 [wtP] 31.73 [wtP]    6.12 [%] 0.28 [wtP] 0.21 [atP]
+      11 0.46 [wtP] 2.91 [wtP] 16.91 [wtP]    3.73 [%] 0.22 [wtP] 3.57 [atP]
+      12 0.58 [wtP] 3.60 [wtP] 28.49 [wtP]    4.89 [%] 0.19 [wtP] 1.95 [atP]
+      13 0.88 [wtP] 5.24 [wtP] 38.04 [wtP]   10.12 [%] 0.30 [wtP] 1.35 [atP]
+      14 0.84 [wtP] 3.97 [wtP] 31.67 [wtP]    4.30 [%] 0.18 [wtP] 0.83 [atP]
+                CaO       TiO2        MnO      FeOtot FeOtot_err2SD       ZnO
+      1  2.11 [wtP] 0.52 [wtP] 0.20 [wtP] 43.83 [wtP]   4.120 [wtP]  5.64 [%]
+      2  2.34 [wtP] 0.49 [wtP] 0.54 [wtP] 51.02 [wtP]   3.890 [wtP]  4.07 [%]
+      3  2.00 [wtP] 0.55 [wtP] 0.10 [wtP] 30.59 [wtP]   2.940 [wtP]  6.87 [%]
+      4  3.91 [wtP] 0.27 [wtP] 0.70 [wtP] 46.09 [wtP]   3.500 [wtP]  5.87 [%]
+      5  1.79 [wtP] 0.24 [wtP] 1.00 [wtP] 31.91 [wtP]   2.870 [wtP]  2.26 [%]
+      6  2.06 [wtP] 0.18 [wtP] 0.49 [wtP] 36.57 [wtP]   5.486 [wtP]  1.66 [%]
+      7    NA [wtP]   NA [wtP] 1.52 [wtP] 35.52 [wtP]   2.660 [wtP]  2.46 [%]
+      8  4.28 [wtP]   NA [wtP] 0.40 [wtP] 41.20 [wtP]   3.890 [wtP] 11.65 [%]
+      9  4.08 [wtP] 0.15 [wtP] 0.46 [wtP] 48.26 [wtP]   2.268 [wtP]  6.80 [%]
+      10 7.14 [wtP]   NA [wtP] 0.24 [wtP]  5.26 [wtP]   0.305 [wtP]  0.03 [%]
+      11 2.50 [wtP] 0.14 [wtP] 0.21 [wtP] 56.36 [wtP]   6.763 [wtP]  4.89 [%]
+      12 1.39 [wtP] 0.17 [wtP] 0.13 [wtP] 47.64 [wtP]   4.235 [wtP]  6.56 [%]
+      13 2.10 [wtP] 0.34 [wtP] 1.32 [wtP] 43.21 [wtP]   2.161 [wtP]  1.52 [%]
+      14 3.51 [wtP] 0.24 [wtP] 0.34 [wtP] 45.90 [wtP]   2.387 [wtP]  0.85 [%]
+                K2O         Cu         As        LOI           Ag           Sn
+      1  1.34 [wtP] 0.11 [wtP] 0.02 [wtP] 1.89 [wtP]  500 [ug/kg]   85 [µg/ml]
+      2  1.26 [wtP] 0.22 [wtP] 0.01 [wtP]   NA [wtP]  200 [ug/kg]   85 [µg/ml]
+      3  1.54 [wtP] 0.74 [wtP]   NA [wtP]   NA [wtP]  210 [ug/kg]   98 [µg/ml]
+      4  1.66 [wtP] 0.13 [wtP] 0.01 [wtP]   NA [wtP]  350 [ug/kg]   45 [µg/ml]
+      5  2.15 [wtP] 0.14 [wtP] 0.03 [wtP] 3.57 [wtP]  400 [ug/kg]   14 [µg/ml]
+      6  1.21 [wtP] 0.09 [wtP] 0.11 [wtP] 5.66 [wtP]  250 [ug/kg]  181 [µg/ml]
+      7  2.26 [wtP] 0.21 [wtP] 0.23 [wtP] 0.12 [wtP]  120 [ug/kg]  566 [µg/ml]
+      8  1.86 [wtP] 0.43 [wtP] 0.02 [wtP] 0.89 [wtP]  430 [ug/kg]  203 [µg/ml]
+      9  2.01 [wtP] 0.59 [wtP] 0.08 [wtP] 4.75 [wtP]  300 [ug/kg] 5594 [µg/ml]
+      10 1.34 [wtP] 0.07 [wtP] 0.06 [wtP]   NA [wtP] 2420 [ug/kg]   32 [µg/ml]
+      11 1.34 [wtP] 0.15 [wtP] 0.01 [wtP] 4.22 [wtP]  140 [ug/kg]   23 [µg/ml]
+      12 0.98 [wtP] 0.16 [wtP] 0.02 [wtP] 6.01 [wtP] 1610 [ug/kg]   45 [µg/ml]
+      13 1.48 [wtP] 0.27 [wtP] 0.05 [wtP] 3.22 [wtP]  100 [ug/kg]   15 [µg/ml]
+      14 1.32 [wtP] 0.09 [wtP] 0.14 [wtP]   NA [wtP]  100 [ug/kg]  146 [µg/ml]
+                   Sb         Te          Bi          U          V          Cr
+      1   370 [mg/kg]  2 [mg/kg]  18 [mg/kg]  3 [mg/kg] 60 [mg/kg] 160 [mg/kg]
+      2   210 [mg/kg]  2 [mg/kg]  20 [mg/kg]  2 [mg/kg] 60 [mg/kg] 140 [mg/kg]
+      3   256 [mg/kg] NA [mg/kg]  47 [mg/kg] NA [mg/kg] 63 [mg/kg]  99 [mg/kg]
+      4   400 [mg/kg]  2 [mg/kg]  NA [mg/kg]  2 [mg/kg] 45 [mg/kg]  30 [mg/kg]
+      5   410 [mg/kg]  2 [mg/kg]   6 [mg/kg]  3 [mg/kg] 55 [mg/kg]  75 [mg/kg]
+      6   453 [mg/kg]  6 [mg/kg]   9 [mg/kg]  3 [mg/kg] 54 [mg/kg]  41 [mg/kg]
+      7   701 [mg/kg]  8 [mg/kg]   9 [mg/kg] NA [mg/kg] 75 [mg/kg] 106 [mg/kg]
+      8   231 [mg/kg] 15 [mg/kg]   3 [mg/kg] NA [mg/kg] 84 [mg/kg]  91 [mg/kg]
+      9   107 [mg/kg]  6 [mg/kg]   5 [mg/kg]  5 [mg/kg] 32 [mg/kg]  63 [mg/kg]
+      10 3310 [mg/kg]  9 [mg/kg] 150 [mg/kg] NA [mg/kg] 61 [mg/kg]  45 [mg/kg]
+      11   95 [mg/kg]  5 [mg/kg]   1 [mg/kg]  3 [mg/kg] 37 [mg/kg]  36 [mg/kg]
+      12   78 [mg/kg]  5 [mg/kg]   4 [mg/kg]  3 [mg/kg] 40 [mg/kg]  33 [mg/kg]
+      13  610 [mg/kg]  2 [mg/kg]   1 [mg/kg]  2 [mg/kg] 80 [mg/kg] 190 [mg/kg]
+      14  390 [mg/kg] NA [mg/kg]  NA [mg/kg]  1 [mg/kg] 40 [mg/kg] 100 [mg/kg]
+                 Co          Ni          Sr         Se FeOtot/SiO2 (Na2O+K2O)/SiO2
+      1  60 [mg/kg]  60 [mg/kg] 130 [mg/kg] NA [mg/kg]       1.386         0.14037
+      2  70 [mg/kg] 100 [mg/kg] 120 [mg/kg] NA [mg/kg]       1.756         0.11906
+      3  55 [mg/kg]  87 [mg/kg] 121 [mg/kg] 15 [mg/kg]       1.003         0.21443
+      4  30 [mg/kg]  20 [mg/kg] 250 [mg/kg] NA [mg/kg]       1.791         0.33269
+      5  NA [mg/kg]  10 [mg/kg] 160 [mg/kg] NA [mg/kg]       0.734         0.11839
+      6   8 [mg/kg]  47 [mg/kg] 258 [mg/kg] NA [mg/kg]       1.081         0.15696
+      7  22 [mg/kg]  19 [mg/kg] 122 [mg/kg]  3 [mg/kg]       1.051         0.16741
+      8  47 [mg/kg]  34 [mg/kg] 280 [mg/kg]  3 [mg/kg]       1.561         0.13869
+      9  17 [mg/kg]  37 [mg/kg] 310 [mg/kg] NA [mg/kg]       2.279         0.33569
+      10 NA [mg/kg]   4 [mg/kg] 287 [mg/kg]  5 [mg/kg]       0.166         0.17145
+      11 69 [mg/kg]  39 [mg/kg] 155 [mg/kg] NA [mg/kg]       3.333         0.28031
+      12 43 [mg/kg]  39 [mg/kg] 165 [mg/kg] NA [mg/kg]       1.672         0.19235
+      13 20 [mg/kg]  50 [mg/kg] 100 [mg/kg] NA [mg/kg]       1.136         0.12303
+      14 10 [mg/kg]  65 [mg/kg] 120 [mg/kg] NA [mg/kg]       1.449         0.15851
+         206Pb/204Pb 206Pb/204Pb_err2SD 207Pb/204Pb 207Pb/204Pb_err2SD 208Pb/204Pb
+      1     18.61147            0.01082    15.65405            0.00915    38.75630
+      2     18.61617            0.01629    15.65682            0.01341    38.76404
+      3     18.61615            0.01698    15.65740            0.01441    38.76411
+      4     18.61272            0.00926    15.65677            0.00747    38.76097
+      5     18.63562            0.01189    15.64765            0.00989    38.73583
+      6     18.63952            0.01802    15.65502            0.01513    38.75729
+      7     18.64465            0.01468    15.65836            0.01210    38.76615
+      8     18.83274            0.01608    15.82161            0.01350    39.20161
+      9     18.83312            0.01875    15.82042            0.01619    39.20067
+      10    18.80371            0.03129    15.81646            0.02600    39.15557
+      11    18.80760            0.01550    15.82128            0.01309    39.17608
+      12    18.81356            0.01557    15.82045            0.01305    39.17244
+      13    18.81501            0.01535    15.80824            0.01350    39.13346
+      14    18.82037            0.01483    15.81119            0.01252    39.13715
+         208Pb/204Pb_err2SD 207Pb/206Pb 207Pb/206Pb_err2SD 208Pb/206Pb
+      1             0.02270     0.84110            0.00006     2.08239
+      2             0.03450     0.84103            0.00013     2.08229
+      3             0.03452     0.84107            0.00014     2.08228
+      4             0.01820     0.84119            0.00010     2.08251
+      5             0.02561     0.83966            0.00009     2.07858
+      6             0.04157     0.83989            0.00012     2.07923
+      7             0.02716     0.83984            0.00009     2.07928
+      8             0.03356     0.84852            0.00007     2.10238
+      9             0.04173     0.84846            0.00007     2.10235
+      10            0.06458     0.84955            0.00007     2.10313
+      11            0.03091     0.84963            0.00012     2.10388
+      12            0.03263     0.84931            0.00007     2.10297
+      13            0.03109     0.84859            0.00007     2.10076
+      14            0.03182     0.84851            0.00007     2.10030
+         208Pb/206Pb_err2SD
+      1             0.00017
+      2             0.00033
+      3             0.00033
+      4             0.00025
+      5             0.00023
+      6             0.00032
+      7             0.00023
+      8             0.00017
+      9             0.00018
+      10            0.00026
+      11            0.00022
+      12            0.00022
+      13            0.00018
+      14            0.00024
+
+---
+
+    Code
+      print(test_input)
+    Output
+      archchem table
+      Analytical columns: 206Pb/204Pb, Al2O3, SiO2+Al2O3, 204Pb, K2O, d18O, SiO2/FeO, Mn, Zn, SiO2/(FeO+MnO), Sb/As, Sn, K2O+MgO+Na2O
+      Contextual columns: other, other2 
+      # A data frame: 1 x 16
+        ID    `206Pb/204Pb`     Al2O3 `SiO2+Al2O3` `204Pb` other other2   K2O  d18O
+        <chr>         <dbl> [count/s]      [ng/kg] [mg/kg] <chr>  <dbl> [wtP] <dbl>
+      1 troet           0.5         3           20       7 troet     27    23 -5.32
+      # i 7 more variables: `SiO2/FeO` <dbl>, Mn [atP], Zn [mg/kg],
+      #   `SiO2/(FeO+MnO)` <dbl>, `Sb/As` <dbl>, Sn [µg/g], `K2O+MgO+Na2O` [wtP]
 
diff --git a/tests/testthat/setup.R b/tests/testthat/setup.R
index 6e5e215..ea93e54 100644
--- a/tests/testthat/setup.R
+++ b/tests/testthat/setup.R
@@ -2,7 +2,12 @@
 
 # Additional packages to load for testing or setting up the test environment
 
+library(testthat)
+library(ASTR)
+library(tibble)
+library(ggplot2)
 library(vdiffr)
+library(readxl)
 
 # reference data sets
 
diff --git a/tests/testthat/test_archchem_basic.R b/tests/testthat/test_archchem_basic.R
index d4a8491..8f20c35 100644
--- a/tests/testthat/test_archchem_basic.R
+++ b/tests/testthat/test_archchem_basic.R
@@ -6,9 +6,59 @@ test_input <- read_archchem(
   context = c("other", "other2")
 )
 
+test_input_xlsx <- read_archchem(
+  system.file("extdata", "input_format.xlsx", package = "ASTR"),
+  id_column = "other",
+  context = c("other", "other2")
+)
+
+test_input2 <- suppressWarnings(
+  read_archchem(
+    system.file("extdata", "test_data_input_good.csv", package = "ASTR"),
+    id_column = "Sample",
+    context = c("Lab no.", "Site", "latitude", "longitude", "Type", "method_comp")
+  )
+)
+
+test_input3 <- readr::read_csv(system.file("extdata", "input_format.csv", package = "ASTR"))
+test_input3[2, ] <- test_input3[1, ]
+test_input3 <- suppressWarnings(
+  as_archchem(test_input3, id_column = "other2", context = "other")
+)
+
 test_that("reading of a basic example table works as expected", {
   expect_snapshot({
     # turn to data.frame to render the entire table
     as.data.frame(test_input)
   })
+  expect_equal(test_input_xlsx, test_input)
+  expect_snapshot({
+    # turn to data.frame to render the entire table
+    as.data.frame(test_input2)
+  })
+  expect_snapshot({
+    print(test_input)
+  })
+  # checks that automatic renaming of ID values works
+  expect_all_equal(test_input3$ID[2], "27_2")
+})
+
+# parsing throws expected errors
+
+test_input3 <- readr::read_csv(system.file("extdata", "input_format.csv", package = "ASTR"))
+test_input3[2, ] <- test_input3[1, ]
+
+test_that("archem functions result in expected errors and warnings", {
+  expect_error(
+    suppressWarnings(as_archchem(test_input3, id_column = "other")),
+    "Column name .* could not be parsed"
+  )
+  expect_warning(
+    as_archchem(test_input3, id_column = "other", context = "other2"),
+    "Detected multiple data rows with the same ID"
+  )
+  expect_error(
+    validate(2),
+    "x is not an object of class archchem"
+  )
 })
diff --git a/tests/testthat/test_archchem_column_selectionc.R b/tests/testthat/test_archchem_column_selectionc.R
new file mode 100644
index 0000000..6349d8f
--- /dev/null
+++ b/tests/testthat/test_archchem_column_selectionc.R
@@ -0,0 +1,38 @@
+# test columns selection function not checked elsewhere
+
+test_input <- suppressWarnings(
+  read_archchem(
+    system.file("extdata", "test_data_input_good.csv", package = "ASTR"),
+    id_column = "Sample",
+    context = c("Lab no.", "Site", "latitude", "longitude", "Type", "method_comp")
+  )
+)
+
+test_that("column selection based on archchem column types", {
+  expect_all_true(
+    colnames(get_isotope_columns(test_input)) ==
+      c(
+        "ID", "143Nd/144Nd", "d65Cu", "206Pb/204Pb", "207Pb/204Pb", "208Pb/204Pb",
+        "207Pb/206Pb", "208Pb/206Pb"
+      )
+  )
+  expect_all_true(
+    colnames(get_element_columns(test_input)) ==
+      c("ID", "FeOtot/SiO2", "(Na2O+K2O)/SiO2")
+  )
+  expect_all_true(
+    colnames(get_ratio_columns(test_input)) ==
+      c(
+        "ID", "143Nd/144Nd", "d65Cu", "FeOtot/SiO2", "(Na2O+K2O)/SiO2", "206Pb/204Pb", "207Pb/204Pb",
+        "208Pb/204Pb", "207Pb/206Pb", "208Pb/206Pb"
+      )
+  )
+  expect_all_true(
+    colnames(get_concentration_columns(test_input)) ==
+      c(
+        "ID", "Na2O", "BaO", "Pb", "MgO", "Al2O3", "SiO2", "P2O5", "S", "CaO", "TiO2",
+        "MnO", "FeOtot", "ZnO", "K2O", "Cu", "As", "LOI", "Ag", "Sn", "Sb", "Te", "Bi",
+        "U", "V", "Cr", "Co", "Ni", "Sr", "Se"
+      )
+  )
+})
diff --git a/vignettes/VG.ASTRschema.0.0.2.Rmd b/vignettes/VG.ASTRschema.0.0.2.Rmd
index bc4f461..597c556 100644
--- a/vignettes/VG.ASTRschema.0.0.2.Rmd
+++ b/vignettes/VG.ASTRschema.0.0.2.Rmd
@@ -26,7 +26,7 @@ All columns that contain element and oxide compositions and analytical errors, i
 
 - The names of oxides and trace elements are self-explanatory (e.g. `SiO2` or `Si`). Total iron, if given, should be expressed as: `FeOtot` or `Fe2O3tot`. Loss on ignition, where known, should be expressed as `LOI`.
 
-- Units for values should follow element or oxide, as `_<unit>`. Units supported include *all SI units*, as well as *ppm*, *ppb*, *ppt*, *%*, *wt%*, *at%*, *w/w%*, *‰*, *counts*, and *cps*, noted as such.
+- Units for values should follow element or oxide, as `_<unit>`. Units supported include *all SI units*, as well as *ppm*, *ppb*, *ppt*, *ppq*, *%*, *wt%*, *at%*, *‰*, *counts*, and *cps*, noted as such. For relative units, the type of concentration can be specified, e.g. *w/w%* or *ppm(m/v)*
 
 - Where known, specify *wt%* and *at%* instead of using *%*. For *‘per mille’*, use the symbol *‰*.