update doc; no slicing in `plot_spc()`

DESCRIPTION

@@ -38,6 +38,6 @@ Imports:
     broom
 License: GPL-2
 LazyData: TRUE
-RoxygenNote: 7.1.0
+RoxygenNote: 7.2.3
 Roxygen: list(markdown = TRUE)
 Encoding: UTF-8

NAMESPACE

@@ -51,4 +51,5 @@ importFrom(purrr,modify_depth)
 importFrom(rlang,ensym)
 importFrom(rlang,quo_name)
 importFrom(rlang,set_names)
+importFrom(stringr,str_replace)
 importFrom(tidyselect,one_of)

R/plot_spc.R

@@ -32,10 +32,6 @@
 #' \code{expression(paste("Wavenumber [", cm^-1, "]"))}.
 #' @param ylab Character string or mathematical expression
 #' (use \code{expression}) for the y axis title. Default is \code{"absorbance"}.
-#' @param slice Logical whether to slice the data sets (select rows by position).
-#' Default is \code{TRUE}.
-#' This argument will soon be supported to group spectra based id variables present
-#' in spc_tbl such as the `sample_id` and slicing the data set prior to plotting.
 #' @param alpha Double in between 0 and 1. Sets the transparency for the plotted
 #' spectra lines.
 #' @param legend Logical whether to plot a legend for the spectra describing
@@ -56,17 +52,12 @@ plot_spc <- function(spc_tbl, spc_tbl_2 = NULL,
                      graph_id_1 = "Set 1", graph_id_2 = "Set 2",
                      graph_id_1_col = "black", graph_id_2_col = "red",
                      xlab = expression(paste("Wavenumber [", cm^-1, "]")),
-                     ylab = "Absorbance", slice = TRUE, alpha = 0.2,
+                     ylab = "Absorbance", alpha = 0.2,
                      legend = TRUE) {
 
   # Fix `R CMD check NOTE`: "no visible binding for global variable ‘...‘"
   graph_id <- id <- variable <- value <- NULL
 
-  # (0) Slice spectra tibble to remove triplicate spectra (reps)
-  # only sample_id level
-  if(slice == TRUE) {
-    spc_tbl <- dplyr::slice(spc_tbl)
-  }
   # (1) Gather spectra into one data.table
   if(!is.null(spc_tbl_2)) {
     if(y == "spc") {

R/select-spc.R

@@ -7,7 +7,7 @@
 #' @param lcol_xvalues List-column containing x-values, specified with
 #' column name as symbols or 1L character vector.
 #' @param every Every n-th spectral positions to keep as 1L integer vector.
-#' @return
+#' @return a spectral tibble
 #' @export
 select_spc_vars <- function(spc_tbl,
                             lcol_spc = "spc_pre",

man/assess_multimodels.Rd

@@ -4,15 +4,19 @@
 \alias{assess_multimodels}
 \title{Assess multiple pairs of measured and predicted values}
 \usage{
-assess_multimodels(data, ..., .metrics = c("simplerspec", "yardstick"),
-  .model_name = "model")
+assess_multimodels(
+  data,
+  ...,
+  .metrics = c("simplerspec", "yardstick"),
+  .model_name = "model"
+)
 }
 \arguments{
 \item{data}{Data frame with all measured (observed) and predicted variables.}
 
 \item{...}{Multiple arguments with observed (measured)-predicted pairs,
 specified with \code{dplyr::vars(o = <column_name>, p = <column_name>)}.
-Column names can strings or symbols. The arguments in `...` need to be named.}
+Column names can strings or symbols. The arguments in \code{...} need to be named.}
 
 \item{.metrics}{Character vector with package used for metrics calculation.
 Default is \code{"simplerspec"}, which uses

man/fit_pls.Rd

@@ -5,32 +5,62 @@
 \alias{pls_ken_stone}
 \title{Calibration sampling, model tuning, and PLS regression}
 \usage{
-fit_pls(spec_chem, response, variable = NULL, center = TRUE, scale = TRUE,
-  evaluation_method = "test_set", validation = TRUE,
-  split_method = "ken_stone", ratio_val = 1/3, ken_sto_pc = 2, pc,
-  invert = TRUE, tuning_method = "resampling",
-  resampling_method = "kfold_cv", cv = NULL, resampling_seed = 123,
-  pls_ncomp_max = 20, ncomp_fixed = 5, print = TRUE,
-  env = parent.frame())
-
-pls_ken_stone(spec_chem, response, variable = NULL, center = TRUE,
-  scale = TRUE, evaluation_method = "test_set", validation = TRUE,
-  split_method = "ken_stone", ratio_val = 1/3, ken_sto_pc = 2, pc,
-  invert = TRUE, tuning_method = "resampling",
-  resampling_method = "kfold_cv", cv = NULL, resampling_seed = 123,
-  pls_ncomp_max = 20, ncomp_fixed = 5, print = TRUE,
-  env = parent.frame())
+fit_pls(
+  spec_chem,
+  response,
+  variable = NULL,
+  center = TRUE,
+  scale = TRUE,
+  evaluation_method = "test_set",
+  validation = TRUE,
+  split_method = "ken_stone",
+  ratio_val = 1/3,
+  ken_sto_pc = 2,
+  pc,
+  invert = TRUE,
+  tuning_method = "resampling",
+  resampling_method = "kfold_cv",
+  cv = NULL,
+  resampling_seed = 123,
+  pls_ncomp_max = 20,
+  ncomp_fixed = 5,
+  print = TRUE,
+  env = parent.frame()
+)
+
+pls_ken_stone(
+  spec_chem,
+  response,
+  variable = NULL,
+  center = TRUE,
+  scale = TRUE,
+  evaluation_method = "test_set",
+  validation = TRUE,
+  split_method = "ken_stone",
+  ratio_val = 1/3,
+  ken_sto_pc = 2,
+  pc,
+  invert = TRUE,
+  tuning_method = "resampling",
+  resampling_method = "kfold_cv",
+  cv = NULL,
+  resampling_seed = 123,
+  pls_ncomp_max = 20,
+  ncomp_fixed = 5,
+  print = TRUE,
+  env = parent.frame()
+)
 }
 \arguments{
 \item{spec_chem}{Tibble that contains spectra, metadata and chemical
 reference as list-columns. The tibble to be supplied to \code{spec_chem} can
-be generated by the `join_chem_spc() function`}
+be generated by the \verb{join_chem_spc() function}}
 
 \item{response}{Response variable as symbol or name
 (without quotes, no character string). The provided response symbol needs to be
 a column name in the \code{spec_chem} tibble.}
 
-\item{variable}{Depreciated and replaced by `response`}
+\item{variable}{Depreciated and replaced by \code{response}}
 
 \item{center}{Logical whether to perform mean centering of each spectrum column
 (e.g. wavenumber or wavelength) after common spectrum preprocessing. Default is
@@ -66,7 +96,7 @@ Kennard-Stone algorithm.
 Default is \code{ken_sto_pc = 2}, which will use the first two PCA
 components.}
 
-\item{pc}{Depreciated; renamed argument is `ken_sto_pc`.}
+\item{pc}{Depreciated; renamed argument is \code{ken_sto_pc}.}
 
 \item{invert}{Logical}
 

man/fit_rf.Rd

@@ -4,22 +4,35 @@
 \alias{fit_rf}
 \title{Calibration sampling, and random forest model tuning and evaluation}
 \usage{
-fit_rf(spec_chem, response, variable = NULL, evaluation_method = "test_set",
-  validation = NULL, split_method = "ken_stone", ratio_val,
-  ken_sto_pc = 2, pc = NULL, invert = TRUE,
-  tuning_method = "resampling", resampling_seed = 123, cv = NULL,
-  ntree_max = 500, print = TRUE, env = parent.frame())
+fit_rf(
+  spec_chem,
+  response,
+  variable = NULL,
+  evaluation_method = "test_set",
+  validation = NULL,
+  split_method = "ken_stone",
+  ratio_val,
+  ken_sto_pc = 2,
+  pc = NULL,
+  invert = TRUE,
+  tuning_method = "resampling",
+  resampling_seed = 123,
+  cv = NULL,
+  ntree_max = 500,
+  print = TRUE,
+  env = parent.frame()
+)
 }
 \arguments{
 \item{spec_chem}{Tibble that contains spectra, metadata and chemical
 reference as list-columns. The tibble to be supplied to \code{spec_chem} can
-be generated by the `join_chem_spc() function`}
+be generated by the \verb{join_chem_spc() function}}
 
 \item{response}{Response variable as symbol or name
 (without quotes, no character string). The provided response symbol needs to be
 a column name in the \code{spec_chem} tibble.}
 
-\item{variable}{Depreciated and replaced by `response`}
+\item{variable}{Depreciated and replaced by \code{response}}
 
 \item{evaluation_method}{Character string stating evaluation method.
 Either \code{"test_set"} (default) or \code{"resampling"}. \code{"test_set"}
@@ -47,7 +60,7 @@ Kennard-Stone algorithm.
 Default is \code{ken_sto_pc = 2}, which will use the first two PCA
 components.}
 
-\item{pc}{Depreciated; renamed argument is `ken_sto_pc`.}
+\item{pc}{Depreciated; renamed argument is \code{ken_sto_pc}.}
 
 \item{invert}{Logical}
 

man/merge_dts.Rd

@@ -5,8 +5,13 @@
 \title{Merge list-columns of spectra, x-axis values, metadata and additional
 measured variables into a single long form data.table}
 \usage{
-merge_dts(spc_tbl, lcols_spc = c("spc", "spc_pre"), lcol_measure = NULL,
-  spc_id = "unique_id", group_id = "sample_id")
+merge_dts(
+  spc_tbl,
+  lcols_spc = c("spc", "spc_pre"),
+  lcol_measure = NULL,
+  spc_id = "unique_id",
+  group_id = "sample_id"
+)
 }
 \arguments{
 \item{spc_tbl}{Tibble data frame containing spectra, x-axis values, metadata

man/merge_dts_l.Rd

@@ -5,8 +5,13 @@
 \title{Wrapper function around \code{merge_dts()} for list of tibbles to
 aggregate data for plotting.}
 \usage{
-merge_dts_l(spc_tbl_l, lcols_spc = c("spc", "spc_pre"), lcol_measure = NULL,
-  spc_id = "unique_id", group_id = "sample_id")
+merge_dts_l(
+  spc_tbl_l,
+  lcols_spc = c("spc", "spc_pre"),
+  lcol_measure = NULL,
+  spc_id = "unique_id",
+  group_id = "sample_id"
+)
 }
 \arguments{
 \item{spc_tbl_l}{List of spectral tibbles (data frames).}

man/plot_spc.Rd

@@ -57,11 +57,6 @@ set. Default is \code{"red"}.}
 \item{ylab}{Character string or mathematical expression
 (use \code{expression}) for the y axis title. Default is \code{"absorbance"}.}
 
-\item{slice}{Logical whether to slice the data sets (select rows by position).
-Default is \code{TRUE}.
-This argument will soon be supported to group spectra based id variables present
-in spc_tbl such as the `sample_id` and slicing the data set prior to plotting.}
-
 \item{alpha}{Double in between 0 and 1. Sets the transparency for the plotted
 spectra lines.}
 

man/plot_spc_ext.Rd

@@ -4,13 +4,27 @@
 \alias{plot_spc_ext}
 \title{ggplot2 wrapper for extended spectra plotting}
 \usage{
-plot_spc_ext(spc_tbl, spc_tbl_l = NULL, lcols_spc = "spc",
-  lcol_measure = NULL, lcol_measure_col_palette = "Spectral",
-  lcol_measure_col_direction = -1, spc_id = "unique_id",
-  group_id = "sample_id", group_id_order = TRUE, group_color = TRUE,
-  group_color_palette = NULL, group_panel = TRUE, group_legend = FALSE,
-  ncol = NULL, relabel_spc = TRUE, ylab = "Spectrum value", alpha = 0.5,
-  line_width = 0.2, ...)
+plot_spc_ext(
+  spc_tbl,
+  spc_tbl_l = NULL,
+  lcols_spc = "spc",
+  lcol_measure = NULL,
+  lcol_measure_col_palette = "Spectral",
+  lcol_measure_col_direction = -1,
+  spc_id = "unique_id",
+  group_id = "sample_id",
+  group_id_order = TRUE,
+  group_color = TRUE,
+  group_color_palette = NULL,
+  group_panel = TRUE,
+  group_legend = FALSE,
+  ncol = NULL,
+  relabel_spc = TRUE,
+  ylab = "Spectrum value",
+  alpha = 0.5,
+  line_width = 0.2,
+  ...
+)
 }
 \arguments{
 \item{spc_tbl}{Tibble data frame containing spectra, x-axis values, metadata
@@ -27,14 +41,14 @@ of the measure columns. This argument is optional. Default is \code{NULL},
 which does not extract an additional measure column.}
 
 \item{lcol_measure_col_palette}{Palette value supplied to
-`ggplot::scale_colour_brewer()`. Default is `"Spectral"`, but you can set
-it to the default argument `1` (will use
-`scale_colour_brewer(..., palette = 1)`).}
+\code{ggplot::scale_colour_brewer()}. Default is \code{"Spectral"}, but you can set
+it to the default argument \code{1} (will use
+\code{scale_colour_brewer(..., palette = 1)}).}
 
 \item{lcol_measure_col_direction}{Sets the the order of colours in the scale
 that is based on a measure column. Default is \code{-1} which reverses the
-scale. Argument is passed on to the function `ggplot2::sclae_colour_brewer()`
-as argument `direction`.}
+scale. Argument is passed on to the function \code{ggplot2::sclae_colour_brewer()}
+as argument \code{direction}.}
 
 \item{spc_id}{Character vector denoting column name for a unique spectrum ID.
 Default is \code{"unique_id"}.}
@@ -51,8 +65,8 @@ numbers. Default is \code{TRUE}.}
 specified by \code{group_id}.}
 
 \item{group_color_palette}{Character (1L) defining the diverging colour
-scales from colorbrewer.org; see `?scale_colour_brewer` for supported
-diverging colur types (`palette` argument).}
+scales from colorbrewer.org; see \code{?scale_colour_brewer} for supported
+diverging colur types (\code{palette} argument).}
 
 \item{group_panel}{Logical defining whether spectra are arranged into panels
 by groups specified in \code{group_id}. Default is \code{TRUE}.}

man/preprocess_spc.Rd

@@ -4,8 +4,7 @@
 \alias{preprocess_spc}
 \title{Preprocess spectra}
 \usage{
-preprocess_spc(spc_tbl, select, column_in = "spc_mean",
-  custom_function = NULL)
+preprocess_spc(spc_tbl, select, column_in = "spc_mean", custom_function = NULL)
 }
 \arguments{
 \item{spc_tbl}{Tibble that contains spectra to be preprocessed within

man/read_asd.Rd

@@ -14,7 +14,7 @@ remaining columns are sample spectra referred by an ID name provided in the
 first row of these columns.}
 }
 \value{
-Spectra data in tibble data frame (class `tbl_df`) that contains
+Spectra data in tibble data frame (class \code{tbl_df}) that contains
 columns \code{sample_id} (derived from 2nd and following column names of
 tab delimited ASD exported text file),
 \code{spc} (list-column of spectral matrices)

man/select_spc_vars.Rd

@@ -24,7 +24,7 @@ column name as symbols or 1L character vector.}
 \item{every}{Every n-th spectral positions to keep as 1L integer vector.}
 }
 \value{
-
+a spectral tibble
 }
 \description{
 Select every n-th spectral variable for all spectra and x-values in spectral

man/slice_xvalues.Rd

@@ -4,8 +4,12 @@
 \alias{slice_xvalues}
 \title{Slice spectra into defined x-axis ranges}
 \usage{
-slice_xvalues(spc_tbl, xunit_lcol = "wavenumbers", spc_lcol = "spc",
-  xvalues_cut = NULL)
+slice_xvalues(
+  spc_tbl,
+  xunit_lcol = "wavenumbers",
+  spc_lcol = "spc",
+  xvalues_cut = NULL
+)
 }
 \arguments{
 \item{spc_tbl}{Spectral data in a tibble object (classes "tibble_df", "tbl"