add maldipickr vignette for improved quickstart

this fixes #48

DESCRIPTION

@@ -41,6 +41,7 @@ Imports:
     tools,
     utils
 Suggests:
+    coop,
     knitr,
     rmarkdown,
     spelling,

README.Rmd

@@ -36,67 +36,6 @@ knitr::opts_chunk$set(
 
 Illustration (click for a bigger version) of the data flow when using `{maldipickr}` to cherry-pick bacterial isolates with MALDI Biotyper. It depicts the two possible approaches using either taxonomic identification reports (left) or spectra data (right).
 
-## Quickstart
-
-How to **cherry-pick bacterial isolates** with MALDI Biotyper:
-
-* [using taxonomic identification report](#using-taxonomic-identification-report)
-* [using spectra data](#using-spectra-data)
-
-
-### Using taxonomic identification report
-
-```{r quickstart_report, eval=TRUE}
-library(maldipickr)
-# Import Biotyper CSV report
-#  and glimpse at the table
-report_tbl <- read_biotyper_report(
-  system.file("biotyper_unknown.csv", package = "maldipickr")
-)
-report_tbl %>%
-  dplyr::select(name, bruker_species, bruker_log)
-
-
-# Delineate clusters from the identifications after filtering the reliable ones
-#   and cherry-pick one representative spectra.
-#   The chosen ones are indicated by `to_pick` column
-report_tbl <- report_tbl %>%
-  dplyr::mutate(
-      bruker_species = dplyr::if_else(bruker_log >= 2, bruker_species,
-                                      "not reliable identification")
-  )
-report_tbl %>%
-  delineate_with_identification() %>%
-  pick_spectra(report_tbl, criteria_column = "bruker_log") %>%
-  dplyr::relocate(name, to_pick, bruker_species)
-```
-
-### Using spectra data
-
-```{r quickstart_spectra}
-library(maldipickr)
-# Set up the directory location of your spectra data
-spectra_dir <- system.file("toy-species-spectra", package = "maldipickr")
-
-# Import and process the spectra
-processed <- spectra_dir %>%
-  import_biotyper_spectra() %>%
-  process_spectra()
-
-# Delineate spectra clusters using Cosine similarity
-#  and cherry-pick one representative spectra.
-#  The chosen ones are indicated by `to_pick` column
-processed %>%
-  list() %>%
-  merge_processed_spectra() %>%
-  coop::tcosine() %>%
-  delineate_with_similarity(threshold = 0.92) %>%
-  set_reference_spectra(processed$metadata) %>%
-  pick_spectra() %>%
-  dplyr::relocate(name, to_pick)
-```
-
-
 
 ## Installation
 

dev/config_fusen.yaml

@@ -79,3 +79,16 @@ keep:
   - tests/testthat/test-remove_spectra_logical.R
   - tests/testthat/test-remove_spectra.R
   vignettes: []
+maldipickr.Rmd:
+  path: dev/maldipickr.Rmd
+  state: active
+  R: []
+  tests: []
+  vignettes: vignettes/maldipickr.Rmd
+  inflate:
+    flat_file: dev/maldipickr.Rmd
+    vignette_name: maldipickr
+    open_vignette: true
+    check: false
+    document: true
+    overwrite: ask

dev/maldipickr.Rmd

@@ -0,0 +1,110 @@
+---
+title: "maldipickr"
+output: html_document
+editor_options: 
+  chunk_output_type: console
+---
+
+```{r development-load}
+# Load already included functions if relevant
+pkgload::load_all(export_all = FALSE)
+```
+
+## Quickstart
+
+The `{maldipickr}` package helps microbiologists reduce duplicate/clonal bacteria from their cultures and eventually exclude previously selected bacteria. `{maldipickr}` achieve this feat by grouping together data from MALDI Biotyper and helps choose representative bacteria from each group using user-relevant metadata -- a process known as **cherry-picking**.
+
+`{maldipickr}` cherry-picks bacterial isolates with MALDI Biotyper:
+
+* [using taxonomic identification report](#using-taxonomic-identification-report)
+* [using spectra data](#using-spectra-data)
+
+
+### Using taxonomic identification report
+
+First make sure `{maldipickr}` is installed and loaded, alternatively [follow the instructions to install the package](https://clavellab.github.io/maldipickr/index.html#installation).
+
+Cherry-picking four isolates based on their taxonomic identification by the MALDI Biotyper is done in a few steps with `{maldipickr}`.
+
+#### Get example data
+
+We import an example Biotyper CSV report and glimpse at the table.
+
+```{r quickstart_report_data, eval=TRUE}
+report_tbl <- read_biotyper_report(
+  system.file("biotyper_unknown.csv", package = "maldipickr")
+)
+report_tbl %>%
+  dplyr::select(name, bruker_species, bruker_log) %>% knitr::kable()
+```
+
+#### Delineate clusters and cherry-pick
+
+Delineate clusters from the identifications after filtering the reliable ones and cherry-pick one representative spectra.
+
+Unreliable identifications based on the log-score are replaced by "not reliable identification", but stay tuned as they do not represent the same isolates!
+
+```{r quickstart_report_filter, eval=TRUE}
+report_tbl <- report_tbl %>%
+  dplyr::mutate(
+      bruker_species = dplyr::if_else(bruker_log >= 2, bruker_species,
+                                      "not reliable identification")
+  )
+knitr::kable(report_tbl)
+```
+
+The chosen ones are indicated by `to_pick` column.
+
+```{r quickstart_report_delineate, eval=TRUE}
+report_tbl %>%
+  delineate_with_identification() %>%
+  pick_spectra(report_tbl, criteria_column = "bruker_log") %>%
+  dplyr::relocate(name, to_pick, bruker_species) %>% 
+  knitr::kable()
+```
+
+### Using spectra data
+
+In parallel to taxonomic identification reports, `{maldipickr}` process spectra data.
+Make sure `{maldipickr}` is installed and loaded, alternatively [follow the instructions to install the package](https://clavellab.github.io/maldipickr/index.html#installation).
+
+Cherry-picking six isolates from three species based on their spectra data obtained from the MALDI Biotyper is done in a few steps with `{maldipickr}`.
+
+#### Get example data
+
+We set up the directory location of our example spectra data, but adjust for your requirements. We import and process the spectra which gives us a named list of three objects: spectra, peaks and metadata (more details in Value section of `process_spectra()`).
+
+
+```{r quickstart_spectra_data, eval=TRUE}
+spectra_dir <- system.file("toy-species-spectra", package = "maldipickr")
+
+processed <- spectra_dir %>%
+  import_biotyper_spectra() %>%
+  process_spectra()
+```
+
+#### Delineate clusters and cherry-pick
+
+Delineate spectra clusters using Cosine similarity and cherry-pick one representative spectra.
+The chosen ones are indicated by `to_pick` column.
+
+```{r quickstart_spectra_delineate, eval=TRUE}
+processed %>%
+  list() %>%
+  merge_processed_spectra() %>%
+  coop::tcosine() %>%
+  delineate_with_similarity(threshold = 0.92) %>%
+  set_reference_spectra(processed$metadata) %>%
+  pick_spectra() %>%
+  dplyr::relocate(name, to_pick) %>% 
+  knitr::kable()
+```
+
+This provides only a brief overview of the features of `{maldipickr}`, browse the  others vignettes to learn more about additional features.
+
+```{r development-inflate, eval=FALSE}
+# Run but keep eval=FALSE to avoid infinite loop
+# Execute in the console directly
+fusen::inflate(flat_file = "dev/maldipickr.Rmd", vignette_name = "maldipickr")
+```
+

vignettes/maldipickr.Rmd

@@ -0,0 +1,123 @@
+---
+title: "maldipickr"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{maldipickr}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+```{r setup}
+library(maldipickr)
+```
+
+<!-- WARNING - This vignette is generated by {fusen} from dev/maldipickr.Rmd: do not edit by hand -->
+
+## Quickstart
+
+The `{maldipickr}` package helps microbiologists reduce duplicate/clonal bacteria from their cultures and eventually exclude previously selected bacteria. `{maldipickr}` achieve this feat by grouping together data from MALDI Biotyper and helps choose representative bacteria from each group using user-relevant metadata -- a process known as **cherry-picking**.
+
+`{maldipickr}` cherry-picks bacterial isolates with MALDI Biotyper:
+
+* [using taxonomic identification report](#using-taxonomic-identification-report)
+* [using spectra data](#using-spectra-data)
+
+
+
+### Using taxonomic identification report
+
+First make sure `{maldipickr}` is installed and loaded, alternatively [follow the instructions to install the package](https://clavellab.github.io/maldipickr/index.html#installation).
+
+Cherry-picking four isolates based on their taxonomic identification by the MALDI Biotyper is done in a few steps with `{maldipickr}`.
+
+
+#### Get example data
+
+We import an example Biotyper CSV report and glimpse at the table.
+
+
+```{r quickstart_report_data, eval = TRUE}
+report_tbl <- read_biotyper_report(
+  system.file("biotyper_unknown.csv", package = "maldipickr")
+)
+report_tbl %>%
+  dplyr::select(name, bruker_species, bruker_log) %>% knitr::kable()
+```
+
+#### Delineate clusters and cherry-pick
+
+Delineate clusters from the identifications after filtering the reliable ones and cherry-pick one representative spectra.
+
+Unreliable identifications based on the log-score are replaced by "not reliable identification", but stay tuned as they do not represent the same isolates!
+
+
+```{r quickstart_report_filter, eval = TRUE}
+report_tbl <- report_tbl %>%
+  dplyr::mutate(
+      bruker_species = dplyr::if_else(bruker_log >= 2, bruker_species,
+                                      "not reliable identification")
+  )
+knitr::kable(report_tbl)
+```
+
+The chosen ones are indicated by `to_pick` column.
+
+
+```{r quickstart_report_delineate, eval = TRUE}
+report_tbl %>%
+  delineate_with_identification() %>%
+  pick_spectra(report_tbl, criteria_column = "bruker_log") %>%
+  dplyr::relocate(name, to_pick, bruker_species) %>% 
+  knitr::kable()
+```
+
+### Using spectra data
+
+In parallel to taxonomic identification reports, `{maldipickr}` process spectra data.
+Make sure `{maldipickr}` is installed and loaded, alternatively [follow the instructions to install the package](https://clavellab.github.io/maldipickr/index.html#installation).
+
+Cherry-picking six isolates from three species based on their spectra data obtained from the MALDI Biotyper is done in a few steps with `{maldipickr}`.
+
+
+#### Get example data
+
+We set up the directory location of our example spectra data, but adjust for your requirements. We import and process the spectra which gives us a named list of three objects: spectra, peaks and metadata (more details in Value section of `process_spectra()`).
+
+
+
+```{r quickstart_spectra_data, eval = TRUE}
+spectra_dir <- system.file("toy-species-spectra", package = "maldipickr")
+
+processed <- spectra_dir %>%
+  import_biotyper_spectra() %>%
+  process_spectra()
+```
+
+#### Delineate clusters and cherry-pick
+
+Delineate spectra clusters using Cosine similarity and cherry-pick one representative spectra.
+The chosen ones are indicated by `to_pick` column.
+
+
+```{r quickstart_spectra_delineate, eval = TRUE}
+processed %>%
+  list() %>%
+  merge_processed_spectra() %>%
+  coop::tcosine() %>%
+  delineate_with_similarity(threshold = 0.92) %>%
+  set_reference_spectra(processed$metadata) %>%
+  pick_spectra() %>%
+  dplyr::relocate(name, to_pick) %>% 
+  knitr::kable()
+```
+
+This provides only a brief overview of the features of `{maldipickr}`, browse the  others vignettes to learn more about additional features.
+
+