Update documentation, update News.md. Addresses #1.

.Rbuildignore

@@ -14,3 +14,5 @@
 ^pkgdown$
 ^\.github$
 ^cran-comments\.md$
+^man/figures/logo-hex\.svg$
+^man/figures/logo-hex\.png$

.gitignore

@@ -6,3 +6,4 @@ ir.Rproj
 /doc/
 /Meta/
 docs
+/man/figures/logo-hex.svg

NEWS.md

@@ -1,5 +1,6 @@
-# ir 0.1.0.9999
+# ir 0.2.0.
 
+* First CRAN release.
 * Added a `NEWS.md` file to track changes to the package.
 * Added tidyverse methods for `ir` objects.
 * `ir` objects no longer require columns `measurement_id` and `sample_id`, but only the column `spectra`.

README.Rmd

@@ -15,13 +15,17 @@ knitr::opts_chunk$set(
 )
 ```
 
-# ir
+# ir <img src='man/figures/logo-hex.png' align="right" height="139" alt="logo" style="float:right; height:200px;" />
 
 <!-- badges: start -->
-[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental) [![DOI](https://zenodo.org/badge/234117897.svg)](https://zenodo.org/badge/latestdoi/234117897)
+[![DOI](https://zenodo.org/badge/234117897.svg)](https://zenodo.org/badge/latestdoi/234117897)
+[![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
 <!-- badges: end -->
+
 
-ir is an R package that contains simple functions to import, handle and preprocess infrared spectra. Infrared spectra are stored as list columns in data frames to enable efficient storage of metadata along with the spectra and support further analyses containing other data for the same samples.
+## Overview
+
+'ir' is an R package that contains simple functions to import, handle and preprocess infrared spectra. Infrared spectra are stored as list columns in data frames to enable efficient storage of metadata along with the spectra and support further analyses containing other data for the same samples.
 
 **Supported file formats for import are:**
 
@@ -55,22 +59,22 @@ ir is an R package that contains simple functions to import, handle and preproce
 
 ### How to install
 
-You can install ir from CRAB using R via:
+You can install 'ir' from CRAB using R via:
 
-```{r installation-cran}
+```{r installation-cran, eval = FALSE}
 install.packages("ir")
 ```
 
 
-You can install ir from GitHub using R via:
+You can install 'ir' from GitHub using R via:
 
 ```{r installation-github, eval = FALSE}
 remotes::install_github(repo = "henningte/ir")
 ```
 
 ### How to use
 
-You can load ir in R with:
+You can load 'ir' in R with:
 
 ```{r load_ir}
 # load ir package
@@ -87,7 +91,7 @@ For brief introductions, see below and the two vignettes:
 
 #### Sample workflow
 
-A simple workflow would be, for example, to baseline correct the spectra, then bin them to bins with a width of 10 wavenumber units, then normalize them so that the maximum intensity value is 1 and the minimum intensity value is 0 and then plot the baseline corrected spectra for each sample and sample type. Here's the ir code using the built-in sample data `ir_sample_data`.
+A simple workflow would be, for example, to baseline correct the spectra, then bin them to bins with a width of 10 wavenumber units, then normalize them so that the maximum intensity value is 1 and the minimum intensity value is 0 and then plot the baseline corrected spectra for each sample and sample type. Here's the 'ir' code using the built-in sample data `ir_sample_data`.
 
 ```{r sample_data_workflow}
 ir_sample_data %>%                                      # data
@@ -127,13 +131,13 @@ Please cite this R package as:
 
 ### Companion packages
 
-[irpeat](https://github.com/henningte/irpeat/) builds on ir. irpeat provides functions to analyze infrared spectra of peat (humification indices, prediction models).
+[irpeat](https://github.com/henningte/irpeat/) builds on 'ir'. irpeat provides functions to analyze infrared spectra of peat (humification indices, prediction models).
 
 
 
 ### Licenses
 
-**Text and figures :**  [CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)
+**Text and figures :**  [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
 
 **Code :** See the [DESCRIPTION](DESCRIPTION) file
 
@@ -145,7 +149,7 @@ We welcome contributions from everyone. Before you get started, please see our [
 
 ### Sources
 
-The complete data in this package is derived from @Hodgkins.2018 and was restructured to match the requirements of ir. The original article containing the data can be downloaded from https://www.nature.com/articles/s41467-018-06050-2 and is distributed under the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). The data on Klason lignin and holocellulose content was originally derived from @LaCruz.2016.
+The complete data in this package is derived from @Hodgkins.2018 and was restructured to match the requirements of 'ir'. The original article containing the data can be downloaded from https://www.nature.com/articles/s41467-018-06050-2 and is distributed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). The data on Klason lignin and holocellulose content was originally derived from @LaCruz.2016.
 
 This packages was developed in R (`r R.Version()$version.string`) [@RCoreTeam.2019] using functions from devtools [@Wickham.2019], usethis [@Wickham.2019b], rrtools [@Marwick.2019] and roxygen2 [@Wickham.2019c].
 

README.md

@@ -1,20 +1,22 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# ir
+# ir <img src='man/figures/logo-hex.png' align="right" height="139" alt="logo" style="float:right; height:200px;" />
 
 <!-- badges: start -->
 
-[![Lifecycle:
-experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental)
 [![DOI](https://zenodo.org/badge/234117897.svg)](https://zenodo.org/badge/latestdoi/234117897)
+[![Lifecycle:
+stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable)
 <!-- badges: end -->
 
-ir is an R package that contains simple functions to import, handle and
-preprocess infrared spectra. Infrared spectra are stored as list columns
-in data frames to enable efficient storage of metadata along with the
-spectra and support further analyses containing other data for the same
-samples.
+## Overview
+
+‘ir’ is an R package that contains simple functions to import, handle
+and preprocess infrared spectra. Infrared spectra are stored as list
+columns in data frames to enable efficient storage of metadata along
+with the spectra and support further analyses containing other data for
+the same samples.
 
 **Supported file formats for import are:**
 
@@ -51,15 +53,21 @@ samples.
 
 ### How to install
 
-You can install ir from GitHub using R via:
+You can install ‘ir’ from CRAB using R via:
+
+``` r
+install.packages("ir")
+```
+
+You can install ‘ir’ from GitHub using R via:
 
 ``` r
 remotes::install_github(repo = "henningte/ir")
 ```
 
 ### How to use
 
-You can load ir in R with:
+You can load ‘ir’ in R with:
 
 ``` r
 # load ir package
@@ -72,7 +80,7 @@ library(ggplot2)
 
 For brief introductions, see below and the two vignettes:
 
-1.  [Introduction to the ir package](vignettes/ir-introduction.Rmd)
+1.  [Introduction to the ‘ir’ package](vignettes/ir-introduction.Rmd)
 2.  [Introduction to the `ir`class](vignettes/ir-class.Rmd)
 
 #### Sample workflow
@@ -81,7 +89,7 @@ A simple workflow would be, for example, to baseline correct the
 spectra, then bin them to bins with a width of 10 wavenumber units, then
 normalize them so that the maximum intensity value is 1 and the minimum
 intensity value is 0 and then plot the baseline corrected spectra for
-each sample and sample type. Here’s the ir code using the built-in
+each sample and sample type. Here’s the ‘ir’ code using the built-in
 sample data `ir_sample_data`.
 
 ``` r
@@ -156,13 +164,19 @@ Column `x` represents the x values (in this case wavenumbers
 Please cite this R package as:
 
 > Henning Teickner (2022). *ir: Functions to Handle and Preprocess
-> Infrared Spectra’*. DOI: 10.5281/zenodo.5747170. Accessed 20 Apr 2022.
+> Infrared Spectra*. DOI: 10.5281/zenodo.5747170. Accessed 21 Apr 2022.
 > Online at <https://zenodo.org/record/5747170>.
 
+### Companion packages
+
+[irpeat](https://github.com/henningte/irpeat/) builds on ‘ir’. irpeat
+provides functions to analyze infrared spectra of peat (humification
+indices, prediction models).
+
 ### Licenses
 
 **Text and figures :** [CC
-BY 4.0](http://creativecommons.org/licenses/by/4.0/)
+BY 4.0](https://creativecommons.org/licenses/by/4.0/)
 
 **Code :** See the [DESCRIPTION](DESCRIPTION) file
 
@@ -180,11 +194,11 @@ participating in this project you agree to abide by its terms.
 ### Sources
 
 The complete data in this package is derived from Hodgkins et al. (2018)
-and was restructured to match the requirements of ir. The original
+and was restructured to match the requirements of ‘ir’. The original
 article containing the data can be downloaded from
 <https://www.nature.com/articles/s41467-018-06050-2> and is distributed
 under the Creative Commons Attribution 4.0 International License
-(<http://creativecommons.org/licenses/by/4.0/>). The data on Klason
+(<https://creativecommons.org/licenses/by/4.0/>). The data on Klason
 lignin and holocellulose content was originally derived from De La Cruz,
 Florentino B., Osborne, and Barlaz (2016).
 

_pkgdown.yml

@@ -12,7 +12,7 @@ navbar:
     articles:
       text: Articles
       menu:
-      - text: Introduction to the ir package
+      - text: Introduction to the 'ir' package
         href: articles/ir-introduction.html
       - text: Introduction to the `ir`class
         href: articles/ir-class.html

inst/WORDLIST

@@ -31,7 +31,6 @@ Preprocess
 Readme
 René
 Savitzky
-Spectra’
 Thermo
 Tibbles
 Tidyverse
@@ -55,12 +54,12 @@ et
 ggplot
 github
 holocellulose
-http
 https
 humification
 hyperspec
 ing
 irpeat
+ir’
 magrittr
 magrittr's
 mth
@@ -85,9 +84,11 @@ summarise
 tibble
 tidyr
 tidyverse
+un
 unchopping
 ungrouped
 unnest
+unnested
 unnesting
 unsmoothed
 usethis

vignettes/ir-class.Rmd

@@ -23,9 +23,9 @@ knitr::opts_chunk$set(
 
 ## Purpose
 
-The purpose of this vignette is to describe the structure and methods of objects of class `ir`. `ir` objects are used by the ir package to store spectra and their metadata. This vignette could be helpful if you want to understand better how the ir package works, how to handle metadata, how to manipulate `ir` objects, or if you want to construct a subclass based on the `ir`class.
+The purpose of this vignette is to describe the structure and methods of objects of class `ir`. `ir` objects are used by the 'ir' package to store spectra and their metadata. This vignette could be helpful if you want to understand better how the 'ir' package works, how to handle metadata, how to manipulate `ir` objects, or if you want to construct a subclass based on the `ir`class.
 
-This vignette does not give an overview on how to use the ir package, on functions for spectral preprocessing, and on how to plot `ir` objects. For this, see vignette [`r rmarkdown::yaml_front_matter("ir-introduction.Rmd")$title`](ir-introduction.html). 
+This vignette does not give an overview on how to use the 'ir' package, on functions for spectral preprocessing, and on how to plot `ir` objects. For this, see vignette [`r rmarkdown::yaml_front_matter("ir-introduction.Rmd")$title`](ir-introduction.html). 
 
 ## Structure
 
@@ -43,7 +43,7 @@ In part [Special functions to manipulate `ir objects`], I will present some more
 
 ## Preparation
 
-To follow this vignette, you have to install the ir package as described in the Readme file and you have to load it:
+To follow this vignette, you have to install the 'ir' package as described in the Readme file and you have to load it:
 
 ```{r load-package-ir}
 library(ir)
@@ -269,7 +269,7 @@ ir_sample_data[2, ] / ir_sample_data[3, ]
 
 # Further information
 
-Many more functions and options to handle and process spectra are available in the ir package. These are described in the documentation. In the documentation, you can also read more details about the functions and options presented here.  
+Many more functions and options to handle and process spectra are available in the 'ir' package. These are described in the documentation. In the documentation, you can also read more details about the functions and options presented here.  
 To learn more about how `ir` objects can be useful can be plotted, and the spectral preprocessing functions, see the vignette [`r rmarkdown::yaml_front_matter("ir-introduction.Rmd")$title`](ir-introduction.html).
 
 

vignettes/ir-introduction.Rmd

@@ -1,9 +1,9 @@
 ---
-title: "Introduction to the ir package"
+title: "Introduction to the 'ir' package"
 output: rmarkdown::html_vignette
 author: Henning Teickner
 vignette: >
-  %\VignetteIndexEntry{Introduction to the ir package}
+  %\VignetteIndexEntry{Introduction to the 'ir' package}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 bibliography: "../inst/REFERENCES.bib"  
@@ -28,7 +28,7 @@ library(kableExtra)
 
 ## Purpose
 
-This vignette shows you the main functionalities of the ir package. This includes data import,  functions for spectral preprocessing, and plotting.  
+This vignette shows you the main functionalities of the 'ir' package. This includes data import,  functions for spectral preprocessing, and plotting.  
 This vignette does not explain the data structure of `ir` objects (the objects the package ir uses to store spectra) in detail and it does not describe general data manipulation functions (e.g. subsetting rows or columns, modifying variables) (for this, see vignette [`r rmarkdown::yaml_front_matter("ir-class.Rmd")$title`](ir-class.html)). Moreover, this vignette does not explain the purpose of the spectral preprocessing functions.
 
 ## Structure
@@ -39,13 +39,13 @@ The vignette has three parts:
 2. Plotting spectra
 3. Spectral preprocessing
 
-In part [Data import and export], I will show how spetra can be imported from `csv` files and from Thermo Galactic's spectral files (file extension `.spc`). I will also show how `ir` objects can be exported as `csv` files. To this end, I will use sample data which comes along with the ir package. 
+In part [Data import and export], I will show how spetra can be imported from `csv` files and from Thermo Galactic's spectral files (file extension `.spc`). I will also show how `ir` objects can be exported as `csv` files. To this end, I will use sample data which comes along with the 'ir' package. 
 In part [Plotting spectra], I will show how spectra can be plotted and how these plots can be modified.  
-In part [Spectral preprocessing] I will demonstrate the main preprocessing functions included in the ir package and how these can be combined to execute complex preprocessing pipelines.
+In part [Spectral preprocessing] I will demonstrate the main preprocessing functions included in the 'ir' package and how these can be combined to execute complex preprocessing pipelines.
 
 ## Preparation
 
-To follow this vignette, you have to install the ir package as described in the Readme file and you have to load it:
+To follow this vignette, you have to install the 'ir' package as described in the Readme file and you have to load it:
 
 ```{r load-package-ir}
 library(ir)
@@ -57,7 +57,7 @@ library(ir)
 
 ## Data import
 
-To test importing spectra from files, I'll use sample data which is contained in the ir package (in folder `inst/extdata`). First, I'll show how to import spectra from csv files and then how to import  Thermo Galactic's spectral files (file extension `.spc`).
+To test importing spectra from files, I'll use sample data which is contained in the 'ir' package (in folder `inst/extdata`). First, I'll show how to import spectra from csv files and then how to import  Thermo Galactic's spectral files (file extension `.spc`).
 
 ### `csv` files
 
@@ -124,10 +124,29 @@ d_spc <- ir_import_spc("../inst/extdata/1.spc")
 In this case, names for the spectra and other metadata are extracted from the `spc` file(s) and added to the `ir` object. You can inspect `d_spc` to see these additional variables.
 
 
+## Data export
+
+Data in `ir` objects can in principle be exported in many ways. Here, I show hot to export to a `csv` file with the same format as the sample data we imported in subsection [`csv` files].  
+
+To this end, we first have to "flatten" the `spectra` column in `ir_sample_data` (using `ir_flatten()`) and export this as `csv` file using `write.csv()`. Second, to export the metadata, we have to drop the spectra from `ir_sample_data` (using `ir_drop_spectra()`), and then write the remaining data to a separate `csv` file using `write.csv()`:   
+
+```{r export-csv-1}
+# export only the spectra
+ir_sample_data %>%
+  ir_flatten() %>%
+  write.csv(tempfile("ir_sample_data_spectra", fileext = "csv"), row.names = FALSE)
+
+# export only the metadata
+ir_sample_data %>%
+  ir_drop_spectra() %>%
+  write.csv(tempfile("ir_sample_data_metadata", fileext = "csv"), row.names = FALSE)
+```
+
+
 
 # Plotting spectra
 
-The ir package provides a function to create simple plots of spectra out-of-the box:
+The 'ir' package provides a function to create simple plots of spectra out-of-the box:
 
 ```{r plot-1}
 plot(d_csv)
@@ -295,7 +314,7 @@ Now, we have a baseline corrected spectrum, `"area"` normalized, clipped to [650
 
 # Further information
 
-Many more functions and options to handle and process spectra are available in the ir package. These are described in the documentation. In the documentation, you can also read more details about the functions and options presented here.  
+Many more functions and options to handle and process spectra are available in the 'ir' package. These are described in the documentation. In the documentation, you can also read more details about the functions and options presented here.  
 To learn more about the structure and general functions to handle `ir` objects, see the vignette [`r rmarkdown::yaml_front_matter("ir-class.Rmd")$title`](ir-class.html).