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rOpensci acceptance edits, addresses #188 (#189)
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* DESCRIPTION for rOpensci

* News out of README.md

* Create NEWS.md

* Links to ropensci in README.md

* ropensci in codemeta.json

* Create codemeta.json

* Addressed CRAN gotchas

See https://devguide.ropensci.org/building.html#crangotchas

* NEWS.md link from README

* reword code of conduct lines in README

As described in https://devguide.ropensci.org/collaboration.html

* Update CONTRIBUTING.md

* Removed duplicated development

Co-authored-by: Samuel Joseph Haynes <[email protected]>
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4 changes: 2 additions & 2 deletions .github/CONTRIBUTING.md
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Expand Up @@ -40,7 +40,7 @@ If you’ve found a bug, please file an issue that illustrates the bug with a mi

## Code of Conduct

Please note that the tidyqpcr project follows the [code of conduct from the tidyverse](https://dplyr.tidyverse.org/CODE_OF_CONDUCT).
By contributing to this project you agree to abide by its terms.
Please note that the tidyqpcr package is released with a [Contributor Code of Conduct from rOpensci](https://ropensci.org/code-of-conduct/).
By contributing to this project, you agree to abide by its terms.

This contributing guide is adapted from the tidyverse contributing guide available at https://raw.githubusercontent.com/r-lib/usethis/master/inst/templates/tidy-contributing.md
8 changes: 4 additions & 4 deletions DESCRIPTION
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Package: tidyqpcr
Type: Package
Title: Quantitative PCR Analysis with the Tidyverse
Version: 0.5
Version: 1.0
Authors@R: c(person("Edward", "Wallace", email = "[email protected]", role = c("aut", "cre")),
person("Sam", "Haynes", email = "[email protected]", role = c("ctb")))
Description: This package is for reproducible quantitative PCR (qPCR) analysis using packages from the tidyverse, notably dplyr and ggplot2. It normalizes (by ddCq), summarizes, and plots pre-calculated Cq data, and plots raw amplification and melt curves from Roche lightcycler machines. It does NOT (yet) calculate Cq data from amplification curves.
Description: For reproducible quantitative PCR (qPCR) analysis building on packages from the ’tidyverse’, notably ’dplyr’ and ’ggplot2’. It normalizes (by ddCq), summarizes, and plots pre-calculated Cq data, and plots raw amplification and melt curves from Roche Lightcycler (tm) machines. It does NOT (yet) calculate Cq data from amplification curves.
Depends:
R (>= 3.1.0),
tibble
Expand All @@ -27,6 +27,6 @@ License: Apache License (>= 2)
LazyData: true
RoxygenNote: 7.1.2
Encoding: UTF-8
URL: https://github.com/ewallace/tidyqpcr, https://ewallace.github.io/tidyqpcr/
BugReports: https://github.com/ewallace/tidyqpcr/issues
URL: https://docs.ropensci.org/tidyqpcr, https://github.com/ropensci/tidyqpcr
BugReports: https://github.com/ropensci/tidyqpcr/issues
Language: en-GB
9 changes: 9 additions & 0 deletions NEWS.md
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## News

* June 2022, removed plot helper functions `scale_..._nice` and `scale_loglog` from tidyqpcr, because those capabilities are now available in the [scales package](https://scales.r-lib.org) using `label_log` and similar functions. Older code may need to change `scale_y_log10nice` to `scale_y_log10(labels = scales::label_log())`, for example.
* May 2022, Improvements in documentation and testing. Reorganized `display_plate` function to be more flexible, so older code will need to use `display_plate_qpcr` to ensure that `sample_id` and `target_id` info displays. Updated to v0.5.
* January 2022, Improvements in documentation and argument-checking for v0.4.
* October 2021, Unit tests now cover over 75% of tidyqpcr code.
* June 2021, [tidyqpcr blogpost in eLife labs](https://elifesciences.org/labs/f23e268f/tidyqpcr-quantitative-pcr-analysis-in-the-tidyverse)
* August 2020, relative quantification (delta delta Cq) added with function `calculate_deltadeltacq_bytargetid`, and a vignette illustrating this with a small data set from a 96-well plate.
* June 2020, upgrades that break previous code. All function and variable names have been changed to snake case, i.e. lower case with underscore. Commits up to #ee6d192 change variable and function names. tidyqpcr now uses `sample_id` for nucleic acid sample (replaces Sample or SampleID), `target_id` for primer set/ probe (replaces TargetID or Probe), `prep_type` for nucleic acid preparation type (replaces Type), and `cq` for quantification cycle (replaces Cq or Ct). It should be possible to upgrade old analysis code by (case-sensitive) search and replace.
50 changes: 20 additions & 30 deletions README.md
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<!-- badges: start -->
[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![Codecov test coverage](https://codecov.io/gh/DimmestP/tidyqpcr/branch/main/graph/badge.svg)](https://codecov.io/gh/DimmestP/tidyqpcr/branch/main)
[![Codecov test coverage](https://codecov.io/gh/ropensci/tidyqpcr/branch/main/graph/badge.svg)](https://codecov.io/gh/ropensci/tidyqpcr/branch/main)
[![fair-software.eu](https://img.shields.io/badge/fair--software.eu-%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F%20%20%E2%97%8F-green)](https://fair-software.eu)
[![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/5287/badge)](https://bestpractices.coreinfrastructure.org/projects/5287)
[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/tidyqpcr)](https://cran.r-project.org/package=tidyqpcr)
[![R-CMD-check](https://github.com/ewallace/tidyqpcr/workflows/R-CMD-check/badge.svg)](https://github.com/ewallace/tidyqpcr/actions)
[![R-CMD-check](https://github.com/ropensci/tidyqpcr/workflows/R-CMD-check/badge.svg)](https://github.com/ropensci/tidyqpcr/actions)
<!-- badges: end -->

# tidyqpcr - Quantitative PCR analysis in the tidyverse.
Expand All @@ -22,7 +22,7 @@
* [Installing tidyqpcr](#Installing-tidyqpcr)
* [Using tidyqpcr](#Using-tidyqpcr)
* [Status](#Status)
* [News](#News)
* [News - see NEWS.md](NEWS.md)
* [Features](#Features)
* [Current features](#Current-features)
* [Future priorities](#Future-priorities)
Expand Down Expand Up @@ -73,17 +73,17 @@ Next, you need a working installation of [Rtools](https://cran.r-project.org/bin

Jeffrey Leek made [slides on installation and testing of Rtools](http://jtleek.com/modules/01_DataScientistToolbox/02_10_rtools/).

### For all R users
### Install via devtools (for all R users)

Install the devtools R package, see [devtools installation instructions](https://www.r-project.org/nosvn/pandoc/devtools.html).

```
library(devtools)
devtools::install_github("ewallace/tidyqpcr",build_vignettes = TRUE) ## Vignettes require cowplot package
devtools::install_github("ropensci/tidyqpcr", build_vignettes = TRUE)
## Alternatively, install without building the vignetttes to remove cowplot dependency
## Alternatively, install without building the vignettes
## (Not recommended as vignettes contain the tutorials on using tidyqpcr)
devtools::install_github("ewallace/tidyqpcr")
devtools::install_github("ropensci/tidyqpcr")
```

**Note**
Expand All @@ -95,27 +95,28 @@ Then load tidyqpcr as a standard package:
```
library(tidyqpcr)
```

**Note**
tidyqpcr automatically imports and loads several external packages for basic functionality, including; tidy, dplyr and ggplot2.
This allows tidyqpcr to be used immediately but may cause NAMESPACE clashes if the user already has many other package libraries loaded.
Restarting the R session and loading tidyqpcr separately may solve such issues.

## Using tidyqpcr

The best place to start is by viewing the articles on the [tidyqpcr website](https://ewallace.github.io/tidyqpcr/index.html).
The best place to start is by viewing the articles on the [tidyqpcr website](https://docs.ropensci.org/tidyqpcr/index.html).
Here you will find the vignettes, which offer tutorials and example data analyses including figures.
Currently there are 4 vignettes:

* [IntroDesignPlatesetup](https://ewallace.github.io/tidyqpcr/articles/platesetup_vignette.html) - Introduction to designing an experiment and setting up a plate plan in tidyqpcr.
* [DeltaCq96wellExample](https://ewallace.github.io/tidyqpcr/articles/deltacq_96well_vignette.html) - Example analysis of 96-well RT-qPCR data including relative quantification with delta Cq, from a real experiment.
* [MultifactorialExample](https://ewallace.github.io/tidyqpcr/articles/multifactor_vignette.html) - Example design and analysis of a (real) multifactorial RT-qPCR experiment.
* [PrimerCalibration](https://ewallace.github.io/tidyqpcr/articles/calibration_vignette.html) - Example design and analysis of calibrating qPCR primer sets from a (real) experimental test
* [IntroDesignPlatesetup](https://docs.ropensci.org/tidyqpcr/articles/platesetup_vignette.html) - Introduction to designing an experiment and setting up a plate plan in tidyqpcr.
* [DeltaCq96wellExample](https://docs.ropensci.org/tidyqpcr/articles/deltacq_96well_vignette.html) - Example analysis of 96-well RT-qPCR data including relative quantification with delta Cq, from a real experiment.
* [MultifactorialExample](https://docs.ropensci.org/tidyqpcr/articles/multifactor_vignette.html) - Example design and analysis of a (real) multifactorial RT-qPCR experiment.
* [PrimerCalibration](https://docs.ropensci.org/tidyqpcr/articles/calibration_vignette.html) - Example design and analysis of calibrating qPCR primer sets from a (real) experimental test

To find these from your R session, enter `browseVignettes(package="tidyqpcr")`.

Individual R functions are also documented, use R's standard help system after loading the package, e.g. `?create_blank_plate`. To see a list of all the functions and links to their help pages use `help(package="tidyqpcr")`.

A basic use case for designing a 12 well plate is given below, see [IntroDesignPlatesetup](https://ewallace.github.io/tidyqpcr/articles/platesetup_vignette.html) for more details.
A basic use case for designing a 12 well plate is given below, see [IntroDesignPlatesetup](https://docs.ropensci.org/tidyqpcr/articles/platesetup_vignette.html) for more details.

```
rowkey4 <- tibble(
Expand Down Expand Up @@ -143,27 +144,15 @@ display_plate_qpcr(plate_plan12)

# Status

As of May 2022, this software is fully useable, while still being in development.
As of June 2022, this software is fully useable, and under active development.
It is particularly good at designing qPCR experiments in microwell plates (96-well and 384-well), and at relative quantification by the delta Cq method.

[Edward Wallace](https://github.com/ewallace) wrote basic functions and documentation needed to do qPCR analysis in [the Wallace lab](https://ewallace.github.io/), and is making them freely available.
[Edward Wallace](https://github.com/ewallace) wrote basic functions and documentation needed to do qPCR analysis in [the Wallace lab](https://ewallace.github.io/), then started building them into an R package.
[Sam Haynes](https://github.com/dimmestp) is actively developing, initially as part of the [eLife Open Innovation Leaders programme 2020](https://elifesciences.org/labs/fdcb6588/innovation-leaders-2020-introducing-the-cohort).

If there is a feature that you need for your work, please ask us!

## News

* June 2022, removed plot helper functions `scale_..._nice` and `scale_loglog` from tidyqpcr, because those capabilities are now available in the [scales package](https://scales.r-lib.org) using `label_log` and similar functions. Older code may need to change `scale_y_log10nice` to `scale_y_log10(labels = scales::label_log())`, for example.
* May 2022, Improvements in documentation and testing. Reorganized `display_plate` function to be more flexible, so older code will need to use `display_plate_qpcr` to ensure that `sample_id` and `target_id` info displays. Updated to v0.5.
* January 2022, Improvements in documentation and argument-checking for v0.4.
* October 2021, Unit tests now cover over 75% of tidyqpcr code.
* June 2021, [tidyqpcr blogpost in eLife labs](https://elifesciences.org/labs/f23e268f/tidyqpcr-quantitative-pcr-analysis-in-the-tidyverse)
* August 2020, relative quantification (delta delta Cq) added with function `calculate_deltadeltacq_bytargetid`, and a vignette illustrating this with a small data set from a 96-well plate.
* June 2020, upgrades that break previous code. All function and variable names have been changed to snake case, i.e. lower case with underscore. Commits up to #ee6d192 change variable and function names. tidyqpcr now uses `sample_id` for nucleic acid sample (replaces Sample or SampleID), `target_id` for primer set/ probe (replaces TargetID or Probe), `prep_type` for nucleic acid preparation type (replaces Type), and `cq` for quantification cycle (replaces Cq or Ct).
It should be possible to upgrade old analysis code by (case-sensitive) search and replace.

Alternatively, pre-April 2020 analysis code should run from release v0.1-alpha, see [releases](https://github.com/ewallace/tidyqpcr/releases).

## News - see [NEWS.md](NEWS.md).

# Features

Expand Down Expand Up @@ -226,11 +215,12 @@ Note:

# Contribute

We would be delighted to work with you to answer questions, add features, and fix problems. Please [file an issue](https://github.com/ewallace/tidyqpcr/issues) or email Edward dot Wallace at his University email address, (ed.ac.uk).
We would be delighted to work with you to answer questions, add features, and fix problems. Please [file an issue](https://github.com/ropensci/tidyqpcr/issues) or email Edward dot Wallace at his University email address, (ed.ac.uk).

## Code of conduct

We will be following the [code of conduct from the tidyverse](https://dplyr.tidyverse.org/CODE_OF_CONDUCT).
This package is released with a [Contributor Code of Conduct](https://ropensci.org/code-of-conduct/).
By contributing to this project, you agree to abide by its terms.

## How to contribute code: style, checking, development cycle

Expand Down
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