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edits paper.md #2

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Expand Up @@ -22,7 +22,7 @@ authors:
affiliation: "3"

affiliations:
- name: Department of Agricultural Statistics, College of Agriculture, Vellayani, Kerala Agricultural Univesity, Kerala, India.
- name: Department of Agricultural Statistics, College of Agriculture, Vellayani, Kerala Agricultural **University**, Kerala, India.
index: 1
- name: ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India.
index: 2
Expand All @@ -35,35 +35,72 @@ bibliography: paper.bib

# Summary

<p><div align="justify">India has one of the world's largest agricultural education system. According to the Indian Council of Agricultural Research (ICAR), the main regulator of agricultural education in India, there are 63 State Agricultural Universities, 4 deemed universities and 3 Central Agricultural Universities. These institutions enrol on annual basis about 15,000 students at Under Graduate and over 7,000 students at Post Graduate and more than 2000 at PhD level. At any point, there are over 75,000 students studying in these institutions. Research activities are performed actively in all these universities.</div></p>


<p><div align="justify">Agricultural experiments demand a wide range of statistical tools for analysis, which includes from Exploratory analysis, Design of experiments and Statistical genetics. Scientist and research students were in trouble to find a suitable platform for their data analysis and also to publish the research output in quality journals. Most of the softwares available for data analysis are proprietary or lacks a simple user interface. Though several online platforms were available for data analysis, they are not much updated up to the current publication standards at international level. Also, these web applications don’t provide opportunity to generate plots and graphs for publication.</div></p>


<p><div align="justify">R which is an open source software, provides an excellent platform for data analysis. This powerful computation platform is not much utilised by researchers in agricultural field. What hinder R from getting utilised by agricultural researchers is the coding and computational knowledge required. Also, researchers in Agriculture prefer a graphical user interface.</div></p>


<p><div align="justify">Efforts should be made to develop a high quality reliable open-source platform with a simple interactive user interface for data analysis in agriculture. In this light utilising the features of shiny package in R, we have developed a collection of shiny apps for agricultural research ``grapesAgri1`` (General R-shiny based Analysis Platform Empowered by Statistics for data analysis in Agriculture-part1) It can be hosted on the web (www.kaugrapes.com), downloadable as a standalone application and we have realised it in to an R package.</div></p>


<p><div align="justify">On using the functions in ``grapesAgri1`` package a Graphical User Interface will pop up. Apps are self-explanatory and works by simple upload of files in CSV format. Results can be downloaded as docx, PDF or HTML format. Plots and Graphs can be generated, which are also downloadable as .png file.</div></p>
<p>

::: {align="justify"}
*Can this paragraph's gist be present in the Statement of need where the author can mention the target audience?*
India has one of the world's largest agricultural education **systems**. According to the Indian Council of Agricultural Research (ICAR), the main regulator of agricultural education in India, there are 63 State Agricultural Universities, 4 deemed universities and 3 Central Agricultural Universities. These institutions enrol on **an** annual basis about 15,000 students at **Undergraduate** and over 7,000 students at Post Graduate and more than 2000 at PhD level. At any point, there are over 75,000 students studying in these institutions. Research activities are performed actively in all these universities.
:::

</p>

<p>

::: {align="justify"}
Agricultural experiments demand a wide range of statistical tools for analysis, which includes ~~from~~ Exploratory analysis, Design of experiments and Statistical genetics. ~~Scientist and research students were in trouble to find a suitable platform for their data analysis and also to publish the research output in quality journals.~~ **It is a challenge for the scientists and the students to find a suitable platform for data analysis and to eventually publish the research outputs in quality journals.** Most of the **software** available for data analysis are proprietary or **lack** a simple user interface. Though several online platforms were available for data analysis, they are not much updated up to the current publication standards at international level. Also, these web applications don't provide **opportunities** to generate plots and graphs for publication.
:::

</p>

<p>

::: {align="justify"}
R**,** which is an open source software, provides an excellent platform for data analysis. This powerful computation platform is not much utilised by researchers in **the** agricultural field. ~~What hinder R from getting utilised by agricultural researchers is the coding and computational knowledge required.~~ **Programming and computational knowledge hinders agricultural researchers from using R for analysis. Agriculture researchers also prefer a graphical user interface.** ~~Also, researchers in Agriculture prefer a graphical user interface.~~
:::

</p>

<p>

::: {align="justify"}
Efforts should be made to develop a high quality reliable open-source platform with a simple interactive user interface for data analysis in agriculture. In this light utilising the features of shiny package in R, we have developed a collection of shiny apps for agricultural research **called** `grapesAgri1` (General R-shiny based Analysis Platform Empowered by Statistics for data analysis in Agriculture-part1). It ~~can be~~ is hosted on the web (www.kaugrapes.com), downloadable as a standalone application and we have realised it in to an R package.
:::

</p>

<p>

::: {align="justify"}
On using the functions in `grapesAgri1` package a Graphical User Interface will pop up. Apps are self-explanatory and works by simple upload of files in CSV format. Results can be downloaded as docx, PDF or HTML format. Plots and Graphs can be generated, which are also downloadable as .png file.
:::

</p>

# Information for Users

<p><div align="justify">``grapesAgri1`` is a collection of six shiny applications. Details of six applications were given below. Model dataset for testing can be downloaded from the main window of the application. Details for preparing CSV file is also included in the main window. User just need to click on the browse and upload the CSV file for analysis. </div></p>
<p>

::: {align="justify"}
`grapesAgri1` is a collection of six shiny applications. Details of six applications ~~were~~ **are** given below. Model dataset for testing can be downloaded from the main window of the application. Details for preparing CSV file is also included in the main window. **Users** just need to click on the browse and upload the CSV file for analysis.
:::

</p>

- **Descriptive Statistics and Visualization**:- includes Summary Statistics, Summary Statistics by Group, Box plot, Histogram, Q-Q plot and Shapiro-Wilk's test

- **Descriptive Statistics and Visualization**:- includes Summary Statistics, Summary Statistics by Group, Box plot, Histogram, Q-Q plot and Shapiro-Wilk's test
- **Correlation Simple correlation**:- includes Correlation Matrix, correlogram and scatterplot
- **Compare Means: Small sample tests** :- includes One sample t-test, Two sample unpaired t-test, paired t-test, Two sample unpaired Welch t-test, F test, Box plot, Paired Plot
- **Completely Randomized Design**:- One-way Analysis of variance (equal and unequal replication), Multiple comparison test, boxplot and barchart with confidence interval
- **Field layout of experiments**:- Field layout of following designs can be obtained: Completely Randomized Design (CRD), Randomized Complete Block Design (RCBD), Split-plot design, Strip-plot design, Augmented RCBD
- **Randomized Block Design**:- includes Two-way Analysis of variance, Multiple comparison test, boxplot and barchart with confidence interval
- **Correlation Simple correlation**:- includes Correlation Matrix, correlogram and scatterplot

- **Compare Means: Small sample tests** :- includes One sample t-test, Two sample unpaired t-test, paired t-test, Two sample unpaired Welch t-test, F test, Box plot, Paired Plot

- The package can be installed from Github using the following code:
``` r
- **Completely Randomized Design**:- One-way Analysis of variance (equal and unequal replication), Multiple comparison test, boxplot and barchart with confidence interval

- **Field layout of experiments**:- Field layout of following designs can be obtained: Completely Randomized Design (CRD), Randomized Complete Block Design (RCBD), Split-plot design, Strip-plot design, Augmented RCBD

- **Randomized Block Design**:- includes Two-way Analysis of variance, Multiple comparison test, boxplot and barchart with confidence interval

- The package can be installed from Github using the following code:

``` {.r}
# Install grapes development version from Github using the code below:
if (!require('devtools')) install.packages('devtools')
devtools::install_github("pratheesh3780/grapesAgri1")
Expand All @@ -80,14 +117,14 @@ grapesAgri1::rbdApp() # Randomized Block Design

Report Issues:

- Questions, feedback, bug reports: please open an issue in the [issue tracker of the project](https://github.com/pratheesh3780/grapesAgri1/issues).
- Questions, feedback, bug reports: please open an issue in the [issue tracker of the project](https://github.com/pratheesh3780/grapesAgri1/issues).

Contribution to the software:

- Please open an issue in the issue tracker of the project that describes the changes you would like to make to the software and open a pull request with the changes. The description of the pull request must references the corresponding issue.
- Please open an issue in the issue tracker of the project that describes the changes you would like to make to the software and open a pull request with the changes. The description of the pull request must **reference** the corresponding issue.

# Acknowledgements

We wish to thank Kerala Agricultural University for the financial support through revolving fund scheme.
We wish to thank Kerala Agricultural University for the financial support through **the** revolving fund scheme.

# References