closes #563; closes #564; closes #565; closes #566; closes #567; closes

#568; closes #569; closes #570; closes #571; closes #572; closes #573; closes #574

paper/paper.Rmd

@@ -41,21 +41,21 @@ knitr::opts_chunk$set(
 
 Graphical displays can reveal problems in a statistical model that might not be
 apparent from purely numerical summaries. Such visualizations can also be
-helpful for the reader to evaluate validity of a model if the said analysis is
-reported in a scholarly publication/report. But, given the onerous costs
-involved, researchers can avoid preparing information-rich graphics and
-exploring several statistical approaches/tests available. The `ggstatsplot`
-package in R programming language [@base2021] provides a one-line syntax to
-create densely informative `ggplot2`-based visualizations with the results from
-statistical analysis embedded in the visualization itself. In doing so, the
-package helps researchers adopt a **rigorous, reliable, and robust** data
-exploratory and reporting workflow.
+helpful for the reader to evaluate the validity of a model if it is reported in
+a scholarly publication/report. But, given the onerous costs involved,
+researchers can avoid preparing information-rich graphics and exploring several
+statistical approaches/tests available. The `ggstatsplot` package in R
+programming language [@base2021] provides a one-line syntax to enrich
+`ggplot2`-based visualizations with the results from statistical analysis
+embedded in the visualization itself. In doing so, the package helps researchers
+adopt a rigorous, reliable, and robust data exploratory and reporting
+workflow.
 
 # Statement of Need
 
 In a typical data analysis workflow, data visualization and statistical modeling
-are two different phases: visualization informs modeling, and modeling in its
-turn can suggest a different visualization method, and so on and so forth
+are two different phases: visualization informs modeling, and in turn, modeling
+can suggest a different visualization method, and so on and so forth
 [@wickham2016r]. The central idea of `ggstatsplot` is simple: combine these two
 phases into one in the form of an informative graphic with statistical details.
 
@@ -70,19 +70,18 @@ library(ggstatsplot)
 ggbetweenstats(penguins, species, body_mass_g)
 ```
 
-As can be seen, with a **single** line of code, the function produces details
-about descriptive statistics, inferential statistics, effect size estimate and
-its uncertainty, pairwise comparisons, Bayesian hypothesis testing, Bayesian
+As can be seen, with a single line of code, the function produces details about
+descriptive statistics, inferential statistics, effect size estimate and its
+uncertainty, pairwise comparisons, Bayesian hypothesis testing, Bayesian
 posterior estimate and its uncertainty. Moreover, these details are juxtaposed
 with informative and well-labeled visualizations. The defaults are designed to
-follow best practices in **both** data visualization [@cleveland1985;
+follow best practices in both data visualization [@cleveland1985;
 @grant2018data; @healy2018data; @tufte2001; @wilke2019fundamentals] and
 (Frequentist/Bayesian) statistical reporting [@apa2019; @van2020jasp]. Without
 `ggstatsplot`, getting these statistical details and customizing a plot would
 require significant amount of time and effort In other words, this package
-removes the trade-off often faced by researchers between ease versus
-thoroughness of exploring data and further cements good data
-sanitation/exploration habits.
+removes the trade-off often faced by researchers between ease and thoroughness
+of data exploration and further cements good data exploration habits.
 
 Internally, data cleaning is carried out using `tidyverse` [@Wickham2019], while
 statistical analysis is carried out via `statsExpressions` [@Patil2021] and
@@ -105,14 +104,14 @@ c. highlights the importance of the effect by providing effect size measures by
 
 d. provides an easy way to evaluate *absence* of an effect using Bayes factors,
 
-e. encourages researchers/readers to evaluate statistical assumptions of a model
-   in the context of the underlying data (Figure 2),
+e. encourages researchers and readers to evaluate statistical assumptions of a
+model in the context of the underlying data (Figure 2),
 
 f. is easy and simple enough that someone with little-to-no coding experience
    can use it without making an error and may even encourage beginners to
-   programmatically (instead of using GUI software) analyze data.
+   programmatically analyze data, instead of using GUI software.
 
-```{r reporting, echo=FALSE, fig.cap="Comparing the 'Standard' approach of reporting statistical analysis in a publication/report with the 'ggstatsplot' approach of reporting the same analysis next to an informative graphic. Note that the results described in the 'Standard' approach are about the 'Dinosaur' dataset plotted on the right. Without the accompanying visualization, it is hard to evaluate the validity of the results. The ideal reporting practice will be a hybrid of these two approaches where the plot contains both the visual and numerical summaries about a statistical model, while the narrative provides interpretive context for the reported statistics."}
+```{r reporting, echo=FALSE, fig.cap="Comparing the 'Standard' approach of reporting statistical analysis in a publication/report with the 'ggstatsplot' approach of reporting the same analysis next to an informative graphic. Note that the results described in the 'Standard' approach are about the 'Dinosaur' dataset plotted on the right. Without the accompanying visualization, it is hard to evaluate the validity of the results. The ideal reporting practice will be a hybrid of these two approaches where the plot contains both the visual and numerical summaries about a statistical model, while the narrative provides interpretative context for the reported statistics."}
 knitr::include_graphics("reporting.png")
 ```
 
@@ -130,11 +129,12 @@ collection of statistical analyses and visualizations.
 
 `ggstatsplot` is licensed under the GNU General Public License (v3.0), with all
 source code stored at [GitHub](https://github.com/IndrajeetPatil/ggstatsplot/).
-In the spirit of honest and open science, requests/tips for fixes, feature
-updates, as well as general questions and concerns via direct interaction with
-contributors and developers are encouraged by filing an
+In the spirit of honest and open science, requests and suggestions for fixes,
+feature updates, as well as general questions and concerns are encouraged via
+direct interaction with contributors and developers by filing an
 [issue](https://github.com/IndrajeetPatil/ggstatsplot/issues) while respecting
-[*Contribution Guidelines*](https://indrajeetpatil.github.io/ggstatsplot/CONTRIBUTING.html).
+[*Contribution
+Guidelines*](https://indrajeetpatil.github.io/ggstatsplot/CONTRIBUTING.html).
 
 # Acknowledgements
 

paper/paper.aux

@@ -31,8 +31,8 @@
 \newlabel{benefits}{{}{2}{Benefits}{section*.4}{}}
 \@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{Future Scope}{2}{section*.6}\protected@file@percent }
 \newlabel{future-scope}{{}{2}{Future Scope}{section*.6}{}}
-\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Comparing the 'Standard' approach of reporting statistical analysis in a publication/report with the 'ggstatsplot' approach of reporting the same analysis next to an informative graphic. Note that the results described in the 'Standard' approach are about the 'Dinosaur' dataset plotted on the right. Without the accompanying visualization, it is hard to evaluate the validity of the results. The ideal reporting practice will be a hybrid of these two approaches where the plot contains both the visual and numerical summaries about a statistical model, while the narrative provides interpretive context for the reported statistics.\relax }}{3}{figure.caption.5}\protected@file@percent }
-\newlabel{fig:reporting}{{2}{3}{Comparing the 'Standard' approach of reporting statistical analysis in a publication/report with the 'ggstatsplot' approach of reporting the same analysis next to an informative graphic. Note that the results described in the 'Standard' approach are about the 'Dinosaur' dataset plotted on the right. Without the accompanying visualization, it is hard to evaluate the validity of the results. The ideal reporting practice will be a hybrid of these two approaches where the plot contains both the visual and numerical summaries about a statistical model, while the narrative provides interpretive context for the reported statistics.\relax }{figure.caption.5}{}}
+\@writefile{lof}{\defcounter {refsection}{0}\relax }\@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Comparing the 'Standard' approach of reporting statistical analysis in a publication/report with the 'ggstatsplot' approach of reporting the same analysis next to an informative graphic. Note that the results described in the 'Standard' approach are about the 'Dinosaur' dataset plotted on the right. Without the accompanying visualization, it is hard to evaluate the validity of the results. The ideal reporting practice will be a hybrid of these two approaches where the plot contains both the visual and numerical summaries about a statistical model, while the narrative provides interpretative context for the reported statistics.\relax }}{3}{figure.caption.5}\protected@file@percent }
+\newlabel{fig:reporting}{{2}{3}{Comparing the 'Standard' approach of reporting statistical analysis in a publication/report with the 'ggstatsplot' approach of reporting the same analysis next to an informative graphic. Note that the results described in the 'Standard' approach are about the 'Dinosaur' dataset plotted on the right. Without the accompanying visualization, it is hard to evaluate the validity of the results. The ideal reporting practice will be a hybrid of these two approaches where the plot contains both the visual and numerical summaries about a statistical model, while the narrative provides interpretative context for the reported statistics.\relax }{figure.caption.5}{}}
 \@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{Licensing and Availability}{3}{section*.7}\protected@file@percent }
 \newlabel{licensing-and-availability}{{}{3}{Licensing and Availability}{section*.7}{}}
 \@writefile{toc}{\defcounter {refsection}{0}\relax }\@writefile{toc}{\contentsline {section}{Acknowledgements}{3}{section*.8}\protected@file@percent }

paper/paper.log

@@ -1,4 +1,4 @@
-This is XeTeX, Version 3.14159265-2.6-0.999992 (TeX Live 2020) (preloaded format=xelatex 2021.3.22)  5 APR 2021 09:45
+This is XeTeX, Version 3.14159265-2.6-0.999992 (TeX Live 2020) (preloaded format=xelatex 2021.3.22)  21 APR 2021 14:51
 entering extended mode
  restricted \write18 enabled.
  %&-line parsing enabled.
@@ -1153,7 +1153,7 @@ Package fancyhdr Warning: \headheight is too small (62.59596pt):
 (fancyhdr)                \addtolength{\topmargin}{-0.95425pt}.
 
 LaTeX Font Info:    Font shape `TU/lmss/m/it' in size <8> not available
-(Font)              Font shape `TU/lmss/m/sl' tried instead on input line 372.
+(Font)              Font shape `TU/lmss/m/sl' tried instead on input line 371.
 [1
 
 ]
@@ -1210,10 +1210,10 @@ Package logreq Info: Writing requests to 'paper.run.xml'.
 
  ) 
 Here is how much of TeX's memory you used:
- 34928 strings out of 478922
- 702613 string characters out of 5866020
- 1508855 words of memory out of 5000000
- 54220 multiletter control sequences out of 15000+600000
+ 34927 strings out of 478922
+ 702590 string characters out of 5866020
+ 1508857 words of memory out of 5000000
+ 54219 multiletter control sequences out of 15000+600000
  410294 words of font info for 74 fonts, out of 8000000 for 9000
  14 hyphenation exceptions out of 8191
  95i,13n,103p,1194b,804s stack positions out of 5000i,500n,10000p,200000b,80000s

paper/paper.md

@@ -15,7 +15,7 @@ authors:
 affiliations:
   - name: Center for Humans and Machines, Max Planck Institute for Human Development, Berlin, Germany
     index: 1
-date: "2021-04-05"
+date: "2021-04-21"
 bibliography: paper.bib
 ---
 
@@ -25,21 +25,21 @@ bibliography: paper.bib
 
 Graphical displays can reveal problems in a statistical model that might not be
 apparent from purely numerical summaries. Such visualizations can also be
-helpful for the reader to evaluate validity of a model if the said analysis is
-reported in a scholarly publication/report. But, given the onerous costs
-involved, researchers can avoid preparing information-rich graphics and
-exploring several statistical approaches/tests available. The `ggstatsplot`
-package in R programming language [@base2021] provides a one-line syntax to
-create densely informative `ggplot2`-based visualizations with the results from
-statistical analysis embedded in the visualization itself. In doing so, the
-package helps researchers adopt a **rigorous, reliable, and robust** data
-exploratory and reporting workflow.
+helpful for the reader to evaluate the validity of a model if it is reported in
+a scholarly publication/report. But, given the onerous costs involved,
+researchers can avoid preparing information-rich graphics and exploring several
+statistical approaches/tests available. The `ggstatsplot` package in R
+programming language [@base2021] provides a one-line syntax to enrich
+`ggplot2`-based visualizations with the results from statistical analysis
+embedded in the visualization itself. In doing so, the package helps researchers
+adopt a rigorous, reliable, and robust data exploratory and reporting
+workflow.
 
 # Statement of Need
 
 In a typical data analysis workflow, data visualization and statistical modeling
-are two different phases: visualization informs modeling, and modeling in its
-turn can suggest a different visualization method, and so on and so forth
+are two different phases: visualization informs modeling, and in turn, modeling
+can suggest a different visualization method, and so on and so forth
 [@wickham2016r]. The central idea of `ggstatsplot` is simple: combine these two
 phases into one in the form of an informative graphic with statistical details.
 
@@ -59,19 +59,18 @@ ggbetweenstats(penguins, species, body_mass_g)
 \includegraphics[width=1\linewidth]{paper_files/figure-latex/penguins-1} \caption{Example plot from the `ggstatsplot` package illustrates its philosophy of juxtaposing informative visualizations with details from statistical analysis. To see all supported plots and statistical analyses, see the package website: https://indrajeetpatil.github.io/ggstatsplot/}\label{fig:penguins}
 \end{figure}
 
-As can be seen, with a **single** line of code, the function produces details
-about descriptive statistics, inferential statistics, effect size estimate and
-its uncertainty, pairwise comparisons, Bayesian hypothesis testing, Bayesian
+As can be seen, with a single line of code, the function produces details about
+descriptive statistics, inferential statistics, effect size estimate and its
+uncertainty, pairwise comparisons, Bayesian hypothesis testing, Bayesian
 posterior estimate and its uncertainty. Moreover, these details are juxtaposed
 with informative and well-labeled visualizations. The defaults are designed to
-follow best practices in **both** data visualization [@cleveland1985;
+follow best practices in both data visualization [@cleveland1985;
 @grant2018data; @healy2018data; @tufte2001; @wilke2019fundamentals] and
 (Frequentist/Bayesian) statistical reporting [@apa2019; @van2020jasp]. Without
 `ggstatsplot`, getting these statistical details and customizing a plot would
 require significant amount of time and effort In other words, this package
-removes the trade-off often faced by researchers between ease versus
-thoroughness of exploring data and further cements good data
-sanitation/exploration habits.
+removes the trade-off often faced by researchers between ease and thoroughness
+of data exploration and further cements good data exploration habits.
 
 Internally, data cleaning is carried out using `tidyverse` [@Wickham2019], while
 statistical analysis is carried out via `statsExpressions` [@Patil2021] and
@@ -94,15 +93,15 @@ c. highlights the importance of the effect by providing effect size measures by
 
 d. provides an easy way to evaluate *absence* of an effect using Bayes factors,
 
-e. encourages researchers/readers to evaluate statistical assumptions of a model
-   in the context of the underlying data (Figure 2),
+e. encourages researchers and readers to evaluate statistical assumptions of a
+model in the context of the underlying data (Figure 2),
 
 f. is easy and simple enough that someone with little-to-no coding experience
    can use it without making an error and may even encourage beginners to
-   programmatically (instead of using GUI software) analyze data.
+   programmatically analyze data, instead of using GUI software.
 
 \begin{figure}
-\includegraphics[width=1\linewidth]{reporting} \caption{Comparing the 'Standard' approach of reporting statistical analysis in a publication/report with the 'ggstatsplot' approach of reporting the same analysis next to an informative graphic. Note that the results described in the 'Standard' approach are about the 'Dinosaur' dataset plotted on the right. Without the accompanying visualization, it is hard to evaluate the validity of the results. The ideal reporting practice will be a hybrid of these two approaches where the plot contains both the visual and numerical summaries about a statistical model, while the narrative provides interpretive context for the reported statistics.}\label{fig:reporting}
+\includegraphics[width=1\linewidth]{reporting} \caption{Comparing the 'Standard' approach of reporting statistical analysis in a publication/report with the 'ggstatsplot' approach of reporting the same analysis next to an informative graphic. Note that the results described in the 'Standard' approach are about the 'Dinosaur' dataset plotted on the right. Without the accompanying visualization, it is hard to evaluate the validity of the results. The ideal reporting practice will be a hybrid of these two approaches where the plot contains both the visual and numerical summaries about a statistical model, while the narrative provides interpretative context for the reported statistics.}\label{fig:reporting}
 \end{figure}
 
 # Future Scope
@@ -119,11 +118,12 @@ collection of statistical analyses and visualizations.
 
 `ggstatsplot` is licensed under the GNU General Public License (v3.0), with all
 source code stored at [GitHub](https://github.com/IndrajeetPatil/ggstatsplot/).
-In the spirit of honest and open science, requests/tips for fixes, feature
-updates, as well as general questions and concerns via direct interaction with
-contributors and developers are encouraged by filing an
+In the spirit of honest and open science, requests and suggestions for fixes,
+feature updates, as well as general questions and concerns are encouraged via
+direct interaction with contributors and developers by filing an
 [issue](https://github.com/IndrajeetPatil/ggstatsplot/issues) while respecting
-[*Contribution Guidelines*](https://indrajeetpatil.github.io/ggstatsplot/CONTRIBUTING.html).
+[*Contribution
+Guidelines*](https://indrajeetpatil.github.io/ggstatsplot/CONTRIBUTING.html).
 
 # Acknowledgements