diff --git a/.gitignore b/.gitignore index a26bef1..cc9b798 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,4 @@ doc Meta docs .lintr +inst/doc diff --git a/vignettes/.gitignore b/vignettes/.gitignore new file mode 100644 index 0000000..097b241 --- /dev/null +++ b/vignettes/.gitignore @@ -0,0 +1,2 @@ +*.html +*.R diff --git a/vignettes/test_choice_tree.tex b/vignettes/b_test_choice_tree.Rmd similarity index 68% rename from vignettes/test_choice_tree.tex rename to vignettes/b_test_choice_tree.Rmd index 6345216..90acd28 100644 --- a/vignettes/test_choice_tree.tex +++ b/vignettes/b_test_choice_tree.Rmd @@ -1,81 +1,30 @@ -%\VignetteIndexEntry{Test_choice} -%\VignettePackage{DescrTab2} -%\VignetteEncoding{UTF-8} - - - -\documentclass[10pt, a4paper, landscape]{article}\usepackage[]{graphicx}\usepackage[]{color} -% maxwidth is the original width if it is less than linewidth -% otherwise use linewidth (to make sure the graphics do not exceed the margin) -\makeatletter -\def\maxwidth{ % - \ifdim\Gin@nat@width>\linewidth - \linewidth - \else - \Gin@nat@width - \fi -} -\makeatother - -\definecolor{fgcolor}{rgb}{0.345, 0.345, 0.345} -\newcommand{\hlnum}[1]{\textcolor[rgb]{0.686,0.059,0.569}{#1}}% -\newcommand{\hlstr}[1]{\textcolor[rgb]{0.192,0.494,0.8}{#1}}% -\newcommand{\hlcom}[1]{\textcolor[rgb]{0.678,0.584,0.686}{\textit{#1}}}% -\newcommand{\hlopt}[1]{\textcolor[rgb]{0,0,0}{#1}}% -\newcommand{\hlstd}[1]{\textcolor[rgb]{0.345,0.345,0.345}{#1}}% -\newcommand{\hlkwa}[1]{\textcolor[rgb]{0.161,0.373,0.58}{\textbf{#1}}}% -\newcommand{\hlkwb}[1]{\textcolor[rgb]{0.69,0.353,0.396}{#1}}% -\newcommand{\hlkwc}[1]{\textcolor[rgb]{0.333,0.667,0.333}{#1}}% -\newcommand{\hlkwd}[1]{\textcolor[rgb]{0.737,0.353,0.396}{\textbf{#1}}}% -\let\hlipl\hlkwb - -\usepackage{framed} -\makeatletter -\newenvironment{kframe}{% - \def\at@end@of@kframe{}% - \ifinner\ifhmode% - \def\at@end@of@kframe{\end{minipage}}% - \begin{minipage}{\columnwidth}% - \fi\fi% - \def\FrameCommand##1{\hskip\@totalleftmargin \hskip-\fboxsep - \colorbox{shadecolor}{##1}\hskip-\fboxsep - % There is no \\@totalrightmargin, so: - \hskip-\linewidth \hskip-\@totalleftmargin \hskip\columnwidth}% - \MakeFramed {\advance\hsize-\width - \@totalleftmargin\z@ \linewidth\hsize - \@setminipage}}% - {\par\unskip\endMakeFramed% - \at@end@of@kframe} -\makeatother - -\definecolor{shadecolor}{rgb}{.97, .97, .97} -\definecolor{messagecolor}{rgb}{0, 0, 0} -\definecolor{warningcolor}{rgb}{1, 0, 1} -\definecolor{errorcolor}{rgb}{1, 0, 0} -\newenvironment{knitrout}{}{} % an empty environment to be redefined in TeX - -\usepackage{alltt} -\usepackage[utf8]{inputenc} -\usepackage[T1]{fontenc} -\usepackage{amsmath} -\usepackage{amsfonts} -\usepackage{amssymb} -\usepackage{graphicx} -\usepackage{tikz} -\usepackage{tikz-cd} -\usepackage{stackengine} -\usepackage[margin=2in]{geometry} -\tikzcdset{scale cd/.style={every label/.append style={scale=#1}, - cells={nodes={scale=#1}}}, - every arrow/.append style={line width=0.1}} -\parindent=0pt -\pagenumbering{gobble} -\IfFileExists{upquote.sty}{\usepackage{upquote}}{} -\begin{document} -\begin{center} -\section*{Test choice tree} -\end{center} - +--- +title: "Test choice tree" +output: rmarkdown::pdf_document +classoption: landscape +header-includes: +- \usepackage{tikz} +- \usepackage{tikz-cd} +- \usepackage{stackengine} +- \tikzcdset{scale cd/.style={every label/.append style={scale=#1}, cells={nodes={scale=#1}}}, every arrow/.append style={line width=0.1}} +- \parindent=0pt +- \pagenumbering{gobble} +pkgdown: + as_is: true + extension: pdf + title: Test choice tree +vignette: > + %\VignetteIndexEntry{b_test_choice_tree} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- + +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>" +) +``` The graph below describes the test choice algorithm employed in the \texttt{DescrTab2} package. Some of the branching steps are dependent on the data and some can be manipulated by the user. @@ -89,14 +38,14 @@ \section*{Test choice tree} Test choices can be manually overwritten via the appropriately titled parameter \texttt{test\_override}. If, for example, you want to calculate Boschloo's test only for the gender variable and use a chi-squared test (default) for everything else, -you can request this by supplying the following argument: \\ +you can request this by supplying the following argument: \newline \texttt{var\_options=list(gender=list(test\_options = list(test\_override="Boschloo's test")))}. \vspace{1.5cm} -% https://tikzcd.yichuanshen.de/#N4Igdg9gJgpgziAXAbVABwnAlgFyxMJZARgAYAaUgXXJADcBDAGwFcYkQAdTnGADxwAjAGbBuvAcADqACywBjGQAJecHEqxwlizDDBLhEAE4qZMJYyNYGgpuYC+S8fxyvgl+88kB+e-ZD25OiYuPiEKACc5MQ09MxsHF5CoknAAMIMvADmxgrMfgFBIBjYeARExMTRsYys7IhcPC4iYk2SAHIsALYwVvL5-oHBpWEVFABMNfH1jRLJrXPAAPJGUFhgA4XDoeUoAGzkk7S1CQ1JLantEF3rm0PFIWXhyACs5ADMU3WJbfOpAIJwACeXTQOAgeHkBXuJR2zwA7B8vqdZs0Ur9gABRPgMeQ4aFFWFPIgAFnIe2RM1S2Nx6gAsvJ2jAugwjAByLSqfFbB4jXbIKIkyk-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-wAJRJc8ZIaa5gXaeiClSjKYnbZJbwPWl5wvbp+owIagMmipJn7DJZ5uTpkiKe9YPhcgiI-VNor-ZIelAwZoO3aSeaPel7njHwTpZbYMC4xDUUHUFJMqDYdiU8gBz7aRkgAEKYIoTAQBA+7zkz0nU2z7mgXwAAy9Pk4LwFQ097nijArJwO2WhpHIfZBiwrJFVto2SRQwtucAxOkwzFOfXG+O-RimXZTRMjGMAoFKDKhp4E8Wh8EoksAOJ2Zy5vVcAgi3EY6EynAGBgHAFNMxQrPGwyTIsuynIC5bx6yxZhP0YoPUwB2ACKf4LuaiO5kb8seRLUuM5nzy5on1fq1gmva+9zq81AY5wHAPYQMIPbCLgpdM8QQuyYswUB+P0nN7ns3zYZHZoG9uvHf+DcVCzU-alIZWKPum9l96x4J3vkiNs2raq0oSErYO5gjmOUATlOs56+XY2RZf7l2-IaiuUxaSzJkOYwNUmCskDJxBGP9s4E1SBuTQZg079RxC1L+Z9njOT-jXUB5smYTTwTSFqodIA3GYGPAIsRZSBgQCgUAwhXRdCQBQEA4IkBVBAFAwQZVxTgxAOsbAsAQBy0Jm4TQAA6YQtJjCnUsFdAAvEogAKpmZh1w2G0E4YgXMOckiSLgFIpiPQ+gKNZMotR-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-qHVRvYVm35LySWowlcAJN0rmlFrhck0gRbVUkkRcG1V7wIjovbSKgleqiX-ITf2wdKb-WloWQ2kAFT3hVKjeGxt7xm3JM9Y2kk1rkllv3ac1VbLcXnsqdW+9cySRHuSSe99rqv2VNbY28Ys7nWNuxQskke6D03sqaB39yTR2VPndumDLxx1tpfS8RFVB7BAA -\hspace{-4.7cm}\begin{tikzcd}[scale cd=0.31, sep=10pt] + +\hspace{-2.5cm}\begin{tikzcd}[scale cd=0.28, sep=10pt] & & & & & & & & & & \textbf{\text{Which test is chosen for the variable } \texttt{var}\text{?}} \arrow[ld, "\texttt{is.factor(var)==T}" description] \arrow[rd, "\texttt{is.numeric(var)==T}" description] & & & & & & & \\ & & & & & & & & & \textbf{\text{Categorical}} \arrow[rd, "\texttt{is.ordered(var)==T}" description] \arrow[llld, "\texttt{is.ordered(var)==F}" description] & & \textbf{\text{Numerical}} \arrow[ld, "\texttt{nonparametric==T}" description] \arrow[rrrd, "\texttt{nonparametric==F}" description] & & & & & & \\ & & & & & & \textbf{\text{Nominal}} \arrow[ld, "\texttt{exact==F}" description] \arrow[rd, "\texttt{exact==T}" description] & & & & \textbf{\text{Ordinal}} \arrow[ldd, "\texttt{paired==T}" description] \arrow[rdd, "\texttt{paired==F}" description] & & & & \textbf{\text{\stackanchor{Continuous}{and normally distributed}}} \arrow[ldd, "\texttt{paired==T}" description] \arrow[rrdd, "\texttt{paired==F}" description] & & & \\ @@ -105,5 +54,3 @@ \section*{Test choice tree} \text{2x2 table} \arrow[d] & {\text{\stackanchor{K repetitions x L Groups table,}{binary response}}} \arrow[d] & \text{1xL table} \arrow[d] & \text{KxL table} \arrow[d] & \text{2x2 table} \arrow[d] & \text{1xL table} \arrow[d] & \text{2x2 table} \arrow[d] & \text{\stackanchor{KxL table or}{large n}} \arrow[d] & \text{2 groups} \arrow[d] & \geq \text{3 groups} \arrow[d] & \text{1 group} \arrow[d] & \text{2 groups} \arrow[d] & \geq\text{ 3 groups} \arrow[d] & \text{2 groups} \arrow[d] & \geq \text{3 groups} \arrow[d] & \text{1 group} \arrow[d] & \text{2 groups} \arrow[d] & \geq \text{3 groups} \arrow[d] \\ \text{McNemar's test} & \text{Cochrane's Q test} & \text{Chi-squared goodness-of-fit test} & \text{Pearson's Chi-squared test} & \text{Exact McNemar's test} & \text{Exact binomial test} & \text{Boschloo's test} & \text{Fisher's exact test} & \text{Wilcoxon's two-sample signed-rank test} & \text{Friedman's test} & \text{Wilcoxon's one-sample signed-rank test} & \text{Mann-Whitney's U test} & \text{Kruskal-Wallis's one-way ANOVA} & \text{Student's paired t-test} & {\text{\stackanchor{Mixed model ANOVA,}{subject ID as random effect}}} & \text{Student's one-sample t-test} & \text{Welch's t-test} & \text{F-test (ANOVA)} \end{tikzcd} - -\end{document} diff --git a/vignettes/b_test_choice_tree_pdf.Rmd b/vignettes/b_test_choice_tree_pdf.Rmd deleted file mode 100644 index 17fcc46..0000000 --- a/vignettes/b_test_choice_tree_pdf.Rmd +++ /dev/null @@ -1,27 +0,0 @@ ---- -title: "Test choice tree" -header-includes: - - \usepackage{pdfpages} - - \AtBeginDocument{\let\maketitle\relax} - -pkgdown: - as_is: true - extension: pdf - title: Test choice tree - -output: pdf_document - -vignette: > - %\VignetteIndexEntry{Test choice tree} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} - + ---- - -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` - -```{r test-choice-tree, out.width='100%', out.height='100%', echo=FALSE} -knitr::include_graphics("test_choice_tree.pdf") -``` diff --git a/vignettes/test_choice_tree.pdf b/vignettes/test_choice_tree.pdf deleted file mode 100644 index 1542311..0000000 Binary files a/vignettes/test_choice_tree.pdf and /dev/null differ diff --git a/vignettes/test_choice_tree.synctex.gz b/vignettes/test_choice_tree.synctex.gz deleted file mode 100644 index a2db5db..0000000 Binary files a/vignettes/test_choice_tree.synctex.gz and /dev/null differ diff --git a/vignettes/tex/test_choice_tree.aux b/vignettes/tex/test_choice_tree.aux deleted file mode 100644 index b640121..0000000 --- a/vignettes/tex/test_choice_tree.aux +++ /dev/null @@ -1,2 +0,0 @@ -\relax -\gdef \@abspage@last{1} diff --git a/vignettes/tex/test_choice_tree.fdb_latexmk b/vignettes/tex/test_choice_tree.fdb_latexmk deleted file mode 100644 index 3272f86..0000000 --- a/vignettes/tex/test_choice_tree.fdb_latexmk +++ /dev/null @@ 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-Output written on test_choice_tree.pdf (1 page, 63998 bytes). -PDF statistics: - 183 PDF objects out of 1000 (max. 8388607) - 0 named destinations out of 1000 (max. 500000) - 13 words of extra memory for PDF output out of 10000 (max. 10000000) - diff --git a/vignettes/tex/test_choice_tree.pdf b/vignettes/tex/test_choice_tree.pdf deleted file mode 100644 index 1542311..0000000 Binary files a/vignettes/tex/test_choice_tree.pdf and /dev/null differ diff --git a/vignettes/tex/test_choice_tree.synctex.gz b/vignettes/tex/test_choice_tree.synctex.gz deleted file mode 100644 index 102303e..0000000 Binary files a/vignettes/tex/test_choice_tree.synctex.gz and /dev/null differ diff --git a/vignettes/tex/test_choice_tree.tex b/vignettes/tex/test_choice_tree.tex deleted file mode 100644 index 382bdf3..0000000 --- a/vignettes/tex/test_choice_tree.tex +++ /dev/null @@ -1,109 +0,0 @@ -%\VignetteIndexEntry{Test_choice} -%\VignettePackage{DescrTab2} -%\VignetteEncoding{UTF-8} - - - -\documentclass[10pt, a4paper, landscape]{article}\usepackage[]{graphicx}\usepackage[]{color} -% maxwidth is the original width if it is less than linewidth -% otherwise use linewidth (to make sure the graphics do not exceed the margin) -\makeatletter -\def\maxwidth{ % - \ifdim\Gin@nat@width>\linewidth - \linewidth - \else - \Gin@nat@width - \fi -} -\makeatother - -\definecolor{fgcolor}{rgb}{0.345, 0.345, 0.345} -\newcommand{\hlnum}[1]{\textcolor[rgb]{0.686,0.059,0.569}{#1}}% -\newcommand{\hlstr}[1]{\textcolor[rgb]{0.192,0.494,0.8}{#1}}% -\newcommand{\hlcom}[1]{\textcolor[rgb]{0.678,0.584,0.686}{\textit{#1}}}% -\newcommand{\hlopt}[1]{\textcolor[rgb]{0,0,0}{#1}}% -\newcommand{\hlstd}[1]{\textcolor[rgb]{0.345,0.345,0.345}{#1}}% -\newcommand{\hlkwa}[1]{\textcolor[rgb]{0.161,0.373,0.58}{\textbf{#1}}}% -\newcommand{\hlkwb}[1]{\textcolor[rgb]{0.69,0.353,0.396}{#1}}% -\newcommand{\hlkwc}[1]{\textcolor[rgb]{0.333,0.667,0.333}{#1}}% -\newcommand{\hlkwd}[1]{\textcolor[rgb]{0.737,0.353,0.396}{\textbf{#1}}}% -\let\hlipl\hlkwb - -\usepackage{framed} -\makeatletter -\newenvironment{kframe}{% - \def\at@end@of@kframe{}% - \ifinner\ifhmode% - \def\at@end@of@kframe{\end{minipage}}% - \begin{minipage}{\columnwidth}% - \fi\fi% - \def\FrameCommand##1{\hskip\@totalleftmargin \hskip-\fboxsep - \colorbox{shadecolor}{##1}\hskip-\fboxsep - % There is no \\@totalrightmargin, so: - \hskip-\linewidth \hskip-\@totalleftmargin \hskip\columnwidth}% - \MakeFramed {\advance\hsize-\width - \@totalleftmargin\z@ \linewidth\hsize - \@setminipage}}% - {\par\unskip\endMakeFramed% - \at@end@of@kframe} -\makeatother - -\definecolor{shadecolor}{rgb}{.97, .97, .97} -\definecolor{messagecolor}{rgb}{0, 0, 0} -\definecolor{warningcolor}{rgb}{1, 0, 1} -\definecolor{errorcolor}{rgb}{1, 0, 0} -\newenvironment{knitrout}{}{} % an empty environment to be redefined in TeX - -\usepackage{alltt} -\usepackage[utf8]{inputenc} -\usepackage[T1]{fontenc} -\usepackage{amsmath} -\usepackage{amsfonts} -\usepackage{amssymb} -\usepackage{graphicx} -\usepackage{tikz} -\usepackage{tikz-cd} -\usepackage{stackengine} -\usepackage[margin=2in]{geometry} -\tikzcdset{scale cd/.style={every label/.append style={scale=#1}, - cells={nodes={scale=#1}}}, - every arrow/.append style={line width=0.1}} -\parindent=0pt -\pagenumbering{gobble} -\IfFileExists{upquote.sty}{\usepackage{upquote}}{} -\begin{document} -\begin{center} -\section*{Test choice tree} -\end{center} - - -The graph below describes the test choice algorithm employed in the \texttt{DescrTab2} package. -Some of the branching steps are dependent on the data and some can be manipulated by the user. -The type of data (\texttt{numeric}, \texttt{ordered} factor and unordered \texttt{factor}) is automatically determined, -as is the number of groups and the number of categories in categorical variables. The user may choose to apply nonparametric instead -of parametric tests, asymptotic instead of exact and paired instead of unpaired tests. - -The default configuration of arguments is \texttt{test\_options=list(paired=F, nonparametric=F, exact=F)}. -In a scenario with 2 groups, this will give produce Welch's t-test for numerical variables and a chi-squared test for -categorical variables with 2 categories. - -Test choices can be manually overwritten via the appropriately titled parameter \texttt{test\_override}. -If, for example, you want to calculate Boschloo's test only for the gender variable and use a chi-squared test (default) for everything else, -you can request this by supplying the following argument: \\ -\texttt{var\_options=list(gender=list(test\_options = list(test\_override="Boschloo's test")))}. - - -\vspace{1.5cm} - -% https://tikzcd.yichuanshen.de/#N4Igdg9gJgpgziAXAbVABwnAlgFyxMJZARgAYAaUgXXJADcBDAGwFcYkQAdTnGADxwAjAGbBuvAcADqACywBjGQAJecHEqxwlizDDBLhEAE4qZMJYyNYGgpuYC+S8fxyvgl+88kB+e-ZD25OiYuPiEKACc5MQ09MxsHF5CoknAAMIMvADmxgrMfgFBIBjYeARExMTRsYys7IhcPC4iYk2SAHIsALYwVvL5-oHBpWEVFABMNfH1jRLJrXPAAPJGUFhgA4XDoeUoAGzkk7S1CQ1JLantEF3rm0PFIWXhyACs5ADMU3WJbfOpAIJwACeXTQOAgeHkBXuJR2zwA7B8vqdZs0Ur9gABRPgMeQ4aFFWFPIgAFnIe2RM1S2Nx6gAsvJ2jAugwjAByLSqfFbB4jXbIKIkyk-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-wAJRJc8ZIaa5gXaeiClSjKYnbZJbwPWl5wvbp+owIagMmipJn7DJZ5uTpkiKe9YPhcgiI-VNor-ZIelAwZoO3aSeaPel7njHwTpZbYMC4xDUUHUFJMqDYdiU8gBz7aRkgAEKYIoTAQBA+7zkz0nU2z7mgXwAAy9Pk4LwFQ097nijArJwO2WhpHIfZBiwrJFVto2SRQwtucAxOkwzFOfXG+O-RimXZTRMjGMAoFKDKhp4E8Wh8EoksAOJ2Zy5vVcAgi3EY6EynAGBgHAFNMxQrPGwyTIsuynIC5bx6yxZhP0YoPUwB2ACKf4LuaiO5kb8seRLUuM5nzy5on1fq1gmva+9zq81AY5wHAPYQMIPbCLgpdM8QQuyYswUB+P0nN7ns3zYZHZoG9uvHf+DcVCzU-alIZWKPum9l96x4J3vkiNs2raq0oSErYO5gjmOUATlOs56+XY2RZf7l2-IaiuUxaSzJkOYwNUmCskDJxBGP9s4E1SBuTQZg079RxC1L+Z9njOT-jXUB5smYTTwTSFqodIA3GYGPAIsRZSBgQCgUAwhXRdCQBQEA4IkBVBAFAwQZVxTgxAOsbAsAQBy0Jm4TQAA6YQtJjCnUsFdAAvEogAKpmZh1w2G0E4YgXMOckiSLgFIpiPQ+gKNZMotR-haC8P4YI4RWBRH3E0awxA3DdHSR4TYex4UhExycewcRhj3LSOMLAd6FijBWPUTyVxXCdEQCQGSAxvwjFSPCb0WUUSrEbhsd4vhTABF+Mcc4oo8TEApN0W8VJcw3D8FpCovJYiCm+IIv4kR7AXEsOSYkpABxWlFIcQE0RwS0nuQaXiFRsTulaL0X0xAXjEHjOAJAMAa9Jw9BwH0aZ+S7FDJKSMrp5SemLIWTaWpLg3BryUlAXZLT9nFPaaU45TDTleN0VEQZTzFQdMCS05ZdT3I3Pek0jRpyLm6LILMtxkKknuOIDCpAXyoXvCRYgFF8LiDjHRfoqFXzHnDM6QCm2QLVkEA2QOGA2yFBgriac5UHCsUvHRcQAZUL4Sso+Viq0rL2FQpJKyzFSBxjsMBVc4F687nWIeT4g5zyjn0rmcQYVejuHioENcqVdLbFyp+ZaP5ZS3lzPGNykVnKTkmrxfC8YexcU1N0eMXllq3GioWeMHFLqRUeJtWir1ar3WCv9eMH1IqWX+vZfC943DCWHOJWMslILZT3PRZGpA7x9GxoVfGy5mrJW3Lpei6NCz3h+uNW4jNJag3lvTWmxZFqa2LP5VGu1-qHVRvYVm35LySWowlcAJN0rmlFrhck0gRbVUkkRcG1V7wIjovbSKgleqiX-ITf2wdKb-WloWQ2kAFT3hVKjeGxt7xm3JM9Y2kk1rkllv3ac1VbLcXnsqdW+9cySRHuSSe99rqv2VNbY28Ys7nWNuxQskke6D03sqaB39yTR2VPndumDLxx1tpfS8RFVB7BAA -\hspace{-4.7cm}\begin{tikzcd}[scale cd=0.31, sep=10pt] - & & & & & & & & & & \textbf{\text{Which test is chosen for the variable } \texttt{var}\text{?}} \arrow[ld, "\texttt{is.factor(var)==T}" description] \arrow[rd, "\texttt{is.numeric(var)==T}" description] & & & & & & & \\ - & & & & & & & & & \textbf{\text{Categorical}} \arrow[rd, "\texttt{is.ordered(var)==T}" description] \arrow[llld, "\texttt{is.ordered(var)==F}" description] & & \textbf{\text{Numerical}} \arrow[ld, "\texttt{nonparametric==T}" description] \arrow[rrrd, "\texttt{nonparametric==F}" description] & & & & & & \\ - & & & & & & \textbf{\text{Nominal}} \arrow[ld, "\texttt{exact==F}" description] \arrow[rd, "\texttt{exact==T}" description] & & & & \textbf{\text{Ordinal}} \arrow[ldd, "\texttt{paired==T}" description] \arrow[rdd, "\texttt{paired==F}" description] & & & & \textbf{\text{\stackanchor{Continuous}{and normally distributed}}} \arrow[ldd, "\texttt{paired==T}" description] \arrow[rrdd, "\texttt{paired==F}" description] & & & \\ - & & & & & \textbf{\text{Asymptotic}} \arrow[d, "\texttt{paired==F}" description] \arrow[ld, "\texttt{paired==T}" description] & & \textbf{\text{Exact}} \arrow[ld, "\texttt{paired==T}" description] \arrow[d, "\texttt{paired==F}" description] & & & & & & & & & & \\ - & & & & \textbf{\text{Paired}} \arrow[llld] \arrow[lllld] & \textbf{\text{Independent}} \arrow[lld] \arrow[llld] & \textbf{\text{Paired}} \arrow[lld] & \textbf{\text{Independent}} \arrow[ld] \arrow[d] \arrow[lld] & & \textbf{\text{Paired}} \arrow[ld] \arrow[d] & & \textbf{\text{Independent}} \arrow[rd] \arrow[ld] \arrow[d] & & \textbf{\text{Paired}} \arrow[rd] \arrow[d] & & & \textbf{\text{Independent}} \arrow[ld] \arrow[d] \arrow[rd] & \\ -\text{2x2 table} \arrow[d] & {\text{\stackanchor{K repetitions x L Groups table,}{binary response}}} \arrow[d] & \text{1xL table} \arrow[d] & \text{KxL table} \arrow[d] & \text{2x2 table} \arrow[d] & \text{1xL table} \arrow[d] & \text{2x2 table} \arrow[d] & \text{\stackanchor{KxL table or}{large n}} \arrow[d] & \text{2 groups} \arrow[d] & \geq \text{3 groups} \arrow[d] & \text{1 group} \arrow[d] & \text{2 groups} \arrow[d] & \geq\text{ 3 groups} \arrow[d] & \text{2 groups} \arrow[d] & \geq \text{3 groups} \arrow[d] & \text{1 group} \arrow[d] & \text{2 groups} \arrow[d] & \geq \text{3 groups} \arrow[d] \\ -\text{McNemar's test} & \text{Cochrane's Q test} & \text{Chi-squared goodness-of-fit test} & \text{Pearson's Chi-squared test} & \text{Exact McNemar's test} & \text{Exact binomial test} & \text{Boschloo's test} & \text{Fisher's exact test} & \text{Wilcoxon's two-sample signed-rank test} & \text{Friedman's test} & \text{Wilcoxon's one-sample signed-rank test} & \text{Mann-Whitney's U test} & \text{Kruskal-Wallis's one-way ANOVA} & \text{Student's paired t-test} & {\text{\stackanchor{Mixed model ANOVA,}{subject ID as random effect}}} & \text{Student's one-sample t-test} & \text{Welch's t-test} & \text{F-test (ANOVA)} -\end{tikzcd} - -\end{document} diff --git a/vignettes/usage_guide.html b/vignettes/usage_guide.html deleted file mode 100644 index adb1a1d..0000000 --- a/vignettes/usage_guide.html +++ /dev/null @@ -1,4683 +0,0 @@ - - - - - - - - - - - - - - - -Usage guidance - - - - - - - - - - - - - - - - - - - - - - - - - -

Usage guidance

-

2021-11-07

- - - -
-

Introduction

-

DescrTab2 is the replacement of the DescrTab package. It supports a variety of different customization options and can be used in .Rmd files in conjunction with knitr.

-
-
-

Preamble settings

-

DescrTab2 works in your R-console, as well as in .Rmd documents corresponding to output formats of the type pdf_documument, html_document and word_document. It even supports YAML-headers with multiple output formats! For example, if your YAML-header looks like the example below, DescrTab2 should automagically detect the output format depending on the rendering option you choose from the dropdown menue (the arrow next to the “Knit” button on the top menue bar).

-
---
-title: "DescrTab2 tutorial"
-output:
-  word_document: default
-  pdf_document: default
-  html_document: default
----
-

Required LaTeX packages should be loaded automatically as well when rendering as a pdf.

-
-
-

Getting started

-

Make sure you include the DescrTab2 library by typing

-
library(DescrTab2)
-

somewhere in the document before you use it. You are now ready to go!

-

For instructive purposes, we will use the following dataset:

-
dat <- iris[, c("Species", "Sepal.Length")]
-dat %<>% mutate(animal= c("Mammal", "Fish") %>% rep(75) %>% factor())
-dat %<>% mutate(food= c("fries", "wedges") %>% sample(150, TRUE) %>% factor())
-

Producing beautiful descriptive tables is now as easy as typing:

-
descr(dat)
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Total -
-		 -
-p -
-
- -(N=150) - -
-Species -
-setosa - -50 (33%) - ->0.999Chi -
-versicolor - -50 (33%) - -
-virginica - -50 (33%) - -
-Sepal.Length -
-N - -150 - -<0.001Stu -
-mean - -5.8 - -
-sd - -0.83 - -
-median - -5.8 - -
-Q1 - Q3 - -5.1 – 6.4 - -
-min - max - -4.3 – 7.9 - -
-animal -
-Fish - -75 (50%) - ->0.999Chi -
-Mammal - -75 (50%) - -
-food -
-fries - -75 (50%) - ->0.999Chi -
-wedges - -75 (50%) - -
-Chi Chi-squared goodness-of-fit test -
-Stu Student’s one-sample t-test -
-
-
-

Accessing table elements

-

The object returned from the descr function is basically just a named list. You may be interested in referencing certain summary statistics from the table in your document. To do this, you can save the list returned by descr:

-
my_table <- descr(dat)
-

You can then access the elements of the list using the $ operator.

-
my_table$variables$Sepal.Length$results$Total$mean
-#> NULL
-

Rstudios autocomplete suggestions are very helpful when navigating this list.

-

The print function returns a formatted version of this list, which you can also save and access using the same syntax.

-
my_table <- descr(dat) %>% print(silent=TRUE)
-
-
-

Specifying a group

-

Use the group option to specify the name of a grouping variable in your data:

-
descr(dat, "Species")
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001F-t -
-mean - -5 - -5.9 - -6.6 - -5.8 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-animal -
-Fish - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - ->0.999Pea -
-Mammal - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - -
-food -
-fries - -32 (64%) - -22 (44%) - -21 (42%) - -75 (50%) - -0.052Pea -
-wedges - -18 (36%) - -28 (56%) - -29 (58%) - -75 (50%) - -
-F-t F-test (ANOVA) -
-Pea Pearson’s chi-squared test -
-
-
-

Assigning labels

-

Use the group_labels option to assign group labels and the var_labels option to assign variable labels:

-
descr(dat, "Species", group_labels=list(setosa="My custom group label"), var_labels = list(Sepal.Length = "My custom variable label"))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-My custom group label -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=150) - - -
-My custom variable label -
-N - -50 - -50 - -50 - -150 - -<0.001F-t -
-mean - -5 - -5.9 - -6.6 - -5.8 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-animal -
-Fish - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - ->0.999Pea -
-Mammal - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - -
-food -
-fries - -32 (64%) - -22 (44%) - -21 (42%) - -75 (50%) - -0.052Pea -
-wedges - -18 (36%) - -28 (56%) - -29 (58%) - -75 (50%) - -
-F-t F-test (ANOVA) -
-Pea Pearson’s chi-squared test -
-
-
-

Assigning a table caption

-

Use the caption member of the format_options argument to assign a table caption:

-
descr(dat, "Species", format_options = list(caption="Description of our example dataset."))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-Description of our example dataset. -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001F-t -
-mean - -5 - -5.9 - -6.6 - -5.8 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-animal -
-Fish - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - ->0.999Pea -
-Mammal - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - -
-food -
-fries - -32 (64%) - -22 (44%) - -21 (42%) - -75 (50%) - -0.052Pea -
-wedges - -18 (36%) - -28 (56%) - -29 (58%) - -75 (50%) - -
-F-t F-test (ANOVA) -
-Pea Pearson’s chi-squared test -
-
-
-

Confidence intervals for two group comparisons

-

For 2-group comparisons, decrtab automatically calculates confidence intervals for differences in effect measures:

-
descr(dat, "animal")
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Fish -
-		 -
-Mammal -
-		 -
-Total -
-		 -
-p -
-		 -
-CI -
-
- -(N=75) - -(N=75) - -(N=150) - - -
-Species -
-setosa - -25 (33%) - -25 (33%) - -50 (33%) - ->0.999Pea - -
-versicolor - -25 (33%) - -25 (33%) - -50 (33%) - - -
-virginica - -25 (33%) - -25 (33%) - -50 (33%) - - -
-Sepal.Length -
-N - -75 - -75 - -150 - -0.961Wel - -Mean dif. CI -
-mean - -5.8 - -5.8 - -5.8 - - -[-0.26, 0.27] -
-sd - -0.86 - -0.81 - -0.83 - - -
-median - -5.7 - -5.8 - -5.8 - - -
-Q1 - Q3 - -5.1 – 6.4 - -5.1 – 6.5 - -5.1 – 6.4 - - -
-min - max - -4.3 – 7.9 - -4.4 – 7.7 - -4.3 – 7.9 - - -
-food -
-fries - -37 (49%) - -38 (51%) - -75 (50%) - -0.870Pea - -Prop. dif. CI -
-wedges - -38 (51%) - -37 (49%) - -75 (50%) - - -[-0.17, 0.15] -
-Pea Pearson’s chi-squared test -
-Wel Welch’s two-sample t-test -
-
-
-

Different tests

-

There are a lot of different tests available. Check out the test_choice vignette for details: https://imbi-heidelberg.github.io/DescrTab2/articles/test_choice_tree_pdf.pdf

-

Here are some different tests in action:

-
descr(dat %>% select(-"Species"), "animal", test_options = list(exact=TRUE, nonparametric=TRUE))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Fish -
-		 -
-Mammal -
-		 -
-Total -
-		 -
-p -
-		 -
-CI -
-
- -(N=75) - -(N=75) - -(N=150) - - -
-Sepal.Length -
-N - -75 - -75 - -150 - -0.870Man - -HL CI -
-mean - -5.8 - -5.8 - -5.8 - - -[-0.3, 0.3] -
-sd - -0.86 - -0.81 - -0.83 - - -
-median - -5.7 - -5.8 - -5.8 - - -
-Q1 - Q3 - -5.1 – 6.4 - -5.1 – 6.5 - -5.1 – 6.4 - - -
-min - max - -4.3 – 7.9 - -4.4 – 7.7 - -4.3 – 7.9 - - -
-food -
-fries - -37 (49%) - -38 (51%) - -75 (50%) - -0.935Bos - -Prop. dif. CI -
-wedges - -38 (51%) - -37 (49%) - -75 (50%) - - -[-0.17, 0.15] -
-Man Mann-Whitney’s U test -
-Bos Boschloo’s test -
-
descr(dat %>% select(c("Species", "Sepal.Length")), "Species", test_options = list(nonparametric=TRUE))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001Kru -
-mean - -5 - -5.9 - -6.6 - -5.8 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-Kru Kruskal-Wallis’s one-way ANOVA -
-
-
-

Paired observations

-

In situations with paired data, the group variable usually denotes the timing of the measurement (e.g. “before” and “after” or “time 1”, “time 2”, etc.). In these scenarios, you need an additional index variable that specifies which observations from the different timepoints should be paired. The test_options =list(paired=TRUE, indices = <Character name of index variable name or vector of indices>) option can be used to specify the pairing indices, see the example below. DescrTab2 only works with data in “long format”, see e.g. ?reshape or ?tidyr::pivot_longer for information on how to transoform your data from wide to long format.

-
descr(dat %>% mutate(animal = fct_recode(animal, Before="Fish", After="Mammal")) %>% select(-"Species"), "animal", test_options = list(paired=TRUE, indices=rep(1:75, each=2)))
-#> You specified paired tests and did not explicitly
-#> specify format_options$print_Total. print_Total is set to FALSE.
-#> Warning in sig_test(var, group, test_options, test_override, var_name): Confidence intervals for differences in proportions ignore the paired structure of the data.
-#> Use Exact McNemar's test if you want confidence intervals which use the test statistic of the
-#> exact McNemar's test.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Before -
-		 -
-After -
-		 -
-p -
-		 -
-CI -
-
- -(N=75) - -(N=75) - - -
-Sepal.Length -
-N - -75 - -75 - -0.937Stu - -Mean dif. CI -
-mean - -5.8 - -5.8 - - -[-0.16, 0.18] -
-sd - -0.86 - -0.81 - - -
-median - -5.7 - -5.8 - - -
-Q1 - Q3 - -5.1 – 6.4 - -5.1 – 6.5 - - -
-min - max - -4.3 – 7.9 - -4.4 – 7.7 - - -
-food -
-fries - -37 (49%) - -38 (51%) - ->0.999McN - -Prop. dif. CI -
-wedges - -38 (51%) - -37 (49%) - - -[-0.17, 0.15] -
-Stu Student’s paired t-test -
-McN McNemar’s test -
-

-descr(dat %>% mutate(animal = fct_recode(animal, Before="Fish", After="Mammal"), idx = rep(1:75, each=2)) %>% select(-"Species"), "animal", test_options = list(paired=TRUE, indices="idx" ))
-#> You specified paired tests and did not explicitly
-#> specify format_options$print_Total. print_Total is set to FALSE.
-#> Warning in sig_test(var, group, test_options, test_override, var_name): Confidence intervals for differences in proportions ignore the paired structure of the data.
-#> Use Exact McNemar's test if you want confidence intervals which use the test statistic of the
-#> exact McNemar's test.
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Before -
-		 -
-After -
-		 -
-p -
-		 -
-CI -
-
- -(N=75) - -(N=75) - - -
-Sepal.Length -
-N - -75 - -75 - -0.937Stu - -Mean dif. CI -
-mean - -5.8 - -5.8 - - -[-0.16, 0.18] -
-sd - -0.86 - -0.81 - - -
-median - -5.7 - -5.8 - - -
-Q1 - Q3 - -5.1 – 6.4 - -5.1 – 6.5 - - -
-min - max - -4.3 – 7.9 - -4.4 – 7.7 - - -
-food -
-fries - -37 (49%) - -38 (51%) - ->0.999McN - -Prop. dif. CI -
-wedges - -38 (51%) - -37 (49%) - - -[-0.17, 0.15] -
-Stu Student’s paired t-test -
-McN McNemar’s test -
-
-
-

Significant digits

-

Every summary statistic in DescrTab2 is formatted by a corresponding formatting function. You can exchange these formatting functions as you please:

-
descr(dat, "Species", format_summary_stats = list(mean=function(x)formatC(x, digits = 4)) )
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001F-t -
-mean - -5.006 - -5.936 - -6.588 - -5.843 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-animal -
-Fish - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - ->0.999Pea -
-Mammal - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - -
-food -
-fries - -32 (64%) - -22 (44%) - -21 (42%) - -75 (50%) - -0.052Pea -
-wedges - -18 (36%) - -28 (56%) - -29 (58%) - -75 (50%) - -
-F-t F-test (ANOVA) -
-Pea Pearson’s chi-squared test -
-
-
-

Omitting summary statistics

-

Let’s say you don’t want to calculate quantiles for your numeric variables. You can specify the summary_stats_cont option to include all summary statistics but quantiles:

-
descr(dat, "Species", summary_stats_cont = list(N = DescrTab2:::.N, Nmiss = DescrTab2:::.Nmiss, mean =
-    DescrTab2:::.mean, sd = DescrTab2:::.sd, median = DescrTab2:::.median, min = DescrTab2:::.min, max =
-    DescrTab2:::.max))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001F-t -
-mean - -5 - -5.9 - -6.6 - -5.8 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-animal -
-Fish - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - ->0.999Pea -
-Mammal - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - -
-food -
-fries - -32 (64%) - -22 (44%) - -21 (42%) - -75 (50%) - -0.052Pea -
-wedges - -18 (36%) - -28 (56%) - -29 (58%) - -75 (50%) - -
-F-t F-test (ANOVA) -
-Pea Pearson’s chi-squared test -
-
-
-

Adding summary statistics

-

Let’s say you have a categorical variable, but for some reason it’s levels are numerals and you want to calculate the mean. No problem:

-
# Create example dataset
-dat2 <- iris
-dat2$cat_var <- c(1,2) %>% sample(150, TRUE) %>% factor()
-dat2 <- dat2[, c("Species", "cat_var")]
-
-descr(dat2, "Species", summary_stats_cat=list(mean=DescrTab2:::.factormean))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-cat_var -
-1 - -24 (48%) - -21 (42%) - -25 (50%) - -70 (47%) - -0.706Pea -
-2 - -26 (52%) - -29 (58%) - -25 (50%) - -80 (53%) - -
-mean - -1.5 - -1.6 - -1.5 - -1.5 - -
-Pea Pearson’s chi-squared test -
-
-
-

Combining mean and sd

-

Use the format_options = list(combine_mean_sd=TRUE) option:

-
descr(dat, "Species", format_options = c(combine_mean_sd=TRUE))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001F-t -
-mean ± sd - -5 ± 0.35 - -5.9 ± 0.52 - -6.6 ± 0.64 - -5.8 ± 0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-animal -
-Fish - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - ->0.999Pea -
-Mammal - -25 (50%) - -25 (50%) - -25 (50%) - -75 (50%) - -
-food -
-fries - -32 (64%) - -22 (44%) - -21 (42%) - -75 (50%) - -0.052Pea -
-wedges - -18 (36%) - -28 (56%) - -29 (58%) - -75 (50%) - -
-F-t F-test (ANOVA) -
-Pea Pearson’s chi-squared test -
-
-
-

Omitting p-values

-

You can declare the format_options = list(print_p = FALSE) option to omit p-values:

-
descr(dat, "animal", format_options = list(print_p = FALSE))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Fish -
-		 -
-Mammal -
-		 -
-Total -
-		 -
-CI -
-
- -(N=75) - -(N=75) - -(N=150) - -
-Species -
-setosa - -25 (33%) - -25 (33%) - -50 (33%) - -
-versicolor - -25 (33%) - -25 (33%) - -50 (33%) - -
-virginica - -25 (33%) - -25 (33%) - -50 (33%) - -
-Sepal.Length -
-N - -75 - -75 - -150 - -Mean dif. CI -
-mean - -5.8 - -5.8 - -5.8 - -[-0.26, 0.27] -
-sd - -0.86 - -0.81 - -0.83 - -
-median - -5.7 - -5.8 - -5.8 - -
-Q1 - Q3 - -5.1 – 6.4 - -5.1 – 6.5 - -5.1 – 6.4 - -
-min - max - -4.3 – 7.9 - -4.4 – 7.7 - -4.3 – 7.9 - -
-food -
-fries - -37 (49%) - -38 (51%) - -75 (50%) - -Prop. dif. CI -
-wedges - -38 (51%) - -37 (49%) - -75 (50%) - -[-0.17, 0.15] -
-

Similarily for Confidence intervals:

-
descr(dat, "animal", format_options = list(print_CI = FALSE))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Fish -
-		 -
-Mammal -
-		 -
-Total -
-		 -
-p -
-
- -(N=75) - -(N=75) - -(N=150) - -
-Species -
-setosa - -25 (33%) - -25 (33%) - -50 (33%) - ->0.999Pea -
-versicolor - -25 (33%) - -25 (33%) - -50 (33%) - -
-virginica - -25 (33%) - -25 (33%) - -50 (33%) - -
-Sepal.Length -
-N - -75 - -75 - -150 - -0.961Wel -
-mean - -5.8 - -5.8 - -5.8 - -
-sd - -0.86 - -0.81 - -0.83 - -
-median - -5.7 - -5.8 - -5.8 - -
-Q1 - Q3 - -5.1 – 6.4 - -5.1 – 6.5 - -5.1 – 6.4 - -
-min - max - -4.3 – 7.9 - -4.4 – 7.7 - -4.3 – 7.9 - -
-food -
-fries - -37 (49%) - -38 (51%) - -75 (50%) - -0.870Pea -
-wedges - -38 (51%) - -37 (49%) - -75 (50%) - -
-Pea Pearson’s chi-squared test -
-Wel Welch’s two-sample t-test -
-
-
-

Controling options on a per-variable level

-

You can use the var_options list to control formatting and test options on a per-variable basis. Let’s say in the dataset iris, we want that only the Sepal.Length variable has more digits in the mean and a nonparametric test:

-
descr(iris, "Species", var_options = list(Sepal.Length = list(
-  format_summary_stats = list(
-    mean = function(x)
-      formatC(x, digits = 4)
-  ),
-  test_options = c(nonparametric = TRUE)
-)))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-setosa -
-		 -
-versicolor -
-		 -
-virginica -
-		 -
-Total -
-		 -
-p -
-
- -(N=50) - -(N=50) - -(N=50) - -(N=150) - -
-Sepal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001Kru -
-mean - -5.006 - -5.936 - -6.588 - -5.843 - -
-sd - -0.35 - -0.52 - -0.64 - -0.83 - -
-median - -5 - -5.9 - -6.5 - -5.8 - -
-Q1 - Q3 - -4.8 – 5.2 - -5.6 – 6.3 - -6.2 – 6.9 - -5.1 – 6.4 - -
-min - max - -4.3 – 5.8 - -4.9 – 7 - -4.9 – 7.9 - -4.3 – 7.9 - -
-Sepal.Width -
-N - -50 - -50 - -50 - -150 - -<0.001F-t -
-mean - -3.4 - -2.8 - -3 - -3.1 - -
-sd - -0.38 - -0.31 - -0.32 - -0.44 - -
-median - -3.4 - -2.8 - -3 - -3 - -
-Q1 - Q3 - -3.2 – 3.7 - -2.5 – 3 - -2.8 – 3.2 - -2.8 – 3.3 - -
-min - max - -2.3 – 4.4 - -2 – 3.4 - -2.2 – 3.8 - -2 – 4.4 - -
-Petal.Length -
-N - -50 - -50 - -50 - -150 - -<0.001F-t -
-mean - -1.5 - -4.3 - -5.6 - -3.8 - -
-sd - -0.17 - -0.47 - -0.55 - -1.8 - -
-median - -1.5 - -4.3 - -5.5 - -4.3 - -
-Q1 - Q3 - -1.4 – 1.6 - -4 – 4.6 - -5.1 – 5.9 - -1.6 – 5.1 - -
-min - max - -1 – 1.9 - -3 – 5.1 - -4.5 – 6.9 - -1 – 6.9 - -
-Petal.Width -
-N - -50 - -50 - -50 - -150 - -<0.001F-t -
-mean - -0.25 - -1.3 - -2 - -1.2 - -
-sd - -0.11 - -0.2 - -0.27 - -0.76 - -
-median - -0.2 - -1.3 - -2 - -1.3 - -
-Q1 - Q3 - -0.2 – 0.3 - -1.2 – 1.5 - -1.8 – 2.3 - -0.3 – 1.8 - -
-min - max - -0.1 – 0.6 - -1 – 1.8 - -1.4 – 2.5 - -0.1 – 2.5 - -
-Kru Kruskal-Wallis’s one-way ANOVA -
-F-t F-test (ANOVA) -
-
-
-

Use user defined test statistics

-

DescrTab2 has many predefined significance tests, but sometimes you may need to use a custom test. In this case, you can use the test_override option in test_options (or as a part of per variable options, see above)

-
custom_ttest <- list(
-  name = "custom t-test",
-  abbreviation = "custom",
-  p = function(var) {
-    return(t.test(var, alternative = "greater")$p.value)
-  }
-)
-
-descr(iris %>% select(-Species), test_options = list(test_override = custom_ttest))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
-
-Variables -
-		 -
-Total -
-		 -
-p -
-
- -(N=150) - -
-Sepal.Length -
-N - -150 - -<0.001cus -
-mean - -5.8 - -
-sd - -0.83 - -
-median - -5.8 - -
-Q1 - Q3 - -5.1 – 6.4 - -
-min - max - -4.3 – 7.9 - -
-Sepal.Width -
-N - -150 - -<0.001cus -
-mean - -3.1 - -
-sd - -0.44 - -
-median - -3 - -
-Q1 - Q3 - -2.8 – 3.3 - -
-min - max - -2 – 4.4 - -
-Petal.Length -
-N - -150 - -<0.001cus -
-mean - -3.8 - -
-sd - -1.8 - -
-median - -4.3 - -
-Q1 - Q3 - -1.6 – 5.1 - -
-min - max - -1 – 6.9 - -
-Petal.Width -
-N - -150 - -<0.001cus -
-mean - -1.2 - -
-sd - -0.76 - -
-median - -1.3 - -
-Q1 - Q3 - -0.3 – 1.8 - -
-min - max - -0.1 – 2.5 - -
-cus custom t-test -
-
- - - - - - - - - - -