\documentclass[10pt,a4paper]{article}

% Packages
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\usepackage{seqsplit}           % Splits long words.
%\usepackage{opensans}          % Can't make this work so far. Shame. Would be lovely.
\usepackage[normalem]{ulem}     % For underlining links
% Most of the following are not required for the majority
% of cheat sheets but are needed for some symbol support.
\usepackage{amsmath}            % Symbols
\usepackage{MnSymbol}           % Symbols
\usepackage{wasysym}            % Symbols
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% Document Info
\author{Darktotoro}
\pdfinfo{
  /Title (rstudio.pdf)
  /Creator (Cheatography)
  /Author (Darktotoro)
  /Subject (RStudio Cheat Sheet)
}

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% Header and Footer
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\fancyhead{} % Set header to blank
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\fancyhead[L]{
\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{C}
    \SetRowColor{DarkBackground}
    \vspace{-7pt}
    {\parbox{\dimexpr\textwidth-2\fboxsep\relax}{\noindent
        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
    }
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\columnbreak
\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{RStudio Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{Darktotoro} via \textcolor{DarkBackground}{\uline{cheatography.com/133678/cs/44804/}}}
\end{tabulary}
\end{multicols}}

\fancyfoot[L]{ \footnotesize
\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{LL}
  \SetRowColor{FootBackground}
  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}Darktotoro \\
  \uline{cheatography.com/darktotoro} \\
  \end{tabulary}
\vfill
\columnbreak
\begin{tabulary}{5.8cm}{L}
  \SetRowColor{FootBackground}
  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 26th October, 2024.\\
   Page {\thepage} of \pageref{LastPage}.
\end{tabulary}
\vfill
\columnbreak
\begin{tabulary}{5.8cm}{L}
  \SetRowColor{FootBackground}
  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Sponsor}}  \\
  \SetRowColor{white}
  \vspace{-5pt}
  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
\end{tabulary}
\end{multicols}}




\begin{document}
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% www.emerson.emory.edu/services/latex/latex_169.html
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\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{GENERAL}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{rm(list = ls()) \newline dat = read.table('coches.txt', header = T) \newline dat = read.csv('gastos.csv', header = T) \newline dat = na.omit(d)  \# Cojo solo observ. completas \newline names(d) \newline dat\$sexo  = factor(dat\$sexo, labels=c('H','M'))  \# Paso a factor \newline dat\$titu  = relevel(d\$titu, ref = 'GITI') \# cambiar ref si \textgreater{}2 vars \newline dat2 = dat{[}  ,3:8{]}    \# c(3,4,..,8) - ordenar datos \newline dat = dat{[} ,-1{]}                     \# Elimino columna 1 \newline row.names(dat2) = dat\$VX \newline  \newline round(X, digits=2) \newline sum(dat\$SEXO == 'mujer') \newline dim(dat)  \# Dimensiones base de datos} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{ESTADÍSTICA DESCRIPTIVA}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{hist(dat\$V1) \newline hist(d\$V1{[}d\$V2 == 4{]}) \newline hist(d{[}1:50 , 1{]} ) \newline  \newline boxplot(dat\$V1)                \# también en dos variables \newline summary(dat\$V1) \newline plot( V1 \textasciitilde{} V2, data = dat) \newline par(mfrow = c(1,2))           \# Para dibujar dos gráficos a la vez} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{TÉCNICAS DE REGRESIÓN}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{mod = lm(V1 \textasciitilde{} V2, data = dat)  \# modelo regresion \newline mod = lm(V1 \textasciitilde{} V2 + V3, data = dat)  \newline summary(mod)  \# beta0 y beta1 \newline abline(mod)   \# Recta de Regresion \newline pred1=data.frame(V1=,V2=...) \newline predict(mod,pred1) \# predicc puntual \newline predict(mod,pred1, interval='prediction', level=0.9)  \newline \# predicc nueva obs \newline predict(mod,pred1, interval='confidence') \# prmedia \newline  \newline confint(mod6) \# intervalo de conf - si E0 no sign \newline mod9 = step(mod8)  \# Selección de variables singif \newline  \newline res = residuals(mod9)   \# residuos \newline var(dat)      \# varianzas covarianzas \newline cor(dat)      \# coefs. correlacion (matriz) \newline pairs(dat)    \# dispersion multiple} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{LDA - ANÁLISIS DISCRIMINANTE}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{{\bf{library(MASS)}} \newline (m1 = lda( SEXO \textasciitilde{}  . , data = dat )) \newline m1\$scaling  \# Coefs de la f.discrimante (no stand) \newline (a0 = -sum(m1\$scaling * colMeans(dat{[} , -1{]}))) \newline plot(m1) \newline (pred1 = predict(m1)) \newline table(pred = pred1\$class, real = dat\$SEXO) \newline  \newline prediccion\$class    \# Decisión del modelo \newline prediccion\$posterior \# Seguridad decisión \newline prediccion\$x      \#score o puntuación \newline dat\$SEXO{[}5{]}    \# La quinta obs. es mujer \newline prediccion\$class{[}5{]} \# modelo dice  \newline  \newline {\bf{library(multiUS)}} \newline \# .f.discriminante coef. estandarizados \newline m2 = ldaPlus(x = dat{[},  -1{]}, grouping = dat\$SEXO) \newline m2\$standCoefWithin   \#Coeficientes estandarizados \newline \# Con estos coef. variables son más impor. \newline \# Si hay multicolinealidad: no se pueden interpretar  \newline m2\$class\$orgTab    \# M CONFUSIÓN:absoluta \newline m2\$class\$perTab    \# M CONFUSIÓN:relativa \newline m2\$classCV\$orgTab    \# MC VC absoluta \newline m2\$classCV\$perTab    \# MC VC relativa \newline m2\$centroids         \# Centroides \newline pred1 = predict(m1, flor) \newline points(pred1\$x, cex = 3) \newline plot(m2, dimen = 1)} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{ANÁLISIS COMPONENTES PRINCIPALES}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{plot(fit, type = 'lines')   \# grafico codo - num CP \newline biplot(fit)   \# añadir rownames A parecido B?  \newline dat\$T100 = dat\$T100\textasciicircum{}-1      \# si invrs proporcional \newline  \newline fit = princomp(datos, cor = T) \newline fit\$sdev\textasciicircum{}2    \# importancia \newline cumsum(m\$sdev\textasciicircum{}2/7) \# Importancias acumuladas \newline round(fit\$sdev\textasciicircum{}2/6*100, digits= 2)  \# imp rel \newline fit\$loadings  \# pesos (weights) \newline fit\$scores    \# puntuaciones - valores CP \newline cor(datos, fit\$scores)      \# cargas \newline rowSums(cor(datos, fit\$scores{[}, 1:2{]})\textasciicircum{}2)  \# comunalidad \newline 1-rowSums(cor(datos, fit\$scores{[}, 1:2{]})\textasciicircum{}2)\# especifidad \newline  \newline {\bf{source('prinfact.R')}} \newline sol = prinfact(datos, 6)  \#num CP \newline sol\$loadings  \# Cargas \newline sol\$scores    \# puntuaciones \newline sol\$variances \# imporancias \newline sol\$eig\$vectors{[} ,1{]} \# Pesos {[}CP1{]} \newline  \newline {\bf{CORRPLOT}} \newline install.packages('corrplot')  \# descargar paquete \newline library(corrplot)             \# activar paquete \newline r = cor(dat) \newline corrplot(r, method = 'ellipse')} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{17.67cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{17.67cm}}{\bf\textcolor{white}{ANÁLISIS COMPONENTES PRINCIPALES}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{17.67cm}}{plot(fit, type = 'lines')   \# grafico codo - num CP \newline biplot(fit)   \# añadir rownames A parecido B?  \newline dat\$T100 = dat\$T100\textasciicircum{}-1      \# si invrs proporcional \newline  \newline fit = princomp(datos, cor = T) \newline fit\$sdev\textasciicircum{}2    \# importancia \newline cumsum(m\$sdev\textasciicircum{}2/7) \# Importancias acumuladas \newline round(fit\$sdev\textasciicircum{}2/6*100, digits= 2)  \# imp rel \newline fit\$loadings  \# pesos (weights) \newline fit\$scores    \# puntuaciones - valores CP \newline cor(datos, fit\$scores)      \# cargas \newline rowSums(cor(datos, fit\$scores{[}, 1:2{]})\textasciicircum{}2)  \# comunalidad \newline 1-rowSums(cor(datos, fit\$scores{[}, 1:2{]})\textasciicircum{}2)\# especifidad \newline  \newline {\bf{source('prinfact.R')}} \newline sol = prinfact(datos, 6)  \#num CP \newline sol\$loadings  \# Cargas \newline sol\$scores    \# puntuaciones \newline sol\$variances \# imporancias \newline sol\$eig\$vectors{[} ,1{]} \# Pesos {[}CP1{]} \newline  \newline {\bf{CORRPLOT}} \newline install.packages('corrplot')  \# descargar paquete \newline library(corrplot)             \# activar paquete \newline r = cor(dat) \newline corrplot(r, method = 'ellipse')} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}



\end{document}