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% Document Info
\author{Niki (worlddoit)}
\pdfinfo{
  /Title (python-vs-r-1.pdf)
  /Creator (Cheatography)
  /Author (Niki (worlddoit))
  /Subject (python vs r 1 Cheat Sheet)
}

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\noindent
\begin{multicols}{3}
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    \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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\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{python vs r 1 Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{Niki (worlddoit)} via \textcolor{DarkBackground}{\uline{cheatography.com/170195/cs/35896/}}}
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\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{LL}
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}Niki (worlddoit) \\
  \uline{cheatography.com/worlddoit} \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 6th December, 2022.\\
   Page {\thepage} of \pageref{LastPage}.
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  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
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\begin{document}
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\begin{multicols*}{3}

\begin{tabularx}{5.377cm}{X}
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Intro}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{{\bf{R}} statistical analysis, statistical support  \newline % Row Count 1 (+ 1)
{\bf{Python}} general data science. \newline % Row Count 2 (+ 1)
\{\{fa-bolt\}\}R \textgreater{} functional, Python  \textgreater{} object-oriented. \newline % Row Count 4 (+ 2)
\{\{fa-bolt\}\}R \textgreater{} data analysis functionality built-in, Python relies on packages. \newline % Row Count 6 (+ 2)
\{\{fa-bolt\}\}Python \textgreater{} non-statistical tasks. \newline % Row Count 7 (+ 1)
\{\{fa-bolt\}\}Both can handle huge size of database. \newline % Row Count 8 (+ 1)
\{\{fa-bolt\}\}Python is faster, better for deep learning. \newline % Row Count 10 (+ 2)
\{\{fa-bolt\}\}R is better for data visualization. \newline % Row Count 11 (+ 1)
Resources: \newline % Row Count 12 (+ 1)
\{\{link="https://www.dataquest.io/blog/python-vs-r/"\}\}main, \{\{/link\}\}\{\{link="https://www.guru99.com/r-vs-python.html\#:\textasciitilde{}:text=their\%20respective\%20catalog.-,R\%20is\%20mainly\%20used\%20for\%20statistical\%20analysis\%20while\%20Python\%20provides,of\%20course\%2C\%20the\%20ideal\%20solution."\}\}definition, \{\{/link\}\}\{\{link="https://www.educba.com/r-vs-python/"\}\}comparison\{\{/link\}\}% Row Count 20 (+ 8)
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\begin{tabularx}{5.377cm}{X}
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\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{R}}  \tn
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\mymulticolumn{1}{x{5.377cm}}{{\bf{Importing a CSV, Data Look}} \newline library(readr)\{\{noshy\}\} \newline nba \textless{}- read\_csv("nba\_2013.csv")\{\{noshy\}\} \newline  \newline dim(nba)\{\{noshy\}\} \newline  \newline head(nba, 1)\{\{noshy\}\} \newline  \newline {\bf{Averages for Each Statistic}} \newline library(purrr)\{\{noshy\}\} \newline library(dplyr)\{\{noshy\}\} \newline nba \%\textgreater{}\%\{\{noshy\}\} \newline  select\_if(is.numeric) \%\textgreater{}\%\{\{noshy\}\} \newline  map\_dbl(mean, na.rm = TRUE)\{\{noshy\}\} \newline  \newline {\bf{Scatterplots (see below results)}} \newline library(GGally)\{\{noshy\}\} \newline nba \%\textgreater{}\%\{\{noshy\}\} \newline select(ast, fg, trb) \%\textgreater{}\%\{\{noshy\}\} \newline gpairs()\{\{noshy\}\} \newline  \newline {\bf{Data into Training and Testing Sets}} \newline trainRowCount \textless{}- floor(0.8 * nrow(nba))\{\{noshy\}\} \newline set.seed(1)\{\{noshy\}\} \newline trainIndex \textless{}- sample(1:nrow(nba), \{\{noshy\}\} \newline +trainRowCount)\{\{noshy\}\} \newline train \textless{}- nba{[}trainIndex,{]}\{\{noshy\}\} \newline test \textless{}- nba{[}-trainIndex,{]}\{\{noshy\}\} \newline  \newline {\bf{Univariate Linear Regression}} \newline fit \textless{}- lm(ast \textasciitilde{} fg, data=train)\{\{noshy\}\} \newline predictions \textless{}- predict(fit, test)\{\{noshy\}\} \newline  \newline {\bf{Summary Statistics}} \newline summary(fit)\{\{noshy\}\} \newline  \newline {\bf{Web Scrapping}} \newline library(RCurl)\{\{noshy\}\} \newline url \textless{}- "http"\{\{noshy\}\} \newline data \textless{}- readLines(url)\{\{noshy\}\}} \tn 
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\mymulticolumn{1}{x{5.377cm}}{{\bf{Importing a CSV, Data Look}} \newline import pandas\{\{noshy\}\} \newline nba = pandas.read\_csv\{\{noshy\}\} \newline ("nba\_2013.csv")\{\{noshy\}\} \newline  \newline nba.shape\{\{noshy\}\} \newline  \newline nba.head(1)\{\{noshy\}\} \newline  \newline {\bf{Averages for Each Statistic}} \newline nba.mean()\{\{noshy\}\} \newline  \newline {\bf{Scatterplots (see below results)}} \newline import seaborn as sns\{\{noshy\}\} \newline span class="token keyword"\{\{noshy\}\} \newline import matplotlib.pyplot as plt\{\{noshy\}\} \newline sns.pairplot(nba{[}{[}"ast", "fg", "trb"{]}{]})\{\{noshy\}\} \newline plt.show()\{\{noshy\}\} \newline  \newline {\bf{Data into Training/Testing Set}} \newline train = nba.sample(frac=0.8, random\_state=1)\{\{noshy\}\} \newline test = nba.loc{[}\textasciitilde{}nba.index.isin\{\{noshy\}\} \newline (train.index){]}\{\{noshy\}\} \newline  \newline {\bf{Univariate Linear Regression}} \newline fit \textless{}- lm(ast \textasciitilde{} fg, data=train)\{\{noshy\}\} \newline predictions \textless{}- predict(fit, test)\{\{noshy\}\} \newline  \newline {\bf{Summary Statistics}} \newline import statsmodels.formula.api \{\{noshy\}\} \newline as sm\{\{noshy\}\} \newline model = sm.ols(formula='ast \textasciitilde{} fga' \{\{noshy\}\} \newline ,data=train)\{\{noshy\}\} \newline fitted = model.fit()\{\{noshy\}\} \newline fitted.summary()\{\{noshy\}\} \newline  \newline {\bf{Web Scrapping}} \newline import requests\{\{noshy\}\} \newline url = "http"\{\{noshy\}\} \newline data = requests.get(url).content\{\{noshy\}\}} \tn 
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% That's all folks
\end{multicols*}

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