\documentclass{beamer}
\usepackage{graphicx}
\usepackage{multicol}
\usepackage{hyperref}
\usepackage{lipsum} % for placeholder text
\graphicspath{{pdf_images/}}


\title{ECON 4530/5530 \\ Computational Economics}
\author{Alex Gebben}

\begin{document}

% Title Slide
\begin{frame}
  \titlepage
\end{frame}

%%%%%%%%%%%%%%%%%%

\begin{frame}{Class Exercise}
	In this class we will be starting with the question 
\vspace{1em}

	``Which states have oil production rates that are similar to Wyoming?'' 
	\begin{itemize}
	\onslide<2->{\item Method applied: correlation matrix}
	\onslide<3->{\item Data needed: Oil production volumes for each US state over time}
	\onslide<4->{\item	\href{https://www.eia.gov/dnav/pet/pet_crd_crpdn_adc_mbbl_m.htm}{Petroleum \& Other Liquids from the EIA}
	}
\end{itemize}
\end{frame}


\begin{frame}{Loading Data}

% Top half: two columns
\vspace{-0.5em}
\begin{columns}[T,onlytextwidth]
    \column{0.5\textwidth}
    \textbf{Functions to read data}
    \begin{enumerate}
        \item \texttt{read.csv("FilePath")}
        \item \texttt{read.delim("FilePath")}
        \item \texttt{read.table("FilePath")}
    \end{enumerate}

    \column{0.48\textwidth}
    \textbf{Important inputs}
    \begin{enumerate}
        \item \texttt{file}
        \item \texttt{header}
        \item \texttt{skip}
        \item \texttt{colnames}
    \end{enumerate}
\end{columns}

\vspace{1em}

% Bottom half: full width
\textbf{Notes}
\begin{itemize}
    \item You can load data directly from a URL or point the file variable to the local computer.
    \item R uses \texttt{/} for file path, based on Unix standards.
    \item Enclose URLs or paths with single quotes when they include a space.
    \item You can see where R starts looking for a file (default working directory) with \texttt{getwd()}.
\end{itemize}
\end{frame}
%%%%%%%%%%%%%%
\begin{frame}{Other data loading options}
Other libraries can be installed to load data in other ways. Keep these in mind for future projects.

\begin{itemize}
    \item \texttt{read\_xls(x)}: library \texttt{read excel}
    \item \texttt{getSymbols}: library \texttt{quantmod}
    \item \texttt{fredr}: library \texttt{fredr}
    \item \texttt{vroom}: library \texttt{vroom}
    \item \texttt{read\_html}: library \texttt{rvest}
    \item \texttt{read\_csv}: library \texttt{tidyverse}
    \item \texttt{read\_sheet}: library \texttt{googlesheets4}
\end{itemize}

\vspace{1em}
Almost any type of data can be loaded. Search the web for the right library.

\end{frame}
%%%%%%%%%%%%%%%%%%
\begin{frame}{Class Exercise}
	\huge
	Download relevant data

	\normalsize
	It is good practice to store the data is a separate folder in the location of the Rscript. Such as a "Data" folder.
\end{frame}
%%%%%%%%
\begin{frame}{Variables}
\onslide<1->{
Variables store any type of data.\\
\vspace{1em}
Can be a single values, a row of data, or a table of data.


\vspace{1em}
}
\onslide<2->{
\begin{center}
\includegraphics[width=0.3\textwidth]{image_16_1} 
\end{center}
}

\end{frame}
%%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Variable Tips}

\begin{itemize}
    \item Variables can be created with either \texttt{\(<-\)}, \texttt{\(->\)}, or \texttt{=}. But arrows are best practice.
    \item You can view the names of all variables being used with \texttt{ls()}.
    \item You can delete a variable with \texttt{rm(variable)}.
    \item Variables can be re-assigned using themselves: \texttt{a = 1} then \texttt{a = a + 1} means \texttt{a = 2}.
\end{itemize}

\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Data Frames}

Think of data frames as a table, with rows and columns.


The specific data in a data frame named \texttt{TBL} can be accessed with:
\onslide<2->{
\begin{itemize}
	\onslide<2->{ \item \texttt{TBL[row\_num, column\_num]}}
	\onslide<3->{ \item \texttt{TBL[row\_num, "column\_name"]}}
	\onslide<4->{   \item \texttt{TBL\$column\_name[row\_num]}}
\end{itemize}
}


\onslide<5->{
You can use these to assign values just as you would a variable.\\
\texttt{TBL[4,5] <- 7} will change the value of row 4, column 5 of the data frame to 7.
}

\onslide<5->{
\vspace{1em}
Row or column names can be viewed or assigned with \texttt{colnames()} and \texttt{rownames()}.
}
\end{frame}


%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Vectors}

% Slide 1 (Page 10)
\only<1>{

Think of vectors as single rows of data.\\
Data frames are made up of many vectors put together.
}

% Slide 2 (Page 11)
\only<2>{

You can pull out a vector from a data frame. To get a column you can use:
\begin{itemize}
    \item \texttt{TBL\$column\_name}
    \item \texttt{TBL[, column\_num]}
    \item \texttt{TBL[, "column\_name"]}
\end{itemize}

Rows are typically not named, so to extract a row you can use:
\begin{itemize}
    \item \texttt{TBL[row\_num, ]}
\end{itemize}

The \texttt{\$} extracts a column by name.\\
Use \texttt{colnames()} or \texttt{names()} to view or modify column names.
}

% Slide 3 (Page 13)

% Slide 6 (Page 16)
\only<3>{
	\begin{center}
\includegraphics[width=0.45\textwidth]{image_23_1} % Replace with actual image if available
\end{center}
}

\only<4>{

A vector can be created in several ways. The most common is:
\begin{itemize}
    \item \texttt{c(item1, item2, item3, ...)}
    \item \texttt{1:100} creates a sequence from 1 to 100
    \item \texttt{seq(0, 100, by = 5)} creates a sequence from 0 to 100 by 5
    \item \texttt{rep("a", 100)} creates a vector of "a" repeated 100 times
\end{itemize}
}

% Slide 4 (Page 14)
\only<5>{

These can be combined:
\begin{itemize}
    \item \texttt{rep(1:10, 5)} creates a vector from 1 to 10 repeated 5 times (length 50)
    \item \texttt{c("a", rep(1:10, 5), "b")} starts with "a", repeats 1 to 10 five times, ends with "b"
\end{itemize}
}

% Slide 5 (Page 15)
\only<6>{

Like data frames, vector values can be accessed with \texttt{[ ]}, but vectors are one-dimensional.

Example:
\begin{itemize}
    \item \texttt{a = c(1, 3, 5.8, 7)}
    \item \texttt{a[2]} is 3
    \item \texttt{a[3]} is 5.8
    \item \texttt{a[2] * a[4]} is 21
\end{itemize}
}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%
\begin{frame}{Class Exercise}
	\huge
	Review existing column names
\end{frame}


%%%%%%%%%%%%%%%%
\begin{frame}{Manipulating Data Frames}
	\only<1-7>{
Some useful R functions to review and update data frames.
	\begin{itemize}
		\onslide<2->{\item{\texttt{ncol()}: Provides the number of columns}}
		\onslide<3->{\item{\texttt{nrow()}: Provides the number of rows}}
		\onslide<4->{\item{\texttt{length()}: For data frames, gives the number of columns; for matrices/vectors/lists, gives the number of entries}}
		\onslide<5->{\item{\texttt{rank()}: Provides an index score of a vector from smallest to largest}}
		\onslide<6->{\item{\texttt{order()}: Provides the index of a vector that goes from smallest to largest}}
		\onslide<7->{\item{\texttt{sort()}: Rearranges a vector from smallest to largest}}
\end{itemize}
}
\only<8->{

\begin{center}
\includegraphics[width=0.45\textwidth]{image_57_1}
\end{center}
}

\end{frame}

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Validate Data Frames}
\only<1-7>{
\onslide<1->{
After loading a data set, you need to look for errors. Common issues include:
}

\onslide<2->{
\begin{itemize}
	\onslide<2->{ \item Missing values (\texttt{NA})}
	   \onslide<3->{  \item Wrong data type for the column (number, date, words)}
	  \onslide<4->{   \item Numbers treated as words}
	 \onslide<5->{   \item Values entered incorrectly}
	 \onslide<6->{  \item Implausible data (too large or too small)}
\end{itemize}
}

\onslide<7->{
What is likely to be an issue varies case by case.\\
Consider the unique issues for any data you are loading.
}
}
\only<8->{
\onslide<8->{
Some useful functions to review data:
\begin{itemize}
    \item \texttt{summary()}: Provides summary statistics for a data frame
    \item \texttt{head()}, \texttt{tail()}: Show the top or bottom of a data frame
    \item \texttt{plot()}: Visually plots a vector (can be pulled from a data frame)
    \item \texttt{hist()}: Provides a histogram of values in a vector
    \item \texttt{class()}: Lists the data type of any object (check each column)
\end{itemize}
}
}

\end{frame}

%%%%%%%%%%%%%%%%
\begin{frame}{Data Types}
\begin{enumerate}
    \item \textbf{numeric} (All numbers)
    \item \textbf{integer} (Whole numbers)
    \item \textbf{character} (Letters and words)
    \item \textbf{logical} (True or False)
    \item \textbf{factor} (Categories)
    \item \textbf{date} (Time variables)
\end{enumerate}

\vspace{1em}
You can convert one type to another with \texttt{as.type}.\\
For example:
\begin{itemize}
    \item \texttt{as.numeric(1)} is \texttt{1.00}
    \item \texttt{as.integer(1.00)} is \texttt{1}
\end{itemize}

\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%

% Numeric Slide
\begin{frame}{Numeric}
\onslide<1->{All numbers. Very flexible but can slow down calculations.}

\vspace{1em}
\onslide<2->{Examples:
\begin{itemize}
    \item \texttt{1.001}, \texttt{Inf}, \texttt{-Inf}, \texttt{0}, \texttt{1}, \texttt{-1.08}, \texttt{100.999999}
\end{itemize}
}

\vspace{1em}
\onslide<3->{\textbf{Note:} \texttt{"1.001"} is not numeric — it's a character.}
\end{frame}

% Integer Slide
\begin{frame}{Integer}
\onslide<1->{All whole numbers.}

\vspace{1em}
\onslide<2->{Examples:
\begin{itemize}
    \item \texttt{1}, \texttt{2}, \texttt{-100}
\end{itemize}
}
\end{frame}

% Logical Slide
\begin{frame}{Logical}
\onslide<1->{A true or false value.

\vspace{1em}
Displays as \texttt{TRUE}/\texttt{FALSE}, but in the background is an integer:}
\onslide<2->{
\begin{itemize}
    \item \texttt{TRUE} = 1
    \item \texttt{FALSE} = 0
\end{itemize}
}

\onslide<3->{Examples:
\begin{itemize}
    \item \texttt{TRUE}, \texttt{FALSE}, \texttt{1}, \texttt{0}
\end{itemize}
}
\end{frame}

% Character Slide
\begin{frame}{Character}
\onslide<1->{All characters, treated as words.}

\vspace{1em}
\onslide<2->{For example, \texttt{1} can be a character if written as \texttt{'1'}.

\begin{itemize}
    \item As an integer: \texttt{1 + 1 == 2} → \texttt{TRUE}
    \item As a character: \texttt{'1' + '1'} → error
\end{itemize}
}

\onslide<3->{Examples:
\begin{itemize}
    \item \texttt{m}, \texttt{M}, \texttt{' '}, \texttt{@}, \texttt{'1'}
\end{itemize}
}
\end{frame}

% Factor Slide
\begin{frame}{Factor}
\onslide<1->{Levels of an input. Looks like a word or character but is treated as a distinct group.}

\vspace{1em}

\onslide<2->{In the background, an integer is assigned to each group, but R knows not to perform math on an unordered factor.}

\vspace{1em}
\onslide<3->{\textbf{Unordered examples:}
\begin{itemize}
    \item \texttt{female/male}, \texttt{Wyoming/Colorado/Nebraska}
\end{itemize}
}

\onslide<5->{\textbf{Ordered examples:}
\begin{itemize}
    \item \texttt{Low/Medium/High}
\end{itemize}
}


\onslide<7->{
\vspace{1em}
Useful for analysis where different groups respond to a policy differently.}
\end{frame}

% Dates Slide
\begin{frame}{Dates}
\onslide<1->{Dates are stored as an integer in the background of R. Each is the number of seconds since January 1, 1970.}


\onslide<2->{\vspace{1em}
\begin{center}
\includegraphics[width=0.9\textwidth]{image_37_1}
\end{center}
\vspace{1em}
Dates can be printed in any format. R knows to display the date in a human-readable format. 


}
\end{frame}

% Other Non-Data Types Slide
\begin{frame}{Other Non-Data Types}
\begin{itemize}
    \item \textbf{vector}
\end{itemize}
\begin{itemize}
    \item \textbf{data frame}
\end{itemize}
\begin{itemize}
    \item \textbf{matrix}: Like a data frame but must use the same data type
\end{itemize}
\begin{itemize}
    \item \textbf{list}: Like a vector but can store a data frame
\end{itemize}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Data Conversion}
\only<1>{
You can convert data types in R using the \texttt{as.type} functions. Here are key points to understand:

\begin{itemize}
	\small
    \item \texttt{as.numeric('1')} converts the character \texttt{'1'} to the numeric \texttt{1.00}.
    \item \texttt{as.integer(1.00)} converts the numeric \texttt{1.00} to the integer \texttt{1}.
    \item \texttt{as.logical('1')} will fail to convert directly, but \texttt{as.logical(as.numeric('1'))} will return \texttt{TRUE}.
\end{itemize}
    }
\only<2->{   R makes educated guesses when converting types, but not all types are compatible. If a conversion fails, R will insert \texttt{NA} values.
\vspace{1em}

}
\only<3->{When converting a data frame to a matrix using \texttt{as.matrix()}, all elements must be of the same type.}
\vspace{1em}

	  \only<4->{  For example, if a column contains the word \texttt{"zero"}, it will become \texttt{NA} in the matrix, not \texttt{0}.}


\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Working with Characters}
\only<1-9>{
Key functions\:
\begin{itemize}
	\onslide<2->{\item{\texttt{paste}: Combines two strings with spaces}}
		\onslide<3->{\item{\texttt{paste0}: Combines two strings without spaces}}
		\onslide<4->{\item{\texttt{grep}: Finds matching characters}}
		\onslide<5->{\item{\texttt{grepl}: Finds matching characters returns TRUE or FALSE}}
		\onslide<6->{\item{\texttt{toupper}, \texttt{tolower}: Converts the whole string to upper or lower case}}
		\onslide<7->{\item{\texttt{substr}: Selects only part of the string}}
		\onslide<8->{\item{\texttt{sub}: Replaces the first occurrence}}
		\onslide<9->{\item{\texttt{gsub}: Replaces all occurrences}}
\end{itemize}
}


\only<10>{
	\begin{center}
\includegraphics[width=0.6\textwidth]{image_51_1} % Replace with actual image if available
\end{center}
}

\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}{Using Logicals}
\only<1>{
The logical data type can be used to conditionally perform calculations or to select data.

\vspace{1em}
Some important functions related to logicals include:
\begin{itemize}
    \item \texttt{which}, \texttt{which.min}, \texttt{which.max}
    \item \texttt{is.na}
    \item \texttt{is.infinite}
    \item \texttt{is.nan}
    \item \texttt{==} (double equals)
    \item \texttt{>}, \texttt{<}, \texttt{>=}, \texttt{<=}
    \item \texttt{!} (logical NOT)
\end{itemize}
}
\only<2->{
\begin{columns}
    \column{0.5\textwidth}
\includegraphics[width=\textwidth]{image_41_1}
    \column{0.5\textwidth}
    \includegraphics[width=\textwidth]{image_41_2} 
\end{columns}
}

\end{frame}

\begin{frame}{Class Exercise}
	\huge
Correct all observed issues	
	\normalsize
\end{frame}

 

\end{document}
