diff --git a/Class7/Class_Examples/apply.r b/Class7/Class_Examples/apply.r
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+DAT <- 1:8
+?apply
+apply(DAT,mean)
+?mapply
+x <- as.data.frame(cbind(x1 = c(3,NA,12,7), x2 = c(4:1, 2:5)) )
+dimnames(x)[[1]] <- letters[1:8]
+apply(x, 2, mean, trim = .2,na.rm=TRUE)
+?mean
\ No newline at end of file
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diff --git a/Class7/Slides/Loops_Tidy.tex b/Class7/Slides/Loops_Tidy.tex
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+\documentclass{beamer}
+\usepackage{graphicx}
+\usepackage{multicol}
+\usepackage{hyperref}
+\usepackage{verbatim}
+\usepackage{lipsum} % for placeholder text
+\graphicspath{{pdf_images/}}
+
+
+\title{ECON 4530/5530 \\ Computational Economics}
+\subtitle{Data transformation and Loops}
+\author{Alex Gebben}
+
+\begin{document}
+
+% Title Slide
+\begin{frame}
+  \titlepage
+\end{frame}
+%%%%%%%%%%%%%%%%%%
+\begin{frame}{Loops}
+	\only<1->{There are three types of loops in R }
+	\begin{enumerate}
+		\onslide<2->{	\item{A \emph{for} loop runs for each value in a list}}
+	\begin{itemize}
+		\onslide<3->{\item \texttt{for(i in 1:10)\{Code to run\}}}
+	\end{itemize}
+		\onslide<4->{	\item{A \emph{While} loop runs if a condtion is metif a condtion is met.\emph{Can lead to infinite loops}}}
+	\begin{itemize}
+		\onslide<5->{\item \texttt{while(a==b)\{Code to run\}}}
+	\end{itemize}
+		\onslide<4->{apply functions }
+	\begin{itemize}
+		\onslide<5->{\item \texttt{while(a==b)\{Code to run\}}}
+	\end{itemize}
+	\end{enumerate}
+\end{frame}
+
+\begin{frame}{Loop examples}
+	\only<1>{\includegraphics[width=\textwidth]{For_Loops.png}}
+	\only<2>{\includegraphics[width=\textwidth]{While_Loops.png}}
+	\only<3>{\includegraphics[width=0.5\textwidth]{While_Loops_Inf.png}}
+	\only<4>{\includegraphics[width=\textwidth]{apply_functions.png}}
+	\only<5>{\includegraphics[width=\textwidth]{sapply_loop.png}}
+\end{frame}
+\begin{frame}{Introduction}
+\begin{itemize}
+  \item Data rarely comes in the form you need.
+  \item Transformation helps prepare data for analysis and visualization.
+  \item We'll use the \texttt{dplyr} package from the tidyverse.
+\end{itemize}
+\end{frame}
+
+% Slide 2
+\begin{frame}{Core dplyr Verbs}
+\begin{itemize}
+  \item \texttt{filter()} – select rows based on conditions
+  \item \texttt{arrange()} – reorder rows
+  \item \texttt{select()} – choose columns
+  \item \texttt{mutate()} – add new columns
+  \item \texttt{summarize()} – reduce multiple values to one
+  \item \texttt{group\_by()} – group data for summary
+\end{itemize}
+\end{frame}
+
+% Slide 3
+\begin{frame}{Using the Pipe Operator}
+\begin{itemize}
+  \item Pipe: \texttt{|>} passes output to next function
+  \item Example:
+  %\begin{verbatim}
+%flights |>
+%  filter(dest == "IAH") |>
+%  group_by(year, month, day) |>
+%  summarize(arr_delay = mean(arr_delay, na.rm = TRUE))
+%  \end{verbatim}
+\end{itemize}
+\end{frame}
+
+% Slide 4
+\begin{frame}{Working with Rows}
+\begin{itemize}
+  \item \texttt{filter()} – keep rows meeting conditions
+  \item \texttt{arrange()} – sort rows
+  \item \texttt{distinct()} – remove duplicates
+\end{itemize}
+\end{frame}
+
+% Slide 5
+\begin{frame}{Working with Columns}
+\begin{itemize}
+  \item \texttt{select()} – choose columns
+  \item \texttt{rename()} – rename columns
+  \item \texttt{mutate()} – create new columns
+\end{itemize}
+\end{frame}
+
+% Slide 6
+\begin{frame}{Grouped Operations}
+\begin{itemize}
+  \item \texttt{group\_by()} – group data
+  \item \texttt{summarize()} – compute summaries per group
+  \item Useful for aggregation and comparisons
+\end{itemize}
+\end{frame}
+
+% Slide 8
+\begin{frame}{Class Example}
+
+	\textbf{Example:} dataset to apply dplyr: \texttt{airquality}
+\begin{itemize}
+	\item Convert to a tibble
+	\item Remove any entries with NA values
+	\item Remove wind speed outliers 
+	\item Calculate the average temperature in each month 
+	\item Find the number of observations in each month
+	\item Find the max, min and standard deviation of Wind speed
+	\item Use pipes to complete all tasks in one line
+\end{itemize}
+\end{frame}
+\begin{frame}{Why Join Data?}
+\begin{itemize}
+  \item Real-world data often comes in multiple tables.
+  \item Joins combine related data based on common keys.
+  \item \texttt{dplyr} provides intuitive functions for joining.
+\end{itemize}
+\end{frame}
+
+% Slide 2
+\begin{frame}{Types of Joins}
+\begin{itemize}
+  \item \texttt{left\_join()} – keep all rows from left table
+  \item \texttt{right\_join()} – keep all rows from right table
+  \item \texttt{inner\_join()} – keep only matching rows
+  \item \texttt{full\_join()} – keep all rows from both tables
+\end{itemize}
+\end{frame}
+
+% Slide 3
+\begin{frame}{left\_join()}
+
+\texttt{left\_join(df1, df2, by = "id")}
+\begin{itemize}
+  \item Keeps all rows from the left table.
+  \item Adds matching rows from the right table.
+  \item Missing matches are filled with \texttt{NA}.
+\end{itemize}
+\end{frame}
+
+% Slide 4
+\begin{frame}{right\_join()}
+
+\texttt{right\_join(df1, df2, by = "id")}
+\begin{itemize}
+  \item Keeps all rows from the right table.
+  \item Adds matching rows from the left table.
+\end{itemize}
+\end{frame}
+
+% Slide 5
+\begin{frame}{inner\_join()}
+\texttt{inner\_join(df1, df2, by = "id")}
+\begin{itemize}
+  \item Keeps only rows with matching keys in both tables.
+  \item Most commonly used for filtering to shared data.
+\end{itemize}
+\end{frame}
+
+% Slide 6
+\begin{frame}{full\_join()}
+\texttt{full\_join(df1, df2, by = "id")}
+\begin{itemize}
+  \item Keeps all rows from both tables.
+  \item Missing matches are filled with \texttt{NA}.
+\end{itemize}
+\end{frame}
+
+% Slide 7
+\begin{frame}{Common Issues}
+\begin{itemize}
+  \item Mismatched column names 
+  \item Duplicate keys – can lead to unexpected row duplication
+  \item Data types must match, both keys should be character or numeric
+  \item Missing value joins will introduce \texttt{NA}s
+\end{itemize}
+\end{frame}
+
+% Slide 8
+\begin{frame}{Best Practices}
+\begin{itemize}
+  \item Inspect keys before joining: \texttt{unique()}
+  \item Use \texttt{anti\_join()} to find unmatched rows
+  \item Validate results with \texttt{summary()} and \texttt{count()}
+\end{itemize}
+\end{frame}
+
+% Slide 9
+\begin{frame}{Class Exercise}
+\begin{itemize}
+  \item Joins are essential for combining data.
+  \item Choose the right join based on your goal.
+  \item Always check for common issues before and after joining.
+\end{itemize}
+\end{frame}
+
+\end{document}
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