From 3a33a9359251b964fd2faac809e2227f07e80f24 Mon Sep 17 00:00:00 2001
From: Alex Gebben Work <agebben@uwyo.edu>
Date: Mon, 15 Sep 2025 17:43:46 -0600
Subject: [PATCH] Cleanup

---
 Class7/Slides/Loops_Tidy.tex | 116 ++++++-----------------------------
 1 file changed, 19 insertions(+), 97 deletions(-)

diff --git a/Class7/Slides/Loops_Tidy.tex b/Class7/Slides/Loops_Tidy.tex
index 15be558..9778135 100644
--- a/Class7/Slides/Loops_Tidy.tex
+++ b/Class7/Slides/Loops_Tidy.tex
@@ -29,9 +29,9 @@
 	\begin{itemize}
 		\onslide<5->{\item \texttt{while(a==b)\{Code to run\}}}
 	\end{itemize}
-		\onslide<4->{apply functions }
+		\onslide<6->{\item apply functions }
 	\begin{itemize}
-		\onslide<5->{\item \texttt{while(a==b)\{Code to run\}}}
+		\onslide<7->{\item \texttt{while(a==b)\{Code to run\}}}
 	\end{itemize}
 	\end{enumerate}
 \end{frame}
@@ -43,7 +43,16 @@
 	\only<4>{\includegraphics[width=\textwidth]{apply_functions.png}}
 	\only<5>{\includegraphics[width=\textwidth]{sapply_loop.png}}
 \end{frame}
-\begin{frame}{Introduction}
+
+\begin{frame}{Class Exercise}
+	\begin{itemize}
+		\item Create a loop that downloads a set of files when given a list of URLs.
+		\item Name each file based on another list.
+		\item Repeat for each type of loop (for, while, apply) 
+	\end{itemize}
+\end{frame}
+
+\begin{frame}{Data transformation}
 \begin{itemize}
   \item Data rarely comes in the form you need.
   \item Transformation helps prepare data for analysis and visualization.
@@ -66,14 +75,7 @@
 % 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}
+	\item Pipe: \texttt{|>} or \texttt{\%>\%} passes output to next function
 \end{itemize}
 \end{frame}
 
@@ -103,9 +105,14 @@
   \item Useful for aggregation and comparisons
 \end{itemize}
 \end{frame}
+\begin{frame}[plain]
+	\only<1>{\includegraphics[width=\textwidth]{Pipe_Example.png}}
+	\only<2>{\includegraphics[width=0.8\textwidth]{Filter_Example.png}}
+	\only<3>{\includegraphics[width=\textwidth]{group_and Summarize.png}}
+\end{frame}
 
 % Slide 8
-\begin{frame}{Class Example}
+\begin{frame}{Class Exercise}
 
 	\textbf{Example:} dataset to apply dplyr: \texttt{airquality}
 \begin{itemize}
@@ -118,89 +125,4 @@
 	\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}