Update tidyr pivoting

aphalo · Jul 27, 2023 · 6fd8127 · 6fd8127
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diff --git a/R.data.Rnw b/R.data.Rnw
diff --git a/R.data.containers.Rnw b/R.data.containers.Rnw
@@ -855,6 +855,15 @@ head(my_data_frame.df, 2)
 Use of \Rscoping{attach()} and \Rscoping{detach()}, which function as a pair of ON and OFF switches, can result in an undesired after-effect on name lookup if the script terminates after \Rscoping{attach()} is executed but before \Rscoping{detach()} is called, as cleanup is not automatic. In contrast, \Rscoping{with()} and \Rscoping{within()}, being self-contained, guarantee that cleanup takes place. Consequently, the usual recommendation is to give preference to the use of \Rscoping{with()} and \Rscoping{within()} over \Rscoping{attach()} and \Rscoping{detach()}. Use of these functions not only saves typing but also makes code more readable.
 \end{explainbox}
 
+\section{Reshaping data frames}\label{sec:calc:reshape}
+\index{data frames!long vs.\ wide shape}
+
+As mentioned above, in most cases in \Rlang data rows represent measurement events or observations possibly on multiple response variables and factors describing groupings, i.e., long shape. However, when measurements are repeated in time, columns rather frequently represent observations of the same response variable at different times, i.e., wide shape. Other cases exist where reshaping is needed. Function \Rfunction{reshape()} can convert wide data frames into long data frames and vice versa.
+
+<<data-frames-reshape-01>>=
+## reshape() example needed
+@
+
 \index{data frames|)}
 
 <<data-frames-cleanup,echo=FALSE,cache=FALSE>>=

diff --git a/R.data.tex b/R.data.tex
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+% !Rnw root = appendix.main.Rnw
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diff --git a/appendixes.prj b/appendixes.prj
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