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biggest #8

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2 changes: 1 addition & 1 deletion tidy-data.tex
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Expand Up @@ -322,7 +322,7 @@ \subsection{Manipulation}

In \proglang{R}, filtering and transforming are performed by the base \proglang{R} functions \code{subset()} and \code{transform()}. These are input and output-tidy. The \code{aggregate()} function performs group-wise aggregation. It is input-tidy. Provided that a single aggregation method is used, it is also output-tidy . The \pkg{plyr} package provides tidy \code{summarise()} and \code{arrange()} functions for aggregation and sorting.

The four verbs can be, and often are, modified by the ``by'' preposition. We often need to perform group-wise aggregation, transformation and subsetting, to pick the biggest ?? in each group, to average over replicates and so on. Combining a verb with a ``by'' operator is a concise way to apply that operation to subsets of a data frame. Many \proglang{SAS} {\sc proc}s possess a {\sc by} statement which allows the operation to be performed by group. They are generally input-tidy. Base \proglang{R} possesses a \code{by()} function, which is input-tidy, but, because it produces a list, is not output-tidy. The \code{ddply()} function from the \pkg{plyr} package is a tidy alternative.
The four verbs can be, and often are, modified by the ``by'' preposition. We often need to perform group-wise aggregation, transformation and subsetting, to find the largest value in each group, to average over replicates and so on. Combining a verb with a ``by'' operator is a concise way to apply that operation to subsets of a data frame. Many \proglang{SAS} {\sc proc}s possess a {\sc by} statement which allows the operation to be performed by group. They are generally input-tidy. Base \proglang{R} possesses a \code{by()} function, which is input-tidy, but, because it produces a list, is not output-tidy. The \code{ddply()} function from the \pkg{plyr} package is a tidy alternative.

% Some aggregations occur so frequently they deserve their own optimised implementations. One such operation is (weighted) counting. Base R provides the {\tt table} function for this, but it is not output-tidy: it returns a multidimensional array. An tidy alternative is the {\tt count} function from {\tt plyr}, which returns a tidy dataset with a column for each of the input variables plus a new variable {\tt freq}, which records the number of records in each category.

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