conversion | R Documentation |
The functions toWide()
, toLong()
, toCompiled()
toRaw()
and toTabular()
converts the data into various formats.
toWide(w)
toLong(w)
toCompiled(w)
toRaw(w)
toTabular(w)
w |
An instance of an ANOFA object. |
The classification of a set of $n$ participants can be
given using many formats. One basic format (called wide
herein)
has $n$ lines, one per participants, and category names assigned
to each.
Another format (called compiled
herein) is to have a list of all
the categories and the number of participants falling in each
cells. This last format is typically much more compact (if there
are 6 categories, the data are all contained in six lines).
However, we fail to see each individual contributing to the counts.
See the vignette DataFormatsForFrequencies for more.
A third possible format (called raw
herein) put one column per
category and 1 is the observation matches this category, 0 otherwise.
This format results in $n$ lines, one participants, and as many
columns are there are categories.
Lastly, a fourth format (called long
herein) as, on a line, the
factor name and the category assigned in that factor. If there are
$f$ factors and $n$ participants, the data are in $f*n$ lines.
See the vignette DataFormatsForFrequencies for more.
a data frame in the requested format.
# The minimalExample contains $n$ of 20 participants categorized according
# to two factors $f = 2$, namely `Intensity` (three levels)
# and Pitch (two levels) for 6 possible cells.
minimalExample
# Lets incorporate the data in an anofa data structure
w <- anofa( Frequency ~ Intensity * Pitch, minimalExample )
# The data presented using various formats looks like
toWide(w)
# ... has 20 lines ($n$) and 2 columns ($f$)
toLong(w)
# ... has 40 lines ($n \times f$) and 3 columns (participant's `Id`, `Factor` name and `Level`)
toRaw(w)
# ... has 20 lines ($n$) and 5 columns ($2+3$)
toCompiled(w)
# ... has 6 lines ($2 \times 3$) and 3 columns ($f$ + 1)
toTabular(w)
# ... has one table with $2 \times 3$ cells. If there had been
# more than two factors, the additional factor(s) would be on distinct layers.
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