## CS代写|R语言代写R language代考|Summaries

The summaries discussed in this section can be superimposed on raw data plots, or plotted on their own. Beware, that if scale limits are manually set, the summaries will be calculated from the subset of observations within these limits. Scale limits can be altered when explicitly defining a scale or by means of functions $\times 1 i \mathrm{m()}$ and ylimo. See section $7.9$ on page 272 for an explanation of how coordinate limits can be used to zoom into a plot without excluding of $x$ and $y$ values from the data.

It is possible to summarize data on the fly when plotting. We describe in the same section the calculation of measures of central tendency and of variation, as stat_summary() allows them to be calculated simultaneously and added together with a single layer.

For use in the examples, we generate some normally distributed artificial data.
fake. data <- data. frame
$y=c($ rnorm $(10$, mean $=2,5 d=0.5)$,
rnorm $(10$, mean $=4, s \mathrm{~d}=0.7))$,
group $=\operatorname{factor}\left(c\left(\operatorname{rep}\left({ }^{\prime \prime} A^{\prime \prime}, 10\right), \operatorname{rep}\left({ }^{\prime \prime} B^{\prime \prime}, 10\right)\right)\right)$
)
We will reuse a “base” scatter plot in a series of examples, so that the differences are easier to appreciate. We first add just the mean. In this case, we need to pass as an argument to stat_summary(), the geom to use, as the default one, geom_pointrange(), expects data for plotting error bars in addition to the mean. This example uses a hyphen character as the constant value of shape (see the example for geom_point() on page 219 on the use of digits as shape). Instead of passing “mean” as an argument to parameter fun (earlier called fun.y), we can pass, if desired, other summary functions like “median”. In the case of these functions that return a single computed value, we pass them, or character strings with their names, as an argument to parameter fun.

## CS代写|R语言代写R language代考|Frequencies and counts

When the number of observations is rather small, we can rely on the density of graphical elements to convey the density of the observations. For example, scatter plots using well-chosen values for a7pha can give a satisfactory impression of the density. Rug plots, described in section $7.4 .2$ on page 221 , can also satisfactorily convey the density of observations along $x$ and/or $y$ axes. Such approaches do not involve computations, while the statistics described in this section do. Frequencies by value-range (or bins) and empirical density functions are summaries especially useful when the number of observations is large. These summaries can be computed in one or more dimensions.

Histograms are defined by how the plotted values are calculated. Although histograms are most frequently plotted as bar plots, many bar or “column” plots are not histograms. Although rarely done in practice, a histogram could be plotted using a different geometry using stat_bin(), the statistic used by default by geom_histogram(). This statistic does binning of observations before computing frequencies, and is suitable for continuous $x$ scales. When a factor is mapped to $\mathrm{x}$, stat_count() should be used, which is the default stat for geom_bar(). These two geometries are described in this section about statistics, because they default to using statistics different from stat_identity() and consequently summarize the data.
As before, we generate suitable artificial data.

# R语言代考

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## CS代写|R语言代写R language代考|Frequencies and counts

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