Summary of high-level plotting functions

Basic skills in R: Basic graphics

Summary of high-level plotting functions

High-level plotting functions are designed to generate a complete plot of the data passed as arguments to the function. Where appropriate, axes, labels and titles are automatically generated (unless you request otherwise.) High-level plotting commands always start a new plot, erasing the current plot if necessary.

One of the most frequently used plotting functions in R is the function plot(), where the output depends on the type or class of the first argument.

`plot(x, y)` `plot(xy)`	If `x` and `y` are vectors, `plot(x, y)` produces a scatter plot of `y` against `x`. The same effect can be produced by supplying `xy` as either a list containing two elements `x` and `y` or a two-column matrix.

`plot(f)` `plot(f, y)`	Here, `f` is a factor object and `y` is a numeric vector. The first form generates a bar graph of `f`; The second form produces box-and-whisker plots of `y` for each level of `f`.

`plot(df)`	If `df` is a data frame, distributional plots of the variables in the data frame are produced.

Other commonly used high-level plotting functions are listed below.

`hist(x)` `hist(x, nclass = ...)` `hist(x, breaks = ...)`	The function `hist()` produces a histogram of the numeric vector `x`. A sensible number of classes is usually chosen, but a suggestion can be given with the argument `nclass`. Alternatively, the breaks can be specified exactly with the argument `breaks`.

`barplot(x)`	If `x` is a numeric vector, a bar diagram is produced of which the heights of the bars are given by the vector. If `x` is a crosstable or a matrix and the argument beside = TRUE is given, then each bar of the diagram corresponds to a column of `x`, with the values in the column giving the heights of juxtaposed sub-bars making up the bar.

`boxplot(y)` `boxplot(y ~ x)`	If `y` is a numeric vector, a box-and-whisker diagram is produced. In case a formula is used has the form `y ~ x` where y is a numeric vector, then the box-and-whisker diagrams are grouped according to the value of the (categorical) variable `x`.

`qqnorm(x) qqline(x) qqplot(x, y)`	The functions produce a quantile-quantile diagram, better known as QQ-plot. The first form plots the numeric vector `x` against the expected normal distribution quantiles and the second adds a straight line to such a plot by drawing a line through the distribution and data quantiles. The third form plots the quantiles of `x` against those of `y` to compare their respective distributions.