favstats2: Aggregating functions

In rpruim/DemoCourse: Package to Support a Demo Course

Description

The mosaic package makes several summary statistic functions (like mean and sd) formula aware.

Usage

favstats2(x, ..., data = NULL, groups = NULL, na.rm = TRUE)

Arguments

x	a numeric vector or a formula
...	additional arguments
data	a data frame in which to evaluate formulas (or bare names). Note that the default is data = parent.frame(). This makes it convenient to use this function interactively by treating the working envionment as if it were a data frame. But this may not be appropriate for programming uses. When programming, it is best to use an explicit data argument – ideally supplying a data frame that contains the variables mentioned.
groups	a grouping variable, typically a name of a variable in data
na.rm	a logical indicating whether NAs should be removed before computing

Details

Many of these functions mask core R functions to provide an additional formula interface. Old behavior should be unchanged. But if the first argument is a formula, that formula, together with data are used to generate the numeric vector(s) to be summarized. Formulas of the shape x ~ a or ~ x | a can be used to produce summaries of x for each subsect defined by a. Two-way aggregation can be achieved using formulas of the form x ~ a + b or x ~ a | b. See the examples.

Note

Earlier versions of these functions supported a "bare name + data frame" interface. This functionality has been removed since it was (a) ambiguous in some cases, (b) unnecessary, and (c) difficult to maintain.

Examples

mean(HELPrct$age)
mean( ~ age, data = HELPrct)
mean( ~ drugrisk, na.rm = TRUE, data = HELPrct)
mean(age ~ shuffle(sex), data = HELPrct)
mean(age ~ shuffle(sex), data = HELPrct, .format = "table")
# wrap in data.frame() to auto-convert awkward variable names
data.frame(mean(age ~ shuffle(sex), data = HELPrct, .format = "table"))
mean(age ~ sex + substance, data = HELPrct)
mean( ~ age | sex + substance, data = HELPrct)
mean( ~ sqrt(age), data = HELPrct)
sum( ~ age, data = HELPrct)
sd(HELPrct$age)
sd( ~ age, data = HELPrct)
sd(age ~ sex + substance, data = HELPrct)
var(HELPrct$age)
var( ~ age, data = HELPrct)
var(age ~ sex + substance, data = HELPrct)
IQR(width ~ sex, data = KidsFeet)
iqr(width ~ sex, data = KidsFeet)
favstats(width ~ sex, data = KidsFeet)

cor(length ~ width, data = KidsFeet)
cov(length ~ width, data = KidsFeet)
tally(is.na(mcs) ~ is.na(pcs), data = HELPmiss)
cov(mcs ~ pcs, data = HELPmiss)             # NA because of missing data
cov(mcs ~ pcs, data = HELPmiss, use = "complete")  # ignore missing data
# alternative approach using filter explicitly
cov(mcs ~ pcs, data = HELPmiss %>% filter(!is.na(mcs) & !is.na(pcs)))

rpruim/DemoCourse documentation built on May 24, 2019, 7:16 a.m.