mosaic (version 1.9.1)

mean_: Aggregating functions

Description

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

Usage

mean_(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))
mean(x, ...)
median(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))
range(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))
sd(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))
max(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))
min(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))
sum(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))
IQR(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))
fivenum(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))
iqr(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))
prod(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))
sum(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))
favstats(x, ..., data = NULL, groups = NULL, na.rm = TRUE)
quantile(x, ..., data = NULL, groups = NULL, na.rm = getOption("na.rm", FALSE))
var(x, y = NULL, na.rm = getOption("na.rm", FALSE), ..., data = NULL)
cor(x, y = NULL, ..., data = NULL)
cov(x, y = NULL, ..., data = NULL)

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 environment 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

y

a numeric vector or a formula

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 subset defined by a. Two-way aggregation can be achieved using formulas of the form x ~ a + b or x ~ a | b. See the examples.

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)))