Compute knot points of an average shifted histogram

Description

Mainly a utility for the lattice and ggplot2 plotting functions, ash_points() returns the points to be plotted.

Usage

ash_points(x, binwidth = NULL, adjust = 1)

Arguments

Value

A data frame containing x and y coordinates of the resulting ASH plot.

Extract coefficients from a function

Description

coef will extract the coefficients attribute from a function. Functions created by applying link{makeFun} to a model produced by lm(), glm(), or nls() store the model coefficients there to enable this extraction.

Usage

## S3 method for class ''function''
coef(object, ...)

Arguments

Examples

if (require(mosaicData)) {
  model <- lm( width ~ length, data = KidsFeet)
  f <- makeFun( model )
  coef(f)
}

return a vector of row or column indices

Description

return a vector of row or column indices

Usage

columns(x, default = c())

rows(x, default = c())

Arguments

Value

if x has rows or columns, a vector of indices, else default

Examples

dim(iris)
columns(iris)
rows(iris)
columns(NULL)
columns("this doesn't have columns")

Campare two numeric vectors

Description

This wrapper around sign() provides a more intuitive labeling.

Usage

compare(x, y, verbose = FALSE)

Arguments

Value

a factor with three levels (<, =, and > if verbose is FALSE)

Examples

tally( ~ compare(mcs, pcs), data = mosaicData::HELPrct)
tally( ~ compare(mcs, pcs, verbose = TRUE), data = mosaicData::HELPrct)
tally( ~ compare(sexrisk, drugrisk), data = mosaicData::HELPrct)

Compute all proportions or counts

Description

Compute vector of counts, proportions, or percents for each unique value (and NA if there is missing data) in a vector.

Usage

counts(x, ...)

## S3 method for class 'factor'
counts(x, ..., format = c("count", "proportion", "percent"))

## Default S3 method:
counts(x, ..., format = c("count", "proportion", "percent"))

## S3 method for class 'formula'
counts(x, data, ..., format = "count")

props(x, ..., format = "proportion")

percs(x, ..., format = "percent")

Arguments

See Also

mosaic::prop()

mosaic::count()

Examples

if (require(mosaicData)) {
  props(HELPrct$substance)
  # numeric version tallies missing values as well
  props(HELPmiss$link)
  # Formula version omits missing data with warning (by default)
  props( ~ link, data = HELPmiss)                       # omit NAs with warning
  props( ~ link, data = HELPmiss, na.action = na.pass)  # no warning; tally NAs
  props( ~ link, data = HELPmiss, na.action = na.omit)  # no warning, omit NAs
  props( ~ substance | sex, data = HELPrct)
  props( ~ substance | sex, data = HELPrct, format = "percent")
  percs( ~ substance | sex, data = HELPrct)
  counts( ~ substance | sex, data = HELPrct)
  df_stats( ~ substance | sex, data = HELPrct, props, counts)
  df_stats( ~ substance | sex, data = HELPmiss, props, na.action = na.pass)
}

Interval statistics

Description

Calculate coverage intervals and confidence intervals for the sample mean, median, sd, proportion, ... Typically, these will be used within df_stats(). For the mean, median, and sd, the variable x must be quantitative. For proportions, the x can be anything; use the success argument to specify what value you want the proportion of. Default for success is TRUE for x logical, or the first level returned by unique for categorical or numerical variables.

Usage

coverage(x, level = 0.95, na.rm = TRUE)

ci.mean(x, level = 0.95, na.rm = TRUE)

ci.median(x, level = 0.9, na.rm = TRUE)

ci.sd(x, level = 0.95, na.rm = TRUE)

ci.prop(
  x,
  success = NULL,
  level = 0.95,
  method = c("Clopper-Pearson", "binom.test", "Score", "Wilson", "prop.test", "Wald",
    "Agresti-Coull", "Plus4")
)

Arguments

Details

Methods: ci.mean() uses the standard t confidence interval. ci.median() uses the normal approximation method. ci.sd() uses the chi-squared method. ci.prop() uses the binomial method. In the usual situation where the mosaic package is available, ci.prop() uses mosaic::binom.test() internally, which provides several methods for the calculation. See the documentation for binom.test() for details about the available methods. Clopper-Pearson is the default method. When used with df_stats(), the confidence interval is calculated for each group separately. For "pooled" confidence intervals, see methods such as lm() or glm().

Value

a named numerical vector with components lower and upper, and, in the case of ci.prop(), center. When used the df_stats(), these components are formed into a data frame.

Note

When using these functions with df_stats(), omit the x argument, which will be supplied automatically by df_stats(). See examples.

See Also

df_stats(), mosaic::binom.test(), mosaic::t.test()

Examples

# The central 95% interval
df_stats(hp ~ cyl, data = mtcars, c95 = coverage(0.95))
# The confidence interval on the mean
df_stats(hp ~ cyl, data = mtcars, mean, ci.mean)
# What fraction of cars have 6 cylinders?
df_stats(mtcars, ~ cyl, six_cyl_prop = ci.prop(success = 6, level = 0.90))
# Use without `df_stats()` (rare)
ci.mean(mtcars$hp)

Calculate statistics for "response" variables

Description

Creates a data frame of statistics calculated on one or more response variables, possibly for each group formed by combinations of additional variables. The resulting data frame has one column for each of the statistics requested as well as columns for any grouping variables and a column identifying the response variable for which the statistics was calculated.

Usage

df_stats(
  formula,
  data,
  ...,
  drop = TRUE,
  fargs = list(),
  sep = "_",
  format = c("wide", "long"),
  groups = NULL,
  long_names = FALSE,
  nice_names = FALSE,
  na.action = "na.warn"
)

Arguments

Details

Use a one-sided formula to compute summary statistics for the right hand side expression over the entire data. Use a two-sided formula to compute summary statistics for the left hand (response) expression(s) for each combination of levels of the expressions occurring on the right hand side. This is most useful when the left hand side is quantitative and each expression on the right hand side has relatively few unique values. A function like mosaic::ntiles() is often useful to create a few groups of roughly equal size determined by ranges of a quantitative variable. See the examples.

Note that unlike dplyr::summarise(), df_stats() ignores any grouping defined in data if data is a grouped tibble.

Value

A data frame. Names of columns in the resulting data frame consist of three parts separated by sep. The first part is the argument name, if it exists, else the function. The second part is the name of the variable being summarised if long_names == TRUE and the first part is the function name, else "" The third part is the names of the object returned by the summarizing function, if they exist, else a sequence of consecutive integers or "" if there is only one component returned by the summarizing function. See the examples.

Cautions Regarding Formulas

The use of | to define groups is tricky because (a) stats::model.frame() doesn't handle this sort of thing and (b) | is also used for logical or. The current algorithm for handling this will turn the first occurrence of | into an attempt to condition, so logical or cannot be used before conditioning in the formula. If you have need of logical or, we suggest creating a new variable that contains the results of evaluating the expression.

Similarly, addition (+) is used to separate grouping variables, not for arithmetic.

Examples

df_stats( ~ hp, data = mtcars)
# There are several ways to specify functions
df_stats( ~ hp, data = mtcars, mean, trimmed_mean = mean(trim = 0.1), "median",
  range, Q = quantile(c(0.25, 0.75)))
# When using ::, be sure to include parens, even if there are no additional arguments.
df_stats( ~ hp, data = mtcars, mean = base::mean(), trimmed_mean = base::mean(trim = 0.1))

# force names to by syntactically valid
df_stats( ~ hp, data = mtcars, Q = quantile(c(0.25, 0.75)), nice_names = TRUE)
# longer names
df_stats( ~ hp, data = mtcars, mean, trimmed_mean = mean(trim = 0.1), "median", range,
  long_names = TRUE)
# wide vs long format
df_stats( hp ~ cyl, data = mtcars, mean, median, range)
df_stats( hp + wt + mpg ~ cyl, data = mtcars, mean, median, range)
df_stats( hp ~ cyl, data = mtcars, mean, median, range, format = "long")
# More than one grouping variable -- 4 ways.
df_stats( hp ~ cyl + gear, data = mtcars, mean, median, range)
df_stats( hp ~ cyl | gear, data = mtcars, mean, median, range)
df_stats( hp ~ cyl, groups = ~gear, data = mtcars, mean, median, range)
df_stats( hp ~ cyl, groups = gear, data = mtcars, mean, median, range)

# because the result is a data frame, df_stats() is also useful for creating plots
if(require(ggformula)) {
  gf_violin(hp ~ cyl, data = mtcars, group = ~ cyl) |>
  gf_point(mean ~ cyl, data = df_stats(hp ~ cyl, data = mtcars, mean),
    color = ~ "mean") |>
  gf_point(median ~ cyl, data = df_stats(hp ~ cyl, data = mtcars, median),
    color = ~"median") |>
  gf_labs(color = "")
}

# magrittr style piping is also supported
if (require(ggformula)) {
  mtcars |>
    df_stats(hp ~ cyl, mean, median, range)
  mtcars |>
    df_stats(hp ~ cyl + gear, mean, median, range) |>
    gf_point(mean ~ cyl, color = ~ factor(gear)) |>
    gf_line(mean ~ cyl, color = ~ factor(gear))
}

# can be used with a categorical response, too
if (require(mosaic)) {
  df_stats(sex ~ substance, data = HELPrct, table, prop_female = prop)
}
if (require(mosaic)) {
  df_stats(sex ~ substance, data = HELPrct, table, props)
}

apply-type function for data frames

Description

An apply-type function for data frames.

Usage

dfapply(data, FUN, select = TRUE, ...)

Arguments

See Also

apply(), sapply(), tapply(), lapply(), inspect()

Examples

dfapply(iris, mean, select = is.numeric)
dfapply(iris, mean, select = c(1,2))
dfapply(iris, mean, select = c("Sepal.Length", "Petal.Length"))
if (require(mosaicData)) {
  dfapply(HELPrct, table, select = is.factor)
  do.call(rbind, dfapply(HELPrct, mean, select = is.numeric))
}

Lagged Differences with equal length

Description

Often when creating lagged differences, it is awkward that the differences vector is shorter than the original. ediff pads with pad.value to make its output the same length as the input.

Usage

ediff(
  x,
  lag = 1,
  differences = 1,
  pad = c("head", "tail", "symmetric"),
  pad.value = NA,
  frontPad,
  ...
)

Arguments

See Also

diff() since ediff is a thin wrapper around diff().

Examples

ediff(1:10)
ediff(1:10, pad.value = 0)
ediff(1:10, 2)
ediff(1:10, 2, 2)
x <- cumsum(cumsum(1:10))
ediff(x, lag = 2)
ediff(x, differences = 2)
ediff(x, differences = 2, pad = "symmetric")
ediff(.leap.seconds)

Evaluate a formula

Description

Evaluate a formula

Usage

evalFormula(formula, data = parent.frame(), subset, ops = c("+", "&"))

Arguments

Value

a list containing data frames corresponding to the left, right, and condition slots of formula

Examples

if (require(mosaicData)) {
data(CPS85)
cps <- CPS85[1:6,]
cps
evalFormula(wage ~ sex & married & age | sector & race, data=cps)
}

Evaluate a part of a formula

Description

Evaluate a part of a formula

Usage

evalSubFormula(x, data = NULL, ops = c("+", "&"), env = parent.frame())

Arguments

Value

a data frame containing the terms of the evaluated subformula

Examples

if (require(mosaicData)) {
data(CPS85)
cps <- CPS85[1:6,]
cps
evalSubFormula( rhs( ~ married & sector), data=cps )
}

Fit a distribution to data and return a function

Description

Given the name of a family of 1-dimensional distributions, this function chooses a particular member of the family that fits the data and returns a function in the selected p, d, q, or r format. When analytical solutions do not exist, MASS::fitdistr() is used to estimate the parameters by numerical maximum likelihood.

Usage

fit_distr_fun(data, formula, dist, start = NULL, ...)

Arguments

Value

A function of one variable that acts like, say, pnorm(), dnorm(), qnorm(), or rnorm(), but with the default values of the parameters set to their maximum likelihood estimates.

Examples

fit_distr_fun( ~ cesd, data = mosaicData::HELPrct, dist = "dnorm")
fit_distr_fun( ~ cesd, data = mosaicData::HELPrct, dist = "pnorm")
fit_distr_fun( ~ cesd, data = mosaicData::HELPrct, dist = "qpois")

Convert lazy objects into formulas

Description

Convert lazy objects into a formula

Usage

formularise(lazy_formula, envir = parent.frame())

Arguments

Details

The expression of the lazy object is evaluated in its environment. If the result is not a formula, then the formula is created with an empty left hand side and the expression on the right hand side.

Value

a formula

Examples

formularise(rlang::quo(foo))
formularise(rlang::quo(y ~ x))
bar <- a ~ b
formularise(rlang::quo(bar))

Infer a Back-Transformation

Description

For a handful of transformations on y, infer the reverse transformation. If the transformation is not recognized, return the identity function. This is primarily intended to be used for setting a default value in other functions.

Usage

infer_transformation(formula, warn = TRUE)

Arguments

Value

A function.

Inspect objects

Description

Print a short summary of the contents of an object. Most useful as a way to get a quick overview of the variables in data frame.

Usage

inspect(object, ...)

## S3 method for class 'list'
inspect(object, max.level = 2, ...)

## S3 method for class 'character'
inspect(object, ...)

## S3 method for class 'logical'
inspect(object, ...)

## S3 method for class 'numeric'
inspect(object, ...)

## S3 method for class 'factor'
inspect(object, ...)

## S3 method for class 'Date'
inspect(object, ...)

## S3 method for class 'POSIXt'
inspect(object, ...)

## S3 method for class 'data.frame'
inspect(object, select = TRUE, digits = getOption("digits", 3), ...)

## S3 method for class 'inspected_data_frame'
print(x, digits = NULL, ...)

Arguments

Examples

if (require(mosaicData)) {
  inspect(Births78)
  inspect(Births78, is.numeric)
}

Join data frames

Description

Join data frames

Usage

joinFrames(...)

joinTwoFrames(left, right)

Arguments

Value

a data frame containing columns from each of data frames being joined.

Convert logical vector into factor

Description

Turn logicals into factors with levels ordered with TRUE before FALSE. Other inputs are returned unaltered.

Usage

logical2factor(x, ...)

## Default S3 method:
logical2factor(x, ...)

## S3 method for class 'data.frame'
logical2factor(x, ...)

Arguments

Value

If x is a vector either x or the result of converting x into a factor with levels TRUE and FALSE (in that order); if x is a data frame, a data frame with all logicals converted to factors in this manner.

Logit and inverse logit functions

Description

Logit and inverse logit functions

Usage

logit(x)

ilogit(x)

Arguments

Value

For logit the value is

log(x/(1 - x))

For ilogit the value is

exp(x)/(1 + exp(x))

Examples

p <- seq(.1, .9, by=.10)
l <- logit(p); l
ilogit(l)
ilogit(l) == p

Create a function from a formula

Description

Provides an easy mechanism for creating simple "mathematical" functions via a formula interface.

Usage

makeFun(object, ...)

## S3 method for class ''function''
makeFun(
  object,
  ...,
  strict.declaration = TRUE,
  use.environment = TRUE,
  suppress.warnings = FALSE
)

## S3 method for class 'formula'
makeFun(
  object,
  ...,
  strict.declaration = TRUE,
  use.environment = TRUE,
  suppress.warnings = TRUE
)

## S3 method for class 'lm'
makeFun(object, ..., transformation = NULL)

## S3 method for class 'glm'
makeFun(object, ..., type = c("response", "link"), transformation = NULL)

## S3 method for class 'nls'
makeFun(object, ..., transformation = NULL)

Arguments

Details

The definition of the function is given by the left side of a two-sided formula or the right side of a one-sided formula. The right side lists at least one of the inputs to the function. The inputs to the function are all variables appearing on either the left or right sides of the formula. Those appearing in the right side will occur in the order specified. Those not appearing in the right side will appear in an unspecified order.

When creating a function from a model created with lm, glm, or nls, the function produced is a wrapper around the corresponding version of predict. This means that having variables in the model with names that match arguments of predict will lead to potentially ambiguous situations and should be avoided.

Value

a function

Examples

f <- makeFun( sin(x^2 * b) ~ x & y & a); f
g <- makeFun( sin(x^2 * b) ~ x & y & a, a = 2 ); g
h <- makeFun( a * sin(x^2 * b) ~ b & y, a = 2, y = 3); h
ff <- makeFun(~ a*x^b + y ); ff # one sided formula
gg <- makeFun(cos(a*x^b + y) ~ . ); gg # dummy right-hand side
if (require(mosaicData)) {
  model <- lm( log(length) ~ log(width), data = KidsFeet)
  f <- makeFun(model, transformation = exp)
  f(8.4)
  head(KidsFeet, 1)
}

if (require(mosaicData)) {
  model <- lm(wage ~ poly(exper, degree = 2), data = CPS85)
  fit <- makeFun(model)
  if (require(ggformula)) {
    gf_point(wage ~ exper, data = CPS85) |>
    gf_fun(fit(exper) ~ exper, color = "red")
  }
}
if (require(mosaicData)) {
model <- glm(wage ~ poly(exper, degree = 2), data = CPS85, family = gaussian)
fit <- makeFun(model)
  if (require(ggformula)) {
    gf_jitter(wage ~ exper, data = CPS85) |>
    gf_fun(fit(exper) ~ exper, color = "red")
    gf_jitter(wage ~ exper, data = CPS85) |>
    gf_function(fun = fit, color = "blue")
  }
}
if (require(mosaicData)) {
model <- nls( wage ~ A + B * exper + C * exper^2, data = CPS85, start = list(A = 1, B = 1, C = 1) )
fit <- makeFun(model)
  if (require(ggformula)) {
    gf_point(wage ~ exper, data = CPS85) |>
    gf_fun(fit(exper) ~ exper, color = "red")
  }
}

Convert to a data frame

Description

A generic and several methods for converting objects into data frames.

Usage

make_df(object, ...)

## S3 method for class 'list'
make_df(object, ...)

## S3 method for class 'matrix'
make_df(object, ...)

## S3 method for class 'numeric'
make_df(object, ...)

## Default S3 method:
make_df(object, ...)

Arguments

Details

These methods are primarily for internal use inside df_stats(), but are exported in case they have other uses. The conversion works as follows. Data frames are left as is. Matrices are converted column-by-column and the columns assembled with as.data.frame(); this allows matrices that are lists to be converted into data frames where columns can have differing types. The names are then set to the column names of object, even if that results in NULL. A numeric vector is converted into a data frame with 1 column. If object is a list, each element is converted using vector2df() and the resulting columns are joined with bind_rows().

extract predictor variables from a model

Description

extract predictor variables from a model

Usage

modelVars(model)

Arguments

Value

a vector of variable names

Examples

if (require(mosaicData)) {
  model <- lm( wage ~ poly(exper, degree = 2), data = CPS85 )
  modelVars(model)
}

Convert formulas into standard shapes

Description

These functions convert formulas into standard shapes, including by incorporating a groups argument.

Usage

mosaic_formula(
  formula,
  groups = NULL,
  envir = parent.frame(),
  max.slots = 3,
  groups.first = FALSE
)

mosaic_formula_q(
  formula,
  groups = NULL,
  max.slots = 3,
  groups.first = FALSE,
  ...
)

Arguments

Details

mosaic_formula_q uses nonstandard evaluation of groups that may be necessary for use within other functions. mosaic_formula is a wrapper around mosaic_formula_q and quotes groups before passing it along.

Examples

mosaic_formula( ~ x | z )
mosaic_formula( ~ x, groups=g )
mosaic_formula( y ~ x, groups=g )
# this is probably not what you want for interactive use.
mosaic_formula_q( y ~ x, groups=g )
# but it is for programming
foo <- function(x, groups=NULL) {
    mosaic_formula_q(x, groups=groups, envir=parent.frame())
}
foo( y ~ x , groups = g)

Internal tally methods

Description

These are used to implement tally() in a way that allows dplyr and mosaicCore to co-exist. End users should call the generics tally() and count().

Usage

mosaic_tally(x, ...)

mosaic_count(x, ...)

Arguments

Counting missing/non-missing elements

Description

Counting missing/non-missing elements

Usage

n_missing(..., type = c("any", "all"))

n_not_missing(..., type = c("any", "all"))

n_total(..., type = c("any", "all"))

Arguments

Value

a vector of counts of missing or non-missing values.

Examples

if (require(NHANES) && require(mosaic) && require(dplyr)) {
  mosaic::tally( ~ is.na(Height) + is.na(Weight), data = NHANES, margins = TRUE)
  NHANES |>
    summarise(
      mean.wt = mean(Weight, na.rm = TRUE),
      missing.Wt = n_missing(Weight),
      missing.WtAndHt = n_missing(Weight, Height, type = "all"),
      missing.WtOrHt = n_missing(Weight, Height, type = "any")
      )
    }

Exclude Missing Data with Warning

Description

Similar to stats::na.exclude() this function excludes missing data. When missing data are excluded, a warning message indicating the number of excluded rows is emited as a caution for the user.

Usage

na.warn(object, ...)

Arguments

List extraction

Description

These functions create subsets of lists based on their names

Usage

named(l)

unnamed(l)

named_among(l, n)

Arguments

Value

A sublist of l determined by names(l).

Nice names

Description

Convert a character vector into a similar character vector that would work better as names in a data frame by avoiding certain awkward characters

Usage

nice_names(x, unique = TRUE)

Arguments

Value

a character vector

Examples

nice_names( c("bad name", "name (crazy)", "a:b", "two-way") )

Parse Formulas

Description

Utilities for extracting portions of formulas.

Usage

parse.formula(formula, ...)

rhs(x, ...)

lhs(x, ...)

condition(x, ...)

operator(x, ...)

## S3 method for class 'formula'
rhs(x, ...)

## S3 method for class 'formula'
lhs(x, ...)

## S3 method for class 'formula'
condition(x, ...)

## S3 method for class 'formula'
operator(x, ...)

## S3 method for class 'parsedFormula'
rhs(x, ...)

## S3 method for class 'parsedFormula'
lhs(x, ...)

## S3 method for class 'parsedFormula'
operator(x, ...)

## S3 method for class 'parsedFormula'
condition(x, ...)

Arguments

Details

currently this is primarily concerned with extracting the operator, left hand side, right hand side (minus any condition) and the condition. Improvements/extensions may come in the future.

Value

an object of class parsedFormula from which information is easy to extract

Modified summaries

Description

msummary provides modified summary objects that typically produce output that is either identical to or somewhat terser than their summary() analogs. The contents of the object itself are unchanged (except for an augmented class) so that other downstream functions should work as before.

Usage

## S3 method for class 'msummary.lm'
print(
  x,
  digits = max(3L, getOption("digits") - 3L),
  symbolic.cor = x$symbolic.cor,
  signif.stars = getOption("show.signif.stars"),
  ...
)

## S3 method for class 'msummary.glm'
print(
  x,
  digits = max(3L, getOption("digits") - 3L),
  symbolic.cor = x$symbolic.cor,
  signif.stars = getOption("show.signif.stars"),
  ...
)

msummary(object, ...)

## Default S3 method:
msummary(object, ...)

## S3 method for class 'lm'
msummary(object, ...)

## S3 method for class 'glm'
msummary(object, ...)

Arguments

Examples

msummary(lm(Sepal.Length ~ Species, data = iris))

Compute proportions, percents, or counts for a single level

Description

Compute proportions, percents, or counts for a single level

Usage

prop(
  x,
  data = parent.frame(),
  useNA = "no",
  ...,
  success = NULL,
  level = NULL,
  long.names = TRUE,
  sep = ".",
  format = c("proportion", "percent", "count"),
  quiet = TRUE,
  pval.adjust = FALSE
)

prop1(..., pval.adjust = TRUE)

count(x, ...)

perc(x, data = parent.frame(), ..., format = "percent")

Arguments

Details

prop1 is intended for the computation of p-values from randomization distributions and differs from prop only in its default value of pval.adjust.

Note

For 0-1 data, success is set to 1 by default since that a standard coding scheme for success and failure.

Examples

if (require(mosaicData)) {
  prop( ~sex, data=HELPrct)
  prop( ~sex, data=HELPrct, success = "male")
  count( ~sex | substance, data=HELPrct)
  prop( ~sex | substance, data=HELPrct)
  perc( ~sex | substance, data=HELPrct)
}

Insert Inhibition of Interpretation/Conversion into formulas

Description

model.frame() assumes that certain operations (e.g. /, *, ^) have special meanings. These can be inhibited using I(). This function inserts I() into a formula when encountering a specified operator or parens.

Usage

reop_formula(x, ops = c("/", "*", "^"))

Arguments

Value

a formula with I() inserted where required to inhibit interpretation/conversion.

Examples

reop_formula(y ~ x * y)
reop_formula(y ~ (x * y))
reop_formula(y ~ x ^ y)
reop_formula(y ~ x * y ^ z)

Return rhs of a formula or expression

Description

Return rhs of a formula or expression

Usage

rhs_or_expr(x)

Arguments

Examples

# This should evaluate to TRUE
rhs_or_expr(~z)
rhs_or_expr(z)
identical(rhs_or_expr(~z), rhs_or_expr(z))

Tabulate categorical data

Description

Tabulate categorical data

Usage

tally(x, ...)

## S3 method for class 'tbl'
mosaic_tally(x, wt, sort = FALSE, ..., envir = parent.frame())

## S3 method for class 'data.frame'
mosaic_tally(x, wt, sort = FALSE, ..., envir = parent.frame())

## S3 method for class 'formula'
mosaic_tally(
  x,
  data = parent.frame(),
  format = c("count", "proportion", "percent", "data.frame", "sparse", "default"),
  margins = FALSE,
  quiet = TRUE,
  subset,
  groups = NULL,
  useNA = "ifany",
  groups.first = FALSE,
  ...
)

Arguments

Details

The dplyr package also exports a dplyr::tally() function. If x inherits from class "tbl" or "data frame", then dplyr's dplyr::tally() is called. This makes it easier to have the two packages coexist.

Otherwise, tally() is designed as an alternative to table() and xtabs(). The primary use case it to describe a (possibly multi-dimensional) table using a formula. For a table of counts, each component of the formula becomes one of the dimensions of the cross table. For tables of proportions or percents, conditional proportions and percents are computed, conditioned on each level of all "secondary" (i.e., conditioning) variables, defined as everything other than the left hand side, if there is a left hand side to the formula; and everything except the right hand side if the left hand side of the formula is empty. Note that groups is folded into the formula prior to this determination and becomes part of the conditioning.

When marginal totals are added, they are added for all of the conditioning dimensions, and proportions should sum to 1 for each level of the conditioning variables. This can be useful to make it clear which conditional proportions are being computed.

See the examples for some typical use cases.

Value

A object of class "table", unless passing through to dplyr or converted to a data frame because format is "data.frame" or "sparse".

Note

The current implementation when format = "sparse" first creates the full data frame and then removes the unneeded rows. So the savings is in terms of space, not time.

Examples

if (require(mosaicData)) {
tally( ~ substance, data = HELPrct)
tally( ~ substance + sex , data = HELPrct)
tally( sex ~ substance, data = HELPrct)   # equivalent to tally( ~ sex | substance, ... )
tally( ~ substance | sex , data = HELPrct)
tally( ~ substance | sex , data = HELPrct, format = 'count', margins = TRUE)
tally( ~ substance + sex , data = HELPrct, format = 'percent', margins = TRUE)
tally( ~ substance | sex , data = HELPrct, format = 'percent', margins = TRUE)
# force NAs to show up
tally( ~ sex, data = HELPrct, useNA = "always")
# show NAs if any are there
tally( ~ link, data = HELPrct)
# ignore the NAs
tally( ~ link, data = HELPrct, useNA = "no")
}

Convert a vector to a data frame

Description

Convert a vector into a 1-row data frame using the names of the vector as column names for the data frame.

Usage

vector2df(x, nice_names = FALSE)

Arguments

Value

A data frame.

Examples

vector2df(c(1, b = 2, `(Intercept)` = 3))
vector2df(c(1, b = 2, `(Intercept)` = 3), nice_names = TRUE)