grapes-grapes-COMPUTE-grapes-grapes: Multivariate computation.

In bruceR: Broadly Useful Convenient and Efficient R Functions

Multivariate computation.

Description

Easily compute multivariate sum, mean, and other scores. Reverse scoring can also be easily implemented without saving extra variables. Alpha function uses a similar method to deal with reverse scoring.

Three ways to specify variables:

1. var + items: common and unique parts of variable names (suggested).

2. vars: a character vector of variable names (suggested).

3. varrange: starting and stopping positions of variables (NOT suggested).

Usage

COUNT(data, var = NULL, items = NULL, vars = NULL, varrange = NULL, value = NA)

MODE(data, var = NULL, items = NULL, vars = NULL, varrange = NULL)

SUM(
  data,
  var = NULL,
  items = NULL,
  vars = NULL,
  varrange = NULL,
  rev = NULL,
  range = likert,
  likert = NULL,
  na.rm = TRUE
)

.sum(
  var = NULL,
  items = NULL,
  vars = NULL,
  varrange = NULL,
  rev = NULL,
  range = likert,
  likert = NULL,
  na.rm = TRUE
)

MEAN(
  data,
  var = NULL,
  items = NULL,
  vars = NULL,
  varrange = NULL,
  rev = NULL,
  range = likert,
  likert = NULL,
  na.rm = TRUE
)

.mean(
  var = NULL,
  items = NULL,
  vars = NULL,
  varrange = NULL,
  rev = NULL,
  range = likert,
  likert = NULL,
  na.rm = TRUE
)

STD(
  data,
  var = NULL,
  items = NULL,
  vars = NULL,
  varrange = NULL,
  rev = NULL,
  range = likert,
  likert = NULL,
  na.rm = TRUE
)

CONSEC(
  data,
  var = NULL,
  items = NULL,
  vars = NULL,
  varrange = NULL,
  values = 0:9
)

Arguments

data	Data frame.
var	[Option 1] Common part across variables: e.g., "RSES", "XX.{i}.pre" (if var string has any placeholder in braces {...}, then items will be pasted into the braces, see examples)
items	[Option 1] Unique part across variables: e.g., 1:10, c("a", "b", "c")
vars	[Option 2] Character vector specifying variables: e.g., c("X1", "X2", "X3", "X4", "X5")
varrange	[Option 3] Character string specifying positions ("start:stop") of variables: e.g., "A1:E5"
value	[Only for COUNT] The value to be counted.
rev	[Optional] Variables that need to be reversed. It can be (1) a character vector specifying the reverse-scoring variables (recommended), or (2) a numeric vector specifying the item number of reverse-scoring variables (not recommended).
range, likert	[Optional] Range of likert scale: e.g., 1:5, c(1, 5). If not provided, it will be automatically estimated from the given data (BUT you should use this carefully).
na.rm	Ignore missing values. Defaults to TRUE.
values	[Only for CONSEC] Values to be counted as consecutive identical values. Defaults to all numbers (0:9).

Value

A vector of computed values.

Functions

• COUNT(): Count a certain value across variables.

• MODE(): Compute mode across variables.

• SUM(): Compute sum across variables.

• .sum(): Tidy version of SUM, only can be used in add()/added()

• MEAN(): Compute mean across variables.

• .mean(): Tidy version of MEAN, only can be used in add()/added()

• STD(): Compute standard deviation across variables.

• CONSEC(): Compute consecutive identical digits across variables (especially useful in detecting careless responding).

Examples

d = data.table(
  x1 = 1:5,
  x4 = c(2,2,5,4,5),
  x3 = c(3,2,NA,NA,5),
  x2 = c(4,4,NA,2,5),
  x5 = c(5,4,1,4,5)
)
d
## I deliberately set this order to show you
## the difference between "vars" and "varrange".

## ====== Usage 1: data.table `:=` ====== ##
d[, `:=`(
  na = COUNT(d, "x", 1:5, value=NA),
  n.2 = COUNT(d, "x", 1:5, value=2),
  sum = SUM(d, "x", 1:5),
  m1 = MEAN(d, "x", 1:5),
  m2 = MEAN(d, vars=c("x1", "x4")),
  m3 = MEAN(d, varrange="x1:x2", rev="x2", range=1:5),
  cons1 = CONSEC(d, "x", 1:5),
  cons2 = CONSEC(d, varrange="x1:x5")
)]
d

## ====== Usage 2: `add()` & `added()` ====== ##
data = as.data.table(psych::bfi)
added(data, {
  gender = as.factor(gender)
  education = as.factor(education)
  E = .mean("E", 1:5, rev=c(1,2), range=1:6)
  A = .mean("A", 1:5, rev=1, range=1:6)
  C = .mean("C", 1:5, rev=c(4,5), range=1:6)
  N = .mean("N", 1:5, range=1:6)
  O = .mean("O", 1:5, rev=c(2,5), range=1:6)
}, drop=TRUE)
data

## ====== New Feature for `var` & `items` ====== ##
d = data.table(
  XX.1.pre = 1:5,
  XX.2.pre = 6:10,
  XX.3.pre = 11:15
)
add(d, { XX.mean = .mean("XX.{i}.pre", 1:3) })
add(d, { XX.mean = .mean("XX.{items}.pre", 1:3) })  # the same
add(d, { XX.mean = .mean("XX.{#$%^&}.pre", 1:3) })  # the same

bruceR documentation built on June 22, 2024, 12:26 p.m.