bruceR: grapes-grapes-COMPUTE-grapes-grapes – R documentation

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grapes-grapes-COMPUTE-grapes-grapes

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 options to specify the variable list:

var + items: use the common and unique parts of variable names.
vars: directly define a variable list.
varrange: use the start and end positions of a variable list.

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,
  likert = NULL,
  na.rm = TRUE
)

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

STD(
  data,
  var = NULL,
  items = NULL,
  vars = NULL,
  varrange = NULL,
  rev = NULL,
  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 multiple variables (e.g., `"RSES", "SWLS"`).
`items`	[option 1] Unique part across multiple variables (e.g., `1:10`).
`vars`	[option 2] Character vector specifying a variable list (e.g., `c("E1", "E2", "E3", "E4", "E5")`).
`varrange`	[option 3] Character with `":"` specifying the start and end positions of a variable list (e.g., `"A1:E5"`).
`value`	[only for `COUNT`] The value to be counted.
`rev`	[optional] Reverse-scoring variables. It can be (1) a numeric vector specifying the positions of reverse-scoring variables (not recommended) or (2) a character vector directly specifying the variable list (recommended).
`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. Default is `TRUE`.
`values`	[only for `CONSEC`] Values to be counted as consecutive identical values. Default is all numbers (`0:9`).

Value

A vector of computed values.

Functions

COUNT: Count a certain value across multiple variables.
MODE: Compute mode across multiple variables.
SUM: Compute sum across multiple variables.
MEAN: Compute mean across multiple variables.
STD: Compute standard deviation across multiple variables.
CONSEC: Compute consecutive identical digits across multiple 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".

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", likert=1:5),
  cons1=CONSEC(d, "x", 1:5),
  cons2=CONSEC(d, varrange="x1:x5")
)]
d

data=as.data.table(bfi)
data[,`:=`(
  E=MEAN(d, "E", 1:5, rev=c(1,2), likert=1:6),
  O=MEAN(d, "O", 1:5, rev=c(2,5), likert=1:6)
)]
data

bruceR

Broadly Useful Convenient and Efficient R Functions

v0.6.2

GPL-3

Authors

Han-Wu-Shuang Bao [aut, cre]

Initial release

2021-04-08