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:
var + items: common and unique parts of variable names (suggested).vars: a character vector of variable names (suggested).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"(ifvarstring has any placeholder in braces{...}, thenitemswill 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).
Functions
COUNT(): Count a certain value across variables.MODE(): Compute mode across variables.SUM(): Compute sum across variables..sum(): Tidy version ofSUM, only can be used inadd()/added()MEAN(): Compute mean across variables..mean(): Tidy version ofMEAN, only can be used inadd()/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
#> x1 x4 x3 x2 x5
#> <int> <num> <num> <num> <num>
#> 1: 1 2 3 4 5
#> 2: 2 2 2 4 4
#> 3: 3 5 NA NA 1
#> 4: 4 4 NA 2 4
#> 5: 5 5 5 5 5
## 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")
)]
#> x1 x4 x3 x2 x5 na n.2 sum m1 m2 m3 cons1
#> <int> <num> <num> <num> <num> <int> <int> <num> <num> <num> <num> <num>
#> 1: 1 2 3 4 5 0 1 15 3.0 1.5 2 0
#> 2: 2 2 2 4 4 0 3 14 2.8 2.0 2 2
#> 3: 3 5 NA NA 1 2 0 9 3.0 4.0 4 0
#> 4: 4 4 NA 2 4 1 1 14 3.5 4.0 4 2
#> 5: 5 5 5 5 5 0 0 25 5.0 5.0 4 5
#> cons2
#> <num>
#> 1: 0
#> 2: 3
#> 3: 0
#> 4: 2
#> 5: 5
d
#> x1 x4 x3 x2 x5 na n.2 sum m1 m2 m3 cons1
#> <int> <num> <num> <num> <num> <int> <int> <num> <num> <num> <num> <num>
#> 1: 1 2 3 4 5 0 1 15 3.0 1.5 2 0
#> 2: 2 2 2 4 4 0 3 14 2.8 2.0 2 2
#> 3: 3 5 NA NA 1 2 0 9 3.0 4.0 4 0
#> 4: 4 4 NA 2 4 1 1 14 3.5 4.0 4 2
#> 5: 5 5 5 5 5 0 0 25 5.0 5.0 4 5
#> cons2
#> <num>
#> 1: 0
#> 2: 3
#> 3: 0
#> 4: 2
#> 5: 5
## ====== 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
#> gender education E A C N O
#> <fctr> <fctr> <num> <num> <num> <num> <num>
#> 1: 1 <NA> 3.8 4.0 2.8 2.80 3.0
#> 2: 2 <NA> 5.0 4.2 4.0 3.80 4.0
#> 3: 2 <NA> 4.2 3.8 4.0 3.60 4.8
#> 4: 2 <NA> 3.6 4.6 3.0 2.80 3.2
#> 5: 1 <NA> 4.8 4.0 4.4 3.20 3.6
#> ---
#> 2796: 1 3 5.0 2.2 6.0 1.00 6.0
#> 2797: 1 4 4.2 4.2 3.2 2.75 4.8
#> 2798: 2 4 5.0 4.0 5.4 2.80 5.0
#> 2799: 1 4 4.6 2.8 4.2 4.20 5.2
#> 2800: 2 4 2.6 3.0 4.2 1.40 4.6
## ====== 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) })
#> XX.1.pre XX.2.pre XX.3.pre XX.mean
#> <int> <int> <int> <num>
#> 1: 1 6 11 6
#> 2: 2 7 12 7
#> 3: 3 8 13 8
#> 4: 4 9 14 9
#> 5: 5 10 15 10
add(d, { XX.mean = .mean("XX.{items}.pre", 1:3) }) # the same
#> XX.1.pre XX.2.pre XX.3.pre XX.mean
#> <int> <int> <int> <num>
#> 1: 1 6 11 6
#> 2: 2 7 12 7
#> 3: 3 8 13 8
#> 4: 4 9 14 9
#> 5: 5 10 15 10
add(d, { XX.mean = .mean("XX.{#$%^&}.pre", 1:3) }) # the same
#> XX.1.pre XX.2.pre XX.3.pre XX.mean
#> <int> <int> <int> <num>
#> 1: 1 6 11 6
#> 2: 2 7 12 7
#> 3: 3 8 13 8
#> 4: 4 9 14 9
#> 5: 5 10 15 10