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fre
returns data.frame with six columns: labels or values, counts,
valid percent (excluding NA), percent (with NA), percent of responses(for
single-column x
it equals to valid percent) and cumulative percent of
responses.
fre(
x,
weight = NULL,
drop_unused_labels = TRUE,
prepend_var_lab = FALSE,
stat_lab = getOption("expss.fre_stat_lab", c("Count", "Valid percent", "Percent",
"Responses, %", "Cumulative responses, %"))
)
vector/data.frame/list. data.frames are considered as multiple
response variables. If x
is list then vertically stacked frequencies
for each element of list will be generated,
numeric vector. Optional case weights. NA's and negative weights treated as zero weights.
logical. Should we drop unused value labels? Default is TRUE.
logical. Should we prepend variable label before value
labels? By default we will add variable labels to value labels only if
x
or predictor is list (several variables).
character. Labels for the frequency columns.
object of class 'etable'. Basically it's a data.frame but class is needed for custom methods.
# NOT RUN {
data(mtcars)
mtcars = modify(mtcars,{
var_lab(vs) = "Engine"
val_lab(vs) = c("V-engine" = 0,
"Straight engine" = 1)
var_lab(am) = "Transmission"
val_lab(am) = c(automatic = 0,
manual=1)
})
fre(mtcars$vs)
# stacked frequencies
fre(list(mtcars$vs, mtcars$am))
# multiple-choice variable
# brands - multiple response question
# Which brands do you use during last three months?
set.seed(123)
brands = data.frame(t(replicate(20,sample(c(1:5,NA),4,replace = FALSE))))
# score - evaluation of tested product
score = sample(-1:1,20,replace = TRUE)
var_lab(brands) = "Used brands"
val_lab(brands) = make_labels("
1 Brand A
2 Brand B
3 Brand C
4 Brand D
5 Brand E
")
var_lab(score) = "Evaluation of tested brand"
val_lab(score) = make_labels("
-1 Dislike it
0 So-so
1 Like it
")
fre(brands)
# stacked frequencies
fre(list(score, brands))
# }
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