table uses the cross-classifying factors to build a contingency table of the counts at each combination of factor levels.
table(…,
exclude = if (useNA == "no") c(NA, NaN),
useNA = c("no", "ifany", "always"),
dnn = list.names(…), deparse.level = 1)<p></p><p>as.table(x, …)
is.table(x)</p><p># S3 method for table
as.data.frame(x, row.names = NULL, …,
responseName = "Freq", stringsAsFactors = TRUE,
sep = "", base = list(LETTERS))</p>| Parameter | Description |
|---|---|
… |
one or more objects which can be interpreted as factors (including character strings), or a list (or data frame) whose components can be so interpreted. (For as.table, arguments passed to specific methods; for as.data.frame, unused.) |
exclude |
levels to remove for all factors in …. If it does not contain NA and useNA is not specified, it implies useNA = "ifany". See ‘Details’ for its interpretation for non-factor arguments. |
useNA |
whether to include NA values in the table. See ‘Details’. Can be abbreviated. |
dnn |
the names to be given to the dimensions in the result (the dimnames names). |
deparse.level |
controls how the default dnn is constructed. See ‘Details’. |
x |
an arbitrary R object, or an object inheriting from class "table" for the as.data.frame method. Note that as.data.frame.table(x, *) may be called explicitly for non-table x for “reshaping” arrays. |
row.names |
a character vector giving the row names for the data frame. |
responseName |
The name to be used for the column of table entries, usually counts. |
stringsAsFactors |
logical: should the classifying factors be returned as factors (the default) or character vectors? |
sep, base |
passed to provideDimnames. |
# NOT RUN {
require(stats) # for rpois and xtabs
## Simple frequency distribution
table(rpois(100, 5))
## Check the design:
with(warpbreaks, table(wool, tension))
table(state.division, state.region)
# simple two-way contingency table
with(airquality, table(cut(Temp, quantile(Temp)), Month))
a <- letters[1:3]
table(a, sample(a)) # dnn is c("a", "")
table(a, sample(a), deparse.level = 0) # dnn is c("", "")
table(a, sample(a), deparse.level = 2) # dnn is c("a", "sample(a)")
## xtabs() <-> as.data.frame.table() :
UCBAdmissions ## already a contingency table
DF <- as.data.frame(UCBAdmissions)
class(tab <- xtabs(Freq ~ ., DF)) # xtabs & table
## tab *is* "the same" as the original table:
all(tab == UCBAdmissions)
all.equal(dimnames(tab), dimnames(UCBAdmissions))
a <- rep(c(NA, 1/0:3), 10)
table(a) # does not report NA's
table(a, exclude = NULL) # reports NA's
b <- factor(rep(c("A","B","C"), 10))
table(b)
table(b, exclude = "B")
d <- factor(rep(c("A","B","C"), 10), levels = c("A","B","C","D","E"))
table(d, exclude = "B")
print(table(b, d), zero.print = ".")
## NA counting:
is.na(d) <- 3:4
d. <- addNA(d)
d.[1:7]
table(d.) # ", exclude = NULL" is not needed
## i.e., if you want to count the NA's of 'd', use
table(d, useNA = "ifany")
## "pathological" case:
d.patho <- addNA(c(1,NA,1:2,1:3))[-7]; is.na(d.patho) <- 3:4
d.patho
## just 3 consecutive NA's ? --- well, have *two* kinds of NAs here :
as.integer(d.patho) # 1 4 NA NA 1 2
##
## In R >= 3.4.0, table() allows to differentiate:
table(d.patho) # counts the "unusual" NA
table(d.patho, useNA = "ifany") # counts all three
table(d.patho, exclude = NULL) # (ditto)
table(d.patho, exclude = NA) # counts none
## Two-way tables with NA counts. The 3rd variant is absurd, but shows
## something that cannot be done using exclude or useNA.
with(airquality,
table(OzHi = Ozone > 80, Month, useNA = "ifany"))
with(airquality,
table(OzHi = Ozone > 80, Month, useNA = "always"))
with(airquality,
table(OzHi = Ozone > 80, addNA(Month)))
# }