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\references{
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\references{
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  \bibshow{R:Becker+Chambers+Wilks:1988}
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  \bibshow{R:Becker+Chambers+Wilks:1988}
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}
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}
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\examples{
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\examples{
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require(stats) # for rpois and xtabs
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require(stats) # for rpois and xtabs
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## Simple frequency distribution
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## Simple frequency distribution
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table(rpois(100, 5))
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table(rpois(100, 5))
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## Check the design:
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## Check the design:
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with(warpbreaks, table(wool, tension))
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with(warpbreaks, table(wool, tension))
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table(state.division, state.region)
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table(state.division, state.region)
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# simple two-way contingency table
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## Simple two-way contingency table:
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with(airquality, table(cut(Temp, quantile(Temp)), Month))
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with(airquality, table(cut(Temp, quantile(Temp)), Month))
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## Dimension names:
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a <- letters[1:3]
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a <- letters[1:3]
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table(a, sample(a))                    # dnn is c("a", "")
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table(a, sample(a))                    # dnn is c("a", "")
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table(a, sample(a), dnn = NULL)        # dimnames() have no names
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table(a, sample(a), dnn = NULL)        # dimnames() have no names
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table(a, sample(a), deparse.level = 0) # dnn is c("", "")
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table(a, sample(a), deparse.level = 0) # dnn is c("", "")
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table(a, sample(a), deparse.level = 2) # dnn is c("a", "sample(a)")
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table(a, sample(a), deparse.level = 2) # dnn is c("a", "sample(a)")
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class(tab <- xtabs(Freq ~ ., DF)) # xtabs & table
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class(tab <- xtabs(Freq ~ ., DF)) # xtabs & table
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## tab *is* "the same" as the original table:
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## tab *is* "the same" as the original table:
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all(tab == UCBAdmissions)
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all(tab == UCBAdmissions)
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all.equal(dimnames(tab), dimnames(UCBAdmissions))
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all.equal(dimnames(tab), dimnames(UCBAdmissions))
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## Excluding levels:
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a <- rep(c(NA, 1/0:3), 10)
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a <- rep(c(NA, 1/0:3), 10)
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table(a)                 # does not report NA's
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table(a)                 # does not report NAs
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table(a, exclude = NULL) # reports NA's
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table(a, exclude = NULL) # reports NAs
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b <- factor(rep(c("A","B","C"), 10))
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b <- factor(rep(c("A","B","C"), 10))
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table(b)
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table(b)
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table(b, exclude = "B")
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table(b, exclude = "B")
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d <- factor(rep(c("A","B","C"), 10), levels = c("A","B","C","D","E"))
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d <- factor(rep(c("A","B","C"), 10), levels = c("A","B","C","D","E"))
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table(d, exclude = "B")
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table(d, exclude = "B")
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## NA counting:
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## NA counting:
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is.na(d) <- 3:4
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is.na(d) <- 3:4
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d. <- addNA(d)
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d. <- addNA(d)
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d.[1:7]
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d.[1:7]
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table(d.) # ", exclude = NULL" is not needed
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table(d.) # ", exclude = NULL" is not needed
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## i.e., if you want to count the NA's of 'd', use
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## i.e., if you want to count the NAs of 'd', use
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table(d, useNA = "ifany")
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table(d, useNA = "ifany")
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## "pathological" case:
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## "pathological" case:
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d.patho <- addNA(c(1,NA,1:2,1:3))[-7]; is.na(d.patho) <- 3:4
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d.patho <- addNA(c(1,NA,1:2,1:3))[-7]; is.na(d.patho) <- 3:4
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d.patho
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d.patho
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## just 3 consecutive NA's ? --- well, have *two* kinds of NAs here :
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## just 3 consecutive NAs ? --- well, have *two* kinds of NAs here :
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as.integer(d.patho) # 1 4 NA NA 1 2
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as.integer(d.patho) # 1 4 NA NA 1 2
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##
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##
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## In R >= 3.4.0, table() allows to differentiate:
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## In R >= 3.4.0, table() allows to differentiate:
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table(d.patho)                   # counts the "unusual" NA
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table(d.patho)                   # counts the "unusual" NA
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table(d.patho, useNA = "ifany")  # counts all three
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table(d.patho, useNA = "ifany")  # counts all three