| table | R Documentation |
Description
table uses the cross-classifying factors to build a contingencytable of the counts at each combination of factor levels.
Usage
table(..., exclude = if (useNA == "no") c(NA, NaN), useNA = c("no", "ifany", "always"), dnn = list.names(...), deparse.level = 1)as.table(x, ...)is.table(x)## S3 method for class 'table'as.data.frame(x, row.names = NULL, ..., responseName = "Freq", stringsAsFactors = TRUE, sep = "", base = list(LETTERS))Arguments
... | one or more objects which can be interpreted as factors(including character strings), or a list (or data frame) whosecomponents can be so interpreted. (For |
exclude | levels to remove for all factors in |
useNA | whether to include |
dnn | the names to be given to the dimensions in the result (thedimnames names). |
deparse.level | controls how the default |
x | an arbitrary R object, or an object inheriting from class |
row.names | a character vector giving the row names for the dataframe. |
responseName | The name to be used for the column of tableentries, usually counts. |
stringsAsFactors | logical: should the classifying factors bereturned as factors (the default) or character vectors? |
sep, base | passed to |
Details
If the argument dnn is not supplied, the internal functionlist.names is called to compute the ‘dimname names’. If thearguments in ... are named, those names are used. For theremaining arguments, deparse.level = 0 gives an empty name,deparse.level = 1 uses the supplied argument if it is a symbol,and deparse.level = 2 will deparse the argument.
Only when exclude is specified (i.e., not by default) andnon-empty, will table potentially drop levels of factorarguments.
useNA controls if the table includes counts of NAvalues: the allowed values correspond to never ("no"), only if the count ispositive ("ifany") and even for zero counts ("always").Note the somewhat “pathological” case of two different kinds ofNAs which are treated differently, depending on bothuseNA and exclude, see d.patho in the‘Examples:’ below.
Both exclude and useNA operate on an “all or none”basis. If you want to control the dimensions of a multiway tableseparately, modify each argument using factor oraddNA.
Non-factor arguments a are coerced via factor(a, exclude=exclude). Since R 3.4.0, care is taken not tocount the excluded values (where they were included in the NAcount, previously).
The summary method for class "table" (used for objectscreated by table or xtabs) which gives basicinformation and performs a chi-squared test for independence offactors (note that the function chisq.test currentlyonly handles 2-d tables).
Value
table() returns a contingency table, an object ofclass "table", an array of integer values.Note that unlike S the result is always an array, a 1Darray if one factor is given.
as.table and is.table coerce to and test for contingencytable, respectively.
The as.data.frame method for objects inheriting from class"table" can be used to convert the array-based representationof a contingency table to a data frame containing the classifyingfactors and the corresponding entries (the latter as componentnamed by responseName). This is the inverse of xtabs.
References
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988)The New S Language.Wadsworth & Brooks/Cole.
See Also
tabulate is the underlying function and allows finercontrol.
Use ftable for printing (and more) ofmultidimensional tables. margin.table,prop.table, addmargins.
addNA for constructing factors with NA asa level.
xtabs for cross tabulation of data frames with aformula interface.
Examples
require(stats) # for rpois and xtabs## Simple frequency distributiontable(rpois(100, 5))## Check the design:with(warpbreaks, table(wool, tension))table(state.division, state.region)# simple two-way contingency tablewith(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 tableDF <- 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'stable(a, exclude = NULL) # reports NA'sb <- 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) <- 3:4d. <- addNA(d)d.[1:7]table(d.) # ", exclude = NULL" is not needed## i.e., if you want to count the NA's of 'd', usetable(d, useNA = "ifany")## "pathological" case:d.patho <- addNA(c(1,NA,1:2,1:3))[-7]; is.na(d.patho) <- 3:4d.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" NAtable(d.patho, useNA = "ifany") # counts all threetable(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)))