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| # Very small (e.g. one line) R functions that just call C. | ||
| # One file wrappers.R to avoid creating lots of small .R files. | ||
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| coalesce = function(...) .Call(Ccoalesce, list(...), FALSE) | ||
| setcoalesce = function(...) .Call(Ccoalesce, list(...), TRUE) | ||
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| fifelse = function(test, yes, no) .Call(CfifelseR,test, yes, no) | ||
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| \name{fifelse} | ||
| \alias{fifelse} | ||
| \title{ Fast ifelse } | ||
| \description{ | ||
| \code{data.table::fifelse} is comparable to \code{base::ifelse}, \code{dplyr::if_else} and \code{hutils::if_else}. | ||
| Like these functions, it returns a value with the same length as \code{test} and filled with value from \code{yes} or \code{no}. | ||
| This function is mostly written in C for speed and unlike \code{base::ifelse} the output type is consistent with those of \code{yes} and \code{no}. | ||
| } | ||
| \usage{ | ||
| fifelse(test, yes, no) | ||
| } | ||
| \arguments{ | ||
| \item{test}{ A logical vector } | ||
| \item{yes, no}{ Values to return depending on \code{TRUE}/\code{FALSE} element of \code{test}. They must be the same type and be either length \code{1} or the same length of \code{test}. Attributes are copied from \code{yes} to the output.} | ||
| } | ||
| \value{ | ||
| A vector of the same length as \code{test} and attributes as \code{yes}. Data values are taken from the values of \code{yes} and \code{no}. | ||
| } | ||
| \examples{ | ||
| # In the following 2 examples, one can see the contrast between ifelse and | ||
| # fifelse. Unlike ifelse, fifelse preserves the | ||
| # type and class of the inputs. Attributes are are taken from the "yes" argument. | ||
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| ### Example 1 - Date class preserved | ||
| dates = as.Date(c("2011-01-01","2011-01-02","2011-01-03","2011-01-04","2011-01-05")) | ||
| ifelse(dates == "2011-01-01", dates - 1, dates) | ||
| # [1] 14974 14976 14977 14978 14979 | ||
| fifelse(dates == "2011-01-01", dates - 1, dates) | ||
| # [1] "2010-12-31" "2011-01-02" "2011-01-03" "2011-01-04" "2011-01-05" | ||
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| ### Example 2 - Factor class preserved | ||
| v = factor(letters[1:3]) | ||
| base::ifelse(c(TRUE,FALSE,TRUE), v, factor("a",levels=levels(v))) | ||
| # [1] 1 1 3 | ||
| fifelse(c(TRUE,FALSE,TRUE), v, factor("a",levels=levels(v))) | ||
| # [1] a a c | ||
| # Levels: a b c | ||
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| \dontrun{ | ||
| # Example 3: | ||
| # Unlike dplyr::if_else and hutils::if_else, fifelse and ifelse | ||
| # allow singleton replacements to be en-listed ( i.e. wrapped correctly in list()). | ||
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| ifelse(c(TRUE, FALSE), 1L, list(0L)) | ||
| # [[1]] | ||
| # [1] 1 | ||
| # | ||
| # [[2]] | ||
| # [1] 0 | ||
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| data.table::fifelse(c(TRUE, FALSE), 1L, list(0L)) | ||
| # [[1]] | ||
| # [1] 1 | ||
| # | ||
| # [[2]] | ||
| # [1] 0 | ||
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| dplyr::if_else(c(TRUE, FALSE), 1L, list(0L)) | ||
| # Error: `false` must be an integer vector, not a list | ||
| # Call `rlang::last_error()` to see a backtrace | ||
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| hutils::if_else(c(TRUE, FALSE), 1L, list(0L)) | ||
| # Error in hutils::if_else(c(TRUE, FALSE), 1L, list(0L)) : | ||
| # typeof(false) == list, yet typeof(true) == integer. Both true and false must have the same type. | ||
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| # The above is useful given that data.table allows list columns as shown below: | ||
| DT1 = data.table(a = 0:5, b = list(1, list(2:3), list(4:6), list(6:4), list(3:2), 1)) | ||
| DT2 = data.table(a = 0:5, b = list(1, list(2:3), list(4:6), list(6:4), list(3:2), 1)) | ||
| all.equal(DT1[ , b := fifelse(a > 2, b, 0)],DT2[ , b := ifelse(a > 2, b, 0)]) | ||
| # [1] TRUE | ||
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| # Example 4 : | ||
| # data.table:fifelse is fast... | ||
| # The below code has been run on a desktop with 32GB RAM and 8 logical CPU | ||
| x <- -3e8L:3e8 < 0 # 2.4GB | ||
| system.time(y1 <- fifelse(x, 1L, 0L)) | ||
| system.time(y2 <- hutils::if_else(x, 1L, 0L)) | ||
| system.time(y3 <- ifelse(x, 1L, 0L)) | ||
| system.time(y4 <- dplyr::if_else(x, 1L, 0L)) | ||
| identical(y1,y2) && identical(y1,y3) && identical(y1,y4) | ||
| # user system elapsed (seconds) | ||
| # 0.55 0.78 1.33 # data.table v1.12.3 | ||
| # 2.52 1.44 3.95 # hutils v1.5.0 | ||
| # 9.46 5.80 15.26 # base v3.6.0 | ||
| # 11.11 9.25 20.38 # dplyr v0.8.3 | ||
| } | ||
| } | ||
| \keyword{ data } |
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