# Last Observation Carried Forward Generic function for replacing each `NA` with the most recent non-`NA` prior to it. ```r na.locf(object, na.rm = TRUE, ...) ## Default S3 method: na.locf(object, na.rm = TRUE, fromLast, rev, maxgap = Inf, rule = 2, ...) na.locf0(object, fromLast = FALSE, maxgap = Inf, coredata = NULL) ``` ## Arguments - `object`: an object. - `na.rm`: logical. Should leading `NA`s be removed? - `fromLast`: logical. Causes observations to be carried backward rather than forward. Default is `FALSE`. With a value of `TRUE` this corresponds to NOCB (next observation carried backward). It is not supported if `x` or `xout` is specified. - `rev`: Use `fromLast` instead. This argument will be eliminated in the future in favor of `fromLast`. - `maxgap`: Runs of more than `maxgap` `NA`s are retained, other `NA`s are removed and the last occurrence in the resulting series prior to each time point in `xout` is used as that time point's output value. (If `xout` is not specified this reduces to retaining runs of more than `maxgap` `NA`s while filling other `NA`s with the last occurrence of a non-`NA`.) - `rule`: See `approx`. - ``...``: further arguments passed to methods. - `coredata`: logical. Should LOCF be applied to the core data of a (time series) object and then assigned to the original object again? By default, this strategy is applied to time series classes (e.g., `ts`, `zoo`, `xts`, etc.) where it preserves the time index. ## Returns An object in which each `NA` in the input object is replaced by the most recent non-`NA` prior to it. If there are no earlier non-`NA`s then the `NA` is omitted (if `na.rm = TRUE`) or it is not replaced (if `na.rm = FALSE`). The arguments `x` and `xout` can be used in which case they have the same meaning as in `approx`. Note that if a multi-column zoo object has a column entirely composed of `NA` then with `na.rm = TRUE`, the default, the above implies that the resulting object will have zero rows. Use `na.rm = FALSE` to preserve the `NA` values instead. The function `na.locf0` is the workhorse function underlying the default `na.locf` method. It has more limited capabilities but is faster for the special cases it covers. Implicitly, it uses `na.rm=FALSE`. ## See Also `zoo` ## Examples ```r az <- zoo(1:6) bz <- zoo(c(2,NA,1,4,5,2)) na.locf(bz) na.locf(bz, fromLast = TRUE) cz <- zoo(c(NA,9,3,2,3,2)) na.locf(cz) # generate and fill in missing dates z <- zoo(c(0.007306621, 0.007659046, 0.007681013, 0.007817548, 0.007847579, 0.007867313), as.Date(c("1993-01-01", "1993-01-09", "1993-01-16", "1993-01-23", "1993-01-30", "1993-02-06"))) g <- seq(start(z), end(z), "day") na.locf(z, xout = g) # similar but use a 2 second grid z <- zoo(1:9, as.POSIXct(c("2010-01-04 09:30:02", "2010-01-04 09:30:06", "2010-01-04 09:30:07", "2010-01-04 09:30:08", "2010-01-04 09:30:09", "2010-01-04 09:30:10", "2010-01-04 09:30:11", "2010-01-04 09:30:13", "2010-01-04 09:30:14"))) g <- seq(start(z), end(z), by = "2 sec") na.locf(z, xout = g) ## get 5th of every month or most recent date prior to 5th if 5th missing. ## Result has index of the date actually used. z <- zoo(c(1311.56, 1309.04, 1295.5, 1296.6, 1286.57, 1288.12, 1289.12, 1289.12, 1285.33, 1307.65, 1309.93, 1311.46, 1311.28, 1308.11, 1301.74, 1305.41, 1309.72, 1310.61, 1305.19, 1313.21, 1307.85, 1312.25, 1325.76), as.Date(c(13242, 13244, 13245, 13248, 13249, 13250, 13251, 13252, 13255, 13256, 13257, 13258, 13259, 13262, 13263, 13264, 13265, 13266, 13269, 13270, 13271, 13272, 13274))) # z.na is same as z but with missing days added (with NAs) # It is formed by merging z with a zero with series having all the dates. rng <- range(time(z)) z.na <- merge(z, zoo(, seq(rng[1], rng[2], by = "day"))) # use na.locf to bring values forward picking off 5th of month na.locf(z.na)[as.POSIXlt(time(z.na))$mday == 5] ## this is the same as the last one except instead of always using the ## 5th of month in the result we show the date actually used # idx has NAs wherever z.na does but has 1, 2, 3, ... instead of # z.na's data values (so idx can be used for indexing) idx <- coredata(na.locf(seq_along(z.na) + (0 * z.na))) # pick off those elements of z.na that correspond to 5th z.na[idx[as.POSIXlt(time(z.na))$mday == 5]] ## only fill single-day gaps merge(z.na, filled1 = na.locf(z.na, maxgap = 1)) ## fill NAs in first column by inflating the most recent non-NA ## by the growth in second column. Note that elements of x-x ## are NA if the corresponding element of x is NA and zero else m <- zoo(cbind(c(1, 2, NA, NA, 5, NA, NA), seq(7)^2), as.Date(1:7)) r <- na.locf(m[,1]) * m[,2] / na.locf(m[,2] + (m[,1]-m[,1])) cbind(V1 = r, V2 = m[,2]) ## repeat a quarterly value every month ## preserving NAs zq <- zoo(c(1, NA, 3, 4), as.yearqtr(2000) + 0:3/4) tt <- as.yearmon(start(zq)) + seq(0, len = 3 * length(zq))/12 na.locf(zq, xout = tt, maxgap = 0) ## na.locf() can also be mimicked with ave() x <- c(NA, 10, NA, NA, 20, NA) f <- function(x) x[1] ave(x, cumsum(!is.na(x)), FUN = f) ## by replacing f() with other functions various generalizations can be ## obtained, e.g., f <- function(x) if (length(x) > 3) x else x[1] # like maxgap f <- function(x) replace(x, 1:min(length(x)), 3) # replace up to 2 NAs f <- function(x) if (!is.na(x[1]) && x[1] > 0) x[1] else x # only positve numbers ```

na.locf function