na.locf
Last Observation Carried Forward
Generic function for replacing each NA
with the most recent
non-NA
prior to it.
- Keywords
- ts
Usage
na.locf(object, na.rm = TRUE, …)
# S3 method for default
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 ofTRUE
this corresponds to NOCB (next observation carried backward). It is not supported ifx
orxout
is specified.- rev
Use
fromLast
instead. This argument will be eliminated in the future in favor offromLast
.- maxgap
Runs of more than
maxgap
NA
s are retained, otherNA
s are removed and the last occurrence in the resulting series prior to each time point inxout
is used as that time point's output value. (Ifxout
is not specified this reduces to retaining runs of more thanmaxgap
NA
s while filling otherNA
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.
Value
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
Examples
# NOT RUN {
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)
# }