# Replace NA by Interpolation Generic functions for replacing each `NA` with interpolated values. ```r na.approx(object, ...) ## S3 method for class 'zoo' na.approx(object, x = index(object), xout, ..., na.rm = TRUE, maxgap = Inf, along) ## S3 method for class 'zooreg' na.approx(object, ...) ## S3 method for class 'ts' na.approx(object, ...) ## Default S3 method: na.approx(object, x = index(object), xout, ..., na.rm = TRUE, maxgap = Inf, along) na.spline(object, ...) ## S3 method for class 'zoo' na.spline(object, x = index(object), xout, ..., na.rm = TRUE, maxgap = Inf, along) ## S3 method for class 'zooreg' na.spline(object, ...) ## S3 method for class 'ts' na.spline(object, ...) ## Default S3 method: na.spline(object, x = index(object), xout, ..., na.rm = TRUE, maxgap = Inf, along) ``` ## Arguments - `object`: object in which `NA`s are to be replaced - `x, xout`: Variables to be used for interpolation as in `approx`. - `na.rm`: logical. If the result of the (spline) interpolation still results in leading and/or trailing `NA`s, should these be removed (using `na.trim`)? - `maxgap`: maximum number of consecutive `NA`s to fill. Any longer gaps will be left unchanged. Note that all methods listed above can accept `maxgap` as it is ultimately passed to the `default` method. In `na.spline` the `maxgap` argument cannot be combined with `xout`, though. - `along`: deprecated. - ``...``: further arguments passed to methods. The `n` argument of `approx` is currently not supported. ## Details Missing values (`NA`s) are replaced by linear interpolation via `approx` or cubic spline interpolation via `spline`, respectively. It can also be used for series disaggregation by specifying `xout`. By default the index associated with `object` is used for interpolation. Note, that if this calls `index.default` this gives an equidistant spacing `1:NROW(object)`. If `object` is a matrix or data.frame, the interpolation is done separately for each column. If `obj` is a plain vector then `na.approx(obj, x, y, xout, ...)` returns `approx(x = x[!na], y = coredata(obj)[!na], xout = xout, ...)` (where `na` indicates observations with `NA`) such that `xout` defaults to `x`. Note that if there are less than two non-`NA`s then `approx()` cannot be applied and thus no `NA`s can be replaced. If `obj` is a `zoo`, `zooreg` or `ts` object its `coredata` value is processed as described and its time index is `xout` if specified and `index(obj)` otherwise. If `obj` is two dimensional then the above is applied to each column separately. For examples, see below. If `obj` has more than one column, the above strategy is applied to each column. ## Returns An object of similar structure as `object` with `NA`s replaced by interpolation. For `na.approx` only the internal `NA`s are replaced and leading or trailing `NA`s are omitted if `na.rm = TRUE` or not replaced if `na.rm = FALSE`. ## See Also `zoo`, `approx`, `na.contiguous`, `na.locf`, `na.omit`, `na.trim`, `spline`, `stinterp` ## Examples ```r z <- zoo(c(2, NA, 1, 4, 5, 2), c(1, 3, 4, 6, 7, 8)) ## use underlying time scale for interpolation na.approx(z) ## use equidistant spacing na.approx(z, 1:6) # with and without na.rm = FALSE zz <- c(NA, 9, 3, NA, 3, 2) na.approx(zz, na.rm = FALSE) na.approx(zz) d0 <- as.Date("2000-01-01") z <- zoo(c(11, NA, 13, NA, 15, NA), d0 + 1:6) # NA fill, drop or keep leading/trailing NAs na.approx(z) na.approx(z, na.rm = FALSE) # extrapolate to point outside of range of time points # (a) drop NA, (b) keep NA, (c) extrapolate using rule = 2 from approx() na.approx(z, xout = d0 + 7) na.approx(z, xout = d0 + 7, na.rm = FALSE) na.approx(z, xout = d0 + 7, rule = 2) # use splines - extrapolation handled differently z <- zoo(c(11, NA, 13, NA, 15, NA), d0 + 1:6) na.spline(z) na.spline(z, na.rm = FALSE) na.spline(z, xout = d0 + 1:6) na.spline(z, xout = d0 + 2:5) na.spline(z, xout = d0 + 7) na.spline(z, xout = d0 + 7, na.rm = FALSE) ## using na.approx for disaggregation zy <- zoo(1:3, 2000:2001) # yearly to monthly series zmo <- na.approx(zy, xout = as.yearmon(2000+0:13/12)) zmo # monthly to daily series sq <- seq(as.Date(start(zmo)), as.Date(end(zmo), frac = 1), by = "day") zd <- na.approx(zmo, x = as.Date, xout = sq) head(zd) # weekly to daily series zww <- zoo(1:3, as.Date("2001-01-01") + seq(0, length = 3, by = 7)) zww zdd <- na.approx(zww, xout = seq(start(zww), end(zww), by = "day")) zdd # The lines do not show up because of the NAs plot(cbind(z, z), type = "b", screen = 1) # use na.approx to force lines to appear plot(cbind(z, na.approx(z)), type = "b", screen = 1) # Workaround where less than 2 NAs can appear in a column za <- zoo(cbind(1:5, NA, c(1:3, NA, 5), NA)); za ix <- colSums(!is.na(za)) > 0 za[, ix] <- na.approx(za[, ix]); za # using na.approx to create regularly spaced series # z has points at 10, 20 and 40 minutes while output also has a point at 30 if(require("chron")) { tt <- as.chron("2000-01-01 10:00:00") + c(1, 2, 4) * as.numeric(times("00:10:00")) z <- zoo(1:3, tt) tseq <- seq(start(z), end(z), by = times("00:10:00")) na.approx(z, xout = tseq) } ```

na.approx function