Skip to contents

Fill missing daily data

Source: R/process.R
gr_fill_gaps.Rd

Use the function to fill the missing daily data by linear interpolation. These can be both missing dates and missing runoff or temperature values. A preliminary summary of missing data can be viewed by gr_get_gaps()

Usage

gr_fill_gaps(hdata, autocorr = 0.7, nobserv = NULL)

Arguments

hdata

data.frame with at least two columns, where the first column is Date, and the remaining columns have numeric type.

autocorr

Autocorrelation value that defines possible length of the period that can be filled. Defaults to 0.7. If nobserv parameter is set, then this parameter is ignored. If both parameters are NULL, then all gaps are filled disregard of their lengths (not recommended).

nobserv

Maximum number of contiguous observations that can be interpolated. Defaults to NULL. If this parameter is set, then autocorr parameter is ignored. If both parameters are NULL, then all gaps are filled disregard of their lengths (not recommended).

Value

data.frame which is a filled version of hdata

Examples

library(grwat)
library(dplyr)

# example Spas-Zagorye data is included with grwat package
path = system.file("extdata", "spas-zagorye.txt", 
                   package = "grwat")

hdata_raw = read.delim(path, header = FALSE, 
                       sep = ' ', na.strings = c('-999', '-999.0', '-'),
                       col.names = c('d', 'm', 'y', 'q'))

hdata = hdata_raw %>% 
  transmute(Date = lubridate::make_date(y, m, d), 
            Q = q)

head(hdata)
#>         Date    Q
#> 1 1956-01-01 5.18
#> 2 1956-01-02 5.18
#> 3 1956-01-03 5.44
#> 4 1956-01-04 5.44
#> 5 1956-01-05 5.44
#> 6 1956-01-06 5.58

# identify gaps
gr_get_gaps(hdata) 
#> # A tibble: 8 × 5
#>     num start_date end_date   duration   type 
#>   <int> <date>     <date>     <drtn>     <chr>
#> 1     1 1956-01-01 1970-04-09  5213 days data 
#> 2     2 1970-04-10 1970-04-12     3 days gap  
#> 3     3 1970-04-13 1979-09-09  3437 days data 
#> 4     4 1979-09-10 1979-09-11     2 days gap  
#> 5     5 1979-09-12 2011-05-09 11563 days data 
#> 6     6 2011-05-10 2011-05-14     5 days gap  
#> 7     7 2011-05-15 2020-12-26  3514 days data 
#> 8     8 2020-12-27 2020-12-31     5 days gap  

# fill gaps
fhdata = gr_fill_gaps(hdata, autocorr = 0.8)
#> grwat: filled 5 observations using 4 days window for each variable

# check the results
gr_get_gaps(fhdata)
#> # A tibble: 4 × 5
#>     num start_date end_date   duration   type 
#>   <int> <date>     <date>     <drtn>     <chr>
#> 1     1 1956-01-01 2011-05-09 20218 days data 
#> 2     2 2011-05-10 2011-05-14     5 days gap  
#> 3     3 2011-05-15 2020-12-26  3514 days data 
#> 4     4 2020-12-27 2020-12-31     5 days gap  

# fill gaps
fhdata = gr_fill_gaps(hdata, nobserv = 7)
#> grwat: filled 10 observations using 7 days window for each variable

# check the results
gr_get_gaps(fhdata)
#> # A tibble: 2 × 5
#>     num start_date end_date   duration   type 
#>   <int> <date>     <date>     <drtn>     <chr>
#> 1     1 1956-01-01 2020-12-26 23737 days data 
#> 2     2 2020-12-27 2020-12-31     5 days gap