Fill in missing values with previous or next value on a SpatVector
Source: R/fill-SpatVector.R
fill.SpatVector.RdFills missing values in selected columns using the next or previous entry. This is useful in the common output format where values are not repeated, and are only recorded when they change.
Arguments
- data
A
SpatVector.- ...
<
tidy-select> Columns to fill.- .direction
Direction in which to fill missing values. Currently either "down" (the default), "up", "downup" (i.e. first down and then up) or "updown" (first up and then down).
Methods
Implementation of the generic tidyr::fill() function for SpatVector.
Grouped SpatVector
With grouped SpatVector created by group_by.SpatVector(), fill() will
be applied within each group, meaning that it won't fill across group
boundaries.
See also
Other tidyr verbs for handling missing values:
drop_na.Spat,
replace_na.Spat
Other tidyr methods:
drop_na.Spat,
pivot_longer.SpatVector(),
pivot_wider.SpatVector(),
replace_na.Spat
Examples
library(dplyr)
lux <- terra::vect(system.file("ex/lux.shp", package = "terra"))
# Leave some blanks for demo purporses
lux_blnk <- lux %>%
mutate(NAME_1 = if_else(NAME_1 != NAME_2, NA, NAME_2))
as_tibble(lux_blnk)
#> # A tibble: 12 × 6
#> ID_1 NAME_1 ID_2 NAME_2 AREA POP
#> <dbl> <chr> <dbl> <chr> <dbl> <int>
#> 1 1 NA 1 Clervaux 312 18081
#> 2 1 Diekirch 2 Diekirch 218 32543
#> 3 1 NA 3 Redange 259 18664
#> 4 1 NA 4 Vianden 76 5163
#> 5 1 NA 5 Wiltz 263 16735
#> 6 2 NA 6 Echternach 188 18899
#> 7 2 NA 7 Remich 129 22366
#> 8 2 Grevenmacher 12 Grevenmacher 210 29828
#> 9 3 NA 8 Capellen 185 48187
#> 10 3 NA 9 Esch-sur-Alzette 251 176820
#> 11 3 Luxembourg 10 Luxembourg 237 182607
#> 12 3 NA 11 Mersch 233 32112
# `fill()` defaults to replacing missing data from top to bottom
lux_blnk %>%
fill(NAME_1) %>%
as_tibble()
#> # A tibble: 12 × 6
#> ID_1 NAME_1 ID_2 NAME_2 AREA POP
#> <dbl> <chr> <dbl> <chr> <dbl> <int>
#> 1 1 NA 1 Clervaux 312 18081
#> 2 1 Diekirch 2 Diekirch 218 32543
#> 3 1 Diekirch 3 Redange 259 18664
#> 4 1 Diekirch 4 Vianden 76 5163
#> 5 1 Diekirch 5 Wiltz 263 16735
#> 6 2 Diekirch 6 Echternach 188 18899
#> 7 2 Diekirch 7 Remich 129 22366
#> 8 2 Grevenmacher 12 Grevenmacher 210 29828
#> 9 3 Grevenmacher 8 Capellen 185 48187
#> 10 3 Grevenmacher 9 Esch-sur-Alzette 251 176820
#> 11 3 Luxembourg 10 Luxembourg 237 182607
#> 12 3 Luxembourg 11 Mersch 233 32112
# direction = "up"
lux_blnk %>%
fill(NAME_1, .direction = "up") %>%
as_tibble()
#> # A tibble: 12 × 6
#> ID_1 NAME_1 ID_2 NAME_2 AREA POP
#> <dbl> <chr> <dbl> <chr> <dbl> <int>
#> 1 1 Diekirch 1 Clervaux 312 18081
#> 2 1 Diekirch 2 Diekirch 218 32543
#> 3 1 Grevenmacher 3 Redange 259 18664
#> 4 1 Grevenmacher 4 Vianden 76 5163
#> 5 1 Grevenmacher 5 Wiltz 263 16735
#> 6 2 Grevenmacher 6 Echternach 188 18899
#> 7 2 Grevenmacher 7 Remich 129 22366
#> 8 2 Grevenmacher 12 Grevenmacher 210 29828
#> 9 3 Luxembourg 8 Capellen 185 48187
#> 10 3 Luxembourg 9 Esch-sur-Alzette 251 176820
#> 11 3 Luxembourg 10 Luxembourg 237 182607
#> 12 3 NA 11 Mersch 233 32112
# Grouping and downup - will restore the initial state
lux_blnk %>%
group_by(ID_1) %>%
fill(NAME_1, .direction = "downup") %>%
as_tibble()
#> # A tibble: 12 × 6
#> # Groups: ID_1 [3]
#> ID_1 NAME_1 ID_2 NAME_2 AREA POP
#> <dbl> <chr> <dbl> <chr> <dbl> <int>
#> 1 1 Diekirch 1 Clervaux 312 18081
#> 2 1 Diekirch 2 Diekirch 218 32543
#> 3 1 Diekirch 3 Redange 259 18664
#> 4 1 Diekirch 4 Vianden 76 5163
#> 5 1 Diekirch 5 Wiltz 263 16735
#> 6 2 Grevenmacher 6 Echternach 188 18899
#> 7 2 Grevenmacher 7 Remich 129 22366
#> 8 2 Grevenmacher 12 Grevenmacher 210 29828
#> 9 3 Luxembourg 8 Capellen 185 48187
#> 10 3 Luxembourg 9 Esch-sur-Alzette 251 176820
#> 11 3 Luxembourg 10 Luxembourg 237 182607
#> 12 3 Luxembourg 11 Mersch 233 32112