aggregate.stars mean by time (by = "2 days")

[alexyshr]

Before using aggregate.stars the values in the original stars object pointsstars1 for 6 days are 0, 0, 0, 0, 0, 0. After aggregating by 2 days using mean the values should be 0, 0, 0 , but they are 0, 1, NaN (stars object pointstarsagg1). In the plot it can be seen that the mean function is returning values for some cells in some aggregated dates (the point appears in the plot), and NA values for the same cells in other aggregated dates (the point disappears in the plot); as the cell values come from the same original stars object pointsstars the aggregation must return all NA or all a value. In other cases the mean value above the original repeated value. Is it a mistake on my side?

library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.3.3
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(sf)
#> Warning: package 'sf' was built under R version 4.3.3
#> Linking to GEOS 3.11.2, GDAL 3.8.2, PROJ 9.3.1; sf_use_s2() is TRUE
library(stars)
#> Warning: package 'stars' was built under R version 4.3.3
#> Loading required package: abind
library(ggplot2)

##################################
###create points
#x and y sequences with names
x <- seq(from = 0, to = 10, by = 1)
y <- seq(from = 10, to = 0, by = -1)
xy <- expand.grid(x = x, y = y)
xy$u <- x

#randomly select 20% of the u values and set them to NA
#using ifelse, runif and dplyr
the_condition <- runif(nrow(xy)) < 0.2
xy <- xy %>%
  dplyr::mutate(u = ifelse(the_condition, NA, u))

#convert the dataframe xy to sf
points <- sf::st_as_sf(xy, coords = c("x", "y"), crs = 4326)

#convert points to stars
pointsstars <- stars::st_as_stars(points)

#create an stars object with 6 dates one per day
pointsstars <- c(pointsstars, pointsstars, pointsstars, 
                 pointsstars, pointsstars, pointsstars)


#convert attributes to dimensions
pointsstars <- merge(pointsstars)

#change the name of the dimensions
pointsstars <- st_set_dimensions(pointsstars, names = c("geometry", "time"))

#create a date sequence with 6 dates
the_dates <- seq.Date(Sys.Date(), by = 1, length.out = 6)

#set time dimension values
pointsstars <- pointsstars %>% st_set_dimensions(which = "time", values = the_dates)
names(pointsstars) <- "u"
pointsstars
#> stars object with 2 dimensions and 1 attribute
#> attribute(s):
#>    Min. 1st Qu. Median     Mean 3rd Qu. Max. NA's
#> u     0       2      5 4.777778       8   10  186
#> dimension(s):
#>          from  to     offset  delta refsys point                        values
#> geometry    1 121         NA     NA WGS 84  TRUE POINT (0 10),...,POINT (10 0)
#> time        1   6 2024-04-15 1 days   Date    NA                          NULL

#select first polygon
pointsstars1 <- pointsstars[ , 1, ]

#remove unused dimensions
pointsstars1 <- pointsstars1 %>% adrop
pointsstars1[[1]]
#> [1] 0 0 0 0 0 0

#aggregate by time (2 days window)
pointstarsagg <- aggregate(
  x = pointsstars,
  by = "2 days",
  FUN = mean,
  na.rm = TRUE
)

#change order of dimensions
pointstarsagg <- pointstarsagg %>% aperm(c(2,1))
pointstarsagg
#> stars object with 2 dimensions and 1 attribute
#> attribute(s):
#>    Min. 1st Qu. Median     Mean 3rd Qu. Max. NA's
#> u     0       2      5 4.777778       8   10   93
#> dimension(s):
#>          from  to     offset  delta refsys point                        values
#> geometry    1 121         NA     NA WGS 84  TRUE POINT (0 10),...,POINT (10 0)
#> time        1   3 2024-04-15 2 days   Date    NA                          NULL

#extract the value for the first polygon
pointstarsagg1 <- pointstarsagg[ , 1, ]
pointstarsagg1[[1]]
#>      [,1] [,2] [,3]
#> [1,]    0    1  NaN
plot(pointstarsagg)

^{Created on 2024-04-15 with reprex v2.0.2}

Session info

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[edzer]

fall-out from d9e84dd

[alexyshr]

Thank you. It is working!