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The goal of paar is to provide useful tools for cleaning
and processing spatial data in precision agriculture.
You can install the released version of paar from CRAN with:
install.packages("paar")You can install the development version from GitHub with:
# install.packages("pak")
pak::pkg_install("PPaccioretti/paar")The package provides a complete protocol for automated error removal. Default values of all functions are optimized for precision agriculture data.
library(paar)
library(sf)
#> Warning: package 'sf' was built under R version 4.5.2
data("barley", package = 'paar')The barley dataset contains grain yield data collected
were using calibrated commercial yield monitors, mounted on combines
equipped with DGPS.
#Convert barley data to an spatial object
barley_sf <- st_as_sf(barley, coords = c("X", "Y"), crs = 32720)
barley_dep <-
depurate(barley_sf, "Yield")
#> Concave hull algorithm is computed with
#> concavity = 2 and length_threshold = 0
# Summary of depurated data
summary(barley_dep)
#> normal point border spatial outlier MP spatial outlier LM
#> 5673 (77%) 964 (13%) 343 (4.6%) 309 (4.2%)
#> global min outlier
#> 99 (1.3%) 6 (0.081%)Spatial yield values before and after the depuration process can be visualized
plot(barley_sf["Yield"], main = "Before depuration")
plot(barley_dep$depurated_data["Yield"], main = "After depuration")

The distribution of yield values can also be compared
boxplot(barley_sf[["Yield"]], main = "Before depuration")
boxplot(barley_dep$depurated_data[["Yield"]], main = "After depuration")

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.