Grid data ready for ASReml analysis — ofe_grid

Optimally rotate and grid a georeferenced dataframe

Usage

ofe_grid_data(data_in, rotation_angle, nrow, npe, ncol, trim_ends = TRUE)

Arguments

data_in

data.frame. The dataframe to be rotated. This should be georeferenced per the sf package.

We assume there are only three column of actual interest in the dataframe, which are:

Yield: The observed yield values.
Treatment: The treatment factor to assess.
Rep: The blocking structure to be preserved

rotation_angle

numeric. Clockwise angle in degrees to rotate the dataframe.

nrow

numeric. The number of rows in the final grid. This should be as large as possible without introducing spurious NA values.

npe

numeric. Number of pseudo-environments to be generated along the strips. PEs will be evenly spaced, so may not be appropriate in all cases. Always assess PEs visually before use.

ncol

numeric. The actual number of strips in the data. This should be counted/confirmed before hand. If there is a gap between strips, count the number of strips in this gap when determining this number.

trim_ends

bool. Boolean determining whether the gridded data should be trimmed to remove NAs occurring at the ends of columns. Defaults to FALSE.

Value

gridded.ofe. list containing the following items:

gridded_data data.frame: The georeferenced gridded data.
data_original data.frame: The input data with additional columns for visualising the rotation process.

Author

Braden Thorne, braden.thorne@curtin.edu.au

Examples

if (FALSE) { # requireNamespace("asreml", quietly = TRUE)
library(CBADASReml)

data <- agridat::lasrosas.corn |>
filter(year == 2001) |>
rename(
  Yield = yield,
  Treatment = nf,
  Rep = rep
 ) |>
 dplyr::select(Yield, Treatment, Rep, long, lat) |>
 st_as_sf(coords = c("long", "lat"), crs = 4326) |>
 st_transform(3395)

ofe_grid_data(data, -60, 80, 5, 18, trim_ends=FALSE)
}