Package 'paar' reference manual

Title:	Precision Agriculture Data Analysis
Description:	Precision agriculture spatial data depuration and homogeneous zones (management zone) delineation. The package includes functions that performs protocols for data cleaning management zone delineation and zone comparison; protocols are described in Paccioretti et al., (2020) <doi:10.1016/j.compag.2020.105556>.
Authors:	Pablo Paccioretti [aut, cre, cph], Mariano Córdoba [aut], Franca Giannini-Kurina [aut], Mónica Balzarini [aut]
Maintainer:	Pablo Paccioretti <[email protected]>
License:	MIT + file LICENSE
Version:	1.0.1
Built:	2024-11-08 05:30:02 UTC
Source:	https://github.com/ppaccioretti/paar

Barley grain yield

Description

A dataset containing Barley grain yield using calibrated commercial yield monitors mounted on combines equipped with DGPS.

Usage

barley
barley

Format

A data frame with 7395 rows and 3 variables:

X: X coordinate, in meters
Y: Y coordinate, in meters
Yield: grain yield, in ton per hectare

Details

Coordinate reference system is "WGS 84 / UTM zone 20S", epsg:32720

Bind outlier condition to an object.

Description

Bind outlier condition to an object.

Usage

## S3 method for class 'paar'
cbind(..., deparse.level = 1)
## S3 method for class 'paar'
cbind(..., deparse.level = 1)

Arguments

`...`	objects to bind.
`deparse.level`	integer controlling the construction of labels in the case of non-matrix-like arguments (for the default method): `deparse.level = 0` constructs no labels; the default `deparse.level = 1` typically and `deparse.level = 2` always construct labels from the argument names, see the ‘Value’ section below.

Value

cbind called with m.

Compare spatial zone means

Description

Compare spatial zone means

Usage

compare_zone(
  data,
  variable,
  zonesCol,
  alpha = 0.05,
  join = sf::st_nearest_feature,
  returnLSD = FALSE,
  grid_dim
)
compare_zone(
  data,
  variable,
  zonesCol,
  alpha = 0.05,
  join = sf::st_nearest_feature,
  returnLSD = FALSE,
  grid_dim
)

Arguments

`data`	`sf` object with zones
`variable`	`character` or `sf` object to use for mean comparison
`zonesCol`	`character` colname from data were zone are specified
`alpha`	`numeric` Significance level to use for comparison
`join`	function to use for st_join if variable is `sf` object
`returnLSD`	`logical` when LSD calculates with spatial variance should be returned
`grid_dim`	`numeric` grid dimentins to estimate spatial variance

Value

list with differences and descriptive_stat

References

Paccioretti, P., Córdoba, M., & Balzarini, M. (2020). FastMapping: Software to create field maps and identify management zones in precision agriculture. Computers and Electronics in Agriculture, 175, 105556 https://doi.org/10.1016/j.compag.2020.105556.

Examples

library(sf)
data(wheat, package = "paar")

##Convert to an sf object
wheat <- sf::st_as_sf(wheat,
                      coords = c("x", "y"),
                      crs = 32720)
clusters <- paar::kmspc(
  wheat,
  variables = c('CE30', 'CE90', 'Elev', 'Pe', 'Tg'),
  number_cluster = 3:4
)
data_clusters <- cbind(wheat, clusters$cluster)
compare_zone(data_clusters,
             "Elev",
             "Cluster_3")
library(sf)
data(wheat, package = "paar")

##Convert to an sf object
wheat <- sf::st_as_sf(wheat,
                      coords = c("x", "y"),
                      crs = 32720)
clusters <- paar::kmspc(
  wheat,
  variables = c('CE30', 'CE90', 'Elev', 'Pe', 'Tg'),
  number_cluster = 3:4
)
data_clusters <- cbind(wheat, clusters$cluster)
compare_zone(data_clusters,
             "Elev",
             "Cluster_3")

Remove errors from spatial data

Description

Data can be filtered by null, edge values, global outliers and spatial outliers or local defective observations. Default values are optimized for precision agricultural data.

Usage

depurate(
  x,
  y,
  toremove = c("edges", "outlier", "inlier"),
  crs = NULL,
  buffer = -10,
  ylimitmax = NA,
  ylimitmin = 0,
  sdout = 3,
  ldist = 0,
  udist = 40,
  criteria = c("LM", "MP"),
  zero.policy = NULL,
  poly_border = NULL
)
depurate(
  x,
  y,
  toremove = c("edges", "outlier", "inlier"),
  crs = NULL,
  buffer = -10,
  ylimitmax = NA,
  ylimitmin = 0,
  sdout = 3,
  ldist = 0,
  udist = 40,
  criteria = c("LM", "MP"),
  zero.policy = NULL,
  poly_border = NULL
)

Arguments

`x`	an `sf` points object
`y`	`character` with the name of the variable to use for depuration/filtering process
`toremove`	`character` vector specifying the procedure to implement for errors removal. Default 'edges', 'outlier', 'inlier'. See Details.
`crs`	coordinate reference system: integer with the EPSG code, or character with proj4string to convert coordinates if `x` has longitude/latitude data
`buffer`	`numeric` distance in meters to be removed. Negative values are recommended
`ylimitmax`	`numeric` of length 1 indicating the maximum limit for the `y` variable. If `NA` `Inf` is assumed
`ylimitmin`	`numeric` of length 1 indicating the minimum limit for the `y` variable. If `NA` `-Inf` is assumed
`sdout`	`numeric` values outside the interval $mean ± sdout × sdout$ values will be removed
`ldist`	`numeric` lower distance bound to identify neighbors
`udist`	`numeric` upper distance bound to identify neighbors
`criteria`	`character` with "LM" and/or "MP" for methods to identify spatial outliers
`zero.policy`	default NULL, use global option value; if FALSE stop with error for any empty neighbors sets, if TRUE permit the weights list to be formed with zero-length weights vectors
`poly_border`	`sf` object with one polygon or NULL. Can be the result of `concaveman::concaveman`

Details

Possible values for toremove are one or more elements of:

edges: All data points for a distance of buffer m from data edges are deleted.
outlier: Values that are outside the mean±sdout are removed
inlier: Local Moran index of spatial autocorrelation is calculated for each datum as a tool to identify inliers

Value

an object of class paar with two elements:

depurated_data: sf object with the data after the removal process
condition: character vector with the condition of each observation

References

Vega, A., Córdoba, M., Castro-Franco, M. et al. Protocol for automating error removal from yield maps. Precision Agric 20, 1030–1044 (2019). https://doi.org/10.1007/s11119-018-09632-8

Examples

library(sf)
data(barley, package = 'paar')
#Convert to an sf object
barley <- st_as_sf(barley,
                   coords = c("X", "Y"),
                   crs = 32720)
depurated <-
  depurate(barley,
           "Yield")

# Summary of depurated data
summary(depurated)

# Keep only depurate data
depurated_data <- depurated$depurated_data
# Combine the condition for all data
all_data_condition <- cbind(depurated, barley)
library(sf)
data(barley, package = 'paar')
#Convert to an sf object
barley <- st_as_sf(barley,
                   coords = c("X", "Y"),
                   crs = 32720)
depurated <-
  depurate(barley,
           "Yield")

# Summary of depurated data
summary(depurated)

# Keep only depurate data
depurated_data <- depurated$depurated_data
# Combine the condition for all data
all_data_condition <- cbind(depurated, barley)

Fuzzy k-means clustering

Description

Performs a vectorized fuzzy k-means clustering, this procedure it is not spatial. The function is almost a wrapper of the function cmeans from the package e1071. Is intended to be used when 'KM-sPC' procedure is not possible because data set has only 1 variable.

Usage

fuzzy_k_means(
  data,
  variables,
  number_cluster = 3:5,
  fuzzyness = 1.2,
  distance = "euclidean"
)
fuzzy_k_means(
  data,
  variables,
  number_cluster = 3:5,
  fuzzyness = 1.2,
  distance = "euclidean"
)

Arguments

`data`	sf object
`variables`	variables to use for clustering, if missing, all numeric variables will be used
`number_cluster`	`numeric` vector with number of final clusters
`fuzzyness`	A number greater than 1 giving the degree of fuzzification.
`distance`	`character` Must be one of the following: If "euclidean", the mean square error, if "manhattan", the mean absolute error is computed. Abbreviations are also accepted.

Value

a list with classification results and indices to select best number of clusters.

Examples

library(sf)
data(wheat, package = 'paar')

# Transform the data.frame into a sf object
wheat_sf <- st_as_sf(wheat,
                     coords = c('x', 'y'),
                     crs = 32720)

# Run the fuzzy_k_means function
fuzzy_k_means_results <- fuzzy_k_means(wheat_sf,
                               variables = 'Tg',
                               number_cluster = 2:4)

# Print the summaryResults
fuzzy_k_means_results$summaryResults

# Print the indices
fuzzy_k_means_results$indices

# Print the cluster
head(fuzzy_k_means_results$cluster, 5)

# Combine the results in a single object
wheat_clustered <- cbind(wheat_sf, fuzzy_k_means_results$cluster)

# Plot the results
plot(wheat_clustered[, "Cluster_2"])
library(sf)
data(wheat, package = 'paar')

# Transform the data.frame into a sf object
wheat_sf <- st_as_sf(wheat,
                     coords = c('x', 'y'),
                     crs = 32720)

# Run the fuzzy_k_means function
fuzzy_k_means_results <- fuzzy_k_means(wheat_sf,
                               variables = 'Tg',
                               number_cluster = 2:4)

# Print the summaryResults
fuzzy_k_means_results$summaryResults

# Print the indices
fuzzy_k_means_results$indices

# Print the cluster
head(fuzzy_k_means_results$cluster, 5)

# Combine the results in a single object
wheat_clustered <- cbind(wheat_sf, fuzzy_k_means_results$cluster)

# Plot the results
plot(wheat_clustered[, "Cluster_2"])

MULTISPATI-PCA clustering

Description

MULTISPATI-PCA clustering

Usage

kmspc(
  data,
  variables,
  number_cluster = 3:5,
  explainedVariance = 70,
  ldist = 0,
  udist = 40,
  center = TRUE,
  fuzzyness = 1.2,
  distance = "euclidean",
  zero.policy = FALSE,
  only_spca_results = TRUE,
  all_results = FALSE
)
kmspc(
  data,
  variables,
  number_cluster = 3:5,
  explainedVariance = 70,
  ldist = 0,
  udist = 40,
  center = TRUE,
  fuzzyness = 1.2,
  distance = "euclidean",
  zero.policy = FALSE,
  only_spca_results = TRUE,
  all_results = FALSE
)

Arguments

`data`	sf object
`variables`	variables to use for clustering, if missing, all numeric variables will be used
`number_cluster`	`numeric` vector with number of final clusters
`explainedVariance`	`numeric` number in percentage of explained variance from PCA analysis to keep and make cluster process
`ldist`	`numeric` lower distance bound to identify neighbors
`udist`	`numeric` upper distance bound to identify neighbors
`center`	a logical or numeric value, centring option if TRUE, centring by the mean if FALSE no centring if a numeric vector, its length must be equal to the number of columns of the data frame df and gives the decentring
`fuzzyness`	A number greater than 1 giving the degree of fuzzification.
`distance`	`character` Must be one of the following: If "euclidean", the mean square error, if "manhattan", the mean absolute error is computed. Abbreviations are also accepted.
`zero.policy`	default NULL, use global option value; if FALSE stop with error for any empty neighbors sets, if TRUE permit the weights list to be formed with zero-length weights vectors
`only_spca_results`	`logical`; should return both PCA and sPCA results (`FALSE`), or only sPCA results (`TRUE`)? This can be a time consuming process if there are multiple variables.
`all_results`	`logical`; should return the results from the sPCA and PCA call?

Value

a list with classification results and indices to select best number of clusters.

Examples

library(sf)
data(wheat, package = 'paar')

# Transform the data.frame into a sf object
wheat_sf <- st_as_sf(wheat,
                     coords = c('x', 'y'),
                     crs = 32720)

# Run the kmspc function
kmspc_results <- kmspc(wheat_sf,
                       number_cluster = 2:4)

# Print the summaryResults
kmspc_results$summaryResults

# Print the indices
kmspc_results$indices

# Print the cluster
head(kmspc_results$cluster, 5)

# Combine the results in a single object
wheat_clustered <- cbind(wheat_sf, kmspc_results$cluster)

# Plot the results
plot(wheat_clustered[, "Cluster_2"])
library(sf)
data(wheat, package = 'paar')

# Transform the data.frame into a sf object
wheat_sf <- st_as_sf(wheat,
                     coords = c('x', 'y'),
                     crs = 32720)

# Run the kmspc function
kmspc_results <- kmspc(wheat_sf,
                       number_cluster = 2:4)

# Print the summaryResults
kmspc_results$summaryResults

# Print the indices
kmspc_results$indices

# Print the cluster
head(kmspc_results$cluster, 5)

# Combine the results in a single object
wheat_clustered <- cbind(wheat_sf, kmspc_results$cluster)

# Plot the results
plot(wheat_clustered[, "Cluster_2"])

Print paar objects

Description

Print paar objects

Usage

## S3 method for class 'paar'
print(x, n = 3, ...)
## S3 method for class 'paar'
print(x, n = 3, ...)

Arguments

`x`	an object used to select a method.
`n`	an integer vector specifying maximum number of rows or elements to print.
`...`	further arguments passed to or from other methods.

Value

invisible object x

Print summarized paar object

Description

Print summarized paar object

Usage

## S3 method for class 'summary.paar'
print(x, digits, ...)
## S3 method for class 'summary.paar'
print(x, digits, ...)

Arguments

`x`	an object used to select a method.
`digits`	minimal number of significant digits, see `print.default`.
`...`	further arguments passed to or from other methods.

Value

A data.frame with the summarized condition of the object.

Remove borders

Description

Remove borders

Usage

remove_border(x, crs = NULL, buffer, poly_border = NULL)
remove_border(x, crs = NULL, buffer, poly_border = NULL)

Arguments

`x`	an `sf` points object
`crs`	coordinate reference system: integer with the EPSG code, or character with proj4string to convert coordinates if `x` has longitude/latitude data
`buffer`	`numeric` distance in meters to be removed. Negative values are recommended
`poly_border`	`sf` object with one polygon or NULL. Can be the result of `concaveman::concaveman`

Details

Removes all points from x that are buffer meters from boundary.

Remove spatial outliers

Description

Removes spatial outliers using Local Moran's I statistic and moran scatterplot.

Usage

remove_inlier(
  x,
  y,
  ldist = 0,
  udist = 40,
  criteria = c("LM", "MP"),
  zero.policy = NULL
)
remove_inlier(
  x,
  y,
  ldist = 0,
  udist = 40,
  criteria = c("LM", "MP"),
  zero.policy = NULL
)

Arguments

`x`	an `sf` points object
`y`	`character` with the name of the variable to use for depuration process
`ldist`	`numeric` lower distance bound to identify neighbors
`udist`	`numeric` upper distance bound to identify neighbors
`criteria`	`character` with "LM" and/or "MP" for methods to identify spatial outliers
`zero.policy`	default NULL, use global option value; if FALSE stop with error for any empty neighbors sets, if TRUE permit the weights list to be formed with zero-length weights vectors

Removes outliers

Description

Removes outliers

Usage

remove_outlier(x, y, ylimitmax = NA, ylimitmin = 0, sdout = 3)
remove_outlier(x, y, ylimitmax = NA, ylimitmin = 0, sdout = 3)

Arguments

`x`	an `sf` points object
`y`	`character` with the name of the variable to use for depuration process
`ylimitmax`	`numeric` of length 1 indicating the maximum limit for the `y` variable. If `NA` `Inf` is assumed
`ylimitmin`	`numeric` of length 1 indicating the minimum limit for the `y` variable. If `NA` `-Inf` is assumed
`sdout`	`numeric` values outside the interval $mean ± sdout × sdout$ values will be removed

Modified t test

Description

Performs a modified version of the t test to assess the correlation between spatial processes. See SpatialPack::modified.ttest for details.

Usage

spatial_t_test(data, variables)
spatial_t_test(data, variables)

Arguments

`data`	`sf` data to extract coordinates or two columns `matrix` or `data.frame` specifying coordinates.
`variables`	`character` vector with column names to perform ttest

Value

a data.frame with the correlation and p-value for each pair of variables

Summarizing paar objects

Description

Summarizing paar objects

Usage

## S3 method for class 'paar'
summary(object, ...)
## S3 method for class 'paar'
summary(object, ...)

Arguments

`object`	an object for which a summary is desired.
`...`	additional arguments affecting the summary produced.

Value

An object of class summary.paar (data.frame) with the following columns:

condition a character vector with the final condition.
n a numeric vector with the number of rows for each condition.
percentage a numeric vector with the percentage of rows for each condition.

Database from a production field under continuous agriculture

Description

A database from a wheat (Triticum aestivum L.) production field (60 ha) under continuous agriculture, located in south-eastern Pampas, Argentina.

Usage

wheat
wheat

Format

A data frame with 5982 rows and 7 variables:

x: X coordinate, in meters
y: Y coordinate, in meters
CE30: apparent electrical conductivity taken at 0–30 cm
CE90: apparent electrical conductivity taken at 0–90 cm
Elev: elevation, in meters
Pe: soil depth, in centimeters
Tg: wheat grain yield

Details

Coordinate reference system is "WGS 84 / UTM zone 20S", epsg:32720 Wheat grain yield was recorded in 2009 using calibrated commercial yield monitors mounted on combines equipped with DGPS. Soil ECa measurements were taken using Veris 3100 (VERIS technologies enr., Salina, KS, USA). Soil depth was measured using a hydraulic penetrometer on a 30 × 30 m regular grid (Peralta et al., 2015). Re-gridding was performed to obtain values of all variables at each intersection point of a 10 × 10 m grid.

References

N.R. Peralta, J.L. Costa, M. Balzarini, M. Castro Franco, M. Córdoba, D. Bullock Delineation of management zones to improve nitrogen management of wheat Comput. Electron. Agric., 110 (2015), pp. 103-113, 10.1016/j.compag.2014.10.017

Package 'paar'

Help Index

Barley grain yield

Description

Usage

Format

Details

Bind outlier condition to an object.

Description

Usage

Arguments

Value

Compare spatial zone means

Description

Usage

Arguments

Value

References

Examples

Remove errors from spatial data

Description

Usage

Arguments

Details

Value

References

Examples

Fuzzy k-means clustering

Description

Usage

Arguments

Value

Examples

MULTISPATI-PCA clustering

Description

Usage

Arguments

Value

Examples

Print paar objects

Description

Usage

Arguments

Value

Print summarized paar object

Description

Usage

Arguments

Value

Remove borders

Description

Usage

Arguments

Details

Remove spatial outliers

Description

Usage

Arguments

Removes outliers

Description

Usage

Arguments

Modified t test

Description

Usage

Arguments

Value

Summarizing paar objects

Description

Usage

Arguments

Value

Database from a production field under continuous agriculture

Description

Usage

Format

Details

References