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Plot SQI Results

Source: R/visualization.R
plot.sqi_result.Rd

S3 plot method for sqi_result objects. Provides multiple visualization types to explore and communicate Soil Quality Index results and analysis components.

Usage

# S3 method for class 'sqi_result'
plot(
  x,
  type = c("distribution", "indicators", "weights", "scree", "biplot"),
  ...
)

Arguments

x

An object of class "sqi_result" returned by compute_sqi, compute_sqi_df, or compute_sqi_properties.

type

Character string specifying the plot type. Options are:

"distribution"

Histogram of SQI values with mean line

"indicators"

Boxplots of indicator scores

"weights"

Bar chart of AHP weights with CR annotation

"scree"

Variance explained by PCs with cumulative line

"biplot"

PCA biplot of observations and variables

...

Additional graphical parameters passed to plotting functions.

Value

NULL (invisibly). The function is called for its side effect of creating a plot.

Details

The function creates different visualizations based on the type parameter:

Distribution Plot: Shows the distribution of SQI values across all samples using a histogram. A vertical line indicates the mean SQI.

Indicators Plot: Displays boxplots for each scored indicator, allowing comparison of score distributions across different soil properties.

Weights Plot: Shows the AHP weights assigned to each indicator as a bar chart. The Consistency Ratio (CR) is annotated on the plot.

Scree Plot: Displays the proportion of variance explained by each principal component, with both individual (bars) and cumulative (line) variance shown.

Biplot: Creates a PCA biplot showing both observations (points) and variable loadings (arrows), useful for understanding relationships between samples and soil properties.

See also

plot_sqi_report for multi-panel visualization, compute_sqi, compute_sqi_df, compute_sqi_properties

Examples

# Create example data
soil_data <- data.frame(
  SampleID = paste0("S", 1:20),
  Sand = rnorm(20, 45, 10),
  Silt = rnorm(20, 30, 5),
  Clay = rnorm(20, 25, 5),
  pH = rnorm(20, 6.5, 0.5),
  OM = rnorm(20, 3, 0.5)
)

# Compute SQI
result <- compute_sqi_df(soil_data, id_column = "SampleID")

# Distribution plot
plot(result, type = "distribution")


# Indicators plot
plot(result, type = "indicators")


# Weights plot
plot(result, type = "weights")


# Scree plot
plot(result, type = "scree")


# Biplot
plot(result, type = "biplot")