Data Exploration and Dynamics

This vignette presents two analyses of growth-related attributes: the Frequency Report summarizes sample distributions across spatial locations, while the Spatial–Temporal Report visualizes interpolated attributes over space and time.


Frequency

The Frequency Report provides frequency distributions across locations, summarizing patterns at the site, tree, and sample levels, and highlighting data completeness, variability, and potential gaps for further analysis.

# loading processed data
# otherwise need to run CFS_format() first as done in data report
 dt.samples_trt <- readRDS(system.file("extdata", "dt.samples_trt.rds", package = "growthTrendR"))
 # Compute frequency statistics at radius level
 dt.freq <- CFS_freq(
   data = dt.samples_trt$tr_all_wide,
   freq.label_data = "demo-samples",
   freq.uid_level  = "uid_radius"
 )
arguments of CFS_freq() function:

data

Metadata table generated by the function CFS_format().

freq.label_data

A short description of the dataset used. This text will appear in the report as the data source for the generated figures.

freq.uid_level

Specifies the hierarchical level at which frequencies are counted (e.g., site, tree, sample, or radius).

generate report: 

generate_report(robj = dt.freq)


arguments of the generate_report() function:

robj

The input for the frequency report is the output of the CFS_freq() function, which assigns the class “cfs_freq” to the resulting object.

output_file

The output_file argument allows users to generate an HTML report at a specified path and file name. If set to NULL (default), the report opens automatically in the browser.

Frequency report



This report presents an analysis of the distribution of samples across geographical locations, stratified by species, providing insights into sampling coverage and spatial patterns.

source data: demo-samples

uid level: uid_radius

cuttoff year: -999

Spatial resolution: 1 degree (longitude); 1 (latitude)



Spatial-Temporal report

This report presents the spatial–temporal dynamics of growth-related attributes (e.g., ring width, basal area increment, chronology indices, or model-predicted values). The variables are interpolated across the study region using the CFS_mapping() function with the inverse distance weighting (IDW) method, and the resulting raster layers are compiled into a GIF for visualization.


# loading processed data
dt.samples_trt <- readRDS(system.file("extdata", "dt.samples_trt.rds", package = "growthTrendR"))


# data processing

cols.meta = c("uid_tree", "uid_site", "longitude", "latitude", "species")

dt.mapping <- dt.samples_trt$tr_all_wide[
  , c(..cols.meta, as.character(1991:1995)), with = FALSE]

results_mapping <- CFS_mapping(data = dt.mapping, year.span = c(1991,1995))
arguments of CFS_mapping() function:

data

The input dataset must include at least five columns: uid_tree, uid_site, longitude, latitude, and species, followed by annual attribute columns labeled by year (e.g., 1991, 1992, …).

year.span

a short description of the input dataset.This text will appear in the report as data source for the generated figures.

generate report: 


# e.g. series to check
generate_report(robj = results_mapping,
                png.text = list(
                  text_top  = "Ring width measurment - ",
                  text_bott = "Source: demo-samples",
                  text_side = "ring width (mm)"
                )
                )
arguments of the generate_report() function:

robj

The input for the spatial-temporal report is the output of the CFS_mapping() function, which assigns the class “cfs_mapping” to the resulting object.

png.text

A list of text labels for the plot. Supported elements are text_top, text_bott, and text_side.

Spatio-Temporal Dynamics Report


The following animation illustrates temporal patterns of a key attribute across a geographic region. Values at discrete locations are interpolated using the inverse distance weighting (IDW) method to create continuous yearly surfaces. The animation provides a dynamic view of spatio-temporal trends, highlighting patterns and potential anomalies in the data.

all.spp

all.spp