Package: stxplore 0.1.0

Sevvandi Kandanaarachchi

stxplore: Exploration of Spatio-Temporal Data

A set of statistical tools for spatio-temporal data exploration. Includes simple plotting functions, covariance calculations and computations similar to principal component analysis for spatio-temporal data. Can use both dataframes and stars objects for all plots and computations. For more details refer 'Spatio-Temporal Statistics with R' (Christopher K. Wikle, Andrew Zammit-Mangion, Noel Cressie, 2019, ISBN:9781138711136).

Authors:Sevvandi Kandanaarachchi [aut, cre], Petra Kuhnert [aut], Andrew Zammit-Mangion [ctb], Christopher Wikle [ctb]

stxplore_0.1.0.tar.gz
stxplore_0.1.0.zip(r-4.7)stxplore_0.1.0.zip(r-4.6)stxplore_0.1.0.zip(r-4.5)
stxplore_0.1.0.tgz(r-4.6-any)stxplore_0.1.0.tgz(r-4.5-any)
stxplore_0.1.0.tar.gz(r-4.7-any)stxplore_0.1.0.tar.gz(r-4.6-any)
stxplore_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
stxplore/json (API)

# Install 'stxplore' in R:
install.packages('stxplore', repos = c('https://sevvandi.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/sevvandi/stxplore/issues

Pkgdown/docs site:https://sevvandi.github.io

Datasets:
  • aerosol_australia - Data from of NASA Earth Observations at https://neo.gsfc.nasa.gov
  • aerosol_world - Data from of NASA Earth Observations at https://neo.gsfc.nasa.gov
  • locs - The locations used in the NOAA dataset.
  • NOAA_df_1990 - National oceanic and atmospheric administration (NOAA) data from 1990 to 1993
  • SSTdatashort - The data from of the Sea Surface Temperature (SST) dataset. A subset of the original dataset is used.
  • SSTlandmaskshort - The land mask for the Sea Surface Temperature (SST) dataset. A subset of the original dataset is used.
  • SSTlonlatshort - The locations of the Sea Surface Temperatures (SST) dataset. A subset of the original dataset is used.
  • Times - The time period in which the NOAA dataset was recorded. This spans from January 1990 to December 1993.
  • Tmax - The maximum temperature values used in the NOAA dataset in a wide dataframe format.

On CRAN:

Conda:

4.70 score 5 stars 7 scripts 208 downloads 13 exports 73 dependencies

Last updated from:bc692a4748. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK199
source / vignettesOK281
linux-release-x86_64OK184
macos-release-arm64OK121
macos-oldrel-arm64OK97
windows-develOK142
windows-releaseOK140
windows-oldrelOK141
wasm-releaseOK141

Exports:%>%autoplotcancor_eofcanonical_correlationemp_orth_funemp_spatial_covhovmollerridgelinesemivariogramspatial_meansspatial_snapshotstemporal_meanstemporal_snapshots

Dependencies:abindaskpassbitopsclassclassIntclicpp11curlDBIdigestdotCall64dplyre1071farverfieldsFNNgenericsggmapggplot2ggridgesgluegridExtragstatgtablehttrintervalsisobandjpegjsonliteKernSmoothlabelinglatticelifecyclelubridatemagrittrmapsMASSmimeopensslpillarpkgconfigplyrpngproxypurrrR6RColorBrewerRcpprlangs2S7scalessfsftimespspacetimespamstarsstringistringrsystibbletidyrtidyselecttimechangeunitsutf8vctrsviridisLitewithrwkxtszoo

Exploration using dataframes

Rendered fromstxplore.Rmdusingknitr::rmarkdownon May 26 2026.

Last update: 2023-01-19
Started: 2023-01-12

Using stars objects

Rendered fromstxplore_stars.Rmdusingknitr::rmarkdownon May 26 2026.

Last update: 2023-01-19
Started: 2023-01-12

Readme and manuals

Help Manual

Help pageTopics
Data from of NASA Earth Observations at https://neo.gsfc.nasa.govaerosol_australia
Data from of NASA Earth Observations at https://neo.gsfc.nasa.govaerosol_world
Performs CCA using Empirical Orthogonal Functions (EOFs) from a lagged datasetautoplot.cancoreof cancor_eof cancor_eof.data.frame cancor_eof.stars
Computes transformed variables from Canonical Correlation Analysis using a dataframe or a stars objectautoplot.cancor canonical_correlation canonical_correlation.data.frame canonical_correlation.stars
Computes empirical orthogonal functions using a dataframe or a stars object.autoplot.emporthfun emp_orth_fun emp_orth_fun.data.frame emp_orth_fun.stars
Computes empirical spatial covariance using a dataframe or a stars objectautoplot.spatialcov emp_spatial_cov emp_spatial_cov.data.frame emp_spatial_cov.stars
Computes the data structure for the Hovmoller plotsautoplot.hovmoller hovmoller hovmoller.data.frame hovmoller.stars
The locations used in the NOAA dataset.locs
National oceanic and atmospheric administration (NOAA) data from 1990 to 1993NOAA_df_1990
Ridgeline plots grouped by an attribute using a dataframe as an input.ridgeline ridgeline.data.frame ridgeline.stars
Computes the semi-variogram using a dataframe or a stars object.autoplot.semivariogramobj semivariogram semivariogram.data.frame semivariogram.stars
Computes spatial empirical means using a dataframe or a stars objectautoplot.spatialmeans spatial_means spatial_means.data.frame spatial_means.stars
Plots spatial snapshots of data through time using a dataframe or a stars object.spatial_snapshots spatial_snapshots.data.frame spatial_snapshots.stars
The data from of the Sea Surface Temperature (SST) dataset. A subset of the original dataset is used.SSTdatashort
The land mask for the Sea Surface Temperature (SST) dataset. A subset of the original dataset is used.SSTlandmaskshort
The locations of the Sea Surface Temperatures (SST) dataset. A subset of the original dataset is used.SSTlonlatshort
Computes temporal empirical means using a dataframe or a stars object.autoplot.temporalmeans temporal_means temporal_means.data.frame temporal_means.stars
Plots temporal snapshots of data for specific spatial locations using a dataframe or a stars object.temporal_snapshots temporal_snapshots.data.frame temporal_snapshots.stars
The time period in which the NOAA dataset was recorded. This spans from January 1990 to December 1993.Times
The maximum temperature values used in the NOAA dataset in a wide dataframe format.Tmax