Package: wdiexplorer 0.1.1

wdiexplorer: Explore World Development Indicators Data

Provides a workflow for exploring World Development Indicators (WDI) country-level panel data. It downloads WDI data using the 'WDI' package and computes diagnostic indices that capture the temporal behaviour of the data by incorporating the grouping structure of the data. The set of diagnostic indices implemented includes variation features, trend and shape features, and sequential temporal features. This method is described in Akinfenwa, Cahill, and Hurley (2025) "wdiexplorer: An R package Designed for Exploratory Analysis of World Development Indicators (WDI) Data" <doi:10.48550/arXiv.2511.07027>. We adapt the clustering diagnostics and visualisation methodology described in Rousseeuw (1987) <doi:10.1016/0377-0427(87)90125-7> and selected time series features from Hyndman and Athanasopoulos (2021) "Forecasting: Principles and Practice" <https://otexts.com/fpp3/>.

Authors:Oluwayomi Akinfenwa [aut, cre], Niamh Cahill [aut, ths], Catherine Hurley [aut, ths]

wdiexplorer_0.1.1.tar.gz
wdiexplorer_0.1.1.zip(r-4.7)wdiexplorer_0.1.1.zip(r-4.6)wdiexplorer_0.1.1.zip(r-4.5)
wdiexplorer_0.1.1.tgz(r-4.6-any)wdiexplorer_0.1.1.tgz(r-4.5-any)
wdiexplorer_0.1.1.tar.gz(r-4.7-any)wdiexplorer_0.1.1.tar.gz(r-4.6-any)
wdiexplorer_0.1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
wdiexplorer/json (API)

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

Bug tracker:https://github.com/oluwayomi-olaitan/wdiexplorer/issues

Datasets:

On CRAN:

Conda:

5.08 score 2 stars 497 downloads 14 exports 88 dependencies

Last updated from:2ae1e64670. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK164
source / vignettesOK320
linux-release-x86_64OK183
macos-release-arm64OK161
macos-oldrel-arm64OK186
windows-develOK139
windows-releaseOK110
windows-oldrelOK112
wasm-releaseOK153

Exports:add_group_infocompute_diagnostic_indicescompute_dissimilaritycompute_temporal_featurescompute_trend_shape_featurescompute_variationget_valid_dataget_wdi_dataplot_data_trajectoriesplot_metric_distributionplot_metric_linkviewplot_metric_partitionplot_missingplot_parallel_coords

Dependencies:anytimebase64encBHbslibcachemcliclustercommonmarkcpp11curldigestdistributionaldplyrevaluatefabletoolsfarverfastmapfeastsfontawesomefontBitstreamVerafontLiberationfontquiverforcatsfsgdtoolsgenericsggdistggiraphggnewscaleggplot2ggtextggtimegluegridtextgtablehighrhtmltoolshtmlwidgetsisobandjpegjquerylibjsonliteknitrlabelinglifecyclelitedownlubridatemagrittrmarkdownMASSmemoisemimenumDerivpatchworkpillarpkgconfigpngprogressrpurrrquadprogR6rappdirsRColorBrewerRcpprlangrmarkdownS7sassscalessliderstringistringrsystemfontstibbletidyrtidyselecttimechangetinytextsibbleutf8vctrsviridisLitewarpWDIwithrxfunxml2yaml

Exploring WDI PISA mathematics data with the wdiexplorer package.
Stage 1: Data Sourcing and Preparation | Data | Identifying Data Gaps | Stage 2: Diagnostic Indices | Variation features | country dissimilarity average | Trend and Shape Features | Sequential Temporal Features | Stage 3: Static and Interactive Visualisations | plot_metric_distribution | plot_metric_partition | plot_data_trajectories | plot_parallel_coords | plot_metric_linkview()
Rendered frompisa_analysis.Rmdusingknitr::rmarkdown

Last update: 2026-04-20
Started: 2025-09-01

Exploring WDI PM2.5 air pollution data with the wdiexplorer package.
Stage 1: Data Sourcing and Preparation | Data | Identifying Data Gaps | Stage 2: Diagnostic Indices | Variation features | country dissimilarity average | Trend and Shape Features | Sequential Temporal Features | Stage 3: Static and Interactive Visualisations | plot_metric_distribution | plot_metric_partition | plot_data_trajectories | plot_parallel_coords | plot_metric_linkview
Rendered frompm_analysis.Rmdusingknitr::rmarkdown

Last update: 2026-04-20
Started: 2025-09-01