Package: AnalysisLin 0.1.0
AnalysisLin: Exploratory Data Analysis
A quick and effective data exploration toolkit. It provides essential features, including a descriptive statistics table for a quick overview of your dataset, interactive distribution plots to visualize variable patterns, Principal Component Analysis for dimensionality reduction and feature analysis, missing value imputation methods, and correlation analysis.
Authors:
AnalysisLin_0.1.0.tar.gz
AnalysisLin_0.1.0.zip(r-4.5)AnalysisLin_0.1.0.zip(r-4.4)AnalysisLin_0.1.0.zip(r-4.3)
AnalysisLin_0.1.0.tgz(r-4.4-any)AnalysisLin_0.1.0.tgz(r-4.3-any)
AnalysisLin_0.1.0.tar.gz(r-4.5-noble)AnalysisLin_0.1.0.tar.gz(r-4.4-noble)
AnalysisLin_0.1.0.tgz(r-4.4-emscripten)AnalysisLin_0.1.0.tgz(r-4.3-emscripten)
AnalysisLin.pdf |AnalysisLin.html✨
AnalysisLin/json (API)
| # Install 'AnalysisLin' in R: |
| install.packages('AnalysisLin', repos = c('https://zhiweilin27.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/zhiweilin27/analysislin/issues
Last updated 10 months agofrom:49d9388b2b. Checks:ERROR: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | FAIL | Oct 26 2024 |
| R-4.5-win | ERROR | Oct 26 2024 |
| R-4.5-linux | ERROR | Oct 26 2024 |
| R-4.4-win | ERROR | Oct 26 2024 |
| R-4.4-mac | ERROR | Oct 26 2024 |
| R-4.3-win | ERROR | Oct 26 2024 |
| R-4.3-mac | ERROR | Oct 26 2024 |
Exports:bar_plotcorr_clustercorr_matrixdens_plotdesc_stathist_plotimpute_missingmissing_values_plotpcapie_plotqq_plot
Dependencies:askpassbackportsbase64encbslibcachemcaretcheckmateclasscliclockclustercodetoolscolorspacecpp11crosstalkcurldata.tablediagramdigestdplyrDTe1071evaluatefansifarverfastmapfontawesomeforeachforeignFormulafsfuturefuture.applygenericsggplot2globalsgluegowergridExtragtablehardhathighrHmischtmlTablehtmltoolshtmlwidgetshttpuvhttripredisobanditeratorsjquerylibjsonliteKernSmoothknitrlabelinglaterlatticelavalazyevallifecyclelistenvlubridatemagrittrMASSMatrixmemoisemgcvmimeModelMetricsmunsellnlmennetnumDerivopensslparallellypillarpkgconfigplotlyplyrpROCprodlimprogressrpromisesproxypurrrR6RANNrappdirsRColorBrewerRcpprecipesreshape2rlangrmarkdownrpartrstudioapisassscalesshapeSQUAREMstringistringrsurvivalsystibbletidyrtidyselecttimechangetimeDatetinytextzdbutf8vctrsviridisviridisLitewithrxfunyaml
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Principle Component Analysis | automate_pca |
| Bar Plots for Categorical Variables | bar_plot |
| Categorical Variables Plots | categoric_plot |
| Correlation Clustering | corr_cluster |
| Correlation Matrix | corr_matrix |
| Numerical Variables Density Plots | dens_plot |
| Descriptive Statistics | desc_stat |
| Histogram Plot for Numerical Variables | hist_plot |
| Missing Value Imputation | impute_missing |
| Missing Values Plot | missing_values_plot |
| Numerical Variables Distribution | numeric_plot |
| Principal Component Analysis (PCA) | pca |
| Pie Plots for Categorical Variables | pie_plot |
| QQ Plots for Numerical Variables | qq_plot |
