pkg_search() starts a new search query, or shows the details of the previous query, if called without arguments.

ps() is an alias to pkg_search().

more() retrieves that next page of results for the previous query.

pkg_search(query = NULL, format = c("short", "long"), from = 1, size = 10)

ps(query = NULL, format = c("short", "long"), from = 1, size = 10)

more(format = NULL, size = NULL)

# S3 method for pkg_search_result
summary(object, ...)

# S3 method for pkg_search_result
print(x, ...)

Arguments

query

Search query string. If this argument is missing or NULL, then the results of the last query are printed, in short and long formats, in turns for successive pkg_search() calls. If this argument is missing, then all other arguments are ignored.

format

Default formatting of the results. short only outputs the name and title of the packages, long also prints the author, last version, full description and URLs. Note that this only affects the default printing, and you can still inspect the full results, even if you specify short here.

from

Where to start listing the results, for pagination.

size

The number of results to list.

object

Object to summarize.

...

Additional arguments, ignored currently.

x

Object to print.

Value

A tibble with columns:

  • score: Score of the hit. See Section Scoring for some details.

  • package: Package name.

  • version: Latest package version.

  • title: Package title.

  • description: Short package description.

  • date: Time stamp of the last release.

  • maintainer_name: Name of the package maintainer.

  • maintainer_email: Email address of the package maintainer.

  • revdeps: Number of (strong and weak) reverse dependencies of the package.

  • downloads_last_month: Raw number of package downloads last month, from the RStudio CRAN mirror.

  • license: Package license.

  • url: Package URL(s).

  • bugreports: URL of issue tracker, or email address for bug reports.

Details

Note that the search needs a working Internet connection.

Examples

# Example ps("survival")
#> - "survival" ---------------------------------- 876 packages in 0.007 seconds - #> # package version by @ title #> 1 100 survival 3.2.7 Terry M Therneau 10d Survival Analysis #> 2 8 flexsurv 1.1.1 Christopher Jackson 2y Flexible Paramet... #> 3 7 survminer 0.4.8 Alboukadel Kassambara 2M Drawing Survival... #> 4 6 rpart 4.1.15 Beth Atkinson 1y Recursive Partit... #> 5 6 randomForestSRC 2.9.3 Udaya B. Kogalur 9M Fast Unified Ran... #> 6 6 rstpm2 1.5.1 Mark Clements 1y Smooth Survival ... #> 7 6 relsurv 2.2.3 Maja Pohar Perme 2y Relative Survival #> 8 5 survRM2 1.0.3 Hajime Uno 4M Comparing Restri... #> 9 5 muhaz 1.2.6.1 David Winsemius 2y Hazard Function ... #> 10 5 pec 2019.11.3 Thomas A. Gerds 1y Prediction Error...
# Pagination ps("networks")
#> - "networks" ---------------------------------- 708 packages in 0.007 seconds - #> # package version by @ title #> 1 100 igraph 1.2.6 Gábor Csárdi 2d Network Analysis and Vis... #> 2 46 network 1.16.1 Carter T. Butts 2d Classes for Relational Data #> 3 44 RCurl 1.98.1.2 CRAN Team 6M General Network (HTTP/FT... #> 4 41 nnet 7.3.14 Brian Ripley 6M Feed-Forward Neural Netw... #> 5 30 DiagrammeR 1.0.6.1 Richard Iannone 5M Graph/Network Visualization #> 6 26 sna 2.6 Carter T. Butts 2d Tools for Social Network... #> 7 25 visNetwork 2.0.9 Benoit Thieurmel 10M Network Visualization us... #> 8 21 snow 0.4.3 Luke Tierney 2y Simple Network of Workst... #> 9 21 ggraph 2.0.3 Thomas Lin Pedersen 5M An Implementation of Gra... #> 10 19 neuralnet 1.44.2 Marvin N. Wright 2y Training of Neural Networks
more()
#> - "networks" ---------------------------------- 708 packages in 0.007 seconds - #> # package version by @ title #> 11 17 bnlearn 4.6.1 Marco Scutari 18d Bayesian Network Struc... #> 12 16 ergm 3.10.4 Pavel N. Krivitsky 1y Fit, Simulate and Diag... #> 13 15 networkD3 0.4 Christopher Gandrud 4y D3 JavaScript Network ... #> 14 12 networkDynamic 0.10.1 Skye Bender-deMoll 9M Dynamic Extensions for... #> 15 12 WGCNA 1.69 Peter Langfelder 7M Weighted Correlation N... #> 16 12 diagram 1.6.5 Karline Soetaert 9d Functions for Visualis... #> 17 12 RSNNS 0.4.12 Christoph Bergmeir 1y Neural Networks using ... #> 18 11 intergraph 2.0.2 Michal Bojanowski 5y Coercion Routines for ... #> 19 9 gRain 1.3.6 Søren Højsgaard 2M Graphical Independence... #> 20 9 ggnetwork 0.5.8 François Briatte 8M Geometries to Plot Net...
# Details ps("visualization")
#> - "visualization" ---------------------------- 1232 packages in 0.007 seconds - #> # package version by @ title #> 1 100 ggplot2 3.3.2 Thomas Lin Pedersen 4M Create Elegant Data Visu... #> 2 65 igraph 1.2.6 Gábor Csárdi 2d Network Analysis and Vis... #> 3 57 scales 1.1.1 Hadley Wickham 5M Scale Functions for Visu... #> 4 42 rgl 0.100.54 Duncan Murdoch 6M 3D Visualization Using O... #> 5 23 vdiffr 0.3.3 Lionel Henry 2d Visual Regression Testin... #> 6 20 ROCR 1.0.11 Felix G.M. Ernst 5M Visualizing the Performa... #> 7 20 DiagrammeR 1.0.6.1 Richard Iannone 5M Graph/Network Visualization #> 8 19 corrplot 0.84 Taiyun Wei 3y Visualization of a Corre... #> 9 17 vcd 1.4.8 David Meyer 18d Visualizing Categorical ... #> 10 16 ggmap 3.0.0 ORPHANED 2y Spatial Visualization wi...
ps()
#> - "visualization" ---------------------------- 1232 packages in 0.007 seconds - #> #> 1 ggplot2 @ 3.3.2 Thomas Lin Pedersen, 4 months ago #> ----------------- #> # Create Elegant Data Visualisations Using the Grammar of Graphics #> A system for 'declaratively' creating graphics, based on "The Grammar #> of Graphics". You provide the data, tell 'ggplot2' how to map #> variables to aesthetics, what graphical primitives to use, and it #> takes care of the details. #> http://ggplot2.tidyverse.org #> https://github.com/tidyverse/ggplot2 #> #> 2 igraph @ 1.2.6 Gábor Csárdi, 2 days ago #> ---------------- #> # Network Analysis and Visualization #> Routines for simple graphs and network analysis. It can handle large #> graphs very well and provides functions for generating random and #> regular graphs, graph visualization, centrality methods and much #> more. #> https://igraph.org #> #> 3 scales @ 1.1.1 Hadley Wickham, 5 months ago #> ---------------- #> # Scale Functions for Visualization #> Graphical scales map data to aesthetics, and provide methods for #> automatically determining breaks and labels for axes and legends. #> https://scales.r-lib.org #> https://github.com/r-lib/scales #> #> 4 rgl @ 0.100.54 Duncan Murdoch, 6 months ago #> ---------------- #> # 3D Visualization Using OpenGL #> Provides medium to high level functions for 3D interactive graphics, #> including functions modelled on base graphics (plot3d(), etc.) as #> well as functions for constructing representations of geometric #> objects (cube3d(), etc.). Output may be on screen using OpenGL, or #> to various standard 3D file formats including WebGL, PLY, OBJ, STL as #> well as 2D image formats, including PNG, Postscript, SVG, PGF. #> https://r-forge.r-project.org/projects/rgl/ #> #> 5 vdiffr @ 0.3.3 Lionel Henry, 2 days ago #> ---------------- #> # Visual Regression Testing and Graphical Diffing #> An extension to the 'testthat' package that makes it easy to add #> graphical unit tests. It provides a Shiny application to manage the #> test cases. #> https://github.com/r-lib/vdiffr #> #> 6 ROCR @ 1.0.11 Felix G.M. Ernst, 5 months ago #> --------------- #> # Visualizing the Performance of Scoring Classifiers #> ROC graphs, sensitivity/specificity curves, lift charts, and #> precision/recall plots are popular examples of trade-off #> visualizations for specific pairs of performance measures. ROCR is a #> flexible tool for creating cutoff-parameterized 2D performance curves #> by freely combining two from over 25 performance measures (new #> performance measures can be added using a standard interface). Curves #> from different cross-validation or bootstrapping runs can be averaged #> by different methods, and standard deviations, standard errors or box #> plots can be used to visualize the variability across the runs. The #> parameterization can be visualized by printing cutoff values at the #> corresponding curve positions, or by coloring the curve according to #> cutoff. All components of a performance plot can be quickly adjusted #> using a flexible parameter dispatching mechanism. Despite its #> flexibility, ROCR is easy to use, with only three commands and #> reasonable default values for all optional parameters. #> http://ipa-tys.github.io/ROCR/ #> #> 7 DiagrammeR @ 1.0.6.1 Richard Iannone, 5 months ago #> ---------------------- #> # Graph/Network Visualization #> Build graph/network structures using functions for stepwise addition #> and deletion of nodes and edges. Work with data available in tables #> for bulk addition of nodes, edges, and associated metadata. Use graph #> selections and traversals to apply changes to specific nodes or #> edges. A wide selection of graph algorithms allow for the analysis of #> graphs. Visualize the graphs and take advantage of any aesthetic #> properties assigned to nodes and edges. #> https://github.com/rich-iannone/DiagrammeR #> #> 8 corrplot @ 0.84 Taiyun Wei, 3 years ago #> ----------------- #> # Visualization of a Correlation Matrix #> A graphical display of a correlation matrix or general matrix. It #> also contains some algorithms to do matrix reordering. In addition, #> corrplot is good at details, including choosing color, text labels, #> color labels, layout, etc. #> https://github.com/taiyun/corrplot #> #> 9 vcd @ 1.4.8 David Meyer, 18 days ago #> ------------- #> # Visualizing Categorical Data #> Visualization techniques, data sets, summary and inference procedures #> aimed particularly at categorical data. Special emphasis is given to #> highly extensible grid graphics. The package was package was #> originally inspired by the book "Visualizing Categorical Data" by #> Michael Friendly and is now the main support package for a new book, #> "Discrete Data Analysis with R" by Michael Friendly and David Meyer #> (2015). #> #> 10 ggmap @ 3.0.0 ORPHANED, 2 years ago #> ---------------- #> # Spatial Visualization with ggplot2 #> A collection of functions to visualize spatial data and models on top #> of static maps from various online sources (e.g Google Maps and #> Stamen Maps). It includes tools common to those tasks, including #> functions for geolocation and routing. #> https://github.com/dkahle/ggmap
# See the underlying tibble ps("ropensci")
#> - "ropensci" ---------------------------------- 249 packages in 0.006 seconds - #> # package version by @ title #> 1 100 rtweet 0.7.0 Michael W. Kearney 9M Collecting Twitter Data #> 2 64 RSelenium 1.7.7 Ju Yeong Kim 8M R Bindings for 'Selen... #> 3 60 webmockr 0.7.0 Scott Chamberlain 8d Stubbing and Setting ... #> 4 50 visdat 0.5.3 Nicholas Tierney 2y Preliminary Visualisa... #> 5 50 wdman 0.2.5 Ju Yeong Kim 8M 'Webdriver'/'Selenium... #> 6 49 tabulizer 0.2.2 Tom Paskhalis 2y Bindings for 'Tabula'... #> 7 48 rfisheries 0.2 Karthik Ram 5y 'Programmatic Interfa... #> 8 45 chromer 0.1 Matthew Pennell 6y Interface to Chromoso... #> 9 45 googleLanguageR 0.3.0 Mark Edmondson 6M Call Google's 'Natura... #> 10 42 jsonld 2.2 Jeroen Ooms 4M JSON for Linking Data
ps()[]
#> # A tibble: 10 x 14 #> score package version title description date maintainer_name #> <dbl> <chr> <pckg_> <chr> <chr> <dttm> <chr> #> 1 700. rtweet 0.7.0 "Col… "An implem… 2020-01-08 22:00:10 Michael W. Kea… #> 2 451. RSelen… 1.7.7 "R B… "Provides … 2020-02-03 19:00:03 Ju Yeong Kim #> 3 418. webmoc… 0.7.0 "Stu… "Stubbing … 2020-09-30 18:40:02 Scott Chamberl… #> 4 351. visdat 0.5.3 "Pre… "Create pr… 2019-02-15 14:30:03 Nicholas Tiern… #> 5 350. wdman 0.2.5 "'We… "There are… 2020-01-31 21:30:02 Ju Yeong Kim #> 6 345. tabuli… 0.2.2 "Bin… "Bindings … 2018-06-07 18:16:06 Tom Paskhalis #> 7 337. rfishe… 0.2 "'Pr… "A program… 2016-02-19 08:50:03 Karthik Ram #> 8 317. chromer 0.1 "Int… "A program… 2015-01-13 10:27:09 Matthew Pennell #> 9 313. google… 0.3.0 "Cal… "Call 'Goo… 2020-04-19 12:40:02 Mark Edmondson #> 10 297. jsonld 2.2 "JSO… "JSON-LD i… 2020-05-27 06:20:03 Jeroen Ooms #> # … with 7 more variables: maintainer_email <chr>, revdeps <int>, #> # downloads_last_month <int>, license <chr>, url <chr>, bugreports <chr>, #> # package_data <I<list>>