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 data frame 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" ---------------------------------- 908 packages in 0.009 seconds -
#>   #     package         version  by                      @ title               
#>   1 100 survival        3.3.1    Terry M Therneau       4M Survival Analysis   
#>   2  10 survminer       0.4.9    Alboukadel Kassambara  1y Drawing Survival ...
#>   3  10 flexsurv        2.2      Christopher Jackson   29d Flexible Parametr...
#>   4   6 rpart           4.1.16   Beth Atkinson          6M Recursive Partiti...
#>   5   6 rstpm2          1.5.7    Mark Clements          3d Smooth Survival M...
#>   6   6 muhaz           1.2.6.4  David Winsemius        1y Hazard Function E...
#>   7   6 relsurv         2.2.7    Damjan Manevski        4M Relative Survival   
#>   8   5 pec             2022.5.4 Thomas A. Gerds        2M Prediction Error ...
#>   9   5 randomForestSRC 3.1.1    Udaya B. Kogalur      10d Fast Unified Rand...
#>  10   5 survRM2         1.0.4    Hajime Uno             1M Comparing Restric...

# Pagination
ps("networks")
#> - "networks" ---------------------------------- 744 packages in 0.008 seconds -
#>   #     package    version  by                    @ title                      
#>   1 100 igraph     1.3.3    Tamás Nepusz         2d Network Analysis and Vis...
#>   2  46 network    1.17.2   Carter T. Butts      2M Classes for Relational Data
#>   3  38 nnet       7.3.17   Brian Ripley         6M Feed-Forward Neural Netw...
#>   4  36 RCurl      1.98.1.7 CRAN Team            1M General Network (HTTP/FT...
#>   5  31 DiagrammeR 1.0.9    Richard Iannone      4M Graph/Network Visualization
#>   6  27 visNetwork 2.1.0    Benoit Thieurmel    10M Network Visualization us...
#>   7  26 ggraph     2.0.5    Thomas Lin Pedersen  1y An Implementation of Gra...
#>   8  26 sna        2.7      Carter T. Butts      2M Tools for Social Network...
#>   9  24 snow       0.4.4    Luke Tierney         9M Simple Network of Workst...
#>  10  18 neuralnet  1.44.2   Marvin N. Wright     3y Training of Neural Networks
more()
#> - "networks" ---------------------------------- 744 packages in 0.011 seconds -
#>   #    package         version by                    @ title                   
#>  11 15 bnlearn         4.7.1   Marco Scutari        4M Bayesian Network Stru...
#>  12 14 spatstat.linnet 2.3.2   Adrian Baddeley      5M Linear Networks Funct...
#>  13 14 ergm            4.2.2   Pavel N. Krivitsky   2M Fit, Simulate and Dia...
#>  14 14 networkD3       0.4     Christopher Gandrud  5y D3 JavaScript Network...
#>  15 13 torch           0.8.0   Daniel Falbel        1M Tensors and Neural Ne...
#>  16 13 WGCNA           1.71    Peter Langfelder     3M Weighted Correlation ...
#>  17 12 intergraph      2.0.2   Michal Bojanowski    7y Coercion Routines for...
#>  18 12 diagram         1.6.5   Karline Soetaert     2y Functions for Visuali...
#>  19 11 networkDynamic  0.11.2  Skye Bender-deMoll   2M Dynamic Extensions fo...
#>  20 11 RSNNS           0.4.14  Christoph Bergmeir   1y Neural Networks using...

# Details
ps("visualization")
#> - "visualization" ---------------------------- 1350 packages in 0.011 seconds -
#>   #     package    version by                    @ title                       
#>   1 100 ggplot2    3.3.6   Thomas Lin Pedersen  2M Create Elegant Data Visua...
#>   2  61 igraph     1.3.3   Tamás Nepusz         2d Network Analysis and Visu...
#>   3  59 scales     1.2.0   Hadley Wickham       3M Scale Functions for Visua...
#>   4  36 rgl        0.109.6 Duncan Murdoch       8d 3D Visualization Using Op...
#>   5  26 vdiffr     1.0.4   Lionel Henry         4M Visual Regression Testing...
#>   6  20 DiagrammeR 1.0.9   Richard Iannone      4M Graph/Network Visualization 
#>   7  18 corrplot   0.92    Taiyun Wei           8M Visualization of a Correl...
#>   8  18 ROCR       1.0.11  Felix G.M. Ernst     2y Visualizing the Performan...
#>   9  17 circlize   0.4.15  Zuguang Gu           2M Circular Visualization      
#>  10  17 visNetwork 2.1.0   Benoit Thieurmel    10M Network Visualization usi...
ps()
#> - "visualization" ---------------------------- 1350 packages in 0.011 seconds -
#> 
#> 1 ggplot2 @ 3.3.6                             Thomas Lin Pedersen, 2 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.
#>   https://ggplot2.tidyverse.org
#>   https://github.com/tidyverse/ggplot2
#> 
#> 2 igraph @ 1.3.3                                        Tamás Nepusz, a day 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
#>   https://igraph.discourse.group/
#> 
#> 3 scales @ 1.2.0                                   Hadley Wickham, 3 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 @ 0.109.6                                      Duncan Murdoch, 8 days 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://github.com/dmurdoch/rgl
#>   https://dmurdoch.github.io/rgl/
#> 
#> 5 vdiffr @ 1.0.4                                     Lionel Henry, 4 months 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://vdiffr.r-lib.org/
#>   https://github.com/r-lib/vdiffr
#> 
#> 6 DiagrammeR @ 1.0.9                              Richard Iannone, 4 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
#> 
#> 7 corrplot @ 0.92                                      Taiyun Wei, 8 months ago
#> -----------------
#>   # Visualization of a Correlation Matrix
#>   Provides a visual exploratory tool on correlation matrix that
#>   supports automatic variable reordering to help detect hidden patterns
#>   among variables.
#>   https://github.com/taiyun/corrplot
#> 
#> 8 ROCR @ 1.0.11                                   Felix G.M. Ernst, 2 years 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/
#> 
#> 9 circlize @ 0.4.15                                    Zuguang Gu, 2 months ago
#> -------------------
#>   # Circular Visualization
#>   Circular layout is an efficient way for the visualization of huge
#>   amounts of information. Here this package provides an implementation
#>   of circular layout generation in R as well as an enhancement of
#>   available software. The flexibility of the package is based on the
#>   usage of low-level graphics functions such that self-defined
#>   high-level graphics can be easily implemented by users for specific
#>   purposes. Together with the seamless connection between the powerful
#>   computational and visual environment in R, it gives users more
#>   convenience and freedom to design figures for better understanding
#>   complex patterns behind multiple dimensional data. The package is
#>   described in Gu et al. 2014 <doi:10.1093/bioinformatics/btu393>.
#>   https://github.com/jokergoo/circlize
#>   https://jokergoo.github.io/circlize_book/book/
#> 
#> 10 visNetwork @ 2.1.0                           Benoit Thieurmel, 10 months ago
#> ---------------------
#>   # Network Visualization using 'vis.js' Library
#>   Provides an R interface to the 'vis.js' JavaScript charting library.
#>   It allows an interactive visualization of networks.
#>   http://datastorm-open.github.io/visNetwork/

# See the underlying data frame
ps("ropensci")
#> - "ropensci" ---------------------------------- 232 packages in 0.007 seconds -
#>   #     package         version by                       @ title               
#>   1 100 rtweet          0.7.0   Michael W. Kearney      3y Collecting Twitte...
#>   2  74 vcr             1.0.2   Scott Chamberlain       1y Record 'HTTP' Cal...
#>   3  59 RSelenium       1.7.7   Ju Yeong Kim            2y R Bindings for 'S...
#>   4  56 webmockr        0.8.0   Scott Chamberlain       1y Stubbing and Sett...
#>   5  39 rfisheries      0.2     Karthik Ram             6y 'Programmatic Int...
#>   6  38 taxize          0.9.100 Zachary Foster          3M Taxonomic Informa...
#>   7  36 wdman           0.2.5   Ju Yeong Kim            2y 'Webdriver'/'Sele...
#>   8  35 googleLanguageR 0.3.0   Mark Edmondson          2y Call Google's 'Na...
#>   9  33 jsonld          2.2     Jeroen Ooms             2y JSON for Linking ...
#>  10  33 tracerer        2.2.2   Richèl J.C. Bilderbeek  1y Tracer from R       
ps()[]
#> # A data frame: 10 × 14
#>    score package   version title description date                maintainer_name
#>    <dbl> <chr>     <pckg_> <chr> <chr>       <dttm>              <chr>          
#>  1  978. rtweet    0.7.0   "Col… "An implem… 2020-01-08 22:00:10 Michael W. Kea…
#>  2  727. vcr       1.0.2   "Rec… "Record te… 2021-05-31 17:00:02 Scott Chamberl…
#>  3  575. RSelenium 1.7.7   "R B… "Provides … 2020-02-03 19:00:03 Ju Yeong Kim   
#>  4  552. webmockr  0.8.0   "Stu… "Stubbing … 2021-03-14 14:50:02 Scott Chamberl…
#>  5  377. rfisheri… 0.2     "'Pr… "A program… 2016-02-19 08:50:03 Karthik Ram    
#>  6  373. taxize    0.9.100 "Tax… "Interacts… 2022-04-22 07:30:02 Zachary Foster 
#>  7  356. wdman     0.2.5   "'We… "There are… 2020-01-31 21:30:02 Ju Yeong Kim   
#>  8  341. googleLa… 0.3.0   "Cal… "Call 'Goo… 2020-04-19 12:40:02 Mark Edmondson 
#>  9  326. jsonld    2.2     "JSO… "JSON-LD i… 2020-05-27 06:20:03 Jeroen Ooms    
#> 10  322. tracerer  2.2.2   "Tra… "'BEAST2' … 2021-05-30 08:40:03 Richèl J.C. Bi…
#> # … with 7 more variables: maintainer_email <chr>, revdeps <int>,
#> #   downloads_last_month <int>, license <chr>, url <chr>, bugreports <chr>,
#> #   package_data <I<list>>