{"Package":"sugrrants","Title":"Supporting Graphs for Analysing Time Series","Version":"0.2.4","Authors@R":"\nc(person(given = \"Earo\",\nfamily = \"Wang\",\nrole = c(\"aut\", \"cre\"),\nemail = \"earo.wang@gmail.com\",\ncomment = c(ORCID = \"0000-0001-6448-5260\")),\nperson(given = \"Di\",\nfamily = \"Cook\",\nrole = c(\"aut\", \"ths\"),\ncomment = c(ORCID = \"0000-0002-3813-7155\")),\nperson(given = \"Rob\",\nfamily = \"Hyndman\",\nrole = c(\"aut\", \"ths\"),\ncomment = c(ORCID = \"0000-0002-2140-5352\")))","Description":"Provides 'ggplot2' graphics for analysing time\nseries data. It aims to fit into the 'tidyverse' and grammar of\ngraphics framework for handling temporal data.","License":"GPL (>= 3)","URL":"https://pkg.earo.me/sugrrants","BugReports":"https://github.com/earowang/sugrrants/issues","Depends":"ggplot2 (>= 2.2.0), R (>= 3.1.3)","Imports":"dplyr (>= 0.7.0), grid, gtable, lubridate (>= 1.7.1), rlang\n(>= 0.2.0)","Suggests":"covr, knitr, plotly, readr, rmarkdown, testthat, tidyr,\ntsibble (>= 0.6.0), viridis","VignetteBuilder":"knitr","Encoding":"UTF-8","Language":"en-GB","LazyData":"true","RoxygenNote":"6.1.1","NeedsCompilation":"no","Packaged":"2019-04-06 03:08:17 UTC; earo","Author":"Earo Wang [aut, cre] (),\nDi Cook [aut, ths] (),\nRob Hyndman [aut, ths] ()","Maintainer":"Earo Wang ","Repository":"CRAN","Date/Publication":"2019-04-06 04:40:03 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/sugrrants_0.2.4.tar.gz","jsonAuthors":[{"name":"Earo Wang","email":"earo.wang@gmail.com","maintainer":true},{"name":"Di Cook","email":null,"maintainer":false},{"name":"Rob Hyndman","email":null,"maintainer":false}],"readme":"\n\n\n# sugrrants\n\n[![Travis-CI Build\nStatus](https://travis-ci.org/earowang/sugrrants.svg?branch=master)](https://travis-ci.org/earowang/sugrrants)\n[![Coverage\nStatus](https://img.shields.io/codecov/c/github/earowang/sugrrants/master.svg)](https://codecov.io/github/earowang/sugrrants?branch=master)\n[![CRAN\\_Status\\_Badge](http://www.r-pkg.org/badges/version/sugrrants)](https://cran.r-project.org/package=sugrrants)\n[![Downloads](http://cranlogs.r-pkg.org/badges/sugrrants?color=brightgreen)](https://cran.r-project.org/package=sugrrants)\n\nThe goal of *sugrrants* is to provide supporting graphs with R for\nanalysing time series data. It aims to fit into the *tidyverse* and\ngrammar of graphics framework for handling temporal data.\n\n## Installation\n\nYou could install the stable version on CRAN:\n\n``` r\ninstall.packages(\"sugrrants\")\n```\n\nYou could also install the development version from Github using:\n\n``` r\n# install.packages(\"devtools\")\ndevtools::install_github(\"earowang/sugrrants\", build_vignettes = TRUE)\n```\n\n## Usage\n\n### Calendar-based graphics\n\nThe fully-fledged faceting calendar `facet_calendar()` unlocks\nday-to-day stories.\n\n``` r\nlibrary(dplyr)\nlibrary(sugrrants)\npedestrian %>%\n filter(Date < as.Date(\"2016-05-01\")) %>% \n ggplot(aes(x = Time, y = Hourly_Counts, colour = Sensor_Name)) +\n geom_line() +\n facet_calendar(~ Date) + # a variable contains dates\n theme(legend.position = \"bottom\")\n```\n\n![](man/figures/facet-calendar-1.png)\n\nOn the other hand, the `frame_calendar()` provides tools for\nre-structuring the data into a compact calendar layout, without using\nthe faceting method. It is fast and light-weight, although it does not\npreserve the values.\n\n``` r\np <- pedestrian %>%\n filter(Sensor_ID == 9, Year == 2016) %>%\n mutate(Weekend = if_else(Day %in% c(\"Saturday\", \"Sunday\"), \"Weekend\", \"Weekday\")) %>%\n frame_calendar(x = Time, y = Hourly_Counts, date = Date) %>% \n ggplot(aes(x = .Time, y = .Hourly_Counts, group = Date, colour = Weekend)) +\n geom_line() +\n theme(legend.position = \"bottom\")\nprettify(p)\n```\n\n![](man/figures/calendar-plot-1.png)\n\n## Google Summer of Code 2017\n\n

\n\nThis package is part of the project—[Tidy data structures and visual\nmethods to support exploration of big temporal-context\ndata](https://summerofcode.withgoogle.com/projects/#4790455121215488),\nwhich has been participated in Google Summer of Code 2017 (gsoc), for [R\nproject for statistical computing](https://www.r-project.org).\n\nA new function `frame_calendar()`\n\\[[here](https://github.com/earowang/sugrrants/blob/master/R/frame-calendar.R)\nand\n[here](https://github.com/earowang/sugrrants/blob/master/R/calendar-fun.R)\\]\nin the **sugrrants** package has been developed and documented for\ncalendar-based graphics. I have also written a vignette\n\\[[source](https://github.com/earowang/sugrrants/blob/master/vignettes/frame-calendar.Rmd)\nand [reader\nview](http://pkg.earo.me/sugrrants/articles/frame-calendar.html)\\],\nwhich introduces and demonstrates the usage of the `frame_calendar()`\nfunction. [Many unit\ntests](https://github.com/earowang/sugrrants/blob/master/tests/testthat/test-calendar.R)\nhave been carried out to ensure the expected performance of this\nfunction. The function implements non-standard evaluation and highlights\nthe [tidy\nevaluation](http://rlang.tidyverse.org/articles/tidy-evaluation.html) in\naction. The initial release (v0.1.0) of the package has been published\non [CRAN](https://CRAN.R-project.org/package=sugrrants) during the gsoc\nsummer time.\n\nI have initialised a new R package\n[**tsibble**](https://github.com/earowang/tsibble) for tidy temporal\ndata, as part of the project. The `tsibble()` function constructs a new\n`tbl_ts` class for temporal data, and the `as_tsibble()` helps to\nconvert a few `ts` objects into the `tbl_ts` class. Some key verbs\n(generics) from the **dplyr** package, such as `mutate()`,\n`summarise()`, `filter()`, have been defined and developed for the\n`tbl_ts` data class. The **tsibble** package was highly experimental\nover the period of the gsoc\n\\[[commits](https://github.com/earowang/tsibble/commit/aba1cfc2eec88966c43232fe5d249522f88e1e27)\\],\nand these functions are very likely to be changed or improved in the\nfuture.\n\nA new package [**rwalkr**](https://github.com/earowang/rwalkr) has been\ncreated and released on\n[CRAN](https://cran.r-project.org/package=rwalkr) during the gsoc\nsummer. This package provides API to Melbourne pedestrian sensor data\nand arrange the data in tidy temporal data form. Two functions including\n[`walk_melb()`](https://github.com/earowang/rwalkr/blob/master/R/scrape.R)\nand\n[`shine_melb()`](https://github.com/earowang/rwalkr/blob/master/R/shiny.R),\nhave been written and documented as the v0.1.0 and v0.2.0 releases on\nCRAN. The majority of the code for the function\n[`run_melb()`](https://github.com/earowang/rwalkr/blob/master/R/soda.R)\nhas been done, but the interface needs improving after the gsoc.\n\n

\n\n## Miscellaneous\n\nThe acronym of *sugrrants* is **SU**pporting **GR**aphs with **R** for\n**AN**alysing **T**ime **S**eries, pronounced as “sugar ants” that are a\nspecies of ant endemic to Australia.\n\n-----\n\nPlease note that this project is released with a [Contributor Code of\nConduct](.github/CODE_OF_CONDUCT.md). By participating in this project\nyou agree to abide by its terms.","jobInfo":{"package":"sugrrants","version":"0.2.4","parsingStatus":"success","parserVersion":1,"parsedAt":"2020-02-21T22:29:47+0000"}}

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