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https://github.com/earowang/sugrrants

SUpporting GRaphics with R for ANalysing Time Series
https://github.com/earowang/sugrrants

r statistical-graphics time-series

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SUpporting GRaphics with R for ANalysing Time Series

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README 

        
---

output: 

  github_document:

    html_preview: false

---



```{r, echo = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE, comment = "#>", fig.path = "man/figures/"

)

options(tibble.print_min = 5)

```



# sugrrants



[![R build status](https://github.com/earowang/sugrrants/workflows/R-CMD-check/badge.svg)](https://github.com/earowang/sugrrants/actions?workflow=R-CMD-check)

[![CRAN_Status_Badge](http://www.r-pkg.org/badges/version/sugrrants)](https://cran.r-project.org/package=sugrrants)

[![Downloads](http://cranlogs.r-pkg.org/badges/sugrrants?color=brightgreen)](https://cran.r-project.org/package=sugrrants)



The goal of *sugrrants* is to provide supporting graphs with R for analysing time series data. It aims to fit into the *tidyverse* and grammar of graphics framework for handling temporal data.



## Installation



You could install the stable version on CRAN:



```{r, eval = FALSE}

install.packages("sugrrants")

```



You could also install the development version from Github using:



```{r, eval = FALSE}

# install.packages("remotes")

remotes::install_github("earowang/sugrrants")

```



## Usage



### Calendar-based graphics



The fully-fledged faceting calendar `facet_calendar()` unlocks day-to-day stories.



```{r facet-calendar, echo = TRUE, message = FALSE, fig.height = 9, fig.width = 9}

library(dplyr)

library(sugrrants)

hourly_peds %>%

  filter(Date < as.Date("2016-05-01")) %>% 

  ggplot(aes(x = Time, y = Hourly_Counts, colour = Sensor_Name)) +

  geom_line() +

  facet_calendar(~ Date) + # a variable contains dates

  theme_bw() +

  theme(legend.position = "bottom")

```



On the other hand, the `frame_calendar()` provides tools for re-structuring the data into a compact calendar layout, without using the faceting method. It is fast and light-weight, although it does not preserve the values.



```{r calendar-plot, echo = TRUE, fig.height = 7, fig.width = 7}

p <- hourly_peds %>%

  filter(Sensor_ID == 9, Year == 2016) %>%

  mutate(Weekend = if_else(Day %in% c("Saturday", "Sunday"), "Weekend", "Weekday")) %>%

  frame_calendar(x = Time, y = Hourly_Counts, date = Date) %>% 

  ggplot(aes(x = .Time, y = .Hourly_Counts, group = Date, colour = Weekend)) +

  geom_line() +

  theme(legend.position = "bottom")

prettify(p)

```



## Google Summer of Code 2017



This package is part of the project---[Tidy data structures and visual methods to support exploration of big temporal-context data](https://summerofcode.withgoogle.com/projects/#4790455121215488), which has been participated in Google Summer of Code 2017 (gsoc), for [R project for statistical computing](https://www.r-project.org). 



A new function `frame_calendar()` [[here](https://github.com/earowang/sugrrants/blob/master/R/frame-calendar.R) and [here](https://github.com/earowang/sugrrants/blob/master/R/calendar-fun.R)] in the **sugrrants** package has been developed and documented for calendar-based graphics. I have also written a vignette [[source](https://github.com/earowang/sugrrants/blob/master/vignettes/frame-calendar.Rmd) and [reader view](https://pkg.earo.me/sugrrants/articles/frame-calendar.html)], which introduces and demonstrates the usage of the `frame_calendar()` function. [Many unit tests](https://github.com/earowang/sugrrants/blob/master/tests/testthat/test-calendar.R) have been carried out to ensure the expected performance of this function. The function implements non-standard evaluation and highlights the [tidy evaluation](https://rlang.r-lib.org) in action. The initial release (v0.1.0) of the package has been published on [CRAN](https://CRAN.R-project.org/package=sugrrants) during the gsoc summer time.



I have initialised a new R package [**tsibble**](https://github.com/tidyverts/tsibble) for tidy temporal data, as part of the project. The `tsibble()` function constructs a new `tbl_ts` class for temporal data, and the `as_tsibble()` helps to convert a few `ts` objects into the `tbl_ts` class. Some key verbs (generics) from the **dplyr** package, such as `mutate()`, `summarise()`, `filter()`, have been defined and developed for the `tbl_ts` data class. The **tsibble** package was highly experimental over the period of the gsoc [[commits](https://github.com/tidyverts/tsibble/commit/aba1cfc2eec88966c43232fe5d249522f88e1e27)], and these functions are very likely to be changed or improved in the future.



A new package [**rwalkr**](https://github.com/earowang/rwalkr) has been created and released on [CRAN](https://cran.r-project.org/package=rwalkr) during the gsoc summer. This package provides API to Melbourne pedestrian sensor data and arrange the data in tidy temporal data form. Two functions including [`walk_melb()`](https://github.com/earowang/rwalkr/blob/master/R/scrape.R) and [`shine_melb()`](https://github.com/earowang/rwalkr/blob/master/R/shiny.R), have been written and documented as the v0.1.0 and v0.2.0 releases on CRAN. The majority of the code for the function [`run_melb()`](https://github.com/earowang/rwalkr/blob/master/R/soda.R) has been done, but the interface needs improving after the gsoc.



## Miscellaneous



The acronym of *sugrrants* is **SU**pporting **GR**aphs with **R** for **AN**alysing **T**ime **S**eries, pronounced as "sugar ants" that are a species of ant endemic to Australia.



---

Please note that this project is released with a [Contributor Code of Conduct](https://github.com/earowang/sugrrants/blob/master/.github/CODE_OF_CONDUCT.md). By participating in this project you agree to abide by its terms.