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.
You could install the stable version on CRAN:
You could also install the development version from Github using:
The fully-fledged faceting calendar
facet_calendar() unlocks day-to-day stories.
filter(Date < as.Date("2016-05-01")) %>%
ggplot(aes(x = Time, y = Hourly_Counts, colour = Sensor_Name)) +
facet_calendar(~ Date) + # a variable contains dates
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.
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)) +
theme(legend.position = "bottom")
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
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()`
in the **sugrrants** package has been developed and documented for
calendar-based graphics. I have also written a vignette
which introduces and demonstrates the usage of the `frame_calendar()`
function. [Many unit
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
I have initialised a new R package
[**tsibble**](https://github.com/earowang/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
and these functions are very likely to be changed or improved in the
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
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
has been done, but the interface needs improving after the gsoc.
The acronym of sugrrants is SUpporting GRaphs with R for ANalysing Time Series, 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. By participating in this project you agree to abide by its terms.