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:
install.packages("sugrrants")
You could also install the development version from Github using:
# install.packages("remotes")
remotes::install_github("earowang/sugrrants")
Usage
Calendar-based graphics
The fully-fledged faceting calendar facet_calendar() unlocks
day-to-day stories.
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.
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, which has been participated in Google Summer of Code 2017 (gsoc), for R project for statistical computing.
A new function frame_calendar()
[here
and
here]
in the sugrrants package has been developed and documented for
calendar-based graphics. I have also written a vignette
[source
and reader
view],
which introduces and demonstrates the usage of the frame_calendar()
function. Many unit
tests
have been carried out to ensure the expected performance of this
function. The function implements non-standard evaluation and highlights
the tidy evaluation in action. The initial
release (v0.1.0) of the package has been published on
CRAN during the gsoc
summer time.
I have initialised a new R package
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],
and these functions are very likely to be changed or improved in the
future.
A new package rwalkr has been
created and released on
CRAN 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()
and
shine_melb(),
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()
has been done, but the interface needs improving after the gsoc.
Miscellaneous
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.