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:
# install.packages("remotes") remotes::install_github("earowang/sugrrants")
The fully-fledged faceting calendar
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
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
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 in action. The initial
release (v0.1.0) of the package has been published on
CRAN during the gsoc
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
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 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
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.