SUpporting GRaphics with R for ANalysing Time Series
R Makefile
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
.github `use_tidy_github()` Jul 15, 2018
R avoid strong dependency on readr Aug 9, 2018
data-raw
data mv tsibble and dplyr-verbs to a new package tsibble Jul 20, 2017
docs
man avoid strong dependency on readr Aug 9, 2018
tests fixed #9 Jul 15, 2018
vignettes cran release Jun 5, 2018
.Rbuildignore
.gitignore export magrittr %>% Jun 11, 2017
.travis.yml added codecov badge into README Jul 22, 2017
DESCRIPTION
Makefile added unit test for frame_calendar.tbl_ts() Jun 2, 2018
NAMESPACE avoid strong dependency on readr Aug 9, 2018
NEWS.md
README.Rmd `use_tidy_github()` Jul 15, 2018
README.md `use_tidy_github()` Jul 15, 2018
_pkgdown.yml
codecov.yml init codecov Jul 22, 2017
cran-comments.md new release to cran Aug 19, 2018
sugrrants.Rproj

README.md

sugrrants

Travis-CI Build Status Coverage Status CRAN_Status_Badge Downloads

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("devtools")
devtools::install_github("earowang/sugrrants", build_vignettes = TRUE)

Usage

Calendar-based graphics

library(dplyr)
library(sugrrants)

calendar_df <- pedestrian %>%
  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, calendar = "monthly"
  )
calendar_df
#> # A tibble: 8,780 x 13
#>   Date_Time           Date        Year Month Mdate Day    Time Sensor_ID
#> * <dttm>              <date>     <int> <ord> <int> <ord> <int>     <int>
#> 1 2016-01-01 00:00:00 2016-01-01  2016 Janu…     1 Frid…     0         9
#> 2 2016-01-01 01:00:00 2016-01-01  2016 Janu…     1 Frid…     1         9
#> 3 2016-01-01 02:00:00 2016-01-01  2016 Janu…     1 Frid…     2         9
#> 4 2016-01-01 03:00:00 2016-01-01  2016 Janu…     1 Frid…     3         9
#> 5 2016-01-01 04:00:00 2016-01-01  2016 Janu…     1 Frid…     4         9
#> # ... with 8,775 more rows, and 5 more variables: Sensor_Name <chr>,
#> #   Hourly_Counts <int>, Weekend <chr>, .Time <dbl>, .Hourly_Counts <dbl>
p <- calendar_df %>%
  ggplot(aes(x = .Time, y = .Hourly_Counts, group = Date, colour = Weekend)) +
  geom_line() +
  theme(legend.position = "bottom")
prettify(p, label.padding = unit(0.08, "lines"))

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.