Supporting Graphs for Analysing Time Series
Provides 'ggplot2' graphics for analysing time
series data. It aims to fit into the 'tidyverse' and grammar of
graphics framework for handling temporal data.
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("devtools") devtools::install_github("earowang/sugrrants", build_vignettes = TRUE)
The fully-fledged faceting calendar
library(dplyr) library(sugrrants) pedestrian %>% 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(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 <- 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) %>% ggplot(aes(x = .Time, y = .Hourly_Counts, group = Date, colour = Weekend)) + geom_line() + theme(legend.position = "bottom") prettify(p)
Google Summer of Code 2017
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_tsclass for temporal data, and the
as_tsibble()helps to convert a few
tsobjects into the
tbl_tsclass. Some key verbs (generics) from the dplyr package, such as
filter(), have been defined and developed for the
tbl_tsdata 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
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.
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.
Functions in sugrrants
|sugrrants-package||sugrrants: supporting graphs for analysing time series|
|stat_acf||Autocorrelation for temporal data|
|frame_calendar||Rearrange a temporal data frame to a calendar-based data format using linear algebra|
|reexports||Objects exported from other packages|
|pedestrian||Pedestrian counts in Melbourne city|
|geom_acf||Autocorrelation for temporal data|
|draw-key||Key drawing functions|
|facet_calendar||Lay out panels in a calendar format|
Vignettes of sugrrants
Last month downloads
|License||GPL (>= 3)|
|Packaged||2019-04-06 03:08:17 UTC; earo|
|Date/Publication||2019-04-06 04:40:03 UTC|
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