README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# ggtime

<!-- badges: start -->
[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://lifecycle.r-lib.org/articles/stages.html#experimental)
[![CRAN status](https://www.r-pkg.org/badges/version/ggtime)](https://CRAN.R-project.org/package=ggtime)
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The ggtime package provides tools for graphically analysing time series, with exploration of trend and seasonality. It utilises the tsibble data format for time series and produces plots with ggplot2.

## Installation

You can install the development version of ggtime from [GitHub](https://github.com/) with:

```r
# install.packages("remotes")
remotes::install_github("tidyverts/ggtime")
```

## Example

```{r example}
library(ggtime)
library(tsibble)
library(ggplot2)
tsibbledata::aus_production %>% 
  autoplot(Bricks)

cal_trans_x <- function() {
  scales::trans_new(
    name = "calendar",
    transform = ggtime:::calendar_wrap,
    inverse = identity,
    breaks = scales::breaks_pretty(),
    domain = c(0, 60*60*24*7)
  )
}

pedestrian[with(pedestrian, Sensor == "Southern Cross Station" & Date < "2015-03-01"),] |> 
  autoplot(Count) +
  # coord_calendar(xlim = c(Sys.time(), Sys.Date() + lubridate::days(1)))
  ggplot2::coord_trans(x = cal_trans_x(), xlim = as.POSIXct(c("2024-03-25 00:00:00", "2024-03-31 23:59:59"))) +
  scale_x_datetime(date_breaks = "day", date_labels = "%a")

pedestrian[with(pedestrian, Sensor == "Southern Cross Station" & Date < "2015-03-01"),] |> 
  ggplot(aes(x = Date_Time, y = Count)) + 
  geom_path() +
  ggplot2::scale_x_continuous(transform = cal_trans_x(), limits = as.POSIXct(c("2024-03-25 00:00:00", "2024-03-31 23:59:59"))) + 
  coord_polar()
```