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README.Rmd
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---
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
message = FALSE,
warning = FALSE,
echo = TRUE,
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/",
out.width = "100%"
)
options(width = 100)
```
# iNZightTS: Time Series Visualisation and Forecasting
[](https://github.com/iNZightVIT/iNZightTS/actions?workflow=R-CMD-check)
[](https://codecov.io/gh/iNZightVIT/iNZightTS?branch=dev)
[](http://www.gnu.org/licenses/gpl-3.0)
[](https://CRAN.R-project.org/package=iNZightTS)
[](https://CRAN.R-project.org/package=iNZightTS)
## Overview
The iNZightTS package provides some simple analysis tools for exploring time series data, which is used by [iNZight](https://github.com/iNZightVIT/iNZight). The package uses [tidyverts](https://tidyverts.org/) to store and process time series data and [ggplot2](https://github.com/tidyverse/ggplot2) to produce customisable graphics.
## Installation
The _stable_ version can be installed from [CRAN](https://cran.r-project.org/web/packages/iNZightTS/index.html):
```{r cran, eval = FALSE}
install.packages("iNZightTS")
```
Install from [GitHub](https://github.com/iNZightVIT/iNZightTS):
```{r github, eval = FALSE}
remotes::install_github("iNZightVIT/iNZightTS")
```
## Usage
```{r pkgs}
library(iNZightTS)
```
### Getting started
Use the `inzightts` function to create a temporal data set to be used by the functions of the package. The data is stored as a [tsibble](https://github.com/tidyverts/tsibble).
```{r data}
data <- visitorsQ |>
tidyr::pivot_longer(!Date, names_to = "Country", values_to = "Visitors") |>
inzightts(key = "Country")
data
```
### Graphics
Exploratory analysis of time series data starts from a smoothed line plot.
```{r raw-plot}
plot(data)
```
### Decompositions
Time series data often exhibit features such as trend, season and cycle. A decomposition plot breaks the data into visual components which simplies the analysis.
```{r decomp}
dcmp <- data |>
dplyr::filter(Country == "Australia") |>
inzightts() |>
decomp()
dcmp
```
```{r decomp-plot}
plot(dcmp, title = "Visitors to Australia")
```
### Visualising seasonal effects
Plots are helpful in revealing the underlying seasonal pattern of time series data.
```{r season-plot}
seasonplot(data)
```
```{r subseries-plot}
subseries(data)
```
### Forecasting
```{r forecast}
pred <- predict(data)
summary(pred)
```
```{r forecast-plot}
plot(pred)
```