This article describes how to produce an **interactive visualization of time series** data frame and objects using the *highcharter* R package.

Contents:

## Prerequisites

```
# Load required R packages
library(tidyverse)
library(highcharter)
# Set highcharter options
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 2)))
```

## Economic time series data

```
data("economics_long", package = "ggplot2")
# Check automatically if the x column is date class
economics_long2 <- economics_long %>%
filter(variable %in% c("pop", "uempmed", "unemploy"))
economics_long2
```

```
## # A tibble: 1,722 x 4
## date variable value value01
## <date> <chr> <dbl> <dbl>
## 1 1967-07-01 pop 198712 0
## 2 1967-08-01 pop 198911 0.00164
## 3 1967-09-01 pop 199113 0.00330
## 4 1967-10-01 pop 199311 0.00492
## 5 1967-11-01 pop 199498 0.00646
## 6 1967-12-01 pop 199657 0.00777
## # … with 1,716 more rows
```

```
hc <- economics_long2 %>%
hchart(
"line",
hcaes(x = date, y = value01, group = variable)
)
```

`hc`

## Visualize a time series object

`hc <- hchart(LakeHuron)`

`hc`

## Multivariate Time series

```
x <- cbind(mdeaths, fdeaths)
hc <- hchart(x)
```

`hc`

## Seasonal Decomposition of Time Series by Loess

```
x <- stl(log(AirPassengers), "per")
hc <- hchart(x)
```

`hc`

## Forecast package

```
library(forecast)
x <- forecast(ets(USAccDeaths), h = 48, level = 95)
hc <- hchart(x)
```

`hc`

## Quantmod package

The highstock extension is used to chart `xts`

and `xts ohlc`

classes from the quantmod package.

```
library(quantmod)
# `xts objects
x <- getFX("USD/JPY", auto.assign = FALSE)
hc <- hchart(x)
```

`hc`

```
# `xts ohlc` objects
y <- getSymbols("SPY", auto.assign = FALSE)
hc <- hchart(y)
```

`hc`

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