# Plotting Financial Time Series Data (Multiple Columns) in R

Let's take one more example of plotting financial time series data. This time we will use the `EuStockMarkets`

data set that comes by default with R. It contains the daily closing prices of major European stock indices from 1991 to 1998.

### Check and Print the Data

Let's first check if the data is a time series and then print a few values from the dataset to get a feel of the data.

```
> is.ts(EuStockMarkets)
[1] TRUE
> head(EuStockMarkets)
DAX SMI CAC FTSE
[1,] 1628.75 1678.1 1772.8 2443.6
[2,] 1613.63 1688.5 1750.5 2460.2
[3,] 1606.51 1678.6 1718.0 2448.2
[4,] 1621.04 1684.1 1708.1 2470.4
[5,] 1618.16 1686.6 1723.1 2484.7
[6,] 1610.61 1671.6 1714.3 2466.8
>
```

As you can see, the dataset is a time series object and contains prices for 4 indices, namely, DAX, SMI, CAC and FTSE.

### Plot the Data

We can plot the data using the `plot()`

function to create a simple plot which will print all 4 series as shown below:

```
> plot(EuStockMarkets)
```

Alternatively we can use the `ts.plot()`

function to get the same plot but with a common y-axis. This time we are also adding a legend to the chart to make it more user-friendly.

```
> ts.plot(EuStockMarkets, col = 1:4, xlab = "Year", ylab = "Prices", main = "European Stock Indices")
> legend("topleft", colnames(EuStockMarkets), lty = 1, col = 1:4, bty = "n")
>
```

Notes: `col`

sets the colors of lines, `lty`

is the line type, and `bty`

is for the type of box to be drawn around the legend. `n`

represents no box.

#### Course Downloads

- Financial Time Series Data
- Exploring Time Series Data in R
- Plotting Time Series in R
- Handling Missing Values in Time Series
- Creating a Time Series Object in R
- Check if an object is a time series object in R
- Plotting Financial Time Series Data (Multiple Columns) in R
- Characteristics of Time Series
- Stationary Process in Time Series
- Transforming a Series to Stationary
- Time Series Transformation in R
- Differencing and Log Transformation
- Autocorrelation in R
- Time Series Models
- ARIMA Modeling
- Simulate White Noise (WN) in R
- Simulate Random Walk (RW) in R
- AutoRegressive (AR) Model in R
- Estimating AutoRegressive (AR) Model in R
- Forecasting with AutoRegressive (AR) Model in R
- Moving Average (MA) Model in R
- Estimating Moving Average (MA) Model in R
- ARIMA Modelling in R
- ARIMA Modelling - Identify Model for a Time Series
- Forecasting with ARIMA Modeling in R - Case Study
- Automatic Identification of Model Using auto.arima() Function in R
- Financial Time Series in R - Course Conclusion

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