test of seasonality I want to test if my series is seasonal, here pacf and the series I'm working on monthly data. is there a test under R seasonality?

 A: There are several tools you can use to explore the presence of seasonality. As you did, you can start by looking at the sample autocorrelation (functions stats::acf and stats::pacf). Significant autocorrelation at seasonal lags would suggest the presence of seasonality. The same would be suggested if significant peaks are observed in the periodogram at seasonal frequencies (function stats::spectrum).
You might as well test the significance of seasonal dummies as suggested by @RichardHardy. 
Other possible approach is to fit the basic structural time series model and check if the variance of the seasonal component is close to zero relatively to the other parameters (function stats::StructTS and package stsm).
You may also find some relevant test statistics in the documentation of function nsdiffs in package forecast.
You must be aware of breaks that may undergo the series (some are graphically observed around year 2009). These effects may distort the interpretation of the results based on the above tools. To handle this issue you may include dummies for these effects or explore the series at different subsamples.
For the series that you show, be aware that the data are annual rates that are obtained by taking seasonal differences in the raw data, this will remove most of the seasonality (if any) so I wouldn't expect a major seasonal pattern in this series.
You may be interested in the monthly index of the harmonised consumer price index, the raw index and the seasonally adjusted series are available here.
A: Any assumed filtering of the original series including taking seasonal differences may either work or possibly inject structure into the resultant series (Slutsky Effect). Consider what happens when you difference a white noise series, you create a a highly correlated resultant series. In this case seasonal dummies a possible approach in many cases proved to be of little value while a rather simple ARIMA model  rendered a set of residuals  free of identifiable structure  . Note that with 214 values the standard error of the acf is 1/sqrt(214) or about .06 can lead to unnecessary worry about the sufficiency of a model. The Actual/Fit/Forecast is here  . Note that the Actual/Cleansed plot is informative about the timing of the exceptional values  which are lost to the human eye due to the strong auto-regressive structure.
The answer to your question is that the data is seasonal as the ARIMA model includes a significant AR(12) coefficient .446 . The acf plot of the original series hints that there might be seasonal structure while an acf plot of first differences would more clearly show this [-.41 for the acf(12) ]
