Based on the following dataset, the attached time series trace the evolution of attendance percentage at the Rotary Club of Shanghai over the period May 1920-September 1935 (inclusive).

• The first graph simply plots the time series using R Studio function "plot.ts() - using "na_interpol" from R package "imputeTS" to fill missing data 
• The second graph plots the same time series transformed into an additive model after calculating the natural lof of the original data using "log()" function. 
• The third graph plots the smoothed time series based a smoothing average of order n = 3 - using the function "SMA()" from R package "TTR" and "na_interpol" from R package "imputeTS" to fill missing data 
• The fourth graph plots the smoothed time series based a smoothing average of order n = 8 - using the function "SMA()" from R package "TTR" and "na_interpol" from R package "imputeTS" to fill missing data 
• The fifth graph plots the smoothed time series based a smoothing average of order n = 16 - using the function "SMA()" from R package "TTR" and "na_interpol" from R package "imputeTS" to fill missing data 
• The last graph plots the smoothed time series based a smoothing average of order n = 20 - using the function "SMA()" from R package "TTR" and "na_interpol" from R package "imputeTS" to fill missing data 

NB Since the attendance time series cannot be described as an additive model, it was not possible to decompose it.