# Does it make sense to convert a time plot to a distribution

I'm plotting time series data where the 5th-95th percentile values overlap so it looks pretty messy. The data is based on a simulation which tracks military 96,000 military personnel purchase of clothing, which is based on a point system. What I'm plotting in the upper chart is the total points used by all personnel in each year from Year 1 (FY 17/18) to Year 7.

To make the charts readable I just used the means in a plot, which looks like this:

You see the mean of S8 rises from year 1 to year 2 and stays nearly constant for the next 5 years.

I thought it would look better if I just plotted the data as a distribution, but I wasn't sure if this made sense given that it's no longer a time series. The chart I got looks like this:

Sample Data for the line and dist charts are as follows:

S8Line <- structure(list(X1.23421911 = c("2 23565938", "3 23583827", "4 23627453", "5 23608291", "6 23600443", "7 23590607", "1 23417838", "2 23619367", "3 23633472", "4 23623865", "5 23599065", "6 23589071", "7 23642366", "1 23433516")), row.names = c(NA, 14L), class = "data.frame")

S8Dist <- structure(list(X1.23421911 = c("2 23565938", "3 23583827", "4 23627453", "5 23608291", "6 23600443", "7 23590607", "1 23417838", "2 23619367", "3 23633472", "4 23623865", "5 23599065", "6 23589071", "7 23642366", "1 23433516")), row.names = c(NA, 14L), class = "data.frame")

• The relationships among your plots are obscure: could you please describe your data and explain how the bottom plots reflect the top plot?
– whuber
Mar 25, 2020 at 21:48
• I added some info in the OP. Mar 25, 2020 at 21:54