Multilevel (Hierarchical) Axis

[bray2016]

Looking for a way to specify two separate axis layers for a line plot. Something like this figure. I've been able to find a lot of examples of how to do this with categorical distinctions, but not with a date variable.

[bjarthur]

you mean you just want to plot multiple lines with a time axis?

julia> using Gadfly, Dates, DataFrames

julia> df = DataFrame(t=Date(2020):Month(1):Date(2021), x=1:13, y=2:14, z=3:15)
13×4 DataFrame
 Row │ t           x      y      z     
     │ Date        Int64  Int64  Int64 
─────┼─────────────────────────────────
   1 │ 2020-01-01      1      2      3
   2 │ 2020-02-01      2      3      4
   3 │ 2020-03-01      3      4      5
   4 │ 2020-04-01      4      5      6
   5 │ 2020-05-01      5      6      7
   6 │ 2020-06-01      6      7      8
   7 │ 2020-07-01      7      8      9
   8 │ 2020-08-01      8      9     10
   9 │ 2020-09-01      9     10     11
  10 │ 2020-10-01     10     11     12
  11 │ 2020-11-01     11     12     13
  12 │ 2020-12-01     12     13     14
  13 │ 2021-01-01     13     14     15

julia> plot(stack(df, [:x, :y, :z]), x=:t, y=:value, color=:variable, Geom.line)

[bray2016]

Im more looking for a way to have month and then have year below each month for multiple years. Something like:

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec ----------------------- 2013------------------------------------------------------2014

Ignore the dashes, they are just to get the years in the right spots.

[Mattriks]

Perhaps like in https://github.com/GiovineItalia/Gadfly.jl/issues/1310#issuecomment-577388786, but with xgroup=:year, x=:month. That style would emphasize the year-to-year differences, if that is of interest.

[bray2016]

Thanks for the suggestion. The issue is I am dealing with about 20 years worth of time series data, so I think showing 20 separate panels of yearly data could be a little overwhelming. Ideally, I could show it all on one graph so one could see the monthly variation for specific years as well as the aggregate trend across years