Creating multi-panel plots

Author

Phillip Alday, Douglas Bates, and Reinhold Kliegl

Published

2024-09-09

This notebook shows creating a multi-panel plot similar to Figure 2 of Fühner et al. (2021).

The data are available from the SMLP2024 example datasets.

Code 
using Arrow
using AlgebraOfGraphics
using CairoMakie   # for displaying static plots
using DataFrames
using Statistics
using StatsBase
using SMLP2024: dataset
tbl = dataset("fggk21")
Arrow.Table with 525126 rows, 7 columns, and schema:
 :Cohort  String
 :School  String
 :Child   String
 :Sex     String
 :age     Float64
 :Test    String
 :score   Float64
typeof(tbl)
Arrow.Table
df = DataFrame(tbl)
typeof(df)
DataFrame

1 Creating a summary data frame

The response to be plotted is the mean score by Test and Sex and age, rounded to the nearest 0.1 years.

The first task is to round the age to 1 digit after the decimal place, which can be done with select applied to a DataFrame. In some ways this is the most complicated expression in creating the plot so we will break it down. select is applied to DataFrame(dat), which is the conversion of the Arrow.Table, dat, to a DataFrame. This is necessary because an Arrow.Table is immutable but a DataFrame can be modified.

The arguments after the DataFrame describe how to modify the contents. The first : indicates that all the existing columns should be included. The other expression can be pairs (created with the => operator) of the form :col => function or of the form :col => function => :newname. (See the documentation of the DataFrames package for details.)

In this case the function is an anonymous function of the form round.(x, digits=1) where “dot-broadcasting” is used to apply to the entire column (see this documentation for details).

transform!(df, :age, :age => (x -> x .- 8.5) => :a1) # centered age (linear)
select!(groupby(df, :Test), :, :score => zscore => :zScore) # z-score
tlabels = [     # establish order and labels of tbl.Test
  "Run" => "Endurance",
  "Star_r" => "Coordination",
  "S20_r" => "Speed",
  "SLJ" => "PowerLOW",
  "BPT" => "PowerUP",
];

The next stage is a group-apply-combine operation to group the rows by Sex, Test and rnd_age then apply mean to the zScore and also apply length to zScore to record the number in each group.

df2 = combine(
  groupby(
    select(df, :, :age => ByRow(x -> round(x; digits=1)) => :age),
    [:Sex, :Test, :age],
  ),
  :zScore => mean => :zScore,
  :zScore => length => :n,
)
120×5 DataFrame
95 rows omitted
Row Sex Test age zScore n
String String Float64 Float64 Int64
1 male S20_r 8.0 -0.0265138 1223
2 male BPT 8.0 0.026973 1227
3 male SLJ 8.0 0.121609 1227
4 male Star_r 8.0 -0.0571726 1186
5 male Run 8.0 0.292695 1210
6 female S20_r 8.0 -0.35164 1411
7 female BPT 8.0 -0.610355 1417
8 female SLJ 8.0 -0.279872 1418
9 female Star_r 8.0 -0.268221 1381
10 female Run 8.0 -0.245573 1387
11 male S20_r 8.1 0.0608397 3042
12 male BPT 8.1 0.0955413 3069
13 male SLJ 8.1 0.123099 3069
⋮ ⋮ ⋮ ⋮ ⋮ ⋮
109 male Star_r 9.0 0.254973 4049
110 male Run 9.0 0.258082 4034
111 female S20_r 9.1 -0.0286172 1154
112 female BPT 9.1 -0.0752301 1186
113 female SLJ 9.1 -0.094587 1174
114 female Star_r 9.1 0.00276252 1162
115 female Run 9.1 -0.235591 1150
116 male S20_r 9.1 0.325745 1303
117 male BPT 9.1 0.616416 1320
118 male SLJ 9.1 0.267577 1310
119 male Star_r 9.1 0.254342 1297
120 male Run 9.1 0.251045 1294

2 Creating the plot

The AlgebraOfGraphics package applies operators to the results of functions such as data (specify the data table to be used), mapping (designate the roles of columns), and visual (type of visual presentation).

let
  design = mapping(:age, :zScore; color=:Sex, col=:Test)
  lines = design * linear()
  means = design * visual(Scatter; markersize=5)
  draw(data(df2) * means + data(df) * lines)
end

  • TBD: Relabel factor levels (Boys, Girls; fitness components for Test)
  • TBD: Relevel factors; why not levels from Tables?
  • TBD: Set range (7.8 to 9.2 and tick marks (8, 8.5, 9) of axes.
  • TBD: Move legend in plot?
Fühner, T., Granacher, U., Golle, K., & Kliegl, R. (2021). Age and sex effects in physical fitness components of 108,295 third graders including 515 primary schools and 9 cohorts. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-97000-4
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