Annual death rates in people of different ages

Author

math4mad

ref

ref :Annual death rates in people of different ages

Code

include("utils.jl")

[ Info: loading success

["Entity", "Code", "Year", "Central death rate - Type: period - Sex: both - Age: 0", "Central death rate - Type: period - Sex: both - Age: 10", "Central death rate - Type: period - Sex: both - Age: 25", "Central death rate - Type: period - Sex: both - Age: 45", "Central death rate - Type: period - Sex: both - Age: 65", "Central death rate - Type: period - Sex: both - Age: 80"]

2. load data->dataframe

Code

df=@pipe CSV.File("./data/annual-death-rates-in-different-age-groups.csv")|>DataFrame
cats=names(df)
rename!(df,  "Central death rate - Type: period - Sex: both - Age: 0"=>:age0,
              "Central death rate - Type: period - Sex: both - Age: 10"=>:age10,
              "Central death rate - Type: period - Sex: both - Age: 25"=>:age25,
              "Central death rate - Type: period - Sex: both - Age: 45"=>:age45,
              "Central death rate - Type: period - Sex: both - Age: 65"=>:age65,
              "Central death rate - Type: period - Sex: both - Age: 80"=>:age80    
)
first(df,10)

10×9 DataFrame

Row	Entity	Code	Year	age0	age10	age25	age45	age65	age80
	String	String15?	Int64	Float64	Float64	Float64	Float64	Float64	Float64
1	Afghanistan	AFG	1950	353.469	7.10599	14.9553	24.7113	72.9251	219.856
2	Afghanistan	AFG	1951	350.066	7.04221	14.8208	24.475	72.3925	218.43
3	Afghanistan	AFG	1952	342.826	6.91651	14.5584	24.0431	71.3978	215.819
4	Afghanistan	AFG	1953	335.489	6.79443	14.3049	23.6353	70.4698	213.431
5	Afghanistan	AFG	1954	328.739	6.88345	14.2338	23.4879	70.0566	211.938
6	Afghanistan	AFG	1955	320.919	6.55437	13.8029	22.8619	68.6513	208.779
7	Afghanistan	AFG	1956	313.825	6.44994	13.5662	22.4995	67.7879	206.572
8	Afghanistan	AFG	1957	306.734	6.31585	13.2953	22.0842	66.7916	204.067
9	Afghanistan	AFG	1958	299.564	6.1871	13.0174	21.6528	65.7387	201.421
10	Afghanistan	AFG	1959	292.941	6.06023	12.7585	21.249	64.7374	198.98

Code

make_title("Part 2: Plot")

Part 2: Plot

Code

france=@chain df begin 
   @filter(Entity=="France")
   @select(3:9)
   @pivot_longer(_, 2:7, names_to = age_group, values_to =rate)
   #@group_by(age_group)
end

two_country=@chain df begin 
   @select(1,3:9)
   @pivot_longer(_, 3:8, names_to = age_group, values_to =rate)
   @filter(Entity ∈ ["France","South Africa"])
   @group_by(Entity)
   @arrange(Year)
   @ungroup
end

1668×4 DataFrame

1643 rows omitted

Row	Entity	Year	age_group	rate
	String	Int64	String	Float64
1	France	1816	age0	205.34
2	France	1816	age10	6.15
3	France	1816	age25	10.25
4	France	1816	age45	14.52
5	France	1816	age65	46.12
6	France	1816	age80	163.13
7	France	1817	age0	200.27
8	France	1817	age10	6.42
9	France	1817	age25	9.64
10	France	1817	age45	14.81
11	France	1817	age65	48.33
12	France	1817	age80	159.54
13	France	1818	age0	201.52
⋮	⋮	⋮	⋮	⋮
1657	South Africa	2020	age0	21.4911
1658	South Africa	2020	age10	0.40998
1659	South Africa	2020	age25	3.14994
1660	South Africa	2020	age45	9.43341
1661	South Africa	2020	age65	35.0478
1662	South Africa	2020	age80	86.7938
1663	South Africa	2021	age0	27.3008
1664	South Africa	2021	age10	0.46976
1665	South Africa	2021	age25	3.63036
1666	South Africa	2021	age45	10.8592
1667	South Africa	2021	age65	41.6478
1668	South Africa	2021	age80	109.58

Code

  data_layer=data(two_country)
  map_layer=mapping(:Year,:rate,layout=:age_group,color=:Entity)
  visual_layer = visual(Lines,linewidth=2)
  
  plt=data_layer*map_layer*visual_layer
  draw(plt,facet = (;linkyaxes = :none),legend=(position=:bottom, titleposition=:left, framevisible=true, padding=2))

┌ Warning: Keyword argument `bgcolor` is deprecated, use `backgroundcolor` instead.
└ @ Makie ~/.julia/packages/Makie/RgxaV/src/makielayout/blocks/legend.jl:23