Private Investment on AI by focus

Author

math4mad

ref

ref :Annual private investment in artificial intelligence, by focus area

Code

include("utils.jl")

[ Info: loading success

Code

make_title("Part 1 : data processing")

Part 1 : data processing

2. load data->dataframe

Code

df=@pipe CSV.File("./data/private-investment-in-artificial-intelligence-by-focus-area.csv")|>DataFrame
rename!(df,"European Union and United Kingdom"=>:EU,"United States"=>:US)
select!(df,Not("Code"))

160×6 DataFrame

135 rows omitted

Row	Entity	Year	World	EU	China	US
	String	Int64	Int64	Int64	Int64	Int64
1	AI ventures	2017	705626292	34720055	233168581	404163765
2	AI ventures	2018	1539250299	30321937	591347599	857474812
3	AI ventures	2019	1212544984	15388467	93042361	962517568
4	AI ventures	2020	1545821073	5170265	91954563	1358836062
5	AI ventures	2021	2379370717	1013359242	258977775	923024967
6	AI ventures	2022	1242838005	14814431	451988999	641729703
7	Agricultural technology	2017	309478503	7554717	10391318	261846503
8	Agricultural technology	2018	280024054	33194058	0	194254290
9	Agricultural technology	2019	502672339	116117865	18018197	283437548
10	Agricultural technology	2020	686735808	25459779	7959031	624487776
11	Agricultural technology	2021	1351178581	469886955	0	838525337
12	Agricultural technology	2022	810137282	76739673	95402811	509921645
13	Augmented or virtual reality	2017	313686338	28642184	156899836	107616472
⋮	⋮	⋮	⋮	⋮	⋮	⋮
149	Total	2017	28434536002	2687729049	7314151975	15548790772
150	Total	2018	46508641939	2955166939	15069705393	22716597215
151	Total	2019	58389972850	6202067697	14892596238	30795184157
152	Total	2020	64505628706	5599432467	17094749150	35531472847
153	Total	2021	125356874235	12500782453	22848295305	73395940859
154	Total	2022	85055265067	10217823819	12412402667	43848726664
155	Venture capital	2017	160103509	9424792	115426266	15476426
156	Venture capital	2018	217600809	0	107910146	109690663
157	Venture capital	2019	106903067	26497349	0	28617137
158	Venture capital	2020	179347432	0	124590452	4502008
159	Venture capital	2021	225784599	0	0	60011724
160	Venture capital	2022	14814431	14814431	0	0

Code

df2=@chain df begin 
   @pivot_longer(_, 3:6, names_to =area, values_to =investment)
   @mutate(investment=investment/10_000_000_000)
   @group_by(Entity)
   @arrange(Year)
   @ungroup
   @filter(Entity ∈ ["Medical and healthcare","Data management","Cybersecurity","Industrial automation","Semiconductors","Drones"])
end

144×4 DataFrame

119 rows omitted

Row	Entity	Year	area	investment
	String	Int64	String	Float64
1	Cybersecurity	2017	World	0.247797
2	Cybersecurity	2017	EU	0.0211647
3	Cybersecurity	2017	China	0.0799647
4	Cybersecurity	2017	US	0.0895792
5	Cybersecurity	2018	World	0.407162
6	Cybersecurity	2018	EU	0.0172524
7	Cybersecurity	2018	China	0.0583573
8	Cybersecurity	2018	US	0.191833
9	Cybersecurity	2019	World	0.411088
10	Cybersecurity	2019	EU	0.0161193
11	Cybersecurity	2019	China	0.196212
12	Cybersecurity	2019	US	0.171356
13	Cybersecurity	2020	World	0.379812
⋮	⋮	⋮	⋮	⋮
133	Semiconductors	2020	World	0.092762
134	Semiconductors	2020	EU	0.00523489
135	Semiconductors	2020	China	0.0207361
136	Semiconductors	2020	US	0.0576268
137	Semiconductors	2021	World	0.0632604
138	Semiconductors	2021	EU	0.00857049
139	Semiconductors	2021	China	0.020434
140	Semiconductors	2021	US	0.0156559
141	Semiconductors	2022	World	0.153211
142	Semiconductors	2022	EU	0.00114812
143	Semiconductors	2022	China	0.0946097
144	Semiconductors	2022	US	0.0536569

Code

 make_title("Part 2 :Plot")

Part 2 :Plot

Code

data_layer=data(df2)
  map_layer=mapping(:Year,:investment,layout=:Entity,color=:area)
  visual_layer = visual(Lines,linewidth=2)
  
  plt=data_layer*map_layer*visual_layer
  draw(plt,legend=(position=:bottom, titleposition=:left, framevisible=true, padding=2),facet = (;linkyaxes = :true))

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