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ch10.jl
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ch10.jl
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# Bogumił Kamiński, 2022
# Codes for chapter 10
# Code for section 10.1
aq = [10.0 8.04 10.0 9.14 10.0 7.46 8.0 6.58
8.0 6.95 8.0 8.14 8.0 6.77 8.0 5.76
13.0 7.58 13.0 8.74 13.0 12.74 8.0 7.71
9.0 8.81 9.0 8.77 9.0 7.11 8.0 8.84
11.0 8.33 11.0 9.26 11.0 7.81 8.0 8.47
14.0 9.96 14.0 8.1 14.0 8.84 8.0 7.04
6.0 7.24 6.0 6.13 6.0 6.08 8.0 5.25
4.0 4.26 4.0 3.1 4.0 5.39 19.0 12.50
12.0 10.84 12.0 9.13 12.0 8.15 8.0 5.56
7.0 4.82 7.0 7.26 7.0 6.42 8.0 7.91
5.0 5.68 5.0 4.74 5.0 5.73 8.0 6.89];
using DataFrames
# Code for listing 10.1
aq1 = DataFrame(aq, ["x1", "y1", "x2", "y2", "x3", "y3", "x4", "y4"])
DataFrame(aq, [:x1, :y1, :x2, :y2, :x3, :y3, :x4, :y4])
# Code for creating DataFrame with automatic column names
DataFrame(aq, :auto)
# Codes for creating DataFrame from vector of vectors
aq_vec = collect(eachcol(aq))
DataFrame(aq_vec, ["x1", "y1", "x2", "y2", "x3", "y3", "x4", "y4"])
DataFrame(aq_vec, :auto)
# Codes for section 10.1.2
data = (set1=(x=aq[:, 1], y=aq[:, 2]),
set2=(x=aq[:, 3], y=aq[:, 4]),
set3=(x=aq[:, 5], y=aq[:, 6]),
set4=(x=aq[:, 7], y=aq[:, 8]));
data.set1.x
DataFrame(x1=data.set1.x, y1=data.set1.y,
x2=data.set2.x, y2=data.set2.y,
x3=data.set3.x, y3=data.set3.y,
x4=data.set4.x, y4=data.set4.y)
DataFrame(:x1 => data.set1.x, :y1 => data.set1.y,
:x2 => data.set2.x, :y2 => data.set2.y,
:x3 => data.set3.x, :y3 => data.set3.y,
:x4 => data.set4.x, :y4 => data.set4.y)
DataFrame([:x1 => data.set1.x, :y1 => data.set1.y,
:x2 => data.set2.x, :y2 => data.set2.y,
:x3 => data.set3.x, :y3 => data.set3.y,
:x4 => data.set4.x, :y4 => data.set4.y]);
[(i, v) for i in 1:4 for v in [:x, :y]]
[string(v, i) for i in 1:4 for v in [:x, :y]]
[string(v, i) => getproperty(data[i], v)
for i in 1:4 for v in [:x, :y]]
DataFrame([string(v, i) => getproperty(data[i], v)
for i in 1:4 for v in [:x, :y]]);
data_dict = Dict([string(v, i) => getproperty(data[i], v)
for i in 1:4 for v in [:x, :y]])
collect(data_dict)
DataFrame(data_dict)
df1 = DataFrame(x1=data.set1.x)
df1.x1 === data.set1.x
df2 = DataFrame(x1=data.set1.x; copycols=false)
df2.x1 === data.set1.x
df = DataFrame(x=1:3, y=1)
df.x
DataFrame(x=[1], y=[1, 2, 3])
using RCall
r_df = R"data.frame(a=1:6, b=1:2, c=1:3)"
julia_df = rcopy(r_df)
# Codes for section 10.1.3
data.set1
DataFrame(data.set1)
DataFrame([(a=1, b=2), (a=3, b=4), (a=5, b=6)])
data
# Code for listing 10.2
aq2 = DataFrame(data)
# Codes for section 10.1.4
aq1
using Statistics
using StatsBase
cor_mat = pairwise(cor, eachcol(aq1))
using Plots
heatmap(names(aq1), names(aq1), cor_mat;
aspect_ratio=:equal, size=(400, 400),
rightmargin=5Plots.mm)
# Codes for listing 10.3
data_dfs = map(DataFrame, data)
# Codes for vertical concatenation examples
vcat(data_dfs.set1, data_dfs.set2, data_dfs.set3, data_dfs.set4)
vcat(data_dfs.set1, data_dfs.set2, data_dfs.set3, data_dfs.set4;
source="source_id")
vcat(data_dfs.set1, data_dfs.set2, data_dfs.set3, data_dfs.set4;
source="source_id"=>string.("set", 1:4))
reduce(vcat, collect(data_dfs);
source="source_id"=>string.("set", 1:4))
# Code for listing 10.4
df1 = DataFrame(a=1:3, b=11:13)
df2 = DataFrame(a=4:6, c=24:26)
vcat(df1, df2)
vcat(df1, df2; cols=:union)
# Code for listing 10.5
df_agg = DataFrame()
append!(df_agg, data_dfs.set1)
append!(df_agg, data_dfs.set2)
# Code for appending tables to a data frame
df_agg = DataFrame()
append!(df_agg, data.set1)
append!(df_agg, data.set2)
# Code for promote keyword argument
df1 = DataFrame(a=1:3, b=11:13)
df2 = DataFrame(a=4:6, b=[14, missing, 16])
append!(df1, df2)
append!(df1, df2; promote=true)
# Code for section 10.2.3
df = DataFrame()
push!(df, (a=1, b=2))
push!(df, (a=3, b=4))
df = DataFrame(a=Int[], b=Int[])
push!(df, [1, 2])
push!(df, [3, 4])
function sim_step(current)
dx, dy = rand(((1,0), (-1,0), (0,1), (0,-1)))
return (x=current.x + dx, y=current.y + dy)
end
using BenchmarkTools
@btime rand(((1,0), (-1,0), (0,1), (0,-1)));
dx, dy = (10, 20)
dx
dy
using FreqTables
using Random
Random.seed!(1234);
proptable([rand(((1,0), (-1,0), (0,1), (0,-1))) for _ in 1:10^7])
using Random
Random.seed!(6);
walk = DataFrame(x=0, y=0)
for _ in 1:10
current = walk[end, :]
push!(walk, sim_step(current))
end
walk
using Plots
plot(walk.x, walk.y;
legend=false,
series_annotations=1:11,
xticks=range(extrema(walk.x)...),
yticks=range(extrema(walk.y)...))
extrema(walk.y)
range(1, 5)
(3/4)^9
# Code for listing 10.6
function walk_unique() #A
walk = DataFrame(x=0, y=0)
for _ in 1:10
current = walk[end, :]
push!(walk, sim_step(current))
end
return nrow(unique(walk)) == nrow(walk) #B
end
Random.seed!(2);
proptable([walk_unique() for _ in 1:10^5])
# code for serialization
using Serialization
serialize("walk.bin", walk)
deserialize("walk.bin") == walk