A day with (the) Julia (language)

In [29]:

    

# Pkg.add("DataFrames")


    

In [30]:

    

using DataFrames # DataFrames to represent tabular datasets
                 # Database-style joins and indexing
                 # Split-apply-combine operations, reshape and pivoting
                 # Formula and model frames


    

In [31]:

    

run(`head data/iris.csv`)


    

    




"SepalLength","SepalWidth","PetalLength","PetalWidth","Species"
5.1,3.5,1.4,0.2,"setosa"
4.9,3.0,1.4,0.2,"setosa"
4.7,3.2,1.3,0.2,"setosa"
4.6,3.1,1.5,0.2,"setosa"
5.0,3.6,1.4,0.2,"setosa"
5.4,3.9,1.7,0.4,"setosa"
4.6,3.4,1.4,0.3,"setosa"
5.0,3.4,1.5,0.2,"setosa"
4.4,2.9,1.4,0.2,"setosa"

In [32]:

    

iris = readtable("data/iris.csv")


    

    
Out[32]:




SepalLength
SepalWidth
PetalLength
PetalWidth
Species
1
5.1
3.5
1.4
0.2
setosa
2
4.9
3.0
1.4
0.2
setosa
3
4.7
3.2
1.3
0.2
setosa
4
4.6
3.1
1.5
0.2
setosa
5
5.0
3.6
1.4
0.2
setosa
6
5.4
3.9
1.7
0.4
setosa
7
4.6
3.4
1.4
0.3
setosa
8
5.0
3.4
1.5
0.2
setosa
9
4.4
2.9
1.4
0.2
setosa
10
4.9
3.1
1.5
0.1
setosa
11
5.4
3.7
1.5
0.2
setosa
12
4.8
3.4
1.6
0.2
setosa
13
4.8
3.0
1.4
0.1
setosa
14
4.3
3.0
1.1
0.1
setosa
15
5.8
4.0
1.2
0.2
setosa
16
5.7
4.4
1.5
0.4
setosa
17
5.4
3.9
1.3
0.4
setosa
18
5.1
3.5
1.4
0.3
setosa
19
5.7
3.8
1.7
0.3
setosa
20
5.1
3.8
1.5
0.3
setosa
21
5.4
3.4
1.7
0.2
setosa
22
5.1
3.7
1.5
0.4
setosa
23
4.6
3.6
1.0
0.2
setosa
24
5.1
3.3
1.7
0.5
setosa
25
4.8
3.4
1.9
0.2
setosa
26
5.0
3.0
1.6
0.2
setosa
27
5.0
3.4
1.6
0.4
setosa
28
5.2
3.5
1.5
0.2
setosa
29
5.2
3.4
1.4
0.2
setosa
30
4.7
3.2
1.6
0.2
setosa
&vellip
&vellip
&vellip
&vellip
&vellip
&vellip

In [33]:

    

describe(iris)


    

    




SepalLength
Min      4.3
1st Qu.  5.1
Median   5.8
Mean     5.843333333333334
3rd Qu.  6.4
Max      7.9
NAs      0
NA%      0.0%

SepalWidth
Min      2.0
1st Qu.  2.8
Median   3.0
Mean     3.0573333333333332
3rd Qu.  3.3
Max      4.4
NAs      0
NA%      0.0%

PetalLength
Min      1.0
1st Qu.  1.6
Median   4.35
Mean     3.7579999999999996
3rd Qu.  5.1
Max      6.9
NAs      0
NA%      0.0%

PetalWidth
Min      0.1
1st Qu.  0.3
Median   1.3
Mean     1.1993333333333331
3rd Qu.  1.8
Max      2.5
NAs      0
NA%      0.0%

Species
Length  150
Type    UTF8String
NAs     0
NA%     0.0%
Unique  3

In [34]:

    

using Gadfly # Similar a ggplot2 de R


    

In [35]:

    

plot(iris, x="Species", y="PetalLength", color="Species", Geom.boxplot)


    

    
Out[35]:

In [36]:

    

plot(iris, color="Species", x="PetalLength", Geom.histogram)


    

    
Out[36]:

In [37]:

    

plot(iris, x=:PetalLength, y=:PetalWidth, color=:Species, Geom.point, Geom.smooth(method=:lm))


    

    
Out[37]:

In [38]:

    

# Pkg.add("GLM")


    

In [39]:

    

using GLM # Generalized linear models

linear = fit(LinearModel, PetalWidth ~ PetalLength, iris) # PetalLength en R: 0.4157554


    

    
Out[39]:





DataFrames.DataFrameRegressionModel{GLM.LinearModel{GLM.DensePredQR{Float64}},Float64}:

Coefficients:
              Estimate  Std.Error  t value Pr(>|t|)
(Intercept)  -0.363076   0.039762 -9.13122   <1e-15
PetalLength   0.415755 0.00958244  43.3872   <1e-85

In [40]:

    

using Clustering


    

In [41]:

    

cl = kmeans(convert(Matrix{Float64}, iris[:, [:PetalWidth, :PetalLength]])', 3)


    

    
Out[41]:





Clustering.KmeansResult{Float64}(2x3 Array{Float64,2}:
 2.0375   0.246  1.34231
 5.59583  1.462  4.26923,[2,2,2,2,2,2,2,2,2,2  …  1,1,1,1,1,1,1,1,1,1],[0.00596,0.00596,0.02836,0.00356,0.00596,0.08036,0.00676,0.00356,0.00596,0.02276  …  0.131424,0.314757,0.264757,0.161424,0.224757,0.22559,0.373924,0.15809,0.107257,0.302257],[48,50,52],[48.0,50.0,52.0],31.371358974358984,5,true)

In [42]:

    

cl.centers


    

    
Out[42]:





2x3 Array{Float64,2}:
 2.0375   0.246  1.34231
 5.59583  1.462  4.26923

In [43]:

    

by(iris, :Species, df -> (mean(df[:PetalWidth]), mean(df[:PetalLength])))


    

    
Out[43]:




Species
x1
1
setosa
(0.24600000000000002,1.462)
2
versicolor
(1.3259999999999998,4.260000000000001)
3
virginica
(2.026,5.5520000000000005)