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In [1]:



    


Pkg.add("Cairo")



    


















    






INFO: No packages to install, update or remove
INFO: Package database updated

















In [2]:



    


Pkg.update()



    


















    






INFO: Updating METADATA...
INFO: Updating RCalling...
INFO: Computing changes...
INFO: No packages to install, update or remove

















In [3]:



    


using Gadfly



    











In [5]:



    


# This is a simple plot
plot(x = rand(10), y = rand(10))

## Add geom.point and geom.line

plot(x = rand(10), y = rand(10), Geom.point, Geom.line)
# Note the case, plots both points and lines



    


















    
Out[5]:









  
    x
  
  
    -1.2
    -1.0
    -0.8
    -0.6
    -0.4
    -0.2
    0.0
    0.2
    0.4
    0.6
    0.8
    1.0
    1.2
    1.4
    1.6
    1.8
    2.0
    2.2
    -1.00
    -0.95
    -0.90
    -0.85
    -0.80
    -0.75
    -0.70
    -0.65
    -0.60
    -0.55
    -0.50
    -0.45
    -0.40
    -0.35
    -0.30
    -0.25
    -0.20
    -0.15
    -0.10
    -0.05
    0.00
    0.05
    0.10
    0.15
    0.20
    0.25
    0.30
    0.35
    0.40
    0.45
    0.50
    0.55
    0.60
    0.65
    0.70
    0.75
    0.80
    0.85
    0.90
    0.95
    1.00
    1.05
    1.10
    1.15
    1.20
    1.25
    1.30
    1.35
    1.40
    1.45
    1.50
    1.55
    1.60
    1.65
    1.70
    1.75
    1.80
    1.85
    1.90
    1.95
    2.00
    -1
    0
    1
    2
    -1.0
    -0.9
    -0.8
    -0.7
    -0.6
    -0.5
    -0.4
    -0.3
    -0.2
    -0.1
    0.0
    0.1
    0.2
    0.3
    0.4
    0.5
    0.6
    0.7
    0.8
    0.9
    1.0
    1.1
    1.2
    1.3
    1.4
    1.5
    1.6
    1.7
    1.8
    1.9
    2.0
  

  
    
      
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    
      
        
      
      
        
          
          
          
          
          
          
          
          
          
          
        
      
    
    
      
        
        
      
      
        
      
      
        
      
      
        
        
      
    
  

  
    -1.5
    -1.0
    -0.5
    0.0
    0.5
    1.0
    1.5
    2.0
    2.5
    -1.00
    -0.95
    -0.90
    -0.85
    -0.80
    -0.75
    -0.70
    -0.65
    -0.60
    -0.55
    -0.50
    -0.45
    -0.40
    -0.35
    -0.30
    -0.25
    -0.20
    -0.15
    -0.10
    -0.05
    0.00
    0.05
    0.10
    0.15
    0.20
    0.25
    0.30
    0.35
    0.40
    0.45
    0.50
    0.55
    0.60
    0.65
    0.70
    0.75
    0.80
    0.85
    0.90
    0.95
    1.00
    1.05
    1.10
    1.15
    1.20
    1.25
    1.30
    1.35
    1.40
    1.45
    1.50
    1.55
    1.60
    1.65
    1.70
    1.75
    1.80
    1.85
    1.90
    1.95
    2.00
    -1
    0
    1
    2
    -1.0
    -0.9
    -0.8
    -0.7
    -0.6
    -0.5
    -0.4
    -0.3
    -0.2
    -0.1
    0.0
    0.1
    0.2
    0.3
    0.4
    0.5
    0.6
    0.7
    0.8
    0.9
    1.0
    1.1
    1.2
    1.3
    1.4
    1.5
    1.6
    1.7
    1.8
    1.9
    2.0
  
  
    y
  


  
  

  
    
  





















In [6]:



    


## Produce more complex plots
plot(x = 1:10, y = 2.^rand(10),
Scale.y_sqrt, Geom.point, Geom.smooth,
Guide.xlabel("Stimulus"), Guide.ylabel("Response"),
Guide.title("Some Training")
)
## We added smotthing line, we added title, and x and y axis labels



    


















    
Out[6]:









  
    Stimulus
  
  
    -12.5
    -10.0
    -7.5
    -5.0
    -2.5
    0.0
    2.5
    5.0
    7.5
    10.0
    12.5
    15.0
    17.5
    20.0
    22.5
    -10.0
    -9.5
    -9.0
    -8.5
    -8.0
    -7.5
    -7.0
    -6.5
    -6.0
    -5.5
    -5.0
    -4.5
    -4.0
    -3.5
    -3.0
    -2.5
    -2.0
    -1.5
    -1.0
    -0.5
    0.0
    0.5
    1.0
    1.5
    2.0
    2.5
    3.0
    3.5
    4.0
    4.5
    5.0
    5.5
    6.0
    6.5
    7.0
    7.5
    8.0
    8.5
    9.0
    9.5
    10.0
    10.5
    11.0
    11.5
    12.0
    12.5
    13.0
    13.5
    14.0
    14.5
    15.0
    15.5
    16.0
    16.5
    17.0
    17.5
    18.0
    18.5
    19.0
    19.5
    20.0
    -10
    0
    10
    20
    -10
    -9
    -8
    -7
    -6
    -5
    -4
    -3
    -2
    -1
    0
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
  

  
    
      
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    
      
        
      
      
        
          
          
          
          
          
          
          
          
          
          
        
      
    
    
      
        
        
      
      
        
      
      
        
      
      
        
        
      
    
  

  
    0.52
    0.62
    0.72
    0.82
    0.92
    1.02
    1.12
    1.22
    1.32
    1.42
    1.52
    1.62
    1.72
    1.82
    1.92
    0.002
    0.022
    0.042
    0.062
    0.082
    0.102
    0.122
    0.142
    0.162
    0.182
    0.202
    0.222
    0.242
    0.262
    0.282
    0.302
    0.322
    0.342
    0.362
    0.382
    0.402
    0.422
    0.442
    0.462
    0.482
    0.502
    0.522
    0.542
    0.562
    0.582
    0.602
    0.622
    0.642
    0.662
    0.682
    0.702
    0.722
    0.742
    0.762
    0.782
    0.802
    0.822
    0.842
    0.862
    0.882
    0.902
    0.922
    0.942
    0.962
    0.982
    1.002
    1.022
    1.042
    1.062
    1.082
    1.102
    1.122
    1.142
    1.162
    1.182
    1.202
    1.222
    1.242
    1.262
    1.282
    1.302
    1.322
    1.342
    1.362
    1.382
    1.402
    1.422
    1.442
    1.462
    1.482
    1.502
    1.522
    1.542
    1.562
    1.582
    1.602
    1.622
    1.642
    1.662
    1.682
    1.702
    1.722
    1.742
    1.762
    1.782
    1.802
    1.822
    0.02
    0.52
    1.02
    1.52
    2.02
    0.002
    0.052
    0.102
    0.152
    0.202
    0.252
    0.302
    0.352
    0.402
    0.452
    0.502
    0.552
    0.602
    0.652
    0.702
    0.752
    0.802
    0.852
    0.902
    0.952
    1.002
    1.052
    1.102
    1.152
    1.202
    1.252
    1.302
    1.352
    1.402
    1.452
    1.502
    1.552
    1.602
    1.652
    1.702
    1.752
    1.802
    1.852
  
  
    Response
  
  
    Some Training
  


  
  

  
    
  





















In [7]:



    


# We can take the previous plot and export to png and pdf and others
myplot = plot(x = 1:10, y = 2.^rand(10),
Scale.y_sqrt, Geom.point, Geom.smooth,
Guide.xlabel("Stimulus"), Guide.ylabel("Response"),
Guide.title("Some Training")
)

draw(PNG("myplot.png", 4inch, 3inch), myplot)



    











In [8]:



    


myplot
# you can see the result by just typing the name of the object



    


















    
Out[8]:









  
    Stimulus
  
  
    -12.5
    -10.0
    -7.5
    -5.0
    -2.5
    0.0
    2.5
    5.0
    7.5
    10.0
    12.5
    15.0
    17.5
    20.0
    22.5
    -10.0
    -9.5
    -9.0
    -8.5
    -8.0
    -7.5
    -7.0
    -6.5
    -6.0
    -5.5
    -5.0
    -4.5
    -4.0
    -3.5
    -3.0
    -2.5
    -2.0
    -1.5
    -1.0
    -0.5
    0.0
    0.5
    1.0
    1.5
    2.0
    2.5
    3.0
    3.5
    4.0
    4.5
    5.0
    5.5
    6.0
    6.5
    7.0
    7.5
    8.0
    8.5
    9.0
    9.5
    10.0
    10.5
    11.0
    11.5
    12.0
    12.5
    13.0
    13.5
    14.0
    14.5
    15.0
    15.5
    16.0
    16.5
    17.0
    17.5
    18.0
    18.5
    19.0
    19.5
    20.0
    -10
    0
    10
    20
    -10
    -9
    -8
    -7
    -6
    -5
    -4
    -3
    -2
    -1
    0
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
  

  
    
      
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    
      
        
      
      
        
          
          
          
          
          
          
          
          
          
          
        
      
    
    
      
        
        
      
      
        
      
      
        
      
      
        
        
      
    
  

  
    0.32
    0.42
    0.52
    0.62
    0.72
    0.82
    0.92
    1.02
    1.12
    1.22
    1.32
    1.42
    1.52
    1.62
    1.72
    1.82
    1.92
    2.02
    0.002
    0.022
    0.042
    0.062
    0.082
    0.102
    0.122
    0.142
    0.162
    0.182
    0.202
    0.222
    0.242
    0.262
    0.282
    0.302
    0.322
    0.342
    0.362
    0.382
    0.402
    0.422
    0.442
    0.462
    0.482
    0.502
    0.522
    0.542
    0.562
    0.582
    0.602
    0.622
    0.642
    0.662
    0.682
    0.702
    0.722
    0.742
    0.762
    0.782
    0.802
    0.822
    0.842
    0.862
    0.882
    0.902
    0.922
    0.942
    0.962
    0.982
    1.002
    1.022
    1.042
    1.062
    1.082
    1.102
    1.122
    1.142
    1.162
    1.182
    1.202
    1.222
    1.242
    1.262
    1.282
    1.302
    1.322
    1.342
    1.362
    1.382
    1.402
    1.422
    1.442
    1.462
    1.482
    1.502
    1.522
    1.542
    1.562
    1.582
    1.602
    1.622
    1.642
    1.662
    1.682
    1.702
    1.722
    1.742
    1.762
    1.782
    1.802
    1.822
    1.842
    1.862
    1.882
    1.902
    1.922
    0.02
    0.52
    1.02
    1.52
    2.02
    0.002
    0.052
    0.102
    0.152
    0.202
    0.252
    0.302
    0.352
    0.402
    0.452
    0.502
    0.552
    0.602
    0.652
    0.702
    0.752
    0.802
    0.852
    0.902
    0.952
    1.002
    1.052
    1.102
    1.152
    1.202
    1.252
    1.302
    1.352
    1.402
    1.452
    1.502
    1.552
    1.602
    1.652
    1.702
    1.752
    1.802
    1.852
    1.902
    1.952
  
  
    Response
  
  
    Some Training
  


  
  

  
    
  





















In [9]:



    


Pkg.add("RDatasets")



    


















    






INFO: Installing RDatasets v0.1.2
INFO: Package database updated

















In [10]:



    


## We can also plot data frames
using DataFrames
using RDatasets



    


















    






WARNING: Base.String is deprecated, use AbstractString instead.
  likely near /Users/arindambose/.julia/v0.4/RDatasets/src/dataset.jl:1
WARNING: Base.String is deprecated, use AbstractString instead.
  likely near /Users/arindambose/.julia/v0.4/RDatasets/src/dataset.jl:1
WARNING: Base.String is deprecated, use AbstractString instead.
  likely near /Users/arindambose/.julia/v0.4/RDatasets/src/datasets.jl:1

















In [12]:



    


plot(dataset("datasets", "iris"), x = "SepalLength",
y = "SepalWidth", Geom.point)

# Here we took a data set iris, and then plotted the two variables



    


















    
Out[12]:









  
    SepalLength
  
  
    -1
    0
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    0.0
    0.2
    0.4
    0.6
    0.8
    1.0
    1.2
    1.4
    1.6
    1.8
    2.0
    2.2
    2.4
    2.6
    2.8
    3.0
    3.2
    3.4
    3.6
    3.8
    4.0
    4.2
    4.4
    4.6
    4.8
    5.0
    5.2
    5.4
    5.6
    5.8
    6.0
    6.2
    6.4
    6.6
    6.8
    7.0
    7.2
    7.4
    7.6
    7.8
    8.0
    8.2
    8.4
    8.6
    8.8
    9.0
    9.2
    9.4
    9.6
    9.8
    10.0
    10.2
    10.4
    10.6
    10.8
    11.0
    11.2
    11.4
    11.6
    11.8
    12.0
    0
    5
    10
    15
    0.0
    0.5
    1.0
    1.5
    2.0
    2.5
    3.0
    3.5
    4.0
    4.5
    5.0
    5.5
    6.0
    6.5
    7.0
    7.5
    8.0
    8.5
    9.0
    9.5
    10.0
    10.5
    11.0
    11.5
    12.0
  

  
    
      
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    
      
        
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
          
        
      
    
    
      
        
        
      
      
        
      
      
        
      
      
        
        
      
    
  

  
    -1.0
    -0.5
    0.0
    0.5
    1.0
    1.5
    2.0
    2.5
    3.0
    3.5
    4.0
    4.5
    5.0
    5.5
    6.0
    6.5
    7.0
    7.5
    -0.5
    -0.4
    -0.3
    -0.2
    -0.1
    0.0
    0.1
    0.2
    0.3
    0.4
    0.5
    0.6
    0.7
    0.8
    0.9
    1.0
    1.1
    1.2
    1.3
    1.4
    1.5
    1.6
    1.7
    1.8
    1.9
    2.0
    2.1
    2.2
    2.3
    2.4
    2.5
    2.6
    2.7
    2.8
    2.9
    3.0
    3.1
    3.2
    3.3
    3.4
    3.5
    3.6
    3.7
    3.8
    3.9
    4.0
    4.1
    4.2
    4.3
    4.4
    4.5
    4.6
    4.7
    4.8
    4.9
    5.0
    5.1
    5.2
    5.3
    5.4
    5.5
    5.6
    5.7
    5.8
    5.9
    6.0
    6.1
    6.2
    6.3
    6.4
    6.5
    6.6
    6.7
    6.8
    6.9
    7.0
    -2.5
    0.0
    2.5
    5.0
    7.5
    -0.6
    -0.4
    -0.2
    0.0
    0.2
    0.4
    0.6
    0.8
    1.0
    1.2
    1.4
    1.6
    1.8
    2.0
    2.2
    2.4
    2.6
    2.8
    3.0
    3.2
    3.4
    3.6
    3.8
    4.0
    4.2
    4.4
    4.6
    4.8
    5.0
    5.2
    5.4
    5.6
    5.8
    6.0
    6.2
    6.4
    6.6
    6.8
    7.0
  
  
    SepalWidth
  


  
  

  
    
  





















In [13]:



    


plot(dataset("car", "SLID"), 
x = "Wages", color = "Language",
Geom.histogram)

# Here we plot a staggered histogram



    


















    
Out[13]:









  
    Wages
  
  
    -60
    -50
    -40
    -30
    -20
    -10
    0
    10
    20
    30
    40
    50
    60
    70
    80
    90
    100
    110
    -50
    -48
    -46
    -44
    -42
    -40
    -38
    -36
    -34
    -32
    -30
    -28
    -26
    -24
    -22
    -20
    -18
    -16
    -14
    -12
    -10
    -8
    -6
    -4
    -2
    0
    2
    4
    6
    8
    10
    12
    14
    16
    18
    20
    22
    24
    26
    28
    30
    32
    34
    36
    38
    40
    42
    44
    46
    48
    50
    52
    54
    56
    58
    60
    62
    64
    66
    68
    70
    72
    74
    76
    78
    80
    82
    84
    86
    88
    90
    92
    94
    96
    98
    100
    -50
    0
    50
    100
    -50
    -45
    -40
    -35
    -30
    -25
    -20
    -15
    -10
    -5
    0
    5
    10
    15
    20
    25
    30
    35
    40
    45
    50
    55
    60
    65
    70
    75
    80
    85
    90
    95
    100
  
  
    
      English
      Other
      French
    
    
      
      
      
    
    
      Language
    
  

  
    
      
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    
      
        
          
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
          
          
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
          
          
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
            
          
        
      
    
    
      
        
        
      
      
        
      
      
        
      
      
        
        
      
    
  

  
    -250
    -200
    -150
    -100
    -50
    0
    50
    100
    150
    200
    250
    300
    350
    400
    450
    -200
    -190
    -180
    -170
    -160
    -150
    -140
    -130
    -120
    -110
    -100
    -90
    -80
    -70
    -60
    -50
    -40
    -30
    -20
    -10
    0
    10
    20
    30
    40
    50
    60
    70
    80
    90
    100
    110
    120
    130
    140
    150
    160
    170
    180
    190
    200
    210
    220
    230
    240
    250
    260
    270
    280
    290
    300
    310
    320
    330
    340
    350
    360
    370
    380
    390
    400
    -200
    0
    200
    400
    -200
    -180
    -160
    -140
    -120
    -100
    -80
    -60
    -40
    -20
    0
    20
    40
    60
    80
    100
    120
    140
    160
    180
    200
    220
    240
    260
    280
    300
    320
    340
    360
    380
    400
  


  
  






















In [14]:



    


## Drawing kernel density
plot(dataset("ggplot2", "diamonds"), x="Price",
Geom.density)



    


















    
Out[14]:









  
    Price
  
  
    -6×10⁴
    -5×10⁴
    -4×10⁴
    -3×10⁴
    -2×10⁴
    -1×10⁴
    0
    1×10⁴
    2×10⁴
    3×10⁴
    4×10⁴
    5×10⁴
    6×10⁴
    7×10⁴
    8×10⁴
    -5.0×10⁴
    -4.8×10⁴
    -4.6×10⁴
    -4.4×10⁴
    -4.2×10⁴
    -4.0×10⁴
    -3.8×10⁴
    -3.6×10⁴
    -3.4×10⁴
    -3.2×10⁴
    -3.0×10⁴
    -2.8×10⁴
    -2.6×10⁴
    -2.4×10⁴
    -2.2×10⁴
    -2.0×10⁴
    -1.8×10⁴
    -1.6×10⁴
    -1.4×10⁴
    -1.2×10⁴
    -1.0×10⁴
    -8.0×10³
    -6.0×10³
    -4.0×10³
    -2.0×10³
    0
    2.0×10³
    4.0×10³
    6.0×10³
    8.0×10³
    1.0×10⁴
    1.2×10⁴
    1.4×10⁴
    1.6×10⁴
    1.8×10⁴
    2.0×10⁴
    2.2×10⁴
    2.4×10⁴
    2.6×10⁴
    2.8×10⁴
    3.0×10⁴
    3.2×10⁴
    3.4×10⁴
    3.6×10⁴
    3.8×10⁴
    4.0×10⁴
    4.2×10⁴
    4.4×10⁴
    4.6×10⁴
    4.8×10⁴
    5.0×10⁴
    5.2×10⁴
    5.4×10⁴
    5.6×10⁴
    5.8×10⁴
    6.0×10⁴
    6.2×10⁴
    6.4×10⁴
    6.6×10⁴
    6.8×10⁴
    7.0×10⁴
    -5×10⁴
    0
    5×10⁴
    1×10⁵
    -5.0×10⁴
    -4.5×10⁴
    -4.0×10⁴
    -3.5×10⁴
    -3.0×10⁴
    -2.5×10⁴
    -2.0×10⁴
    -1.5×10⁴
    -1.0×10⁴
    -5.0×10³
    0
    5.0×10³
    1.0×10⁴
    1.5×10⁴
    2.0×10⁴
    2.5×10⁴
    3.0×10⁴
    3.5×10⁴
    4.0×10⁴
    4.5×10⁴
    5.0×10⁴
    5.5×10⁴
    6.0×10⁴
    6.5×10⁴
    7.0×10⁴
  

  
    
      
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    
      
        
      
    
    
      
        
        
      
      
        
      
      
        
      
      
        
        
      
    
  

  
    -0.0005
    -0.0004
    -0.0003
    -0.0002
    -0.0001
    0.0000
    0.0001
    0.0002
    0.0003
    0.0004
    0.0005
    0.0006
    0.0007
    0.0008
    0.0009
    -0.00040
    -0.00038
    -0.00036
    -0.00034
    -0.00032
    -0.00030
    -0.00028
    -0.00026
    -0.00024
    -0.00022
    -0.00020
    -0.00018
    -0.00016
    -0.00014
    -0.00012
    -0.00010
    -0.00008
    -0.00006
    -0.00004
    -0.00002
    0.00000
    0.00002
    0.00004
    0.00006
    0.00008
    0.00010
    0.00012
    0.00014
    0.00016
    0.00018
    0.00020
    0.00022
    0.00024
    0.00026
    0.00028
    0.00030
    0.00032
    0.00034
    0.00036
    0.00038
    0.00040
    0.00042
    0.00044
    0.00046
    0.00048
    0.00050
    0.00052
    0.00054
    0.00056
    0.00058
    0.00060
    0.00062
    0.00064
    0.00066
    0.00068
    0.00070
    0.00072
    0.00074
    0.00076
    0.00078
    0.00080
    -0.0005
    0.0000
    0.0005
    0.0010
    -0.00040
    -0.00035
    -0.00030
    -0.00025
    -0.00020
    -0.00015
    -0.00010
    -0.00005
    0.00000
    0.00005
    0.00010
    0.00015
    0.00020
    0.00025
    0.00030
    0.00035
    0.00040
    0.00045
    0.00050
    0.00055
    0.00060
    0.00065
    0.00070
    0.00075
    0.00080
  


  
  






















In [15]:



    


## Using pyplot

using PyPlot



    


















    






WARNING: using PyPlot.plot in module Main conflicts with an existing identifier.
WARNING: using PyPlot.draw in module Main conflicts with an existing identifier.

















In [16]:



    


p = scatter(x = rand(10),
y = rand(10))



    


















    

















    
Out[16]:








PyObject <matplotlib.collections.PathCollection object at 0x316bbd350>

















In [17]:



    


Pkg.add("Bokeh")



    


















    






INFO: Cloning cache of Bokeh from git://github.com/bokeh/Bokeh.jl.git
INFO: Cloning cache of Mustache from git://github.com/jverzani/Mustache.jl.git
INFO: Installing Bokeh v0.2.0
INFO: Installing MacroTools v0.2.0
INFO: Installing Mustache v0.0.14
INFO: Installing Requires v0.2.1
INFO: Building Bokeh
INFO: Package database updated

















In [18]:



    


Pkg.available()



    


















    
Out[18]:








755-element Array{AbstractString,1}:
 "AbstractDomains"       
 "Accumulo"              
 "ActiveAppearanceModels"
 "AffineTransforms"      
 "AmplNLWriter"          
 "AndorSIF"              
 "AnsiColor"             
 "AppConf"               
 "AppleAccelerate"       
 "ApproxFun"             
 "Arbiter"               
 "Arduino"               
 "ArgParse"              
 ⋮                       
 "XGBoost"               
 "XSim"                  
 "XSV"                   
 "YAML"                  
 "Yelp"                  
 "Yeppp"                 
 "YT"                    
 "ZChop"                 
 "ZipFile"               
 "Zlib"                  
 "ZMQ"                   
 "ZVSimulator"           

















In [19]:



    


Pkg.add("A*")



    


















    






LoadError: unknown package A*
 in error at /Applications/Julia-0.4.1.app/Contents/Resources/julia/lib/julia/sys.dylib
 [inlined code] from pkg/entry.jl:49
 in anonymous at task.jl:447
while loading In[19], in expression starting on line 1

 in sync_end at /Applications/Julia-0.4.1.app/Contents/Resources/julia/lib/julia/sys.dylib
 [inlined code] from task.jl:422
 in add at pkg/entry.jl:46
 in add at pkg/entry.jl:73
 in anonymous at pkg/dir.jl:31
 in cd at file.jl:22
 in cd at pkg/dir.jl:31
 in add at pkg.jl:23
















In [20]:



    


Pkg.status()



    


















    






21 required packages:
 - Bokeh                         0.2.0
 - Cairo                         0.2.31
 - DataFrames                    0.6.10
 - DataFramesMeta                0.1.0
 - Distances                     0.2.1
 - Distributions                 0.8.7
 - GLM                           0.4.8
 - Gadfly                        0.4.0
 - HypothesisTests               0.2.10
 - IJulia                        1.1.8
 - Interact                      0.2.1
 - MCMC                          0.3.0
 - NHST                          0.0.2
 - PyCall                        1.2.0
 - PyPlot                        2.1.1
 - RCall                         0.3.1
 - RDatasets                     0.1.2
 - Stats                         0.1.0
 - StatsBase                     0.7.4
 - TimeModels                    0.0.3
 - Winston                       0.11.13
54 additional packages:
 - ArrayViews                    0.6.4
 - BinDeps                       0.3.19
 - Calculus                      0.1.14
 - Codecs                        0.1.5
 - ColorTypes                    0.2.0
 - Colors                        0.6.0
 - Compat                        0.7.8
 - Compose                       0.4.0
 - Conda                         0.1.8
 - Contour                       0.0.8
 - DataArrays                    0.2.20
 - DataStructures                0.3.13
 - Dates                         0.4.4
 - Docile                        0.5.19
 - DualNumbers                   0.1.5
 - FactCheck                     0.4.1
 - FixedPointNumbers             0.1.1
 - ForwardDiff                   0.1.2
 - GZip                          0.2.18
 - Graphics                      0.1.3
 - Grid                          0.4.0
 - Hexagons                      0.0.4
 - Homebrew                      0.2.0
 - ImmutableArrays               0.0.11
 - IniFile                       0.2.4
 - Iterators                     0.1.9
 - JSON                          0.5.0
 - KernelDensity                 0.1.2
 - LaTeXStrings                  0.1.6
 - Loess                         0.0.5
 - MacroTools                    0.2.0
 - MathProgBase                  0.3.19
 - Measures                      0.0.1
 - Mustache                      0.0.14
 - NLopt                         0.2.3
 - NaNMath                       0.1.1
 - Nettle                        0.2.0
 - Optim                         0.4.4
 - PDMats                        0.3.6
 - Polynomials                   0.0.4
 - RCalling                      0.0.0-             master (unregistered)
 - Reactive                      0.2.4
 - Reexport                      0.0.3
 - Requires                      0.2.1
 - Roots                         0.1.20
 - SHA                           0.1.2
 - Showoff                       0.0.6
 - SortingAlgorithms             0.0.6
 - StatsFuns                     0.2.0
 - TimeSeries                    0.6.5
 - Tk                            0.3.6
 - URIParser                     0.1.1
 - WoodburyMatrices              0.1.2
 - ZMQ                           0.3.1

















In [21]:



    


pwd()



    


















    
Out[21]:








"/Users/arindambose/Documents/julia"

















In [23]:



    


n = 50
srand(1)
x = rand(n)
y = rand(n)

area = pi .* (15 .* rand(n)).^2
scatter(x, y, s = area, alpha = 0.5)



    


















    

















    
Out[23]:








PyObject <matplotlib.collections.PathCollection object at 0x3183882d0>

















In [24]:



    


help(repmat)



    


















    






LoadError: UndefVarError: help not defined
while loading In[24], in expression starting on line 1

















In [25]:



    


?repmat



    


















    






search: 









    
Out[25]:








repmat(A, n, m)


Construct a matrix by repeating the given matrix n times in dimension 1 and m times in dimension 2.











    






repmat


















In [26]:



    


help(repmat)



    


















    






LoadError: UndefVarError: help not defined
while loading In[26], in expression starting on line 1

















In [28]:



    


using DataFrames
df = DataFrame(A = [1,2],
B = [pi, e],
C = ["xx", "yy"])
show(df)



    


















    






2x3 DataFrames.DataFrame
| Row | A | B       | C    |
|-----|---|---------|------|
| 1   | 1 | 3.14159 | "xx" |
| 2   | 2 | 2.71828 | "yy" |
















In [29]:



    


iris = dataset("datasets", "iris")
show(names(iris))



    


















    






[:SepalLength,:SepalWidth,:PetalLength,:PetalWidth,:Species]
















In [30]:



    


describe(iris:SepalLength)



    


















    






LoadError: UndefVarError: SepalLength not defined
while loading In[30], in expression starting on line 1

















In [31]:



    


describe(:SepalLength)



    


















    






LoadError: MethodError: `describe` has no method matching describe(::Symbol)
Closest candidates are:
  describe(::Any, !Matched::DataFrames.AbstractDataFrame)
  describe{T<:Number}(::Any, !Matched::AbstractArray{T<:Number,N})
  describe{T}(::Any, !Matched::AbstractArray{T,N})
while loading In[31], in expression starting on line 1

















In [32]:



    


head(iris)



    


















    
Out[32]:







SepalLengthSepalWidthPetalLengthPetalWidthSpecies
15.13.51.40.2setosa
24.93.01.40.2setosa
34.73.21.30.2setosa
44.63.11.50.2setosa
55.03.61.40.2setosa
65.43.91.70.4setosa

















In [33]:



    


aggregate(iris, :Species, sum)



    


















    
Out[33]:







SpeciesSepalLength_sumSepalWidth_sumPetalLength_sumPetalWidth_sum
1setosa250.3171.473.112.3
2versicolor296.8138.5213.0000000000000366.3
3virginica329.4148.70000000000002277.6101.3

















In [34]:



    


aggregate(iris, :Species, [sum, mean])



    


















    
Out[34]:







SpeciesSepalLength_sumSepalLength_meanSepalWidth_sumSepalWidth_meanPetalLength_sumPetalLength_meanPetalWidth_sumPetalWidth_mean
1setosa250.35.006171.43.42873.11.46212.30.24600000000000002
2versicolor296.85.936138.52.77213.000000000000034.26000000000000166.31.3259999999999998
3virginica329.46.587999999999999148.700000000000022.974277.65.5520000000000005101.32.026

















In [35]:



    


## reshape data using stack function
help("stack")



    


















    






LoadError: UndefVarError: help not defined
while loading In[35], in expression starting on line 2

















In [36]:



    


?stack



    


















    






search: 









    
Out[36]:







Stacks a DataFrame; convert from a wide to long format


stack(df::AbstractDataFrame, measure_vars, id_vars)
stack(df::AbstractDataFrame, measure_vars)
stack(df::AbstractDataFrame)
melt(df::AbstractDataFrame, id_vars, measure_vars)
melt(df::AbstractDataFrame, id_vars)



Arguments
    

  • df : the AbstractDataFrame to be stacked
    

    • 

  • measure_vars : the columns to be stacked (the measurement   variables), a normal column indexing type, like a Symbol,   Vector{Symbol}, Int, etc.; for melt, defaults to all   variables that are not id_vars
    

    • 

  • id_vars : the identifier columns that are repeated during   stacking, a normal column indexing type; for stack defaults to all   variables that are not measure_vars
    

    • 



If neither measure_vars or id_vars are given, measure_vars defaults to all floating point columns.


Result
    

  • ::DataFrame : the long-format dataframe with column :value   holding the values of the stacked columns (measure_vars), with   column :variable a Vector of Symbols with the measure_vars name,   and with columns for each of the id_vars.
    • 



See also stackdf and meltdf for stacking methods that return a view into the original DataFrame. See unstack for converting from long to wide format.


Examples
d1 = DataFrame(a = repeat([1:3;], inner = [4]),
               b = repeat([1:4;], inner = [3]),
               c = randn(12),
               d = randn(12),
               e = map(string, 'a':'l'))

d1s = stack(d1, [:c, :d])
d1s2 = stack(d1, [:c, :d], [:a])
d1m = melt(d1, [:a, :b, :e])












    






stack stackdf stackplot StackOverflowError vstack hstack unstack


















In [37]:



    


## from wide to long use melt
using StatsBase



    











In [38]:



    


names(iris)



    


















    
Out[38]:








5-element Array{Symbol,1}:
 :SepalLength
 :SepalWidth 
 :PetalLength
 :PetalWidth 
 :Species    

















In [39]:



    


summarystats(iris[:SepalLength])



    


















    
Out[39]:








Summary Stats:
Mean:         5.843333
Minimum:      4.300000
1st Quartile: 5.100000
Median:       5.800000
3rd Quartile: 6.400000
Maximum:      7.900000


















In [40]:



    


describe(iris[:SepalLength])



    


















    






Summary Stats:
Mean:         5.843333
Minimum:      4.300000
1st Quartile: 5.100000
Median:       5.800000
3rd Quartile: 6.400000
Maximum:      7.900000

















In [41]:



    


table(iris[:SepalLength])



    


















    






LoadError: PyError (:PyObject_Call) <type 'exceptions.TypeError'>
TypeError('table() takes exactly 0 arguments (1 given)',)

while loading In[41], in expression starting on line 1

 [inlined code] from /Users/arindambose/.julia/v0.4/PyCall/src/exception.jl:81
 in pycall at /Users/arindambose/.julia/v0.4/PyCall/src/PyCall.jl:361
 in table at /Users/arindambose/.julia/v0.4/PyPlot/src/PyPlot.jl:460
















In [42]:



    


using StatsBase



    











In [43]:



    


mycount = counts(iris[:Species])



    


















    






LoadError: MethodError: `counts` has no method matching counts(::DataArrays.PooledDataArray{ASCIIString,UInt8,1})
while loading In[43], in expression starting on line 1

















In [44]:



    


?counts



    


















    






search: 









    
Out[44]:







No documentation found.


StatsBase.counts is a generic Function.


# 16 methods for generic function "counts":
counts(x::AbstractArray{T<:Integer,N}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:54
counts(x::AbstractArray{T<:Integer,N}, levels::UnitRange{T<:Integer}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:50
counts(x::AbstractArray{T<:Integer,N}, levels::UnitRange{T<:Integer}, wv::StatsBase.WeightVec{W,Vec<:AbstractArray{T<:Real,1}}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:51
counts(x::AbstractArray{T<:Integer,N}, k::Integer) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:52
counts(x::AbstractArray{T<:Integer,N}, k::Integer, wv::StatsBase.WeightVec{W,Vec<:AbstractArray{T<:Real,1}}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:53
counts(x::AbstractArray{T<:Integer,N}, wv::StatsBase.WeightVec{W,Vec<:AbstractArray{T<:Real,1}}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:55
counts(x::AbstractArray{T<:Integer,N}, y::AbstractArray{T<:Integer,N}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:144
counts(x::AbstractArray{T<:Integer,N}, y::AbstractArray{T<:Integer,N}, levels::Tuple{UnitRange{T<:Integer},UnitRange{T<:Integer}}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:130
counts(x::AbstractArray{T<:Integer,N}, y::AbstractArray{T<:Integer,N}, levels::Tuple{UnitRange{T<:Integer},UnitRange{T<:Integer}}, wv::StatsBase.WeightVec{W,Vec<:AbstractArray{T<:Real,1}}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:134
counts(x::AbstractArray{T<:Integer,N}, y::AbstractArray{T<:Integer,N}, levels::UnitRange{T<:Integer}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:137
counts(x::AbstractArray{T<:Integer,N}, y::AbstractArray{T<:Integer,N}, levels::UnitRange{T<:Integer}, wv::StatsBase.WeightVec{W,Vec<:AbstractArray{T<:Real,1}}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:138
counts(x::AbstractArray{T<:Integer,N}, y::AbstractArray{T<:Integer,N}, ks::Tuple{Integer,Integer}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:140
counts(x::AbstractArray{T<:Integer,N}, y::AbstractArray{T<:Integer,N}, ks::Tuple{Integer,Integer}, wv::StatsBase.WeightVec{W,Vec<:AbstractArray{T<:Real,1}}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:141
counts(x::AbstractArray{T<:Integer,N}, y::AbstractArray{T<:Integer,N}, k::Integer) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:142
counts(x::AbstractArray{T<:Integer,N}, y::AbstractArray{T<:Integer,N}, k::Integer, wv::StatsBase.WeightVec{W,Vec<:AbstractArray{T<:Real,1}}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:143
counts(x::AbstractArray{T<:Integer,N}, y::AbstractArray{T<:Integer,N}, wv::StatsBase.WeightVec{W,Vec<:AbstractArray{T<:Real,1}}) at /Users/arindambose/.julia/v0.4/StatsBase/src/counts.jl:145












    






counts addcounts! countlines count_ones count_zeros count countnz


















In [45]:



    


mycount = countmap(iris[:Species])



    


















    
Out[45]:








Dict{Union{ASCIIString,DataArrays.NAtype},Int64} with 3 entries:
  "virginica"  => 50
  "setosa"     => 50
  "versicolor" => 50

















In [46]:



    


proportionmap(iris[:Species])



    


















    
Out[46]:








Dict{Union{ASCIIString,DataArrays.NAtype},Float64} with 3 entries:
  "virginica"  => 0.3333333333333333
  "setosa"     => 0.3333333333333333
  "versicolor" => 0.3333333333333333

















In [51]:



    


Pkg.clone("git://github.com/nalimilan/Tables.jl.git")



    


















    






INFO: Cloning Tables from git://github.com/nalimilan/Tables.jl.git










    






LoadError: Tables already exists
while loading In[51], in expression starting on line 1

















In [48]:



    


using Tables



    


















    






LoadError: LoadError: ArgumentError: NamedArrays not found in path
while loading /Users/arindambose/.julia/v0.4/Tables/src/Tables.jl, in expression starting on line 3
while loading In[48], in expression starting on line 1

 in require at /Applications/Julia-0.4.1.app/Contents/Resources/julia/lib/julia/sys.dylib
 in include at /Applications/Julia-0.4.1.app/Contents/Resources/julia/lib/julia/sys.dylib
 in include_from_node1 at /Applications/Julia-0.4.1.app/Contents/Resources/julia/lib/julia/sys.dylib
 in require at /Applications/Julia-0.4.1.app/Contents/Resources/julia/lib/julia/sys.dylib
















In [50]:



    


Pkg.add("NamedArrays")



    


















    






INFO: Cloning cache of NamedArrays from git://github.com/davidavdav/NamedArrays.jl.git
INFO: Installing NamedArrays v0.4.6
INFO: Package database updated

















In [52]:



    


Pkg.clone("git://github.com/nalimilan/Tables.jl.git")



    


















    






INFO: Cloning Tables from git://github.com/nalimilan/Tables.jl.git










    






LoadError: Tables already exists
while loading In[52], in expression starting on line 1

















In [ ]:

Content source: arinbasu/JuliaWorkshop2015

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