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Data Exploration

This page shows a basic exploration of iris data with R.
Check the dimensionality
> dim(iris)
[1] 150   5


Variable names or column names
> names(iris)
[1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species" 
   

Structure
> str(iris)
'data.frame':   150 obs. of  5 variables:
 $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
 $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
 $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
 $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
 $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...


Attributes
> attributes(iris)
$names
[1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"    

$row.names
  [1]   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20
 [21]  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40
 [41]  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59  60
 [61]  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77  78  79  80
 [81]  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95  96  97  98  99 100
[101] 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
[121] 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140
[141] 141 142 143 144 145 146 147 148 149 150

$class
[1] "data.frame"


Get the first 5 rows
> iris[1:5,]
  Sepal.Length Sepal.Width Petal.Length Petal.Width 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


Get Sepal.Length of the first 10 rows
> iris[1:10, "Sepal.Length"]
 [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9


Same as above
> iris$Sepal.Length[1:10]
 [1] 5.1 4.9 4.7 4.6 5.0 5.4 4.6 5.0 4.4 4.9


Distribution of every variable
> summary(iris)
  Sepal.Length    Sepal.Width     Petal.Length    Petal.Width          Species 
 Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100   setosa    :50 
 1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300   versicolor:50 
 Median :5.800   Median :3.000   Median :4.350   Median :1.300   virginica :50 
 Mean   :5.843   Mean   :3.057   Mean   :3.758   Mean   :1.199                 
 3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800                 
 Max.   :7.900   Max.   :4.400   Max.   :6.900   Max.   :2.500   
              

Frequency
> table(iris$Species)

    setosa versicolor  virginica
        50         50         50


Pie chart
> pie(table(iris$Species))



Variance of Sepal.Length
> var(iris$Sepal.Length)
[1] 0.6856935


Covariance of two variables
> cov(iris$Sepal.Length, iris$Petal.Length)
[1] 1.274315


Correlation of two variables
> cor(iris$Sepal.Length, iris$Petal.Length)
[1] 0.8717538


Histogram
> hist(iris$Sepal.Length)

Histogram

Density
> plot(density(iris$Sepal.Length))



Scatter plot           
> plot(iris$Sepal.Length, iris$Sepal.Width)



Pair plot
> plot(iris)

or
> pairs(iris)

More examples on data exploration with R and other data mining techniques can be found in my book "R and Data Mining: Examples and Case Studies", which is downloadable as a .PDF file at the link.