replace iris dataset with a better version

src/main/groovy/se/alipsa/groovy/datasets/Dataset.groovy

@@ -11,7 +11,7 @@ class Dataset {
      * petal length and petal width, respectively, for 50 flowers from each of 3 species of iris.
      */
     static Matrix iris() {
-        Matrix table = Matrix.create(url('/data/Iris.csv')).convert(
+        Matrix table = Matrix.create(url('/data/iris.csv')).convert(
             Id: Integer,
             'Sepal Length': BigDecimal,
             'Sepal Width': BigDecimal,

src/main/resources/data/iris.csv

@@ -0,0 +1,151 @@
+Sepal Length,Sepal Width,Petal Length,Petal Width,Species
+5.1,3.5,1.4,0.2,setosa
+4.9,3,1.4,0.2,setosa
+4.7,3.2,1.3,0.2,setosa
+4.6,3.1,1.5,0.2,setosa
+5,3.6,1.4,0.2,setosa
+5.4,3.9,1.7,0.4,setosa
+4.6,3.4,1.4,0.3,setosa
+5,3.4,1.5,0.2,setosa
+4.4,2.9,1.4,0.2,setosa
+4.9,3.1,1.5,0.1,setosa
+5.4,3.7,1.5,0.2,setosa
+4.8,3.4,1.6,0.2,setosa
+4.8,3,1.4,0.1,setosa
+4.3,3,1.1,0.1,setosa
+5.8,4,1.2,0.2,setosa
+5.7,4.4,1.5,0.4,setosa
+5.4,3.9,1.3,0.4,setosa
+5.1,3.5,1.4,0.3,setosa
+5.7,3.8,1.7,0.3,setosa
+5.1,3.8,1.5,0.3,setosa
+5.4,3.4,1.7,0.2,setosa
+5.1,3.7,1.5,0.4,setosa
+4.6,3.6,1,0.2,setosa
+5.1,3.3,1.7,0.5,setosa
+4.8,3.4,1.9,0.2,setosa
+5,3,1.6,0.2,setosa
+5,3.4,1.6,0.4,setosa
+5.2,3.5,1.5,0.2,setosa
+5.2,3.4,1.4,0.2,setosa
+4.7,3.2,1.6,0.2,setosa
+4.8,3.1,1.6,0.2,setosa
+5.4,3.4,1.5,0.4,setosa
+5.2,4.1,1.5,0.1,setosa
+5.5,4.2,1.4,0.2,setosa
+4.9,3.1,1.5,0.2,setosa
+5,3.2,1.2,0.2,setosa
+5.5,3.5,1.3,0.2,setosa
+4.9,3.6,1.4,0.1,setosa
+4.4,3,1.3,0.2,setosa
+5.1,3.4,1.5,0.2,setosa
+5,3.5,1.3,0.3,setosa
+4.5,2.3,1.3,0.3,setosa
+4.4,3.2,1.3,0.2,setosa
+5,3.5,1.6,0.6,setosa
+5.1,3.8,1.9,0.4,setosa
+4.8,3,1.4,0.3,setosa
+5.1,3.8,1.6,0.2,setosa
+4.6,3.2,1.4,0.2,setosa
+5.3,3.7,1.5,0.2,setosa
+5,3.3,1.4,0.2,setosa
+7,3.2,4.7,1.4,versicolor
+6.4,3.2,4.5,1.5,versicolor
+6.9,3.1,4.9,1.5,versicolor
+5.5,2.3,4,1.3,versicolor
+6.5,2.8,4.6,1.5,versicolor
+5.7,2.8,4.5,1.3,versicolor
+6.3,3.3,4.7,1.6,versicolor
+4.9,2.4,3.3,1,versicolor
+6.6,2.9,4.6,1.3,versicolor
+5.2,2.7,3.9,1.4,versicolor
+5,2,3.5,1,versicolor
+5.9,3,4.2,1.5,versicolor
+6,2.2,4,1,versicolor
+6.1,2.9,4.7,1.4,versicolor
+5.6,2.9,3.6,1.3,versicolor
+6.7,3.1,4.4,1.4,versicolor
+5.6,3,4.5,1.5,versicolor
+5.8,2.7,4.1,1,versicolor
+6.2,2.2,4.5,1.5,versicolor
+5.6,2.5,3.9,1.1,versicolor
+5.9,3.2,4.8,1.8,versicolor
+6.1,2.8,4,1.3,versicolor
+6.3,2.5,4.9,1.5,versicolor
+6.1,2.8,4.7,1.2,versicolor
+6.4,2.9,4.3,1.3,versicolor
+6.6,3,4.4,1.4,versicolor
+6.8,2.8,4.8,1.4,versicolor
+6.7,3,5,1.7,versicolor
+6,2.9,4.5,1.5,versicolor
+5.7,2.6,3.5,1,versicolor
+5.5,2.4,3.8,1.1,versicolor
+5.5,2.4,3.7,1,versicolor
+5.8,2.7,3.9,1.2,versicolor
+6,2.7,5.1,1.6,versicolor
+5.4,3,4.5,1.5,versicolor
+6,3.4,4.5,1.6,versicolor
+6.7,3.1,4.7,1.5,versicolor
+6.3,2.3,4.4,1.3,versicolor
+5.6,3,4.1,1.3,versicolor
+5.5,2.5,4,1.3,versicolor
+5.5,2.6,4.4,1.2,versicolor
+6.1,3,4.6,1.4,versicolor
+5.8,2.6,4,1.2,versicolor
+5,2.3,3.3,1,versicolor
+5.6,2.7,4.2,1.3,versicolor
+5.7,3,4.2,1.2,versicolor
+5.7,2.9,4.2,1.3,versicolor
+6.2,2.9,4.3,1.3,versicolor
+5.1,2.5,3,1.1,versicolor
+5.7,2.8,4.1,1.3,versicolor
+6.3,3.3,6,2.5,virginica
+5.8,2.7,5.1,1.9,virginica
+7.1,3,5.9,2.1,virginica
+6.3,2.9,5.6,1.8,virginica
+6.5,3,5.8,2.2,virginica
+7.6,3,6.6,2.1,virginica
+4.9,2.5,4.5,1.7,virginica
+7.3,2.9,6.3,1.8,virginica
+6.7,2.5,5.8,1.8,virginica
+7.2,3.6,6.1,2.5,virginica
+6.5,3.2,5.1,2,virginica
+6.4,2.7,5.3,1.9,virginica
+6.8,3,5.5,2.1,virginica
+5.7,2.5,5,2,virginica
+5.8,2.8,5.1,2.4,virginica
+6.4,3.2,5.3,2.3,virginica
+6.5,3,5.5,1.8,virginica
+7.7,3.8,6.7,2.2,virginica
+7.7,2.6,6.9,2.3,virginica
+6,2.2,5,1.5,virginica
+6.9,3.2,5.7,2.3,virginica
+5.6,2.8,4.9,2,virginica
+7.7,2.8,6.7,2,virginica
+6.3,2.7,4.9,1.8,virginica
+6.7,3.3,5.7,2.1,virginica
+7.2,3.2,6,1.8,virginica
+6.2,2.8,4.8,1.8,virginica
+6.1,3,4.9,1.8,virginica
+6.4,2.8,5.6,2.1,virginica
+7.2,3,5.8,1.6,virginica
+7.4,2.8,6.1,1.9,virginica
+7.9,3.8,6.4,2,virginica
+6.4,2.8,5.6,2.2,virginica
+6.3,2.8,5.1,1.5,virginica
+6.1,2.6,5.6,1.4,virginica
+7.7,3,6.1,2.3,virginica
+6.3,3.4,5.6,2.4,virginica
+6.4,3.1,5.5,1.8,virginica
+6,3,4.8,1.8,virginica
+6.9,3.1,5.4,2.1,virginica
+6.7,3.1,5.6,2.4,virginica
+6.9,3.1,5.1,2.3,virginica
+5.8,2.7,5.1,1.9,virginica
+6.8,3.2,5.9,2.3,virginica
+6.7,3.3,5.7,2.5,virginica
+6.7,3,5.2,2.3,virginica
+6.3,2.5,5,1.9,virginica
+6.5,3,5.2,2,virginica
+6.2,3.4,5.4,2.3,virginica
+5.9,3,5.1,1.8,virginica

src/test/groovy/datasets/DatasetTest.groovy

@@ -14,10 +14,20 @@ class DatasetTest {
     void testIris() {
         Matrix iris = Dataset.iris()
         assertEquals(150, iris.rowCount(), 'number of rows')
-        assertEquals(6, iris.columnCount(), 'number of columns')
+        assertEquals(5, iris.columnCount(), 'number of columns')
         assertEquals(876.5, Stat.sum(iris['Sepal Length']))
         assertEquals(Dataset.descIris(), Dataset.describe(iris))
         assertEquals(Dataset.descIris(), Dataset.describe('iris'))
+        assertEquals(['Sepal Length','Sepal Width','Petal Length','Petal Width','Species'], iris.columnNames())
+        def speciesIdx = iris.columnIndex("Species")
+        def setosa = iris.subset {
+            it[speciesIdx] == 'setosa'
+        }
+        def virginica = iris.subset {
+            it[speciesIdx] == 'virginica'
+        }
+        assertEquals(1.462, Stat.mean(setosa['Petal Length']))
+        assertEquals(5.552, Stat.mean(virginica['Petal Length']))
     }