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+---
+title: "Correlation"
+subtitle: "Statistics With R"
+author: "R Workshop"
+output:
+  prettydoc::html_pretty:
+    theme: cayman
+    highlight: github
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+require(prettydoc)
+require(tidyverse)
+require(janitor)
+```
+
+Broom
+============
+
+#### Convert Statistical Analysis Objects into Tidy Data Frames
+
+Convert statistical analysis objects from R into tidy data frames, so that they can more easily be combined, reshaped and otherwise processed with tools like 'dplyr', 'tidyr' and 'ggplot2'. 
+
+The package provides three S3 generics: 
+* **``tidy``**: summarizes a model's statistical findings such as coefficients of a regression; 
+* **``augment``**: adds columns to the original data such as predictions, residuals and cluster assignments
+* **``glance``**: which provides a one-row summary of model-level statistics.
+
+
+
+```R
+# Run once, then comment out
+install.packages("broom")
+
+library(magrittr)
+
+library(broom)
+```
+
+    Installing package into '/home/nbcommon/R'
+    (as 'lib' is unspecified)
+
+
+### Fit a Linear Model
+* Just use a simple example using inbuilt data sets
+* Save it as `myModel`
+
+
+```R
+lm(mpg ~ wt + cyl, data=mtcars)
+```
+
+
+    
+    Call:
+    lm(formula = mpg ~ wt + cyl, data = mtcars)
+    
+    Coefficients:
+    (Intercept)           wt          cyl  
+         39.686       -3.191       -1.508  
+
+
+
+
+```R
+myModel <- lm(mpg ~ wt + cyl, data=mtcars)
+summary(myModel)
+```
+
+
+    
+    Call:
+    lm(formula = mpg ~ wt + cyl, data = mtcars)
+    
+    Residuals:
+        Min      1Q  Median      3Q     Max 
+    -4.2893 -1.5512 -0.4684  1.5743  6.1004 
+    
+    Coefficients:
+                Estimate Std. Error t value Pr(>|t|)    
+    (Intercept)  39.6863     1.7150  23.141  < 2e-16 ***
+    wt           -3.1910     0.7569  -4.216 0.000222 ***
+    cyl          -1.5078     0.4147  -3.636 0.001064 ** 
+    ---
+    Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
+    
+    Residual standard error: 2.568 on 29 degrees of freedom
+    Multiple R-squared:  0.8302,	Adjusted R-squared:  0.8185 
+    F-statistic: 70.91 on 2 and 29 DF,  p-value: 6.809e-12
+
+
+
+#### 1. The `tidy()` function
+
+**``tidy()``** constructs a data frame that summarizes the model's statistical findings. This includes coefficients and p-values for each term in a regression, per-cluster information in clustering applications, or per-test information for multtest functions.
+
+
+
+```R
+tidy(myModel)
+```
+
+
+<table>
+<thead><tr><th>term</th><th>estimate</th><th>std.error</th><th>statistic</th><th>p.value</th></tr></thead>
+<tbody>
+	<tr><td>(Intercept) </td><td>39.686261   </td><td>1.7149840   </td><td>23.140893   </td><td>3.043182e-20</td></tr>
+	<tr><td>wt          </td><td>-3.190972   </td><td>0.7569065   </td><td>-4.215808   </td><td>2.220200e-04</td></tr>
+	<tr><td>cyl         </td><td>-1.507795   </td><td>0.4146883   </td><td>-3.635972   </td><td>1.064282e-03</td></tr>
+</tbody>
+</table>
+
+
+
+
+```R
+myTidyModel <- tidy(myModel)
+```
+
+
+```R
+class(myTidyModel)
+```
+
+
+'data.frame'
+
+
+
+```R
+names(myTidyModel)
+```
+
+
+<ol class="list-inline">
+	<li>'term'</li>
+	<li>'estimate'</li>
+	<li>'std.error'</li>
+	<li>'statistic'</li>
+	<li>'p.value'</li>
+</ol>
+
+
+
+
+```R
+myTidyModel$p.value %>% round(4)
+```
+
+
+<ol class="list-inline">
+	<li>0</li>
+	<li>2e-04</li>
+	<li>0.0011</li>
+</ol>
+
+
+
+#### 2. The `augment()` function
+
+**``augment()``** add columns to the original data that was modeled. 
+This includes predictions, residuals, and cluster assignments.
+
+
+
+```R
+myModel <- lm(mpg ~ wt+cyl, mtcars)
+augment(myModel)
+
+```
+
+
+<table>
+<thead><tr><th>.rownames</th><th>mpg</th><th>wt</th><th>cyl</th><th>.fitted</th><th>.se.fit</th><th>.resid</th><th>.hat</th><th>.sigma</th><th>.cooksd</th><th>.std.resid</th></tr></thead>
+<tbody>
+	<tr><td>Mazda RX4          </td><td>21.0               </td><td>2.620              </td><td>6                  </td><td>22.27914           </td><td>0.6011667          </td><td>-1.27914467        </td><td>0.05482311         </td><td>2.601105           </td><td>0.0050772590       </td><td>-0.51244825        </td></tr>
+	<tr><td>Mazda RX4 Wag      </td><td>21.0               </td><td>2.875              </td><td>6                  </td><td>21.46545           </td><td>0.4976294          </td><td>-0.46544677        </td><td>0.03756521         </td><td>2.611423           </td><td>0.0004442585       </td><td>-0.18478693        </td></tr>
+	<tr><td>Datsun 710         </td><td>22.8               </td><td>2.320              </td><td>4                  </td><td>26.25203           </td><td>0.7252444          </td><td>-3.45202624        </td><td>0.07978891         </td><td>2.522911           </td><td>0.0567764620       </td><td>-1.40157794        </td></tr>
+	<tr><td>Hornet 4 Drive     </td><td>21.4               </td><td>3.215              </td><td>6                  </td><td>20.38052           </td><td>0.4602669          </td><td> 1.01948376        </td><td>0.03213611         </td><td>2.605613           </td><td>0.0018029260       </td><td> 0.40360828        </td></tr>
+	<tr><td>Hornet Sportabout  </td><td>18.7               </td><td>3.440              </td><td>8                  </td><td>16.64696           </td><td>0.7752706          </td><td> 2.05304242        </td><td>0.09117599         </td><td>2.581072           </td><td>0.0235271472       </td><td> 0.83877393        </td></tr>
+	<tr><td>Valiant            </td><td>18.1               </td><td>3.460              </td><td>6                  </td><td>19.59873           </td><td>0.5178496          </td><td>-1.49872807        </td><td>0.04068001         </td><td>2.596911           </td><td>0.0050205614       </td><td>-0.59597493        </td></tr>
+	<tr><td>Duster 360         </td><td>14.3               </td><td>3.570              </td><td>8                  </td><td>16.23213           </td><td>0.7267482          </td><td>-1.93213120        </td><td>0.08012014         </td><td>2.585079           </td><td>0.0178733213       </td><td>-0.78461743        </td></tr>
+	<tr><td>Merc 240D          </td><td>24.4               </td><td>3.190              </td><td>4                  </td><td>23.47588           </td><td>1.0000172          </td><td> 0.92411952        </td><td>0.15170109         </td><td>2.606073           </td><td>0.0091033181       </td><td> 0.39078743        </td></tr>
+	<tr><td>Merc 230           </td><td>22.8               </td><td>3.150              </td><td>4                  </td><td>23.60352           </td><td>0.9793969          </td><td>-0.80351937        </td><td>0.14550945         </td><td>2.607793           </td><td>0.0065061176       </td><td>-0.33855530        </td></tr>
+	<tr><td>Merc 280           </td><td>19.2               </td><td>3.440              </td><td>6                  </td><td>19.66255           </td><td>0.5108741          </td><td>-0.46254751        </td><td>0.03959146         </td><td>2.611439           </td><td>0.0004643600       </td><td>-0.18382951        </td></tr>
+	<tr><td>Merc 280C          </td><td>17.8               </td><td>3.440              </td><td>6                  </td><td>19.66255           </td><td>0.5108741          </td><td>-1.86254751        </td><td>0.03959146         </td><td>2.588159           </td><td>0.0075293380       </td><td>-0.74022926        </td></tr>
+	<tr><td>Merc 450SE         </td><td>16.4               </td><td>4.070              </td><td>8                  </td><td>14.63665           </td><td>0.6544576          </td><td> 1.76335487        </td><td>0.06497359         </td><td>2.590136           </td><td>0.0116847953       </td><td> 0.71025562        </td></tr>
+	<tr><td>Merc 450SL         </td><td>17.3               </td><td>3.730              </td><td>8                  </td><td>15.72158           </td><td>0.6819424          </td><td> 1.57842434        </td><td>0.07054547         </td><td>2.594578           </td><td>0.0102875723       </td><td> 0.63767089        </td></tr>
+	<tr><td>Merc 450SLC        </td><td>15.2               </td><td>3.780              </td><td>8                  </td><td>15.56203           </td><td>0.6718159          </td><td>-0.36202705        </td><td>0.06846591         </td><td>2.612000           </td><td>0.0005228914       </td><td>-0.14609271        </td></tr>
+	<tr><td>Cadillac Fleetwood </td><td>10.4               </td><td>5.250              </td><td>8                  </td><td>10.87130           </td><td>1.1525645          </td><td>-0.47129800        </td><td>0.20151356         </td><td>2.611060           </td><td>0.0035498738       </td><td>-0.20542284        </td></tr>
+	<tr><td>Lincoln Continental</td><td>10.4               </td><td>5.424              </td><td>8                  </td><td>10.31607           </td><td>1.2633704          </td><td> 0.08393115        </td><td>0.24212252         </td><td>2.612898           </td><td>0.0001501537       </td><td> 0.03755005        </td></tr>
+	<tr><td>Chrysler Imperial  </td><td>14.7               </td><td>5.345              </td><td>8                  </td><td>10.56816           </td><td>1.2125441          </td><td> 4.13184435        </td><td>0.22303287         </td><td>2.458216           </td><td>0.3189363624       </td><td> 1.82570047        </td></tr>
+	<tr><td>Fiat 128           </td><td>32.4               </td><td>2.200              </td><td>4                  </td><td>26.63494           </td><td>0.7270859          </td><td> 5.76505710        </td><td>0.08019462         </td><td>2.353101           </td><td>0.1592990291       </td><td> 2.34122168        </td></tr>
+	<tr><td>Honda Civic        </td><td>30.4               </td><td>1.615              </td><td>4                  </td><td>28.50166           </td><td>0.8820281          </td><td> 1.89833840        </td><td>0.11801538         </td><td>2.584888           </td><td>0.0276449872       </td><td> 0.78728146        </td></tr>
+	<tr><td>Toyota Corolla     </td><td>33.9               </td><td>1.835              </td><td>4                  </td><td>27.79965           </td><td>0.7988791          </td><td> 6.10035227        </td><td>0.09681350         </td><td>2.314308           </td><td>0.2233281268       </td><td> 2.50007531        </td></tr>
+	<tr><td>Toyota Corona      </td><td>21.5               </td><td>2.465              </td><td>4                  </td><td>25.78934           </td><td>0.7380797          </td><td>-4.28933528        </td><td>0.08263810         </td><td>2.472103           </td><td>0.0913548207       </td><td>-1.74424120        </td></tr>
+	<tr><td>Dodge Challenger   </td><td>15.5               </td><td>3.520              </td><td>8                  </td><td>16.39168           </td><td>0.7442464          </td><td>-0.89167980        </td><td>0.08402476         </td><td>2.607023           </td><td>0.0040263378       </td><td>-0.36287242        </td></tr>
+	<tr><td>AMC Javelin        </td><td>15.2               </td><td>3.435              </td><td>8                  </td><td>16.66291           </td><td>0.7773252          </td><td>-1.46291244        </td><td>0.09165987         </td><td>2.596811           </td><td>0.0120218543       </td><td>-0.59783451        </td></tr>
+	<tr><td>Camaro Z28         </td><td>13.3               </td><td>3.840              </td><td>8                  </td><td>15.37057           </td><td>0.6623197          </td><td>-2.07056872        </td><td>0.06654404         </td><td>2.581383           </td><td>0.0165559199       </td><td>-0.83469849        </td></tr>
+	<tr><td>Pontiac Firebird   </td><td>19.2               </td><td>3.845              </td><td>8                  </td><td>15.35461           </td><td>0.6616629          </td><td> 3.84538614        </td><td>0.06641213         </td><td>2.502378           </td><td>0.0569730451       </td><td> 1.55006265        </td></tr>
+	<tr><td>Fiat X1-9          </td><td>27.3               </td><td>1.935              </td><td>4                  </td><td>27.48055           </td><td>0.7700721          </td><td>-0.18055052        </td><td>0.08995733         </td><td>2.612717           </td><td>0.0001790454       </td><td>-0.07371481        </td></tr>
+	<tr><td>Porsche 914-2      </td><td>26.0               </td><td>2.140              </td><td>4                  </td><td>26.82640           </td><td>0.7322422          </td><td>-0.82640123        </td><td>0.08133608         </td><td>2.607877           </td><td>0.0033281614       </td><td>-0.33581456        </td></tr>
+	<tr><td>Lotus Europa       </td><td>30.4               </td><td>1.513              </td><td>4                  </td><td>28.82714           </td><td>0.9282190          </td><td> 1.57285924        </td><td>0.13069974         </td><td>2.593440           </td><td>0.0216355209       </td><td> 0.65704006        </td></tr>
+	<tr><td>Ford Pantera L     </td><td>15.8               </td><td>3.170              </td><td>8                  </td><td>17.50852           </td><td>0.9023791          </td><td>-1.70852005        </td><td>0.12352416         </td><td>2.590102           </td><td>0.0237336584       </td><td>-0.71078295        </td></tr>
+	<tr><td>Ferrari Dino       </td><td>19.7               </td><td>2.770              </td><td>6                  </td><td>21.80050           </td><td>0.5342815          </td><td>-2.10049885        </td><td>0.04330261         </td><td>2.581252           </td><td>0.0105550987       </td><td>-0.83641545        </td></tr>
+	<tr><td>Maserati Bora      </td><td>15.0               </td><td>3.570              </td><td>8                  </td><td>16.23213           </td><td>0.7267482          </td><td>-1.23213120        </td><td>0.08012014         </td><td>2.601659           </td><td>0.0072685192       </td><td>-0.50035506        </td></tr>
+	<tr><td>Volvo 142E         </td><td>21.4               </td><td>2.780              </td><td>4                  </td><td>24.78418           </td><td>0.8176667          </td><td>-3.38417906        </td><td>0.10142065         </td><td>2.524357           </td><td>0.0727399065       </td><td>-1.39047125        </td></tr>
+</tbody>
+</table>
+
+
+
+#### 3. The ``glance()`` function
+
+**``glance()``** construct a concise one-row summary of the model. This typically contains values such as R2, adjusted R2, and residual standard error that are computed once for the entire model.
+
+
+
+```R
+glance(myModel)
+
+
+```
+
+
+<table>
+<thead><tr><th>p.value</th><th>df</th><th>logLik</th></tr></thead>
+<tbody>
+	<tr><td>6.808955e-12</td><td>3           </td><td>-74.00503   </td></tr>
+</tbody>
+</table>
+
+
+
+## K-Means
+
+
+```R
+kmeans(iris[,1:4],3 )
+```
+
+
+    K-means clustering with 3 clusters of sizes 96, 33, 21
+    
+    Cluster means:
+      Sepal.Length Sepal.Width Petal.Length Petal.Width
+    1     6.314583    2.895833     4.973958   1.7031250
+    2     5.175758    3.624242     1.472727   0.2727273
+    3     4.738095    2.904762     1.790476   0.3523810
+    
+    Clustering vector:
+      [1] 2 3 3 3 2 2 2 2 3 3 2 2 3 3 2 2 2 2 2 2 2 2 2 2 3 3 2 2 2 3 3 2 2 2 3 2 2
+     [38] 2 3 2 2 3 3 2 2 3 2 3 2 2 1 1 1 1 1 1 1 3 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 1
+     [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1
+    [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+    [149] 1 1
+    
+    Within cluster sum of squares by cluster:
+    [1] 118.651875   6.432121  17.669524
+     (between_SS / total_SS =  79.0 %)
+    
+    Available components:
+    
+    [1] "cluster"      "centers"      "totss"        "withinss"     "tot.withinss"
+    [6] "betweenss"    "size"         "iter"         "ifault"      
+
+
+
+```R
+KMmodel <- kmeans(iris[,1:4],3 )
+summary(KMmodel)
+```
+
+
+                 Length Class  Mode   
+    cluster      150    -none- numeric
+    centers       12    -none- numeric
+    totss          1    -none- numeric
+    withinss       3    -none- numeric
+    tot.withinss   1    -none- numeric
+    betweenss      1    -none- numeric
+    size           3    -none- numeric
+    iter           1    -none- numeric
+    ifault         1    -none- numeric
+
+
+
+```R
+tidy(KMmodel)
+```
+
+
+<table>
+<thead><tr><th>x1</th><th>x2</th><th>x3</th><th>x4</th><th>size</th><th>withinss</th><th>cluster</th></tr></thead>
+<tbody>
+	<tr><td>5.006000</td><td>3.428000</td><td>1.462000</td><td>0.246000</td><td>50      </td><td>15.15100</td><td>1       </td></tr>
+	<tr><td>5.901613</td><td>2.748387</td><td>4.393548</td><td>1.433871</td><td>62      </td><td>39.82097</td><td>2       </td></tr>
+	<tr><td>6.850000</td><td>3.073684</td><td>5.742105</td><td>2.071053</td><td>38      </td><td>23.87947</td><td>3       </td></tr>
+</tbody>
+</table>
+
+
+
+
+```R
+# augment(Model,Data)
+```
+
+
+```R
+augment(KMmodel,iris[,1:4]) %>%head()
+```
+
+
+<table>
+<thead><tr><th>Sepal.Length</th><th>Sepal.Width</th><th>Petal.Length</th><th>Petal.Width</th><th>.cluster</th></tr></thead>
+<tbody>
+	<tr><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>1  </td></tr>
+	<tr><td>4.9</td><td>3.0</td><td>1.4</td><td>0.2</td><td>1  </td></tr>
+	<tr><td>4.7</td><td>3.2</td><td>1.3</td><td>0.2</td><td>1  </td></tr>
+	<tr><td>4.6</td><td>3.1</td><td>1.5</td><td>0.2</td><td>1  </td></tr>
+	<tr><td>5.0</td><td>3.6</td><td>1.4</td><td>0.2</td><td>1  </td></tr>
+	<tr><td>5.4</td><td>3.9</td><td>1.7</td><td>0.4</td><td>1  </td></tr>
+</tbody>
+</table>
+
+
+
+
+```R
+glance(KMmodel)
+```
+
+
+<table>
+<thead><tr><th>totss</th><th>tot.withinss</th><th>betweenss</th><th>iter</th></tr></thead>
+<tbody>
+	<tr><td>681.3706</td><td>78.85144</td><td>602.5192</td><td>2       </td></tr>
+</tbody>
+</table>

term	estimate	std.error	statistic	p.value
(Intercept)	39.686261	1.7149840	23.140893	3.043182e-20
wt	-3.190972	0.7569065	-4.215808	2.220200e-04
cyl	-1.507795	0.4146883	-3.635972	1.064282e-03
.rownames	mpg	wt	cyl	.fitted	.se.fit	.resid	.hat	.sigma	.cooksd	.std.resid
Mazda RX4	21.0	2.620	6	22.27914	0.6011667	-1.27914467	0.05482311	2.601105	0.0050772590	-0.51244825
Mazda RX4 Wag	21.0	2.875	6	21.46545	0.4976294	-0.46544677	0.03756521	2.611423	0.0004442585	-0.18478693
Datsun 710	22.8	2.320	4	26.25203	0.7252444	-3.45202624	0.07978891	2.522911	0.0567764620	-1.40157794
Hornet 4 Drive	21.4	3.215	6	20.38052	0.4602669	1.01948376	0.03213611	2.605613	0.0018029260	0.40360828
Hornet Sportabout	18.7	3.440	8	16.64696	0.7752706	2.05304242	0.09117599	2.581072	0.0235271472	0.83877393
Valiant	18.1	3.460	6	19.59873	0.5178496	-1.49872807	0.04068001	2.596911	0.0050205614	-0.59597493
Duster 360	14.3	3.570	8	16.23213	0.7267482	-1.93213120	0.08012014	2.585079	0.0178733213	-0.78461743
Merc 240D	24.4	3.190	4	23.47588	1.0000172	0.92411952	0.15170109	2.606073	0.0091033181	0.39078743
Merc 230	22.8	3.150	4	23.60352	0.9793969	-0.80351937	0.14550945	2.607793	0.0065061176	-0.33855530
Merc 280	19.2	3.440	6	19.66255	0.5108741	-0.46254751	0.03959146	2.611439	0.0004643600	-0.18382951
Merc 280C	17.8	3.440	6	19.66255	0.5108741	-1.86254751	0.03959146	2.588159	0.0075293380	-0.74022926
Merc 450SE	16.4	4.070	8	14.63665	0.6544576	1.76335487	0.06497359	2.590136	0.0116847953	0.71025562
Merc 450SL	17.3	3.730	8	15.72158	0.6819424	1.57842434	0.07054547	2.594578	0.0102875723	0.63767089
Merc 450SLC	15.2	3.780	8	15.56203	0.6718159	-0.36202705	0.06846591	2.612000	0.0005228914	-0.14609271
Cadillac Fleetwood	10.4	5.250	8	10.87130	1.1525645	-0.47129800	0.20151356	2.611060	0.0035498738	-0.20542284
Lincoln Continental	10.4	5.424	8	10.31607	1.2633704	0.08393115	0.24212252	2.612898	0.0001501537	0.03755005
Chrysler Imperial	14.7	5.345	8	10.56816	1.2125441	4.13184435	0.22303287	2.458216	0.3189363624	1.82570047
Fiat 128	32.4	2.200	4	26.63494	0.7270859	5.76505710	0.08019462	2.353101	0.1592990291	2.34122168
Honda Civic	30.4	1.615	4	28.50166	0.8820281	1.89833840	0.11801538	2.584888	0.0276449872	0.78728146
Toyota Corolla	33.9	1.835	4	27.79965	0.7988791	6.10035227	0.09681350	2.314308	0.2233281268	2.50007531
Toyota Corona	21.5	2.465	4	25.78934	0.7380797	-4.28933528	0.08263810	2.472103	0.0913548207	-1.74424120
Dodge Challenger	15.5	3.520	8	16.39168	0.7442464	-0.89167980	0.08402476	2.607023	0.0040263378	-0.36287242
AMC Javelin	15.2	3.435	8	16.66291	0.7773252	-1.46291244	0.09165987	2.596811	0.0120218543	-0.59783451
Camaro Z28	13.3	3.840	8	15.37057	0.6623197	-2.07056872	0.06654404	2.581383	0.0165559199	-0.83469849
Pontiac Firebird	19.2	3.845	8	15.35461	0.6616629	3.84538614	0.06641213	2.502378	0.0569730451	1.55006265
Fiat X1-9	27.3	1.935	4	27.48055	0.7700721	-0.18055052	0.08995733	2.612717	0.0001790454	-0.07371481
Porsche 914-2	26.0	2.140	4	26.82640	0.7322422	-0.82640123	0.08133608	2.607877	0.0033281614	-0.33581456
Lotus Europa	30.4	1.513	4	28.82714	0.9282190	1.57285924	0.13069974	2.593440	0.0216355209	0.65704006
Ford Pantera L	15.8	3.170	8	17.50852	0.9023791	-1.70852005	0.12352416	2.590102	0.0237336584	-0.71078295
Ferrari Dino	19.7	2.770	6	21.80050	0.5342815	-2.10049885	0.04330261	2.581252	0.0105550987	-0.83641545
Maserati Bora	15.0	3.570	8	16.23213	0.7267482	-1.23213120	0.08012014	2.601659	0.0072685192	-0.50035506
Volvo 142E	21.4	2.780	4	24.78418	0.8176667	-3.38417906	0.10142065	2.524357	0.0727399065	-1.39047125
x1	x2	x3	x4	size	withinss	cluster
5.006000	3.428000	1.462000	0.246000	50	15.15100	1
5.901613	2.748387	4.393548	1.433871	62	39.82097	2
6.850000	3.073684	5.742105	2.071053	38	23.87947	3
Sepal.Length	Sepal.Width	Petal.Length	Petal.Width	.cluster
5.1	3.5	1.4	0.2	1
4.9	3.0	1.4	0.2	1
4.7	3.2	1.3	0.2	1
4.6	3.1	1.5	0.2	1
5.0	3.6	1.4	0.2	1
5.4	3.9	1.7	0.4	1