iris

0. ?iris

help(iris)

1. head(iris)

head(iris)
##   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
## 6          5.4         3.9          1.7         0.4  setosa

2. str(iris)

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 ...

3. summary(iris)

summary(iris)
##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.800   Median :3.000   Median :4.350   Median :1.300  
##  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  
##        Species  
##  setosa    :50  
##  versicolor:50  
##  virginica :50  
##                 
##                 
##

4. summary(lm(iris[,1]~.,data=iris[, -5]))

summary(lm(iris[,1]~.,data=iris[, -5]))
## Warning in summary.lm(lm(iris[, 1] ~ ., data = iris[, -5])): essentially
## perfect fit: summary may be unreliable
## 
## Call:
## lm(formula = iris[, 1] ~ ., data = iris[, -5])
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -2.330e-15 -1.857e-17  1.444e-17  4.207e-17  1.847e-16 
## 
## Coefficients:
##                Estimate Std. Error    t value Pr(>|t|)    
## (Intercept)  -2.595e-15  1.875e-16 -1.384e+01   <2e-16 ***
## Sepal.Length  1.000e+00  5.277e-17  1.895e+16   <2e-16 ***
## Sepal.Width   4.253e-17  5.464e-17  7.780e-01    0.438    
## Petal.Length  3.162e-17  5.204e-17  6.080e-01    0.544    
## Petal.Width  -1.698e-17  8.646e-17 -1.960e-01    0.845    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.006e-16 on 145 degrees of freedom
## Multiple R-squared:      1,  Adjusted R-squared:      1 
## F-statistic: 6.35e+32 on 4 and 145 DF,  p-value: < 2.2e-16

5. plot(lm(iris[,1]~.,data=iris[, -5]))

par(mfrow=c(2, 2))
plot(lm(iris[,1]~.,data=iris[, -5]))

6. boxplot(iris[,1]~iris[,5])

boxplot(iris[,1]~iris[,5])

7. pairs(iris[,-5],col=iris[,5],pch=19)

pairs(iris[,-5],col=iris[,5],pch=19)

8. heatmap(as.matrix(iris[,-5]), cexCol=.7)

heatmap(as.matrix(iris[,-5]), cexCol=.7)

9. plot(hclust(dist(iris[,-5]), “ave”))

plot(hclust(dist(iris[,-5]), "ave"))