This report is automatically generated with the R package knitr (version 1.5) .

# Reading the data ================ Loading data
data(iris)
# ?iris

# Taking a look at the data we have
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 ...
# Creating a data without the Species' labels
iris2 <- iris[, -5]

# A linear model of petal lengh and width
fit <- lm(Petal.Length ~ Petal.Width, data = iris2)
summary(fit)
## 
## Call:
## lm(formula = Petal.Length ~ Petal.Width, data = iris2)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.3354 -0.3035 -0.0295  0.2578  1.3945 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   1.0836     0.0730    14.8   <2e-16 ***
## Petal.Width   2.2299     0.0514    43.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.478 on 148 degrees of freedom
## Multiple R-squared:  0.927,	Adjusted R-squared:  0.927 
## F-statistic: 1.88e+03 on 1 and 148 DF,  p-value: <2e-16
with(iris2, plot(Petal.Length ~ Petal.Width))
abline(fit, col = "darkblue", lwd = 3, lty = 4)
plot of chunk unnamed-chunk-1
# A scatterplot matrix of the 4 variables in our data
Species <- iris[, 5]
col_Species <- c("#7DB0DD", "#86B875", "#E495A5")
pairs(iris[, -5], lower.panel = NULL, col = col_Species[as.numeric(Species)], pch = 19, cex = 1.4)
par(xpd = TRUE)
legend(x = 0, y = 0.4, cex = 2, legend = as.character(levels(Species)), fill = col_Species)
plot of chunk unnamed-chunk-1
par(xpd = NA)
# fix legend: https://stat.ethz.ch/pipermail/r-help/2001-September/015374.html


# Clustering the flowers ======================= Create a heirarchical clustering of the
# flowers
hc <- hclust(dist(scale(iris2)), method = "complete")
# Turning the hclust object into a dendrogram object for plotting
dend <- as.dendrogram(hc)
# install.packages('dendextend')
require(dendextend)
col_clust <- c("burlywood4", "black", "darkgrey")
dend <- color_branches(dend, k = 3, col = col_clust, groupLabels = 3:1)
dend <- color_labels(dend, col = col_Species[as.numeric(Species[order.dendrogram(dend)])])
dend <- hang.dendrogram(dend)

# Viewing our dendrogram with colors per each cluster (cutting for 3 clusters)
plot(dend)
legend("topright", cex = 1.5, legend = as.character(levels(Species)), fill = col_Species)
plot of chunk unnamed-chunk-1

The R session information (including the OS info, R version and all packages used):

sessionInfo()
## R version 3.0.3 (2014-03-06)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## 
## locale:
## [1] LC_COLLATE=Hebrew_Israel.1255  LC_CTYPE=Hebrew_Israel.1255   
## [3] LC_MONETARY=Hebrew_Israel.1255 LC_NUMERIC=C                  
## [5] LC_TIME=Hebrew_Israel.1255    
## 
## attached base packages:
## [1] stats     graphics  grDevices datasets  utils     methods   base     
## 
## other attached packages:
## [1] knitr_1.5            dendextendRcpp_0.5.1 Rcpp_0.11.1          dendextend_0.14.2   
## [5] colorspace_1.2-4     installr_0.14.5     
## 
## loaded via a namespace (and not attached):
## [1] ape_3.1-1       evaluate_0.5.1  formatR_0.10    grid_3.0.3      highr_0.3      
## [6] lattice_0.20-27 nlme_3.1-115    stringr_0.6.2   tools_3.0.3
Sys.time()
## [1] "2014-05-29 09:03:14 IDT"