Week-5

data analysis on iris dataset. present inbuilt on rstudio. The Iris flower dataset is a widely used dataset in machine learning, containing 150 samples of Iris flowers from three species: Iris setosa, Iris virginica, and Iris versicolor.

test_data=iris
summary(test_data)

##   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  
##                 
##                 
## 

str(test_data)

## '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 ...

head(test_data)

##   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

Including Plots using ggplot2 library

You can also embed plots, for example:

# Barplot
library(ggplot2)
data_barplot <- aggregate(Sepal.Length ~ Species, data = iris, mean)
barplot <- ggplot(data_barplot, aes(x = Species, y = Sepal.Length)) +
  geom_bar(stat = "identity", fill = "blue") +
  labs(title = "Barplot of Sepal Length by Species",
       x = "Species", y = "Mean Sepal Length")
barplot

sepal length of virginica is more.

# Histogram
histogram <- ggplot(iris, aes(x = Sepal.Length, fill = Species)) +
  geom_histogram(binwidth = 0.2, position = "identity", alpha = 0.7) +
  labs(title = "Histogram of Sepal Length by Species",
       x = "Sepal Length", y = "Frequency",
       fill = "Species")
print(histogram)

It gives insights into the spread and frequency of Sepal Length values for each species.

#pie chart
data_pie <- table(iris$Species)
piechart <- ggplot() +
  geom_bar(stat = "identity", aes(x = 1, y = as.numeric(data_pie), fill = names(data_pie))) +
  coord_polar(theta = "y") +
  labs(title = "Pie Chart of Species Distribution")
print(piechart)

count of species is same.

# Scatterplot
scatterplot <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species)) +
  geom_point() +
  labs(title = "Scatterplot of Sepal Length vs Sepal Width",
       x = "Sepal Length", y = "Sepal Width",
       color = "Species")
print(scatterplot)

it gives an idea about spread of sepal width and sepal length.

# Boxplot
boxplot <- ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
  geom_boxplot() +
  labs(title = "Boxplot of Sepal Length by Species",
       x = "Species", y = "Sepal Length",
       fill = "Species")
print(boxplot)