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data(iris)

head(iris)

tail(iris)

colnames(iris)

## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width"  "Species"

num_rows <- nrow(iris)
num_cols <- ncol(iris)

num_rows

## [1] 150

num_cols

## [1] 5

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  
##                 
##                 
## 

selected_cols <- iris[, c("Sepal.Length", "Petal.Length")]
head(selected_cols)

virginica_large_sepal <- iris[
  iris$Species == "virginica" & iris$Sepal.Length > 6,
]

virginica_large_sepal

hist(
  iris$Sepal.Length,
  main = "Histogram of Sepal Length",
  xlab = "Sepal Length",
  ylab = "Frequency"
)

pairs(iris[, 1:4], main = "Scatter Plot Matrix of Iris Dataset")