Introduction

Iris dataset is a datset that contains things that are normally contained in a dataset, thereby making it a good dataset among all the other datasets

Data Analysis

Deep analysis of the question number 4 and 5. It is very analytical and full of things that can be analyzed. Analysis, thereby, is very analytical and worth analysing. The end!!!

Wrangling

library("tidyverse")

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.2
## ✔ ggplot2   3.5.2     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

iris %>% 
filter(Petal.Width>5)

## [1] Sepal.Length Sepal.Width  Petal.Length Petal.Width  Species     
## <0 rows> (or 0-length row.names)

This is a very good explanation of the code, first you do what needs to be done and then finish things up!!!

Plotting

iris %>% 
  ggplot(aes(x = Petal.Length, y = Petal.Width)) +
  geom_point()