#Libraries and data
library(dplyr)
library(tidyverse)
data("iris")

Question 1

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 ...
There are 150 observations and 5 variables

Question 2

iris1 <- iris %>%
  filter(Species == "virginica"| Species == "versicolor") %>%
  filter(Sepal.Length > 6 & Sepal.Width >2.5)
str(iris1) 
## 'data.frame':    56 obs. of  5 variables:
##  $ Sepal.Length: num  7 6.4 6.9 6.5 6.3 6.6 6.1 6.7 6.1 6.1 ...
##  $ Sepal.Width : num  3.2 3.2 3.1 2.8 3.3 2.9 2.9 3.1 2.8 2.8 ...
##  $ Petal.Length: num  4.7 4.5 4.9 4.6 4.7 4.6 4.7 4.4 4 4.7 ...
##  $ Petal.Width : num  1.4 1.5 1.5 1.5 1.6 1.3 1.4 1.4 1.3 1.2 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 2 2 2 2 2 2 2 2 2 2 ...
There are 56 observations of 5 variables

Question 3

iris2 <- iris1 %>%
  select(Species, Sepal.Length, Sepal.Width)
str(iris2)
## 'data.frame':    56 obs. of  3 variables:
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ Sepal.Length: num  7 6.4 6.9 6.5 6.3 6.6 6.1 6.7 6.1 6.1 ...
##  $ Sepal.Width : num  3.2 3.2 3.1 2.8 3.3 2.9 2.9 3.1 2.8 2.8 ...
There are 56 observations of 3 variables

Question 4

iris3 <- iris2 %>%
  arrange(desc(Sepal.Length))
head(iris3)
##     Species Sepal.Length Sepal.Width
## 1 virginica          7.9         3.8
## 2 virginica          7.7         3.8
## 3 virginica          7.7         2.6
## 4 virginica          7.7         2.8
## 5 virginica          7.7         3.0
## 6 virginica          7.6         3.0

Question 5

iris4 <- iris3 %>%
  mutate(Sepal.Area = Sepal.Length*Sepal.Width)
str(iris4)
## 'data.frame':    56 obs. of  4 variables:
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ Sepal.Length: num  7.9 7.7 7.7 7.7 7.7 7.6 7.4 7.3 7.2 7.2 ...
##  $ Sepal.Width : num  3.8 3.8 2.6 2.8 3 3 2.8 2.9 3.6 3.2 ...
##  $ Sepal.Area  : num  30 29.3 20 21.6 23.1 ...
There are 56 observations of 4 variables

Question 6

iris5 <- iris4 %>%
  summarise(
    Avg.Sepal.Width = mean(Sepal.Width), 
    Avg.Speal.Length = mean(Sepal.Length),
    Sample.Size = n())

Question 7

iris6 <- iris4 %>%
  group_by(Species) %>%
  summarise(
    Avg.Sepal.Width = mean(Sepal.Width), 
    Avg.Speal.Length = mean(Sepal.Length),
    Sample.Size = n())
print(iris6)
## # A tibble: 2 × 4
##   Species    Avg.Sepal.Width Avg.Speal.Length Sample.Size
##   <fct>                <dbl>            <dbl>       <int>
## 1 versicolor            2.99             6.48          17
## 2 virginica             3.06             6.79          39

Question 8

irisFinal <- iris %>%
  filter(Species == "virginica"| Species == "versicolor") %>%
  filter(Sepal.Length > 6 & Sepal.Width >2.5) %>%
  select(Species, Sepal.Length, Sepal.Width) %>%
  arrange(desc(Sepal.Length)) %>%
  mutate(Sepal.Area = Sepal.Length*Sepal.Width) %>%
  group_by(Species) %>%
  summarise(
    Avg.Sepal.Width = mean(Sepal.Width), 
    Avg.Speal.Length = mean(Sepal.Length),
    Sample.Size = n())

Question 9

irisLonger <- iris %>%
  pivot_longer(cols = c(Sepal.Length,Sepal.Width,Petal.Length,Petal.Width),
               names_to = "Measure",
               values_to = "Value")
head(irisLonger)
## # A tibble: 6 × 3
##   Species Measure      Value
##   <fct>   <chr>        <dbl>
## 1 setosa  Sepal.Length   5.1
## 2 setosa  Sepal.Width    3.5
## 3 setosa  Petal.Length   1.4
## 4 setosa  Petal.Width    0.2
## 5 setosa  Sepal.Length   4.9
## 6 setosa  Sepal.Width    3