Airquality Assignment

Load in the library

library(tidyverse)

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✔ ggplot2   3.5.2     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.4     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Load the dataset into your global environment

data("airquality")

Look at the structure of the data

head(airquality)

  Ozone Solar.R Wind Temp Month Day
1    41     190  7.4   67     5   1
2    36     118  8.0   72     5   2
3    12     149 12.6   74     5   3
4    18     313 11.5   62     5   4
5    NA      NA 14.3   56     5   5
6    28      NA 14.9   66     5   6

Calculate Summary Statistics

mean(airquality$Temp)

[1] 77.88235

Calculate Median, Standard Deviation, and Variance

median(airquality$Temp)

[1] 79

sd(airquality$Wind)

[1] 3.523001

var(airquality$Wind)

[1] 12.41154

Rename the Months from number to names

airquality$Month[airquality$Month == 5]<- "May"
airquality$Month[airquality$Month == 6]<- "June"
airquality$Month[airquality$Month == 7]<- "July"
airquality$Month[airquality$Month == 8]<- "August"
airquality$Month[airquality$Month == 9]<- "September"

Now look at the summary statistics of the dataset

summary(airquality$Month)

   Length     Class      Mode 
      153 character character 

Month is a categorical variable with different levels, called factors.

airquality$Month<-factor(airquality$Month, 
                         levels=c("May", "June","July", "August",
                                  "September"))

Plot 1: Create a histogram categorized by Month

Plot 1 Code

p1 <- airquality |>
  ggplot(aes(x=Temp, fill=Month)) +
  geom_histogram(position="identity")+
  scale_fill_discrete(name = "Month", 
                      labels = c("May", "June","July", "August", "September")) +
  labs(x = "Monthly Temperatures from May - Sept", 
       y = "Frequency of Temps",
       title = "Histogram of Monthly Temperatures from May - Sept, 1973",
       caption = "New York State Department of Conservation and the National Weather Service")  #provide the data source

Plot 1 Output

p1

`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Plot 2: Improve the histogram of Average Temperature by Month

Plot 2 Code

p2 <- airquality |>
  ggplot(aes(x=Temp, fill=Month)) +
  geom_histogram(position="identity", alpha=0.5, binwidth = 5, color = "white")+
  scale_fill_discrete(name = "Month", labels = c("May", "June","July", "August", "September")) +
  labs(x = "Monthly Temperatures from May - Sept", 
       y = "Frequency of Temps",
       title = "Histogram of Monthly Temperatures from May - Sept, 1973",
       caption = "New York State Department of Conservation and the National Weather Service")

Plot 2 Output

p2

Plot 3: Create side-by-side boxplots categorized by Month

Plot 3 Code

p3 <- airquality |>
  ggplot(aes(Month, Temp, fill = Month)) + 
  labs(x = "Months from May through September", y = "Temperatures", 
       title = "Side-by-Side Boxplot of Monthly Temperatures",
       caption = "New York State Department of Conservation and the National Weather Service") +
  geom_boxplot() +
  scale_fill_discrete(name = "Month", labels = c("May", "June","July", "August", "September"))

Plot 3 Output

p3

Plot 4: Side by Side Boxplots in Gray Scale

Plot 4 Code

p4 <- airquality |>
ggplot(aes(Month, Temp, fill = Month)) + 
  labs(x = "Monthly Temperatures", y = "Temperatures", 
       title = "Side-by-Side Boxplot of Monthly Temperatures",
       caption = "New York State Department of Conservation and the National Weather Service") +
  geom_boxplot()+
  scale_fill_grey(name = "Month", labels = c("May", "June","July", "August", "September"))

Plot 4 Output

p4

Plot 5: Create a polar bar plot

Plot 5 Code

p5 <- ggplot(data = airquality) +
  geom_bar(aes(x = Ozone_Bin, fill = Ozone_Bin), 
           show.legend = FALSE, width = 1) +
  theme(aspect.ratio = 1) +
  coord_polar() +
  labs(
    title = "Polar Bar Plot of Ozone Levels",
    x = "Ozone Levels (ppb)",
    y = "Frequency",
    caption = "Data source: New York State Department of Conservation and the National Weather Service"
  ) +
  theme_minimal()

Plot 5: Create a bar-like graph - Polar Plot

PLot 5 Code

bar <- ggplot(data = diamonds) + 
  geom_bar(aes(x = clarity, fill = clarity), 
           show.legend = FALSE, width = 1) + 
  theme(aspect.ratio = 1)

bar + coord_polar()

Essay

This is a polar bar chart, sometimes called a “rose chart.” Think of it as a regular bar chart that’s been wrapped around a circle. Instead of bars going up and down, they spread out from the center, with each segment’s length showing its value.

This plot shows how many diamonds fall into different “clarity” groups. Most diamonds are in the ‘SI1’ and ‘VS2’ clarity levels (the big slices). Very few diamonds are ‘I1’ (lowest clarity) or ‘IF’ (perfect clarity) (the tiny slices). Basically, it shows that most diamonds are somewhere in the middle for clarity.

I used R’s ggplot2 package.

I told it to make a bar chart of clarity from your diamonds data.

Then, the coord_polar() command was used to turn that regular bar chart into the circular one you see.