Airquality Plots

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library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── 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

data("airquality")

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

mean(airquality$Temp)

[1] 77.88235

mean(airquality[,4])

[1] 77.88235

median(airquality$Temp)

[1] 79

sd(airquality$Wind)

[1] 3.523001

var(airquality$Wind)

[1] 12.41154

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"

summary(airquality$Month)

   Length     Class      Mode 
      153 character character 

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

p1 <- ggplot(airquality, aes(x = Temp, fill = Month)) +
geom_histogram(position = "identity", binwidth = 5) + # Set binwidth for clarity
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")

print(p1)

p2 <-ggplot(airquality, 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")

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")

print(p2)

print(p3)

p3 <- airquality |>
 ggplot(aes(Month, Temp, fill = Month)) +
 geom_boxplot() +
 scale_fill_discrete(name = "Month", labels = c("May", "June", "July", "August", "September")) +
 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")

print(p3)

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

print(p4)

# Plot 5 Code
p5 <- airquality |>
 ggplot(aes(x = Ozone, fill = ..count..)) +
 geom_histogram(binwidth = 10, color = "black", alpha = 0.7) +
 scale_fill_viridis_c(name = "Frequency") +
 labs(x = "Ozone Concentration (ppb)",
   y = "Frequency",
   title = "Histogram of Ozone Levels",
   caption = "New York State Department of Conservation and the National Weather Service") +
 theme_minimal()

print(p5)

Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(count)` instead.

Warning: Removed 37 rows containing non-finite outside the scale range
(`stat_bin()`).

In Plot 5, we created a histogram to show how ozone levels are distributed in the dataset. The plot helps us see how common different levels of ozone are. We used a bin width of 10 and added color to show how frequent each level is. The code I used are:

geom_histogram(binwidth = 10, color = “black”, alpha = 0.7)

scale_fill_viridis_c(name = “Frequency”)