Air Quality Tutorial and Homework Assignment

Source: https://universityhealthnews.com/daily/eyes-ears-nose-throat/unhealthy-air-quality-what-should-you-do-when-the-aqi-is-too-high/
Source: https://universityhealthnews.com/daily/eyes-ears-nose-throat/unhealthy-air-quality-what-should-you-do-when-the-aqi-is-too-high/
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

Summary Statistics

mean(airquality$Temp)
## [1] 77.88235
mean(airquality[,4])
## [1] 77.88235

Median, Standard Deviation, and Variance

median(airquality$Temp)
## [1] 79
sd(airquality$Wind)
## [1] 3.523001
var(airquality$Wind)
## [1] 12.41154

Months, Numbers 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"

Summary

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"))
library(ggplot2)

Create Histogram Categorized by months

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
p1
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Improve the Clarity of the Plot using outline and transparency

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")
p2

Side by side box plot categorized by month

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"))
p3

Box plots in grey scale

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"))
p4

Plot 5: Now make one new plot on your own, that is meaningfully different from the 4 I have shown you. You can select any of the variables in this dataset. Be sure to explore the dataset to see which variables are included that we have not explored yet. You may create a scatterplot, histogram, boxplot, or something else.

p5 <- airquality |>
  ggplot(aes(Month, Solar.R, Ozone, fill = Month)) + 
  labs(x = "Months from May through September", y = "Solar Radiation", 
       title = "Side-by-Side Boxplot of Monthly Solar Radiation Levels",
       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"))
p5
## Warning: Removed 7 rows containing non-finite outside the scale range
## (`stat_boxplot()`).

Paragraph:

In my code the ggplot function initializes the plot with Month on the x-axis, Solar.R (solar radiation) on the y-axis, and fill for color-coding the months. The geom_boxplot() function creates the box plots, and scale_fill_discrete() customizes the legend labels for the months. The resulting plot visually compares the monthly solar radiation levels, with appropriate labels, title, and a caption indicating data sources from the New York State Department of Conservation and the National Weather Service.