Airquality Homework

Factory pollution from Shutterstock

Library and Dataset

library(tidyverse) 

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data("airquality")

Data Structure

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

Temperature Mean and Median

mean(airquality$Temp)

[1] 77.88235

median(airquality$Temp)

[1] 79

Wind Standard Deviation and Variance

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 Stats

summary(airquality$Month)

   Length     Class      Mode 
      153 character character 

Months Chronologically

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

Plot 1

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`.

This plot is not the most helpful when it comes to answering questions about monthly temperature values because it is difficult to discern one month from another.

Plot 2

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

While this somewhat improves the readability of the plot, I think that the darker colors which occur due to overlap could confuse the viewer as those colors are not marked on the legend.

Plot 3

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 

Plot 4

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

p5 <- airquality |>
  ggplot(aes(Ozone, Temp)) + 
  labs(x = "Ozone in ppb", y = "Temperature in F", 
       title = "Scatterplot of Temperature and Ozone Levels",
       caption = "New York State Department of Conservation and the National Weather Service") +
  geom_point()
p5 

Warning: Removed 37 rows containing missing values (`geom_point()`).

The plot that I have created shows the correlation between recorded temperature levels in Fahrenheit compared to recorded ozone levels in ppb. It is a scatterplot and I used the geom_point() function under ggplot in order to obtain this plot. I was sure to include a title as well as labeling for my axis and a caption to inform the viewer where the information was from.