Airquality Assignment

Load the library

library(tidyverse)

Load the dateset into your global environment

data("airquality")

View the data using “head” function

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

mean(airquality[,4]) 

[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

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 sourcep1
p1

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

Plot 2: Improve the histogram of Average Temperature by Month

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

Plot 3: Create side-by-side boxplots 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

Plot 4: Side by Side Boxplots in Gray 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: Scatterplot of Ozone Levels vs. Monthly Temperatures from May to September, 1973

p5_scatterplot <- airquality |>
ggplot(aes(x = Temp, y = Ozone, color = factor(Month))) +
  geom_point(na.rm = TRUE)+ 
 scale_color_discrete(name = "Month",labels = c("May", "June", "July", "August", "September")) +
  labs(x = "Monthly Temperatures from May - Sept",
       y = "Ozone Levels", 
       title = "Scatterplot of Ozone Levels vs. Monthly Temperatures from May - Sept, 1973", 
       caption = "New York State Department of Conservation and the National Weather Service")
p5_scatterplot

Brief Essay

For this assignment, I created a scatterplot to show the relationship between temperature and ozone levels. The plot is called “Scatterplot of Ozone Levels vs. Monthly Temperatures from May to September, 1973.” Each point on the plot shows how much ozone was recorded at a certain temperature. I also added colors to the points to represent the months, so it is easier to see how the data changes over time.

From the plot, we can see that ozone levels are not the same across months. Some months have higher ozone levels, while others are lower. The scatterplot makes it clear that temperature and ozone are connected, but the relationship looks different depending on the month. This helps us clearly understand how air quality will change during warmer or cooler parts of the year.

To make this plot, I used special parts of the code. First, I used color = factor(Month) so each month has its own color. This makes it easier to tell them apart. Next, I used geom_point(na.rm = TRUE) to make the scatterplot. At first, I only used geom_point(), but R gave me a warning because some data was missing. By adding na.rm = TRUE, I told R to ignore the missing values and just show the points with real data. This fixed the warning and kept the plot clean.