In this presentation, we will explore how ozone levels are related to:

• Temperature
• Wind speed
• Solar radiation
• Month

The airquality dataset is built into R and can be accessed by every user. It contains daily air quality measurements from New York.

Some of the variables that we will use are:

• Ozone
• Solar.R
• Wind
• Temp
• Month

# Load in packages
library(ggplot2)
library(plotly)
library(dplyr)
library(scales)

# Load in data
data(airquality)

# Show the first 5 rows of the dataset
head(airquality, 5)

##   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

To be able to evaluate the data, we need to make sure that we check and take care of any missing values.

# Check missing values before cleaning
print(colSums(is.na(airquality)))

##   Ozone Solar.R    Wind    Temp   Month     Day 
##      37       7       0       0       0       0

# Remove rows with missing values
air <- na.omit(airquality)

The main question for this project is:

How are ozone levels related to temperature, wind speed, solar radiation, and month?

To explore this, we will use:

• Scatter plots
• Correlation
• Interactive Plotly graph

A simple linear regression line can help show the overall trend in each scatterplot.

\[Ozone = \beta_0 + \beta_1X + \epsilon\]

Here, \(X\) can represent temperature, wind speed, or solar radiation.

• \(\beta_0\) is the intercept
• \(\beta_1\) is the slope — how much ozone changes per unit increase in \(X\)

Correlation helps measure the strength and direction of the relationship between two numerical variables.

\[r = \frac{\sum (x_i - \bar{x})(y_i - \bar{y})} {\sqrt{\sum (x_i - \bar{x})^2 \sum (y_i - \bar{y})^2}}\]

Values close to 1 indicate a strong positive relationship, while values close to -1 indicate a strong negative relationship.

# Calculate correlations between ozone and other variables
air %>%
  summarize(
    Temp_Correlation = cor(Ozone, Temp),
    Wind_Correlation = cor(Ozone, Wind),
    Solar_Correlation = cor(Ozone, Solar.R)
  )

##   Temp_Correlation Wind_Correlation Solar_Correlation
## 1        0.6985414       -0.6124966         0.3483417

Based on the plots and correlation values:

• Higher temperature has a strong correlation with ozone levels (\(r = 0.698\)).
• Higher wind has a strong negative correlation with ozone levels (\(r = -0.612\)).
• Solar radiation has a weaker positive correlation with ozone (\(r = 0.348\)).
• Month helps show seasonal patterns in ozone levels.

Overall, ozone levels seem to be connected to temperature, wind speed, solar radiation, and month.