Airquality HW Assignment

Author

Rebecca Jipdjio

Published

February 3, 2024

Air quality Tutorial and HW Assignment

Load in the library

Because air quality is a pre-built dataset, we can write it to our data directory to store it for later use.

The source for this dataset is the New York State Department of Conservation and the National Weather Service of 1973 for five months from May to September recorded daily.

library(tidyverse)

Load the dataset into your global environment

data("airquality")

Look at the structure of the data

the function, head, will only display the first 6 rows of the dataset. Notice in the global environment to the right, there are 153 observations (rows)

View the data using the “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

If you want to look at specific statistics, here are some variations on coding. Here are 2 different ways to calculate “mean.”

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

Number 5 - 9 to May through September

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

See how Month has changed to have characters instead of numbers

summary(airquality$Month)

   Length     Class      Mode 
      153 character character

Month is a categorical variable with different levels, called factors.

This is one way to reorder the Months so they do not default to alphabetical (you will see another way to reorder DIRECTLY in the chunk that creates the plot below in Plot 1)

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

Plot 1: Create a histogram categorized by Month

Here is a first attempt at viewing a histogram of temperature by the months May through September. We will see that temperatures increase over these months. The median temperature appears to be about 75 degrees.

Reorder the legend so that it is not the default (alphabetical), but rather in chronological order.

fill = Month colors the histogram by months between May - Sept.

scale_fill_discrete(name = “Month”…) provides the month names on the right side as a legend.

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`.

Is this plot useful in answering questions about monthly temperature values?

Plot 2: Improve the histogram using ggplot

Outline the bars in white using the color = “white” command

Use alpha to add some transparency (values between 0 and 1)

Change the binwidth

Histogram of Average Temperature by Month

Add some transparency and white borders around the histogram bars. Here July stands out for having high frequency of 85 degree temperatures. The dark purple color indicates overlaps of months due to the transparency.

p2 <- airquality |>
  ggplot(aes(x=Temp, fill=Month)) +
  geom_histogram(position="identity", alpha=0.5, binwidth = 3, color = "white", linewidth = 0.2)+ 
  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

Did this improve the readability of the plot?

Plot 3: Create side-by-side box plots categorized by Month

We can see that August has the highest temperatures based on the box plot distribution.

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

Notice that the points above and below the box plots in June and July are outliers.

Plot 4: Make the same side-by-side box plots, but in grey-scale

Use the scale_fill_grey command for the grey-scale legend, and again, use fill=Month in the aesthetics

Side by Side Box plots in Gray Scale

Here we just changed the color palette to gray scale using scale_fill_grey

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 and Wind speeds with gradient color
p5 <- airquality |>
  ggplot(aes(Wind, Ozone, color = Ozone)) +
  geom_point(alpha = 0.6, na.rm = TRUE) +
  labs(x = "Wind Speed", y = "Ozone Level", 
       title = "Scatterplot of Ozone Levels vs. Wind Speeds",
       caption = "Data Source: New York State Department of Conservation and the National Weather Service") +
  scale_color_gradient(low = "blue", high = "red")

p5

Essay:

In Plot 5, The scatter plot examines the relationship between Ozone levels and Wind speeds with a gradient color scale. The X-axis represents Wind Speed, measuring how fast the wind is blowing, while the Y-axis gauges the amount of Ozone in the air. Now, each point is adorned with a color gradient ranging from blue to red. Blue points signify lower Ozone concentrations, whereas red points indicate higher concentrations. This color-coded approach provides an additional layer of information, making it easier to spot trends or patterns in the data. To achieve this, we modified the code by incorporating color = Ozone to specify Ozone levels for coloring. The introduction of scale_color_gradient() defines the color gradient, transitioning from blue to red. This modification makes the plot clearer, so you can easily see patterns in Ozone levels at different Wind speeds through the color range.