Airquality Assignment

Author

Sata

Published

May 5, 2023

Airquality Tutorial and Homework Assignment

source : https://images.search.yahoo.com/yhs/search;_ylt=AwrhdUYSQcBll_ozuBoPxQt.;ylu=Y29sbwNiZjEEcG9zAzEEdnRpZANMT0NVSTExNENfMQRzZWMDcGl2cw–?p=air+quality+pictures&type=0_1001_102_7980_104_240201&hsimp=yhs-ext_onelaunch&hspart=reb&ei=UTF-8&fr=yhs-reb-ext_onelaunch#id=1&iurl=https%3A%2F%2Fwww.linquip.com%2Fblog%2Fwp-content%2Fuploads%2F2021%2F04%2FFeatured-Image-Ref_health.harvard.edu-1024x683.jpg&action=click ## Load in the library

Because airquality 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 disply 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

 # what are the months listed in the dataset
unique(airquality$Month)

[1] 5 6 7 8 9

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

barplot(airquality\(Temp , airqulity\)Month)

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")
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 = 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

Did this improve the readability of the plot?

Plot 3: Create side-by-side boxplots categorized by Month

We can see that August has the highest temperatures based on the boxplot 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 boxplots in June and July are outliers.

Plot 4: Make the same side-by-side boxplots, 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 Boxplots 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: 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.

Be sure to include a title, axis label, and caption for the datasource.

p5 <- airquality |>
  ggplot(aes(Month, Ozone, fill = Month)) + 
  labs(x = "Month", y = "Ozone", 
       title = "Side-by-Side Boxplot of Monthly Quantity of Ozone",
       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"))
p5

Warning: Removed 37 rows containing non-finite values (`stat_boxplot()`).

Then write a brief essay, described below.

The provided boxplot appears to represent measurements of ozone levels for various months, with corresponding values such as ozone quantity.The ozone levels during July and August appear to be significant, with relatively higher quantities, while there is a noticeable decrease in ozone levels during September and June. This variation suggests a seasonal pattern, possibly influenced by environmental factors. The elevated ozone levels in July and August may be linked to specific atmospheric conditions or regional influences, warranting further investigation. Conversely, the lower ozone quantities in September and June may indicate a natural fluctuation or a response to different climatic conditions during these months.