Airquality Hw

Author

D Spielman

Load in the library

Load library tidyverse in order to access dplyer and ggplot2

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.5.1     ✔ tibble    3.2.1
✔ lubridate 1.9.4     ✔ tidyr     1.3.1
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Load the dataset into your global environment

Because airquality is a prebuilt dataset, we can write it to our data directory to store it for later use

data("airquality")

Look at the structure of the data

In the global environment, click on the row with the airquality dataset and it will take you to a “spreadsheet” view of the data.

View the data using the “head” function

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)

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

Notice that all the variables are classified as either integers or continuous values .

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

For the second way to calculate the mean, the matrix [row,column] is looking for column #4, which is the Temp column and we use all rows

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

Sometimes we prefer the months to be numerical, but here, we need them as the month names. There are MANY ways to do this. Here is one way to convert numbers 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 (it is now classified as “character” rather than “integer”)

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.

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 in chronological order. This is a different way to order than what was shown above. labs allows us to add a title, axes labels, and a caption for the data source

Plot 1 Code

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

Plot 1 Output

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 of Average Temperature by Month

Outline the bars in white using the color = “white” command Use alpha to add some transparency (values between 0 and 1) Change the binwidth Add some transparency and white borders around the histogram bars.

Plot 2 Code

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")

Plot 2 Output

p2

Here July stands out for having high frequency of 85 degree temperatures. The dark purple color indicates overlaps of months due to the transparency. 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"))

Plot 3

p3

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

Plot 4: Side by Side Boxplots in Gray Scale

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.

Plot 4 Code

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"))

Plot 4

p4

Plot 5 Code

Here I’m making a line graph to show the relationship between temperature and wind speed

p5 <- airquality |>
  ggplot(aes(x=Wind, y=Temp, color = Month))+
    geom_point()+
    labs(x = "Wind Speed (miles/hour)", y = "Temperature (F)", 
       title = "Scatterplot of Wind Speed compared to Temperature",
       caption = "New York State Department of Conservation and the National Weather Service")

Plot 5

p5

Brief Essay

The plot that I made was a scatterplot showing the relationship between the wind speed in miles per hour and the temperature. On top of that, each point in the plot is colored to show which month that data point was from. I like this plot because it helps to show a relationship between the two variables that is difficult to see when only looking at the numbers – that is, in general as the wind speed increases, the temperature decreases. There are also a few other insights that it displays, such as that May has the coldest temperatures and tends to have the highest winds, but June had the day with the single highest speed wind. In terms of the code, there isn’t really anything particularly special that I did to make this plot. In the ggplot() call, instead of writing fill = month I had to write color = month, because you can’t fill in a point. I also called geom_point().