Airquality Homework Assignment1

Author

Viktoriia L.

Airquality Assignment

Load the library tidyverse

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 1.9.3     ✔ 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 data in the global environment

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.

Plot 1: Create a histogram categorized by Month

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

print(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

print(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

print(p3)

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 Output

print(p4)

Plot 5: Heatmap of Ozone vs Temperature and Month

(High ozone levels can indicate pollution or other atmospheric phenomena)

p5 <- airquality |>
  ggplot(aes(x = Temp, y = Month, fill = Ozone)) +
  geom_tile() +
  scale_fill_gradient(low = "yellow", high = "red") + # Color gradient from low to high ozone
  labs(x = "Temperature (F)",
       y = "Month",
       fill = "Ozone (ppb)",
       title = "Heatmap of Ozone Levels by Temperature and Month",
       caption = "Data from New York State Air Quality Measurements, 1973") +
  theme_minimal()

Plot 5 Output

print(p5)

Brief information about Plot 5

- The Heatmap type of plot has been used in this visualization. The plot shows the relationship between temperature, month, and ozone concentration.

The color scale represents the ozone concentration. Depending on the color gradient, lighter or darker colors indicate lowe or higher level of ozone.

- From this visualization we can notice correlation between temperature and Azone level. In August, in the hotest month of the year, the ozone most concentrated. Ozone level in May and September is lower most likely due to cooler temperature.

-In this plot a special function scale_fill_gradient(low = “color1”, high = “color2”) was used. This function very usefull to represent continuous data, data that change with time. In this case ozone levels change over time with temperature change.