Airquality Assignment-

Julia Melo Cavalcante

1- Load in the library

Load library tidyverse in order to access dplyr 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 1.9.3     ✔ 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

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.

2- Load the dataset into your global environment

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

data("airquality")

3- 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.

4- 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 .

5- 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

6- Calculate Median, Standard Deviation, and Variance

median(airquality$Temp)
## [1] 79
sd(airquality$Temp)
## [1] 9.46527
var(airquality$Temp)
## [1] 89.59133

7- 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"

8- 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.

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? Explain No. This graph is not useful to answer questions about monthly temperature values because it is made of stacked bars, which makes it hard to see the values independently. Additionally, one other problem with the graph is that the bars are not outlined, making it hard to analyze the histogram.

Plot 2: Improve the histogram of Average Temperature by Month

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

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

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

Note the points above and below the boxplots in June and July. They are outliers.

Plot 4:

Now make one new plot on your own, that is meaningfully different from the 3 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, axes labels, colors, and caption for the datasource in your Plot 4.

library(colorspace)

p4 <- airquality |>
  ggplot(aes(x = Ozone, y = Temp, color = Solar.R)) +
  geom_point() +
  labs(x = "Ozone (ppb)", y = "Temperature (F)", 
       title = "Scatterplot of relationship between Ozone, Solar Radiation, and Temperature",
       caption = "Data obtained from the New York State Department of Conservation and the National Weather Service") +
  scale_color_continuous_divergingx(palette = "Geyser") +
  theme_minimal()
p4
## Warning: Removed 37 rows containing missing values or values outside the scale range
## (`geom_point()`).

Then, write a brief essay here: To explore the relationship between Ozone, Temperature, and Solar Radiation, I used data from the New York State Department of Conservation and the National Weather Service. The three numeric variables analyzed were ‘Ozone’ (ppb), ‘Temp’ (°F), and ‘Solar.R’ (solar radiation).

In order to see if the variables were correlated I chose to do a scatterplot. In the x-axis I plotted Ozone, in the y-axis I plotted temperature, and colored each point using the solar radiation values with the palette “Geyser”. I chose this palette because it effectively represented solar radiation intensities, where warmer colors (reddish tones) indicated higher values. One other thing that I also included in the graph is a minimalist theme with no background.

The scatterplot reveled a clear positive relationship between Ozone levels (ppb) and Temperature (F). It also appears that higher solar radiation is associated with higher ozone levels and temperatures. Given that, this plot suggests that on hotter and sunnier days, ozone levels tend to be higher.