Airquality-HW

Airquality Assignment-

Lucjan Szulim

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

median(airquality$Temp)

## [1] 79

#std.

sd(airquality$Temp)

## [1] 9.46527

# var

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.

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

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

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.

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.

# Load necessary libraries
library(ggplot2)

# Create the scatterplot comparing Solar Radiation (Solar.R) and Temperature (Temp)
p6 <- airquality |>
  ggplot(aes(x = Solar.R, y = Temp)) + 
  geom_point(aes(color = Temp), alpha = 0.7) +
  labs(
    x = "Solar Radiation in Langleys", 
    y = "Temperature in degrees Fahrenheit", 
    title = "Relationship between Solar Radiation and Temperature",
    caption = "New York State Department of Conservation and the National Weather Service"
  ) +
  scale_color_gradient(low = "lightblue", high = "darkred") +
    theme_minimal()

p6

## Warning: Removed 7 rows containing missing values or values outside the scale range
## (`geom_point()`).

Then, write a brief essay here:

Describe the plot type you have created and the variables you have chosen.
Any insights that the plot shows
Describe your choices in color and any special code you might have used to make this plot.

I created a scatterplot, and the variables I chose were Solar Radiation which is measured in Langley and Temperature which is measured in Farenheit, I used the airquality dataset. I chose to use a scatterplot because it shows a relationship between Solar Radiation and Temperature and is easy to read and understand. The X axis shows Solar Radiation and the Y axis shows Temperature. The scatterplot shows that Solar Radiation does correlate with temperature, as temperature increases solar radiation also increases. The scatterplot also shows us the spread of the data. I also added a color gradient for the points on the Y axis to show that as temperature increases points become more red and as temperature decreases points become more blue. I also made the points semi-transparent because some of the points overlap each other, this is so overlapping points can be visible. I also added the theme_minimal, because the color grading I did made some of the points blend into the gray background, theme_minimal changes the background to white and remove the gridlines. This is because I soley want to focus to be on the scatterplot and I don’t want the background to detract from the viewing experience. The color gradient is also natural as people associate warmer temperatures with red and cooler temperature with blue, transparency of points also shows the density and location of close data points. The plot also shows a relationship between solar radiation and temperature.