Airquality-HW
2025-02-05
Airquality Assignment-
Natalia Solomon
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 errorsThe 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 6Notice 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$Temp)## [1] 77.88235mean(airquality[,4])## [1] 77.88235For the second way to calculate the mean, the matrix [row,column] is looking for column #4, which is the Temp column and we use all rows
6- Calculate Median, Standard Deviation, and Variance
#median
median(airquality$Temp)## [1] 79#std.
sd(airquality$Temp)## [1] 9.46527# var
var(airquality$Temp)## [1] 89.591337- 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
I believe this plot is useful in answering questions about monthly temperatures. However, it is a little difficult to comprehend all of it. The plot could be made to be easier to read.
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.
ggplot(airquality, aes(x = Solar.R, y = Ozone, color = Solar.R)) +
geom_point(size = 3, alpha = 0.5) +
scale_color_gradient(low = "yellow", high = "red") +
labs(title = "Ozone Levels vs. Solar Radiation",
x = "Solar Radiation",
y = "Ozone",
color = "Solar Radiation",
caption = "New York State Department of Conservation and the National Weather Service") +
theme_classic()## Warning: Removed 42 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.
Using the air quality dataset, I created a scatter plot to compare solar radiation and ozone levels. Each point represents a daily measurement, with solar radiation on the x-axis and ozone levels on the y-axis. To make the relationship between the two variables clearer, I used a color gradient from yellow to red, where lower radiation levels are lighter and higher levels are darker. This helps show how ozone levels change with sunlight. I also set the transparency of the points to 0.5 so that overlapping points remain visible, making it easier to see patterns in the data.
To keep the graph simple and easy to read, I chose the classic theme, which removes extra grid lines and background elements. The graph suggests that higher solar radiation is linked to higher ozone levels, showing a positive correlation. This makes sense because sunlight helps produce ozone in the atmosphere. While the trend is clear, other factors like wind speed or pollution might also play a role. Overall, this plot helps visualize how solar radiation affects ozone levels in the air.