Load library tidyverse in order to access dplyr and ggplot2
library(tidyverse)
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✔ ggplot2 3.5.2 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
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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.
Load the daata set 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")
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)
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
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
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")
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?
No beacause overlapping histograms (position = “identity”) can be confusing and it does not show month by month comparison clearly.
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
Did this improve the readability of the plot?
Yes, this chart is a lot easier to follow.
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
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"))p4
Plot 5: Scatterplot of Ozone vs. Temperature
This plot explores whether higher temperatures are associated with higher ozone levels in the air. It is an important relationship for understanding air quality.
Plot 5
airclean <- airquality |>drop_na(Ozone, Temp)p5 <- airclean |>ggplot(aes(x = Temp, y = Ozone, color = Month)) +geom_point(size =3, alpha =0.7) +labs(x ="Temperature",y ="Ozone",title ="Scatterplot of Ozone Levels vs. Temperature (May–Sept, 1973)",caption ="Source: New York State Department of Conservation and the National Weather Service") +scale_color_discrete(name ="Month")p5
Write a brief essay here
This is a scatterplot that visualizes the relationship between temperature and ozone levels from May to September, 1973. Each point represents a day, and the color represents the month in which it was recorded.
There is a clear positive relationship between temperature and ozone levels. As the temperature increases, the ozone concentration in the air tends to rise. This makes sense because ozone formation is often influenced by sunlight and heat. The trend supports this linear relationship.
We can also see that higher ozone values tend to cluster in the hotter months, like July and August, shown by the color grouping.
I used drop_na() from tidyverse to remove rows with missing values for Ozone and Temp, which makes a clean scatterplot.I customized the plot aesthetics using alpha for transparency and size for point clarity. I added the color is used to distinguish months, helping us spot seasonal patterns.
