Because airquality is a pre-built dataset, we can write it in our data directory to store it for later use. 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.
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 dataset into your global environment
data("airquality")
Look at the structure of the data
the function, head, will only display the first 6 rows of the dataset. Notice in the global environment to the right, there are 153 observations (rows)
See how Month has changed to have characters instead of numbers
summary(airquality$Month)
Length Class Mode
153 character character
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.
Reorder the legend so that it is not the default (alphabetical), but rather in chronological order.
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.
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 sourcep1
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
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 using ggplot
Outline the bars in white using the color = “white” command
Use alpha to add some transparency (values between 0 and 1)
Change the binwidth
Histogram of Average Temperature by Month
Add some transparency and white borders around the histogram bars. Here July stands out for having high frequency of 85 degree temperatures. The dark purple color indicates overlaps of months due to the transparency.
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
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"))p3
Notice that the points above and below the boxplots in June and July are outliers.
Plot 4: 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
Side by Side Boxplots in Gray Scale
Here we just changed the color palette to grey-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: Investigating temperatures greater than the 75° median
p5 <- airquality |>filter(Temp >75) |>ggplot(aes(x = Month, y = Temp, color =as.factor(Month))) +labs(x ="Months", y ="Temperature", title ="Scatterplot of Monthly Temperatures Greater Than 75°",caption ="New York State Department of Conservation and the National Weather Service") +geom_point() +scale_color_brewer(palette ="Set1", name ="Month", labels =c("May", "June", "July", "August", "September")) p5
Plot 5 Summary
With global warming affecting weather patterns worldwide, and my interest in this topic, I filtered the New York dataset for temperatures greater than 75° to narrow the analysis. I used scatter plot to explore patterns, trends, and unique characteristics to understand “warmer temperatures.” I updated the color scheme into bold colors to differentiate the months. Although the same months were included in the current analysis, as expected, higher temperatures are more frequent in the summer months (July, August, and September). Although May is best characterized as having cool spring days, it noted some warm days.
