Because airquality is a pre-built dataset, we can write it to 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 3.4.4 ✔ 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 disply 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
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 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: Now make one new plot on your own, that is meaningfully different from the 4 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.
p5 <- airquality |>ggplot(aes(Ozone, Temp, color=Month)) +geom_point() +labs(x ="Ozone Levels", y ="Temperature",title ="Ozone Levels and Temperatures in May - Sept, 1973",caption ="Source: New York State Department of Conservation and the National Weather Service")p5
For Plot 5, I created a scatterplot comparing the ozone levels against the temperature for the respective dates of the dataset. With ozone levels as my independent variable and temperature as my dependent variable, readers would note a positive correlation as well as a few notable outliers. The data points are colored according to their month (see legend), allowing readers to more easily make out when each point occurred and how that might correlate with other factors not pictured. For example, readers might notice how May seems more tightly clustered, yet how the points grow more scattered in warmer months like August, correlating with the shift into the Summer season.
For my code, after setting my dataset as airquality, I began with a standard ggplot. I set my axes (Ozone for X, Temp for Y) and with color=, I set my Month as a third variable to color code the points. I then declared my graph as a scatterplot with geom_point. Then, for organization and ease of reading, I labeled my graph according with labs, setting my x-axis label as “Ozone Levels”, my y-axis label as “Temperature”, titled it “Ozone Levels and Temperatures in May - Sept, 1973” and credited the data with the caption.
