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 1.1.3 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 3.4.3 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ 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`.
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: Scatterplot of Daily Temperature and Wind Speed in August 1973
airquality |>filter(Month =="August") |>ggplot(aes(x = Temp, y = Wind)) +geom_point(alpha =1.0, col ="orange") +geom_smooth(method ="lm", col ="lightblue") +labs(x ="Temperature", y ="Wind Speed",title ="Daily Temperature and Wind Speed, August 1973",caption ="New York State Department of Conservation and the National Weather Service") #provide the data source
`geom_smooth()` using formula = 'y ~ x'
For this assignment I created a scatterplot using the airquality dataset provided. The plot shows daily temperatures and wind speeds for August 1973. Initially, I set out to create a bar graph that showed the fluctuations in temperature averages from May-September 1973, but the visualization looked as uninteresting as its description sounds. So, I decided to move in another direction.
With the scatterplot I created, I tried to explore two variables of the data that might be more interesting: temperature and wind speed. I wanted the visualization to show if there would be a notable correlation between these two variables. Once I got the code down, I took turns plugging in each month (May-September) and settled on August because it showed the most drastic linear regression.
To make this modification I used the code from the tutorial plots 1-4, YouTube videos, the textbook, and various websites as a guide, but what I found was there a several ways to do the same thing in R. After hours of research and trial and error, I found a simple code that served my purpose well enough (see below). I tried to begin the code with p5 <- airquality |> as seen in the tutorial plots, but this did not play well with the other code. I’m sure there is a technical reason that makes a lot of sense, and I look forward to learning what it is.
My Code
airquality |> filter(Month == “August”) |> ggplot(aes(x = Temp, y = Wind)) + geom_point(alpha = 1.0, col = “orange”) + geom_smooth(method = “lm”, col = “lightblue”) + labs(x = “Temperature”, y = “Wind Speed”, title = “Daily Temperature and Wind Speed, August 1973”, caption = “New York State Department of Conservation and the National Weather Service”) #provide the data source
