library(tidyverse)Airquality Assignment
Airquality Tutorial and Homework Assignment
Load in the library
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.
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)
View the data using the “head” function
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 6Calculate 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$Temp)[1] 77.88235mean(airquality[,4])[1] 77.88235Calculate Median, Standard Deviation, and Variance
median(airquality$Temp)[1] 79sd(airquality$Wind)[1] 3.523001var(airquality$Wind)[1] 12.41154Rename the Months from number to names
Number 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"Now look at the summary statistics of the dataset
See how Month has changed to have characters instead of numbers
summary(airquality$Month) Length Class Mode
153 character character Month is a categorical variable with different levels, called factors.
This is one way to reorder the Months so they do not default to alphabetical (you will see another way to reorder DIRECTLY in the chunk that creates the plot below in Plot 1)
airquality$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.
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 source
p1`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.
Be sure to include a title, axis label, and caption for the datasource.
Then write a brief essay, described below.
Be sure to write a brief essay that describes the plot you have created, what the plot shows, and what code you used to make this modification.
p5 <- airquality |>
ggplot(aes(x = Wind, y = Ozone)) +
geom_point(color = "hotpink", size = 2, alpha = 1) +
labs(title = "Correlation Between Wind and Ozone Levels",
x = "Wind in Miles per Hour",
y = "Ozone in Parts per Billion",
caption = "New York State Department of Conservation and the National Weather Service")
p5Warning: Removed 37 rows containing missing values (`geom_point()`).
This visual is a scatter plot that shows the relationship between wind speed and ozone levels. As the wind speed increases, the ozone level decreases. I decided on these two variables because it shows a significant trend that we can make an inference on.
Scatter plots’ code is just using the geom_point code, as opposed to typing out other visual names such as geom_histogram, or geom_boxplot. size = indicates the size of the dots. Everything else was similar to the lesson.
I was able to find out that there are multiple colors we can use to instead of just the standard red, orange, yellow, green, blue, purple, black, and white. My scatter plot is hot pink and I found this so exciting. Here are a couple examples:
p5 <- airquality |>
ggplot(aes(x = Wind, y = Ozone)) +
geom_point(color = "orchid", size = 2, alpha = 1) +
labs(title = "Correlation Between Wind and Ozone Levels",
x = "Wind in Miles per Hour",
y = "Ozone in Parts per Billion",
caption = "New York State Department of Conservation and the National Weather Service")
p5Warning: Removed 37 rows containing missing values (`geom_point()`).
p5 <- airquality |>
ggplot(aes(x = Wind, y = Ozone)) +
geom_point(color = "dodgerblue3", size = 2, alpha = 1) +
labs(title = "Correlation Between Wind and Ozone Levels",
x = "Wind in Miles per Hour",
y = "Ozone in Parts per Billion",
caption = "New York State Department of Conservation and the National Weather Service")
p5Warning: Removed 37 rows containing missing values (`geom_point()`).
p5 <- airquality |>
ggplot(aes(x = Wind, y = Ozone)) +
geom_point(color = "darkseagreen4", size = 2, alpha = 1) +
labs(title = "Correlation Between Wind and Ozone Levels",
x = "Wind in Miles per Hour",
y = "Ozone in Parts per Billion",
caption = "New York State Department of Conservation and the National Weather Service")
p5Warning: Removed 37 rows containing missing values (`geom_point()`).
There are hundreds of more colors you can experiment with at https://sape.inf.usi.ch/quick-reference/ggplot2/colour. Or looking up “ggplot colors in R” also gets you the same results.