library(tidyverse)Warning: package 'dplyr' was built under R version 4.5.3Airquality Assignment
Airquality Tutorial and Homework Assignment
Load in the library
Load library tidyverse in order to access dplyr and ggplot2
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
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)
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 6Notice that all the variables are classified as either integers
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$Temp)[1] 77.88235mean(airquality[,4]) [1] 77.88235For the second way to calculate the mean, the matrix [row,column] is looking for column #4, which is the Temp column and we use all rows
Calculate Median, Standard Deviation, and Variance
median(airquality$Temp)[1] 79sd(airquality$Wind)[1] 3.523001var(airquality$Wind)[1] 12.41154Rename 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
airquality1 <- airquality |>
mutate(month_name = case_when(Month == 4 ~ "April",
Month == 5 ~ "May",
Month == 6 ~ "June",
Month == 7 ~ "July",
Month == 8 ~ "August",
Month == 9 ~ "September"))Now look at the summary statistics of the dataset
See how Month has changed to have characters instead of numbers (it is now classified as “character” rather than “integer”) in the new airquality1 dataframe.
summary(airquality1$month_name) Length Class Mode
153 character character Month is a categorical variable with different levels, called factors.
This is one way to reorder the Months so they do not default to alphabetical (you will see another way to reorder DIRECTLY in the chunk that creates the plot below in Plot #1
airquality1$Month<-factor(airquality1$month_name,
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.
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 <- airquality1 |>
ggplot(aes(x=Temp, fill=month_name)) +
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 sourcePlot 1 Output
p1`stat_bin()` using `bins = 30`. Pick better value `binwidth`.
Is this plot useful in answering questions about monthly temperature values?
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 <- airquality1 |>
ggplot(aes(x=Temp, fill=month_name)) +
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
Here July stands out for having high frequency of 85 degree temperatures. The dark purple color indicates overlaps of months due to the transparency.
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 <- airquality1 |>
ggplot(aes(Month, Temp, fill = month_name)) +
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"))Plot 3 Output
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 <- airquality1 |>
ggplot(aes(Month, Temp, fill = month_name)) +
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"))Plot 4 Output
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, axes labels, and caption for the datasource in your Plot 5. Then finally, below your chunk of code for your plot 5, ….
p5 <- airquality1 |>
ggplot(aes(Ozone, Temp)) +
labs(x = "Ozone",
y = "Temp",
title = "Comparing Temperature and Ozone Levels",
caption = "New York State Department of Conservation and the National Weather Service") +
geom_point(color = "red", shape = 3, size = 2)+
geom_smooth(color = "black", se = FALSE)p5`geom_smooth()` using method = 'loess' and formula = 'y ~ x'Warning: Removed 37 rows containing non-finite outside the scale range
(`stat_smooth()`).Warning: Removed 37 rows containing missing values or values outside the scale range
(`geom_point()`).
Essay
I created a scatterplot for Plot 5 to compare the temperatures and ozone levels. The graphs purpose was to show the relationship between the two. It showed that the majority of the data points are between zero to 50 ozone level. It proves that the higher the ozone level is the higher the temperature gets as well. It also proves there’s a correlation between the two. I used labs() to add the title, labels, and caption for the graph. The points weren’t that easy to see so I changed the color and shape. I changed the shape of each point to a cross point using shape = 3 and color = red to change the color to red. I got rid of the shaded area around the line using geom_smooth(se = FALSE) as it wasn’t needed and distracted from the purpose of the graph.