Load library tidyverse in order to access dplyr and ggplot2
library(tidyverse)
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✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.4 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
data("airquality")
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.
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.
If you want to look at specific statistics, here are some variations on coding. Here are 2 different ways to calculate “mean.”
mean(airquality$Temp)
[1] 77.88235
mean(airquality[,4])
[1] 77.88235
Calculate Median, Standard Deviation, and Variance
median(airquality$Temp)
[1] 79
sd(airquality$Wind)
[1] 3.523001
var(airquality$Wind)
[1] 12.41154
Rename 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
See how Month has changed to have characters instead of numbers (it is now classified as “character” rather than “integer”)
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.
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?
The set-up of the graph is not set up well for learning.
Plot 2: Improve the histogram of Average Temperature by Month
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?
This is much easier to look at because they bars do not blend together, and have more pleasing colors.
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
Plot 4: Side by Side Boxplots in Gray Scale
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:
p5 <- airquality |>ggplot(aes(Ozone, Solar.R, color = Month)) +labs(x ="Ozone Level", y ="Solar Units", title ="Scatterplot Comparing Ozone to Solar Units",caption ="New York State Department of Conservation and the National Weather Service") +geom_point() +scale_fill_discrete(name ="Month", labels =c("May", "June","July", "August", "September"))p5
Warning: Removed 42 rows containing missing values or values outside the scale range
(`geom_point()`).
Essay
I have created a scatter plot that compares Monthly Solar Units to the Ozone level. By observing this polot, I can learn that higher solar units are associated with early summer months such as May, June, and July.
This is compared with August and September, which occupy the middle-lower range of solar units. It can also be learned that overall, the Ozone levels rarely surpass 100. The plot shows the majority of mothns reside in ozone levels below 50, however, the months with higher ozone levels are majority summer months such as July and August.
This graph might not be the best way to display the relationship between Solar units and ozone levels, because majority of ozone levels are in one range. From this comparison we can conclude that Solar Units and Ozone levels have a somewhat direct relationship outside of the norm ozone levels because the few datapoints that surpass an ozone level of 100 also have high solar units. I think this graph could be more informative if more moths were displayed.
I used the “geom_point” input which is new to me, along with the “color=month” input. These created exciting results for my scatter plot. I was able to differentiat the months by giving them their own colors, giving the plot an added vairable for more information.
