Notice that all the variables are classified as either integers or continuous values .
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$Temp)
[1] 77.88235
mean(airquality[,4])
[1] 77.88235
For the second way to calculate the mean, the matrix [row,column] is looking for column #4, which is the Temp column and we use all rows
Calculate Median, Standard Deviation, and Variance
median(airquality$Temp)
[1] 79
79
sd(airquality$Wind)
[1] 3.523001
3.523001
var(airquality$Wind)
[1] 12.41154
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.
-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 <- 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
Plot 1 Output
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 <- 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")
Plot 2 Output
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 <- 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"))
Plot 3
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 <- 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"))
Plot 5:
library(ggplot2)ggplot(data = airquality, aes(x = Wind, fill =factor(Month))) +geom_histogram(position ="dodge", alpha =0.7, binwidth =7, color ="black") +scale_fill_brewer(palette ="GnBu", name ="Month", labels =c("May", "June", "July", "August", "September")) +labs(x ="Wind Level", y ="Frequency",title ="Histogram of Monthly Wind Levels Frequencies from May - Sept, 1973",caption ="New York State Department of Conservation and the National Weather Service")
Using the air quality statistics, this histogram shows the frequency distribution of wind levels (in mph) from May to September 1973. Wind speed is shown by the x-axis, while frequency is represented by the y-axis. To facilitate month-to-month comparisons, each month is represented by a different hue. The plot’s main finding is that some wind speeds are more common than others, most commonly falling between 5 and 15 mph. If there are noticeable peaks, it means that certain wind speeds were more prevalent in that months. In order to guarantee that the bars for various months are positioned next to each other, the plot is created using ggplot2 with geom_histogram(position = “dodge”, alpha = 0.7, binwidth = 2, color = “black”). instead than being stacked. I did this because the previous histogram shown was a little bit confusing to me because I couldnt easily distinguish the different bars and I wanted to properly divide then so that I could easily tell the difference between the wind levels and frequencies.
