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   6

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

sd(airquality$Wind)

[1] 3.523001

var(airquality$Wind)

[1] 12.41154

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”)

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

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

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 4

Plot 5: Scatterplot of Solar Radiation vs. Temperature code

slide responses

1. The histogram plot is useful for analyzing monthly temperature values as it shows the distribution of temperatures for each month with different colors. It helps compare how temperature ranges vary across the months from May to September. To improve clarity, consider adjusting bin width or adding transparency if needed.

2. Yes, this plot improves readability by using side-by-side boxplots to clearly compare temperature distributions across months. It visually distinguishes each month’s temperatures, highlighting August as having the highest temperatures. The plot also identifies outliers in June and July, providing insight into unusual temperature values. The use of distinct colors and clear labels further enhances the plot’s effectiveness in displaying and comparing monthly temperature patterns.

brief essay

Plot Type: The plot is a scatterplot displaying the relationship between Solar Radiation (Solar.R) and Temperature(Temp).

Insights: This scatterplot reveals how solar radiation levels are associated with temperature variations. The plot shows a general trend where higher solar radiation tends to be associated with higher temperatures. The added regression line (black line) provides a visual indication of this relationship, suggesting a positive correlation between the two variables. Different colors represent the months, allowing us to see if this relationship varies by month.

Special Code: I used geom_point() to create the scatterplot and geom_smooth() with method = “lm” to add a linear regression line, which helps to identify the overall trend. The alpha = 0.7 parameter in geom_point() adds transparency to the points, making overlapping points easier to distinguish. The scale_color_discrete() function colors the points by month, adding an additional layer of information to compare how the relationship between solar radiation and temperature might differ across months.