First, we need to load the required packages, which are ggplot2 and datasets:

library(ggplot2)
library(datasets)

Next, we do frequency histograms for four of the numerical quantities which are mean ozone, solar radiation, average wind speed, and maximum daily temperature:

#This histogram is skewed to the right.
qplot(Ozone, ylab = "number of observations", data = airquality, binwidth = 10) 

#This histogram is skewed to the left. 
qplot(Solar.R, ylab = "number of observations", data = airquality, binwidth = 10)

#There are more observations that have a lower wind speed.
qplot(Wind, ylab = "number of observations", data = airquality, binwidth = 10)

#This histogram is skewed to the left. 
qplot(Temp, ylab = "number of observations", data = airquality,
      binwidth = 10)

Next, we obtain the box and whisker plots for solar radiation and maximum daily temperature:

#The mean is at 185.9 Langleys.
boxplot(airquality$`Solar.R`)

#The mean is at 77.88 degrees F
boxplot(airquality$`Temp`)

The summary for the data is as follows:

summary(airquality)
##      Ozone           Solar.R           Wind             Temp      
##  Min.   :  1.00   Min.   :  7.0   Min.   : 1.700   Min.   :56.00  
##  1st Qu.: 18.00   1st Qu.:115.8   1st Qu.: 7.400   1st Qu.:72.00  
##  Median : 31.50   Median :205.0   Median : 9.700   Median :79.00  
##  Mean   : 42.13   Mean   :185.9   Mean   : 9.958   Mean   :77.88  
##  3rd Qu.: 63.25   3rd Qu.:258.8   3rd Qu.:11.500   3rd Qu.:85.00  
##  Max.   :168.00   Max.   :334.0   Max.   :20.700   Max.   :97.00  
##  NA's   :37       NA's   :7                                       
##      Month            Day      
##  Min.   :5.000   Min.   : 1.0  
##  1st Qu.:6.000   1st Qu.: 8.0  
##  Median :7.000   Median :16.0  
##  Mean   :6.993   Mean   :15.8  
##  3rd Qu.:8.000   3rd Qu.:23.0  
##  Max.   :9.000   Max.   :31.0  
##

We also obtain the linear correlation coefficient between temperature and solar radiation as well as between average wind speed and maximum daily temperature:

#There is absolutely no correlation. 
cor(airquality$`Temp`, airquality$`Solar.R`)
## [1] NA
#There is absolutely no correlation.
cor(airquality$`Wind`, airquality$`Ozone`)
## [1] NA

Finally, we obtain a scatterplot for solar radiation and maximum temperature with a line of best fit:

ggplot(data = airquality, aes(x = Solar.R, y = Temp)) + 
geom_point(shape = 1) + 
geom_smooth(method = lm)
## Warning: Removed 7 rows containing missing values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).

Scatterplot for mean ozone and average windspeed with a line of best fit:

ggplot(data = airquality, aes(x = Ozone, y = Wind)) + 
geom_point(shape = 1) + 
geom_smooth(method = lm)
## Warning: Removed 37 rows containing missing values (stat_smooth).
## Warning: Removed 37 rows containing missing values (geom_point).

Scatterplot for solar radiation and maximum temperature with a Loess curve of best fit:

ggplot(data = airquality, aes(x = Solar.R, y = Temp)) + 
geom_point(shape = 1) + 
geom_smooth()
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## Warning: Removed 7 rows containing missing values (stat_smooth).
## Warning: Removed 7 rows containing missing values (geom_point).

Scatterplot for mean ozone and average windspeed with a Loess curve of best fit:

ggplot(data = airquality, aes(x = Ozone, y = Wind)) + 
geom_point(shape = 1) + 
geom_smooth()
## geom_smooth: method="auto" and size of largest group is <1000, so using loess. Use 'method = x' to change the smoothing method.
## Warning: Removed 37 rows containing missing values (stat_smooth).
## Warning: Removed 37 rows containing missing values (geom_point).

#http://rpubs.com/catlin/DPBWeek6
#http://www.cookbook-r.com/Graphs/Scatterplots_(ggplot2)/