Load in the dataset.

Because airquality is pre-built dataset, we can write it to our data directory to store it for later use.

#install.packages("tidyverse")
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.4     v dplyr   1.0.7
## v tidyr   1.1.3     v stringr 1.4.0
## v readr   2.0.1     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Show the data

Look at the structure of the data

str(airquality)
## 'data.frame':    153 obs. of  6 variables:
##  $ Ozone  : int  41 36 12 18 NA 28 23 19 8 NA ...
##  $ Solar.R: int  190 118 149 313 NA NA 299 99 19 194 ...
##  $ Wind   : num  7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
##  $ Temp   : int  67 72 74 62 56 66 65 59 61 69 ...
##  $ Month  : int  5 5 5 5 5 5 5 5 5 5 ...
##  $ Day    : int  1 2 3 4 5 6 7 8 9 10 ...

Calculating Summary Statistics

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

Change the Months from 5-9 to May through September

airquality$Month[airquality$Month == 5]<- "May"
airquality$Month[airquality$Month == 6]<- "June"
airquality$Month[airquality$Month == 7]<- "July"
airquality$Month[airquality$Month == 8]<- "August"
airquality$Month[airquality$Month == 9]<- "September"

Look at the summary statistics of the dataset, and see how Months has changed to have characters instead of numbers

str(airquality)
## 'data.frame':    153 obs. of  6 variables:
##  $ Ozone  : int  41 36 12 18 NA 28 23 19 8 NA ...
##  $ Solar.R: int  190 118 149 313 NA NA 299 99 19 194 ...
##  $ Wind   : num  7.4 8 12.6 11.5 14.3 14.9 8.6 13.8 20.1 8.6 ...
##  $ Temp   : int  67 72 74 62 56 66 65 59 61 69 ...
##  $ Month  : chr  "May" "May" "May" "May" ...
##  $ Day    : int  1 2 3 4 5 6 7 8 9 10 ...
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      
##  Length:153         Min.   : 1.0  
##  Class :character   1st Qu.: 8.0  
##  Mode  :character   Median :16.0  
##                     Mean   :15.8  
##                     3rd Qu.:23.0  
##                     Max.   :31.0  
##

Month is a categorical variable with different levels, called factors

Reorder the Months so they do not default to alphabetical.

airquality$Month<-factor(airquality$Month, levels = c("May","June","July","August","September"))

Plot 1: Create a histogram categorized by Month with qplot

Qplot stands for “Quick-Plot” (in the ggplot2 package)

p1<-qplot(data=airquality,Temp,fill=Month,geom = "histogram",bins = 20)
p1

### Plot 2:Make a histogram using ggplot # ggplot is more sophisticated than qplot, but still uses ggplot2 package. # Reorder the legend so that it is not the default (alphabetical), but rather in order that months come # Outline the bars in white using the color=“white” command

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"))
p2

### Plot3: Create side-by-side boxplots categorized by Months. # fill=Month command fills each boxplot with a different color in the aesthetics # scale_fill_discrete makes the legend on the side for discrete color values.

p3<-airquality %>%
  ggplot(aes(Month,Temp,fill=Month))+
  ggtitle("Temperatures")+
  xlab("Months")+
  ylab("Frequency")+
  geom_boxplot()+
  scale_fill_discrete(name="Month",labels=c("May","June","July","August","September"))
p3

### Plot4: 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.

p4<-airquality%>%
  ggplot(aes(Month,Temp,fill=Month))+
  ggtitle("Monthly Temperature Variations")+
  xlab("Monthly Temperature")+
  ylab("Frequency")+
  geom_boxplot()+
  scale_fill_grey(name="Month",labels=c("May","June","July","August","September"))
p4

Plot5: Make a histogram using ggplot, but the plot is in grey scale.

p5<-airquality%>%
  ggplot(aes(x=Temp,fill=Month))+
  geom_histogram(position="identity",alpha=0.5, binwidth=5,color="white")+
  scale_fill_grey(name="Month",labels=c("May","June","July","August","September"))
p5