Load in the Dataset

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

library(tidyverse)
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## ✓ readr   1.3.1     ✓ forcats 0.5.0
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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

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

Calculating 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 Month 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

Redorder the months so they do not default to alpabetical

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 the 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

Plot 3: Create side-by-side boxplots categorized by Month

fill=Month command fills each boxplot with a different color in the aesthetics

scale_fill_discrete makes the legen 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

Plot 4: Make the same side-by-side boxplots, but in grey-scale

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

p4

Scatterplot of Ozone and Temp

The scatterplot below displays the temperature variable on the y-axis and the ozone variable on the x-axis

airquality2 <- na.omit(airquality)
na_num = 0

for (i in 1:nrow(airquality2)){
  if(is.na(airquality2[i,"Ozone"])){
    na_num = na_num + 1
  }
}

na_num
## [1] 0
plot1 <- airquality2 %>% ggplot(aes(x = Ozone, y = Temp, color = Month)) + geom_point() + scale_x_continuous("Ozone") + scale_y_continuous("Temperature") + ggtitle("Ozone and Temperature") + theme(plot.title = element_text(hjust = 0.5))
plot1

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
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plot2 <- ggplotly(plot1)
plot2