Homework airquality
Tracee Matthias
June 6, 2022
Load in the Dataset
install.packages(“tidyverse”)
library(tidyverse)
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data(airquality)
Show the data
airquality <- airquality
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$Temp)
## [1] 77.88235
mean(airquality[,4])
## [1] 77.88235
Change the Months from 5-9 to May through September
airquality$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 witj 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 ggplot 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
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 legend on the side for discrete color
values
p3 <- airquality %>%
ggplot(aes(Month, Temp, fill = Month)) +
ggtitle("Temperatures") +
xlab("Monthly Temperatures") +
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
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 Temperatures") +
ylab("Frequency") +
geom_boxplot()+
scale_fill_grey(name = "Month", labels = c("May", "June","July", "August", "September"))
p4
Plot 5: Ozone Level Variations
with(airquality, plot(Ozone ~ Temp))
This scatterplot illustrates a strong positive correlation between
temperature and ozone levels. As temperature readings increase, the
ozone level readings increase. I used a with function to create the
scatterplot. The line of code used is with(airquality, plot(Ozone ~
Temp)).