library(tidyverse)
str(airquality)
mean(airquality$Temp) mean(airquality[,4])
median(airquality\(Temp) sd(airquality\)Wind) var(airquality$Wind)
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”
str(airquality) summary(airquality)
airquality\(Month<-factor(airquality\)Month, levels=c(“May”, “June”,“July”, “August”, “September”))
p1 <- qplot(data = airquality,Temp,fill = Month,geom = “histogram”, bins = 20) p1
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
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
p4 <- airquality %>% ggplot(aes(Month, Temp, fill = Month)) + ggtitle(“Temperatures”) + xlab(“Temperatures”) + ylab(“Frequency”) + geom_boxplot()+ scale_fill_grey(name = “Month”, labels = c(“May”, “June”,“July”, “August”, “September”)) p4
p5 <- airquality %>% ggplot(aes(x=Solar.R, 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”)) p5
For plot 5 I chose to visualize the data under the ‘Solar.R’ tab. I created a histogram and used color. To do so, I changed my x variable from Temp to Solar.R