west_roxbury <- mlba::WestRoxbury
library(gridExtra)
library(grid)
library(ggplot2)
g1 <- ggplot(west_roxbury) + geom_boxplot(aes(x = factor(FLOORS), y = TOTAL.VALUE))
g2 <- ggplot(west_roxbury) + geom_boxplot(aes(x = factor(BEDROOMS), y = TOTAL.VALUE))
g3 <- ggplot(west_roxbury) + geom_boxplot(aes(x = factor(ROOMS), y = TOTAL.VALUE))
g4 <- ggplot(west_roxbury) + geom_boxplot(aes(x = factor(FIREPLACE), y = TOTAL.VALUE))
grid.arrange(g1, g2, g3, g4, ncol=2, nrow=2)
As shown, there are many outliers above the fence, indicating that several houses have unusually high property values. There are several houses with unusually low prices.Floors,bedrooms and rooms also have many upper outliers

As shown, there are many outliers above the fence, indicating that several houses have unusually high property values. There are several houses with unusually low prices.Floors,bedrooms and rooms also have many upper outliers

library(ggplot2)
g5 <- ggplot(west_roxbury,aes(x = FLOORS, y = TOTAL.VALUE)) + geom_bar(stat = "identity") 
g6 <- ggplot(west_roxbury,aes(x = BEDROOMS, y = TOTAL.VALUE)) + geom_bar(stat = "identity") 
g7 <- ggplot(west_roxbury,aes(x = ROOMS, y = TOTAL.VALUE)) + geom_bar(stat = "identity") 
g8 <- ggplot(west_roxbury,aes(x = FIREPLACE, y = TOTAL.VALUE)) + geom_bar(stat = "identity") 
grid.arrange(g5,g6,g7,g8, ncol=2, nrow=2)
Obviously,the distrbution of bedrooms and rooms are right-skewed

Obviously,the distrbution of bedrooms and rooms are right-skewed

hist(west_roxbury$TOTAL.VALUE, xlab="TOTAL.VALUE")

library(ggplot2)
ggplot(west_roxbury, aes(x = LIVING.AREA, y = TOTAL.VALUE)) +
  geom_point() +
  geom_smooth(method = "lm", se = FALSE)
## `geom_smooth()` using formula = 'y ~ x'
As shown in the picture,a house with more living area tends to have a higher property value.

As shown in the picture,a house with more living area tends to have a higher property value.

cor_matrix <- west_roxbury[,-14]
heatmap(cor(cor_matrix),Rowv = NA,Colv = NA)
The heat map shows pairwise correlations between all quantitative variables in this dataset.

The heat map shows pairwise correlations between all quantitative variables in this dataset.

library(MASS)
par(mfcol = c(2,1))
parcoord(west_roxbury[west_roxbury$FULL.BATH == 1, -14], main = "FULL.BATH = 1")
parcoord(west_roxbury[west_roxbury$FULL.BATH == 2, -14], main = "FULL.BATH = 2")

library(treemap)
treemap(west_roxbury, index = "ROOMS", vSize = "LIVING.AREA")
The size of the rectangles of the different categories in the tree diagram indicates their corresponding values.

The size of the rectangles of the different categories in the tree diagram indicates their corresponding values.

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