8.1 Correlation plots
Joseph Panetta
10/08/2019
In this exercise you will learn to visualize the pairwise relationships between a set of quantitative variables. To this end, you will make your own note of 8.1 Correlation plots from Data Visualization with R.
# import data
data(SaratogaHouses, package="mosaicData")
# select numeric variables
df <- dplyr::select_if(SaratogaHouses, is.numeric)
# calulate the correlations
r <- cor(df, use="complete.obs")
round(r,2)library(ggplot2)
library(ggcorrplot)
# visualize the correlations
ggcorrplot(r,
hc.order = TRUE,
type = "lower",
lab = TRUE)
Q1 What factors have positve correlation with home price?
The more Fire places a home has the more expensive it will be.
Q2 What factors have strong positve correlation with home price?
More Living spaces, are related to higher home prices. More bathrooms rises home price. The bigger the house the more expensive it gets. ## Q3 What factors have negative correlation with home price? The older the house the less expensive it is. Weak relationship ## Q4 What factors have strong negative correlation with home price? NO facors taht have strong negative corallation to price. ## Q5 What set of two variables has the highest positive Pearson Product-Moment correlation coefficient? What set of two variables has the greatest negative Pearson Product-Moment correlation coefficient? postive- Living area and # of rooms postive coraltion .73 Negative- Strongest negative corallation age and bathrooms.
Q6 What set of two variables has the Pearson Product-Moment correlation coefficent that is closest to zero? Would you be sure that the two variables are not related at all? What would you do to check?
ggplot(SaratogaHouses,
aes(x = landValue,
y = age)) +
geom_point()
Q7 Plot correlation for CPS85 in the same way as above. Repeat Q1-Q6.
Hint: The CPS85 data set is from the mosaicData package. Explain wage instead of home price.
# import data
data( CPS85, package="mosaicData")
# select numeric variables
df <- dplyr::select_if( CPS85, is.numeric)
# calulate the correlations
r <- cor(df, use="complete.obs")
round(r,2)library(ggplot2)
library(ggcorrplot)
# visualize the correlations
ggcorrplot(r,
hc.order = TRUE,
type = "lower",
lab = TRUE)
Q8 Hide the messages, the code and its results on the webpage.
Hint: Use message, echo and results in the chunk options. Refer to the RMarkdown Reference Guide. Q1- Age Q2-Experience Q3-none Q4- experience Q5-wage experience ## Q9 Display the title and your name correctly at the top of the webpage.