8.1 Correlation plots
Brendan Strawn
10/8/2019
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)## price lotSize age landValue livingArea pctCollege bedrooms
## price 1.00 0.16 -0.19 0.58 0.71 0.20 0.40
## lotSize 0.16 1.00 -0.02 0.06 0.16 -0.03 0.11
## age -0.19 -0.02 1.00 -0.02 -0.17 -0.04 0.03
## landValue 0.58 0.06 -0.02 1.00 0.42 0.23 0.20
## livingArea 0.71 0.16 -0.17 0.42 1.00 0.21 0.66
## pctCollege 0.20 -0.03 -0.04 0.23 0.21 1.00 0.16
## bedrooms 0.40 0.11 0.03 0.20 0.66 0.16 1.00
## fireplaces 0.38 0.09 -0.17 0.21 0.47 0.25 0.28
## bathrooms 0.60 0.08 -0.36 0.30 0.72 0.18 0.46
## rooms 0.53 0.14 -0.08 0.30 0.73 0.16 0.67
## fireplaces bathrooms rooms
## price 0.38 0.60 0.53
## lotSize 0.09 0.08 0.14
## age -0.17 -0.36 -0.08
## landValue 0.21 0.30 0.30
## livingArea 0.47 0.72 0.73
## pctCollege 0.25 0.18 0.16
## bedrooms 0.28 0.46 0.67
## fireplaces 1.00 0.44 0.32
## bathrooms 0.44 1.00 0.52
## rooms 0.32 0.52 1.00library(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 only thing that has a positive correlation within home price is Fire PLaces.
Q2 What factors have strong positve correlation with home price?
Two factors that have a strong positive correlation are a bathroom and a living area.
Q3 What factors have negative correlation with home price?
One factor in which has a negative correlation is the amount of rooms provided in the house.
Q4 What factors have strong negative correlation with home price?
N/A (none given)
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?
Age + 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?
Age + Lot Size; by applying your knowledge and using a scatter plot, you will be able to use this 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(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)## price lotSize age landValue livingArea pctCollege bedrooms
## price 1.00 0.16 -0.19 0.58 0.71 0.20 0.40
## lotSize 0.16 1.00 -0.02 0.06 0.16 -0.03 0.11
## age -0.19 -0.02 1.00 -0.02 -0.17 -0.04 0.03
## landValue 0.58 0.06 -0.02 1.00 0.42 0.23 0.20
## livingArea 0.71 0.16 -0.17 0.42 1.00 0.21 0.66
## pctCollege 0.20 -0.03 -0.04 0.23 0.21 1.00 0.16
## bedrooms 0.40 0.11 0.03 0.20 0.66 0.16 1.00
## fireplaces 0.38 0.09 -0.17 0.21 0.47 0.25 0.28
## bathrooms 0.60 0.08 -0.36 0.30 0.72 0.18 0.46
## rooms 0.53 0.14 -0.08 0.30 0.73 0.16 0.67
## fireplaces bathrooms rooms
## price 0.38 0.60 0.53
## lotSize 0.09 0.08 0.14
## age -0.17 -0.36 -0.08
## landValue 0.21 0.30 0.30
## livingArea 0.47 0.72 0.73
## pctCollege 0.25 0.18 0.16
## bedrooms 0.28 0.46 0.67
## fireplaces 1.00 0.44 0.32
## bathrooms 0.44 1.00 0.52
## rooms 0.32 0.52 1.00Q8 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.
ggcorrplot(r,
hc.order = TRUE,
type = "lower",
lab = TRUE)