8.1 Correlation plots
Hunter Jenkins
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?
Bathrooms have a positive correlation with home prices ## Q2 What factors have strong positve correlation with home price? living area has the strongest relationship, bathrooms would have the second strongest
Q3 What factors have negative correlation with home price?
Age and price have a negative correlation
Q4 What factors have strong negative correlation with home price?
Bathrooms and age have a strong negative correlation
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?
Living area and rooms has .73 wich is the highest positive correlation
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)## wage educ exper age
## wage 1.00 0.38 0.09 0.18
## educ 0.38 1.00 -0.35 -0.15
## exper 0.09 -0.35 1.00 0.98
## age 0.18 -0.15 0.98 1.00library(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.