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How to round correlation values in the correlation matrix to zero decimal places in R?
To find the correlation matrix, we simply need to use cor function with the data frame object name. For example, if we have a data frame named as df then the correlation matrix can be found by using cor(df). But the result will have too many decimal places to represent the correlation. If we want to avoid the values after decimal places, we can use round function.
Consider the mtcars data in base R −
Example
data(mtcars) cor(mtcars)
Output
mpg cyl disp hp drat wt mpg 1.0000000 -0.8521620 -0.8475514 -0.7761684 0.68117191 -0.8676594 cyl -0.8521620 1.0000000 0.9020329 0.8324475 -0.69993811 0.7824958 disp -0.8475514 0.9020329 1.0000000 0.7909486 -0.71021393 0.8879799 hp -0.7761684 0.8324475 0.7909486 1.0000000 -0.44875912 0.6587479 drat 0.6811719 -0.6999381 -0.7102139 -0.4487591 1.00000000 -0.7124406 wt -0.8676594 0.7824958 0.8879799 0.6587479 -0.71244065 1.0000000 qsec 0.4186840 -0.5912421 -0.4336979 -0.7082234 0.09120476 -0.1747159 vs 0.6640389 -0.8108118 -0.7104159 - 0.7230967 0.44027846 -0.5549157 am 0.5998324 -0.5226070 -0.5912270 -0.2432043 0.71271113 -0.6924953 gear 0.4802848 -0.4926866 -0.5555692 -0.1257043 0.69961013 -0.5832870 carb -0.5509251 0.5269883 0.3949769 0.7498125 -0.09078980 0.4276059 qsec vs am gear carb mpg 0.41868403 0.6640389 0.59983243 0.4802848 -0.55092507 cyl -0.59124207 -0.8108118 -0.52260705 -0.4926866 0.52698829 disp -0.43369788 -0.7104159 -0.59122704 -0.5555692 0.39497686 hp -0.70822339 -0.7230967 -0.24320426 -0.1257043 0.74981247 drat 0.09120476 0.4402785 0.71271113 0.6996101 -0.09078980 wt -0.17471588 -0.5549157 -0.69249526 -0.5832870 0.42760594 qsec 1.00000000 0.7445354 -0.22986086 -0.2126822 -0.65624923 vs 0.74453544 1.0000000 0.16834512 0.2060233 -0.56960714 am -0.22986086 0.1683451 1.00000000 0.7940588 0.05753435 gear -0.21268223 0.2060233 0.79405876 1.0000000 0.27407284 carb -0.65624923 -0.5696071 0.05753435 0.2740728 1.00000000
Output
qsec vs am gear carb mpg 0.41868403 0.6640389 0.59983243 0.4802848 -0.55092507 cyl -0.59124207 -0.8108118 -0.52260705 -0.4926866 0.52698829 disp -0.43369788 -0.7104159 -0.59122704 -0.5555692 0.39497686 hp -0.70822339 -0.7230967 -0.24320426 -0.1257043 0.74981247 drat 0.09120476 0.4402785 0.71271113 0.6996101 -0.09078980 wt -0.17471588 -0.5549157 -0.69249526 -0.5832870 0.42760594 qsec 1.00000000 0.7445354 -0.22986086 -0.2126822 -0.65624923 vs 0.74453544 1.0000000 0.16834512 0.2060233 -0.56960714 am -0.22986086 0.1683451 1.00000000 0.7940588 0.05753435 gear -0.21268223 0.2060233 0.79405876 1.0000000 0.27407284 carb -0.65624923 -0.5696071 0.05753435 0.2740728 1.00000000
Finding the correlation matrix with correlation coefficients rounded to zero −
Example
round(cor(mtcars),0)
Output
mpg cyl disp hp drat wt qsec vs am gear carb mpg 1 -1 -1 -1 1 -1 0 1 1 0 -1 cyl -1 1 1 1 -1 1 -1 -1 -1 0 1 disp -1 1 1 1 -1 1 0 -1 -1 -1 0 hp -1 1 1 1 0 1 -1 -1 0 0 1 drat 1 -1 -1 0 1 -1 0 0 1 1 0 wt -1 1 1 1 -1 1 0 -1 -1 -1 0 qsec 0 -1 0 -1 0 0 1 1 0 0 -1 vs 1 -1 -1 -1 0 -1 1 1 0 0 -1 am 1 -1 -1 0 1 -1 0 0 1 1 0 gear 0 0 -1 0 1 -1 0 0 1 1 0 carb -1 1 0 1 0 0 -1 -1 0 0 1
Consider the below data frame −
Example
x1<-sample(rexp(5,1),20,replace=TRUE) x2<-sample(runif(5,1,2),20,replace=TRUE) x3<-sample(rnorm(4,0.95,0.04),20,replace=TRUE) df_x<-data.frame(x1,x2,x3) df_x
Output
x1 x2 x3 1 2.89702241 1.764443 0.9478372 2 0.89472590 1.764443 0.9850543 3 0.89472590 1.299860 0.9850543 4 0.07786123 1.377727 0.9661181 5 2.89702241 1.452261 0.9478372 6 0.22655315 1.452261 0.9850543 7 2.89702241 1.452261 0.9478372 8 2.89702241 1.764443 0.9661181 9 0.46248476 1.764443 0.9850543 10 0.22655315 1.452261 0.9731809 11 0.89472590 1.764443 0.9731809 12 0.46248476 1.764443 0.9661181 13 2.89702241 1.452261 0.9731809 14 0.07786123 1.377727 0.9661181 15 0.89472590 1.377727 0.9478372 16 0.07786123 1.180832 0.9731809 17 0.22655315 1.377727 0.9731809 18 0.22655315 1.764443 0.9478372 19 2.89702241 1.764443 0.9731809 20 0.46248476 1.452261 0.9661181
Example
cor(df_x)
Output
x1 x2 x3 x1 1.00000000 0.05458349 -0.2571943 x2 0.05458349 1.00000000 -0.1760571 x3 -0.25719426 -0.17605707 1.0000000
Example
round(cor(df_x),0)
Output
x1 x2 x3 x1 1 0 0 x2 0 1 0 x3 0 0 1
Updated on: 14-Oct-2020
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