1. Kết nối dữ liệu

data(mtcars)
dulieu <-mtcars[,c("mpg","disp","hp","drat","wt","qsec")]
attach(dulieu)
head(dulieu)
##                    mpg disp  hp drat    wt  qsec
## Mazda RX4         21.0  160 110 3.90 2.620 16.46
## Mazda RX4 Wag     21.0  160 110 3.90 2.875 17.02
## Datsun 710        22.8  108  93 3.85 2.320 18.61
## Hornet 4 Drive    21.4  258 110 3.08 3.215 19.44
## Hornet Sportabout 18.7  360 175 3.15 3.440 17.02
## Valiant           18.1  225 105 2.76 3.460 20.22

2. Tính hệ số tương quan 2 biến

2.1 Tính theo phương pháp Pearson

cor(mpg, hp, method = "pearson")
## [1] -0.7761684
cor.test(mpg, hp, method = "pearson")
## 
##  Pearson's product-moment correlation
## 
## data:  mpg and hp
## t = -6.7424, df = 30, p-value = 1.788e-07
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8852686 -0.5860994
## sample estimates:
##        cor 
## -0.7761684

2.2 Tính theo phương pháp Kendall

cor(mpg, hp, method =  "kendall")
## [1] -0.7428125
cor.test(mpg, hp, method =  "kendall")
## Warning in cor.test.default(mpg, hp, method = "kendall"): Cannot compute exact
## p-value with ties
## 
##  Kendall's rank correlation tau
## 
## data:  mpg and hp
## z = -5.871, p-value = 4.332e-09
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
##        tau 
## -0.7428125

2.3 Tính theo phương pháp Spearman

cor(mpg, hp, method =  "spearman")
## [1] -0.8946646
cor.test(mpg, hp, method =  "spearman")
## Warning in cor.test.default(mpg, hp, method = "spearman"): Cannot compute exact
## p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  mpg and hp
## S = 10337, p-value = 5.086e-12
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.8946646

3.Tính tương quan cho data

3.1 Tính tương quan GGally

library(GGally)
## Loading required package: ggplot2
## 
## Attaching package: 'ggplot2'
## The following object is masked from 'dulieu':
## 
##     mpg
## Registered S3 method overwritten by 'GGally':
##   method from   
##   +.gg   ggplot2
ggpairs(dulieu)

3.2 Tính tương quan ggcorplot

tuongquan <- round(cor(dulieu), 1)
tuongquan
##       mpg disp   hp drat   wt qsec
## mpg   1.0 -0.8 -0.8  0.7 -0.9  0.4
## disp -0.8  1.0  0.8 -0.7  0.9 -0.4
## hp   -0.8  0.8  1.0 -0.4  0.7 -0.7
## drat  0.7 -0.7 -0.4  1.0 -0.7  0.1
## wt   -0.9  0.9  0.7 -0.7  1.0 -0.2
## qsec  0.4 -0.4 -0.7  0.1 -0.2  1.0
library(ggcorrplot)
ggcorrplot(tuongquan, hc.order = TRUE, 
           type = "lower", 
           lab = TRUE, 
           lab_size = 3, 
           method="circle", 
           colors = c("tomato2", "white", "springgreen3"), 
           title="Correlogram of mtcars", 
           ggtheme=theme_bw)

3.3 Tính tương quan corrplot

library(corrplot)
## corrplot 0.84 loaded
corrplot(tuongquan, method="number")

corrplot.mixed(tuongquan,tl.col='black')

3.4 Tương quan theo gói PerformanceAnalytics

library(PerformanceAnalytics)
## Loading required package: xts
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## Registered S3 method overwritten by 'xts':
##   method     from
##   as.zoo.xts zoo
## 
## Attaching package: 'PerformanceAnalytics'
## The following object is masked from 'package:graphics':
## 
##     legend
chart.Correlation(mtcars,hist=T)

3.5 Tương quan theo gói psych

library(psych)
## 
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha
pairs.panels(mtcars[,1:6])