a0 <- read.csv(file = "C:/Users/81809/Downloads/RStudio/heart.data.csv")
a <- a0[ ,-1]
library(DT)
datatable(a)

x <- a$biking
y <- a$smoking
z <- a$heart.disease

cor(x,y)

## [1] 0.01513618

cor(x,z)

## [1] -0.9354555

cor(y,z)

## [1] 0.309131

r <- cor(a)
r

##                    biking    smoking heart.disease
## biking         1.00000000 0.01513618    -0.9354555
## smoking        0.01513618 1.00000000     0.3091310
## heart.disease -0.93545547 0.30913098     1.0000000

library(kableExtra)
kable(round(r,2),caption = '相関表') |> kable_classic('striped',full_width = F)

相関表

	biking	smoking	heart.disease
biking	1.00	0.02	-0.94
smoking	0.02	1.00	0.31
heart.disease	-0.94	0.31	1.00

コメント

bikingとsmokingには、相関がない

bikingとheart.diseaseには、強い負の相関がある

smokingとheart.diseaseには、弱い正の相関がある

library(psych)
pairs.panels(a)

cor.plot(a)

library(corrplot)
corrplot.mixed(r,lower = 'ellips',upper = 'number')

library(plotly)

kyokasho <- list(size = 11,color = 'blue',family = 'UD Digi Kyokasho NK-R')

plot_ly(x = rownames(r),
        y = colnames(r),
        z = as.matrix(r),
        text = paste(r),
        type = 'heatmap') |>
  layout(font = kyokasho,
         title = '主タイトル',
         xaxis = list(title = 'x軸カテゴリラベル'),
         yaxis = list(title = 'y軸カテゴリラベル'))