df <- mtcars
# correlation miles per galon of petrol ~ horse power
cor.test(x = df$mpg, y = df$hp)
# get parameters of analysed correlation
fit <- cor.test(x = df$mpg, y = df$hp)
str(fit)
fit$p.value
# the same correlation in 'formula' form (see. ?cor.test)
?cor.test
cor.test(~mpg+hp, df)
# Plot using generic plot function of ggplot from ggplot2 package
plot(x = df$mpg, y = df$hp)
library(ggplot2)
# add color by number of engine's cylinders
ggplot(df, aes(x = mpg, y = hp, col = factor(cyl)))+geom_point(size=5)
# Subset of necessary data from mtcars
df.sub <- df[,c(1,3:7)]
# Scatterplots for all pairs of variables
pairs(df.sub)
# Correlations of all pairs of variables
cor(df.sub)
# Correlation using corr.test function from 'psych' package
# Correlation for all pairs of variables
library(psych)
fit.sub <- corr.test(df.sub)
fit.sub$r # correlations
fit.sub$p # dependecies of variables