I have a data including different types:
a <- data.frame(x=c("a","b","b","c","c","c","d","d","e","f"),y=c(1,2,2,2,3,1,4,7,10,2),m=c("a","d","ab","ac","ac","vc","ed","ed","e","df"),n=c(2,1,5,3,3,2,8,10,10,1))
Actually, the data is more complex than this, probably including date as well. Furthermore, this is an unsupervised issue. So there is no "class labels" here. So I cannot use the methods such as ANOVA. So, how can I find correlation between each two columns?
P.S. I find a function called mixed.cor in psych package, but cannot understand how to use it.
Furthermore, correlation is just representing the linear relation. What function should I use if I want to know the important of every column?
The measure of correlation that most people use for numeric variables (i.e. Pearson correlation) is not defined for categorical data. If you want to measure the association between a numeric variable and a categorical variable, you can use ANOVA. If you want to measure the association between two categorical variables, you can use a Chi-Squared test. If your categorical variable is ordered (e.g. low, medium, high), you can use Spearman rank correlation.