Closed 20 hours ago.

I have a large data frame, the first four rows of my data frame look like this:

I used rmcorr to calculate the correlation coefficient for two groups, one with 50 subjects, and the other with 60 subjects. The first group had 7000 observations while the second group had 8000. There is not an equal number of observations per subject in either group.

I want to determine the correlation and p-value between one variable and all of the others in an R data frame, so I created a correlation matrix with cor_mat, and then cor_get_pval. The relevant p-values were all p<.0001. To test the accuracy, I used cor.test on my one variable and some of the others, individually. I got the same correlation coefficient, but very high p-values (p>.5).

I want to test the first columns of a data frame for correlation with their last columns. I’m implementing this to a bigger function, so I never know the variable names. Therefore I need an automated solution, which I’m not able to find. I tried the following:

i want to test the first columns of a data frame for correlation with their last columns. I’m implementing this to a bigger function, so i never know the variable names. Therefore i need an automated solution, which I’m not able to find.. I tried the following:

I need to calculate the correlation coefficient in R between:
Category 1:
Frequency of watching(very often, often, neutral, less often, rare) and the percentage of each option
Category 2:
Satisfaction level(very satisfied, satisfied, neutral, dissatisfied, very dissatisfied) with the percentages
Is there a way to compute the correlational coefficient? Please advice

when I perform correlation between 2 datasets using cor.test I obtain the error: “not enough finite observations”. The first dataframe is gene expression in patients, the second is proteomic data in the same patients. The two final dataframes have same number of rows (patients) and columns (same genes in the same order).
How to analyze data with NA value?