Hi all, I have a data set (df, n=10 for the sake of simplicity here) where I have two continuous variables (age and weight) and I also have a grouping variable (group, with two levels). I want to run correlations for each group separately (kind of similar to "split file" in SPSS). I've been experimenting with different functions, and I was able to do this correctly using ddply function, but output is a little bit difficult to read when I do the cor.test to get all the data with p values, df, and pearson r (see below). I also tried to do it with by function. Although, with by, it shows the data for two groups separately, it seems like it calculates the same r for both groups. Here is my code for both ddply and by, and the output as well. I was wondering if there is a way to display the output better with ddply or run the correlations correctly for each group using by. Thanks in advance,
1.with "ddply" r<-ddply(df, .(group), summarise, "corr" = cor.test(age, weight, method = "pearson")) Output: Group corr 1 1 Inf 2 1 3 3 1 0 4 1 1 5 1 0 6 1 two.sided 7 1 Pearson's product-moment correlation 8 1 age and weight 9 1 1, 1 10 2 9.722211 11 2 3 12 2 0.002311412 13 2 0.9844986 14 2 0 15 2 two.sided 16 2 Pearson's product-moment correlation 17 2 age and weight 18 2 0.7779640, 0.9990233 2. with "by" r <- by(df, group, FUN = function(x) cor.test(age, weight, method = "pearson")) Output: Group: 1 Pearson's product-moment correlation data: age and weight t = 6.4475, df = 8, p-value = 0.0001988 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.6757758 0.9802100 sample estimates: cor 0.9157592 ------------------------------------------------------------ Group: 2 Pearson's product-moment correlation data: age and weight t = 6.4475, df = 8, p-value = 0.0001988 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: 0.6757758 0.9802100 sample estimates: cor 0.9157592 [[alternative HTML version deleted]]
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