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
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