JSH - 1 year ago 101
R Question

# R: Calculating Pearson correlation and R-squared by group

I am trying to extend the answer of a question R: filtering data and calculating correlation.

To obtain the correlation of temperature and humidity for each month of the year (1 = January), we would have to do the same for each month (12 times).

``````cor(airquality[airquality\$Month == 1, c("Temp", "Humidity")])
``````

Is there any way to do each month automatically?

In my case I have more than 30 groups (not months but species) to which I would like to test for correlations, I just wanted to know if there is a faster way than doing it one by one.

Thank you!

``````cor(airquality[airquality\$Month == 1, c("Temp", "Humidity")])
``````

gives you a `2 * 2` covariance matrix rather than a number. I bet you want a single number for each `Month`, so use

``````## cor(Temp, Humidity | Month)
with(airquality, mapply(cor, split(Temp, Month), split(Humidity, Month)) )
``````

and you will obtain a vector.

Have a read around `?split` and `?mapply`; they are very useful for "by group" operations, although they are not the only option. Also read around `?cor`, and compare the difference between

``````a <- rnorm(10)
b <- rnorm(10)
cor(a, b)
cor(cbind(a, b))
``````

The answer you linked in your question is doing something similar to `cor(cbind(a, b))`.

Reproducible example

The `airquality` dataset in R does not have `Humidity` column, so I will use `Wind` for testing:

``````## cor(Temp, Wind | Month)
x <- with(airquality, mapply(cor, split(Temp, Month), split(Wind, Month)) )

#         5          6          7          8          9
#-0.3732760 -0.1210353 -0.3052355 -0.5076146 -0.5704701
``````

We get a named vector, where `names(x)` gives `Month`, and `unname(x)` gives correlation.

Thank you very much! It worked just perfectly! I was trying to figure out how to obtain a vector with the `R^2` for each correlation too, but I can't... Any ideas?

`cor(x, y)` is like fitting a standardised linear regression model:

``````coef(lm(scale(y) ~ scale(x) - 1))  ## remember to drop intercept
``````

The R-squared in this simple linear regression is just the square of the slope. Previously we have `x` storing correlation per group, now R-squared is just `x ^ 2`.

Recommended from our users: Dynamic Network Monitoring from WhatsUp Gold from IPSwitch. Free Download