I am trying to run a correlation between all numberic values (the dataset contains columns of both numeric and non-numeric values) using the following code:
mydata= read.csv("C:\\full_path\\playerData.csv", header = TRUE)
mydata=data.frame(mydata)
vals=cor(mydata, use="complete.obs", method="pearson")
write.csv(vals,"C:\\Users\\weiler\\Desktop\\RStudioOutput.csv")
based on this site: http://www.statmethods.net/stats/correlations.html I am getting the error:
Error in cor(mydata, use = "complete.obs", method = "pearson") : 'x' must be numeric
My error seems to be because some of the data is non-numeric. Is there a simple way to ignore the non-numeric data?
As David Arenburg said, you can use is.numeric and then subset. Here's an explanation.
mydata <-
data.frame(alpha1=letters[1:10], alpha2=letters[11:20],
num1=runif(10), num2=runif(10))
# alpha1 alpha2 num1 num2
# 1 a k 0.02123999 0.50840184
# 2 b l 0.23963061 0.27622386
# 3 c m 0.32220265 0.69144157
# 4 d n 0.08147787 0.59194675
# 5 e o 0.15875212 0.61067014
# 6 f p 0.87679916 0.65882239
# 7 g q 0.94408782 0.07846614
# 8 h r 0.41669714 0.18997531
# 9 i s 0.35965571 0.90215068
# 10 j t 0.64287793 0.84273345
Which columns are numeric?
sapply(mydata, is.numeric)
# alpha1 alpha2 num1 num2
# FALSE FALSE TRUE TRUE
So, use this boolean vector to subset and you get just the numeric columns
my_num_data <- mydata[, sapply(mydata, is.numeric)]
# num1 num2
# 1 0.5118055 0.82442771
# 2 0.3512970 0.12066818
# 3 0.4344065 0.94698653
# 4 0.1222383 0.72324135
# 5 0.1974004 0.51563337
# 6 0.2794483 0.06022451
# 7 0.1519816 0.32559160
# 8 0.5129894 0.76990432
# 9 0.2433832 0.08038982
# 10 0.7893464 0.45767856
now you can run cor()
cor(my_num_data, use = "complete.obs", method = "pearson")
# num1 num2
# num1 1.0000000 0.2852567
# num2 0.2852567 1.0000000
And to show it as a one-liner:
cor(mydata[, sapply(mydata, is.numeric)],
use = "complete.obs", method = "pearson")
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