library(dplyr) my_df |> group_by(my_category) |> summarise(my_z = cor(my_x, my_y))
On Sat, Apr 9, 2022 at 4:37 AM Richard M. Heiberger <r...@temple.edu> wrote: > look at > ?mapply > Apply a Function to Multiple List or Vector Arguments > > to see if that meets your needs > > > On Apr 08, 2022, at 21:26, Kelly Thompson <kt1572...@gmail.com> wrote: > > > > #Q. How can I "apply" a function that takes two or more vectors as > > arguments, such as cor(x, y), over a "category" or "grouping variable" > > or "index"? > > #I'm using cor() as an example, I'd like to find a way to do this for > > any function that takes 2 or more vectors as arguments. > > > > > > #create example data > > > > my_category <- rep ( c("a","b","c"), 4) > > > > set.seed(12345) > > my_x <- rnorm(12) > > > > set.seed(54321) > > my_y <- rnorm(12) > > > > my_df <- data.frame(my_category, my_x, my_y) > > > > #review data > > my_df > > > > #If i wanted to get the correlation of x and y grouped by category, I > > could use this code and loop: > > > > my_category_unique <- unique(my_category) > > > > my_results <- vector("list", length(my_category_unique) ) > > names(my_results) <- my_category_unique > > > > #start i loop > > for (i in 1:length(my_category_unique) ) { > > my_criteria_i <- my_category == my_category_unique[i] > > my_x_i <- my_x[which(my_criteria_i)] > > my_y_i <- my_y[which(my_criteria_i)] > > my_correl_i <- cor(x = my_x_i, y = my_y_i) > > my_results[i] <- list(my_correl_i) > > } # end i loop > > > > #review results > > my_results > > > > #Q. Is there a better or more "elegant" way to do this, using by(), > > aggregate(), apply(), or some other function? > > > > #This does not work and results in this error message: "Error in > > FUN(dd[x, ], ...) : incompatible dimensions" > > by (data = my_x, INDICES = my_category, FUN = cor, y = my_y) > > > > #This does not work and results in this error message: "Error in > > cor(my_df$x, my_df$y) : ... supply both 'x' and 'y' or a matrix-like > > 'x' " > > by (data = my_df, INDICES = my_category, FUN = function(x, y) { cor > > (my_df$x, my_df$y) } ) > > > > > > #if I wanted the mean of x by category, I could use by() or aggregate(): > > by (data = my_x, INDICES = my_category, FUN = mean) > > > > aggregate(x = my_x, by = list(my_category), FUN = mean) > > > > #Thanks! > > > > ______________________________________________ > > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > > > https://nam10.safelinks.protection.outlook.com/?url=https%3A%2F%2Fstat.ethz.ch%2Fmailman%2Flistinfo%2Fr-help&data=04%7C01%7Crmh%40temple.edu%7C4c8a50fd1bf14b2cf7b408da19c7fe20%7C716e81efb52244738e3110bd02ccf6e5%7C0%7C0%7C637850644148770767%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=23Y%2Fqw7G1gb4ACIz5V41DjBIR8c2IFkkZgud9dGaftE%3D&reserved=0 > > PLEASE do read the posting guide > https://nam10.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.r-project.org%2Fposting-guide.html&data=04%7C01%7Crmh%40temple.edu%7C4c8a50fd1bf14b2cf7b408da19c7fe20%7C716e81efb52244738e3110bd02ccf6e5%7C0%7C0%7C637850644148770767%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000&sdata=3vIZYrMBnAZKZhZCwHcLpILHEE72NuLc03LXAxr%2BXQ4%3D&reserved=0 > > and provide commented, minimal, self-contained, reproducible code. > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.