spearman correlation by group in R

How about this for a base R solution:

df <- data.frame(group = rep(c("G1", "G2"), each = 10),
                 var1 = rnorm(20),
                 var2 = rnorm(20))

r <- by(df, df$group, FUN = function(X) cor(X$var1, X$var2, method = "spearman"))
# df$group: G1
# [1] 0.4060606
# ------------------------------------------------------------ 
# df$group: G2
# [1] 0.1272727

And then, if you want the results in the form of a data.frame:

data.frame(group = dimnames(r)[[1]], corr = as.vector(r))
#   group      corr
# 1    G1 0.4060606
# 2    G2 0.1272727

EDIT: If you prefer a plyr-based solution, here is one:

library(plyr)
ddply(df, .(group), summarise, "corr" = cor(var1, var2, method = "spearman"))

very old question, but this tidy & broom solution is extremely straightforward. Thus I have to share the approach:

set.seed(123)
df <- data.frame(group = rep(c("G1", "G2"), each = 10),
                 var1 = rnorm(20),
                 var2 = rnorm(20))

library(tidyverse)
library(broom)

df  %>% 
  group_by(group) %>%
  summarize(correlation = cor(var1, var2,, method = "sp"))
# A tibble: 2 x 2
  group correlation
  <fct>       <dbl>
1 G1        -0.200 
2 G2         0.0545

# with pvalues and further stats
df %>% 
  nest(-group) %>% 
  mutate(cor=map(data,~cor.test(.x$var1, .x$var2, method = "sp"))) %>%
  mutate(tidied = map(cor, tidy)) %>% 
  unnest(tidied, .drop = T)
# A tibble: 2 x 6
  group estimate statistic p.value method                          alternative
  <fct>    <dbl>     <dbl>   <dbl> <chr>                           <chr>      
1 G1     -0.200        198   0.584 Spearman's rank correlation rho two.sided  
2 G2      0.0545       156   0.892 Spearman's rank correlation rho two.sided 

Since some time/dplyr version, you need to write this to get results like above and no errors:

df %>% 
  nest(data = -group) %>%
  mutate(cor=map(data,~cor.test(.x$var1, .x$var2, method = "sp"))) %>%
  mutate(tidied = map(cor, tidy)) %>% 
  unnest(tidied) %>% 
  select(-data, -cor)