R multidplyr for summarise_at work around?

Question

我想使用multiplyer，但它还没有任何summary_at。我有成百上千，所以summaryat是必要的，但不幸的是，multiplyr中没有。

寻找替代方案来解决它。

library('tidyverse')
df <- tibble(ID = c('a','a','b','c','c','e','e','f','g','g'),
var1 = floor(runif(10, min=0, max=100)),
var2 = floor(runif(10, min=0, max=100)),
var3 = floor(runif(10, min=0, max=100)),
var4 = floor(runif(10, min=0, max=100))
)
library('multidplyr')
cluster <- new_cluster(5)
#works
df %>% 
group_by(ID) %>% 
#partition(cluster) %>% 
summarise_at(.vars = vars(starts_with('var')),sum) 
#collect()
#works
df %>% 
group_by(ID) %>% 
partition(cluster) %>% 
summarise(var1 = sum(var1),
var2 = sum(var2),
var3 = sum(var3)) %>% 
collect()
#doesnt works
df %>% 
group_by(ID) %>% 
partition(cluster) %>%
summarise_at(.vars = vars(starts_with('var')),sum)  %>% 
collect()

我甚至试过这个

#Define character string vector to replace command line
sum_var <- select(df,starts_with('var')) %>% names()
sum_var_str <- paste0(sum_var," = sum(",sum_var,")")
sum_var_str <- str_c(sum_var_str, collapse = ", ")
> sum_var
[1] "var1" "var2" "var3" "var4"
> sum_var_str
[1] "var1 = sum(var1), var2 = sum(var2), var3 = sum(var3), var4 = sum(var4)"
#works
df %>% 
group_by(ID) %>% 
{ eval(parse(text = sprintf("summarise(., %s, .groups = 'drop')", sum_var_str))) }
#doesn't works
df %>% 
group_by(ID) %>% 
partition(cluster) %>%
{ eval(parse(text = sprintf("summarise(., %s, .groups = 'drop')", sum_var_str))) } %>%
collect()

Answer 1

找到解决方案

library('dplyr')
library('multidplyr')
library('parallel')
cluster <- new_cluster(detectCores())
df <- tibble(ID = c('a','a','b','c','c','e','e','f','g','g'),
var1 = floor(runif(10, min=0, max=100)),
var2 = floor(runif(10, min=0, max=100)),
var3 = floor(runif(10, min=0, max=100)),
var4 = floor(runif(10, min=0, max=100))
)
sum_var <- select(df,starts_with('var')) %>% names()
#assign vector to cluster
cluster_assign(cluster, sum_var = sum_var)
cluster_library(cluster, 'dplyr')
df %>% 
group_by(ID) %>% 
partition(cluster) %>% 
summarise(across(all_of(sum_var), sum)) %>% 
collect()
# A tibble: 6 x 5
ID     var1  var2  var3  var4
<chr> <dbl> <dbl> <dbl> <dbl>
1 a        57    72    85   118
2 b        46    50    80    33
3 c        82   156    96   154
4 e       122   107    93   120
5 f        33     7    49    36
6 g        99    79    83    56