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我想使用multidplyr，它还没有任何summarise_at。我有数百甚至数千，所以 summarise_at 是必要的，但不幸的是，在 multidplyr 中不可用。

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()

我什至试过这个

#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()
4

1 回答 1

2

找到了解决方案

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
于 2020-07-28T04:38:29.637 回答