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我在 SparklyR 接口中有一个 Spark 数据框，我正在尝试从数组列中提取元素。

df <- copy_to(sc, data.frame(A=c(1,2),B=c(3,4)))            ## BUILD DATAFRAME
dfnew <- df %>% mutate(C=Array(A,B)) %>% select(C)          ## CREATE ARRAY COL


> dfnew                                                     ## VIEW DATAFRAME
# Source: spark<?> [?? x 1]                       
  C        
  <list>   
1 <dbl [2]>
2 <dbl [2]>


dfnew %>% sdf_schema()                                      ## VERIFY COLUMN TYPE IS ARRAY
$C$name
[1] "C"

$C$type
[1] "ArrayType(DoubleType,true)"

我可以用“mutate”提取一个元素......

dfnew %>% mutate(myfirst_element=C[[1]]) 

# Source: spark<?> [?? x 2]
  C         myfirst_element
  <list>              <dbl>
1 <dbl [2]>               3
2 <dbl [2]>               4

但我想用“select”动态提取一个元素。但是，所有尝试都只返回完整列：

> dfnew %>% select("C"[1]) 
# Source: spark<?> [?? x 1]
  C        
  <list>   
1 <dbl [2]>
2 <dbl [2]>
> dfnew %>% select("C"[[1]]) 
# Source: spark<?> [?? x 1]
  C        
  <list>   
1 <dbl [2]>
2 <dbl [2]>
> dfnew %>% select("C"[[1]][1]) 
# Source: spark<?> [?? x 1]
  C        
  <list>   
1 <dbl [2]>
2 <dbl [2]>
> dfnew %>% select("C"[[1]][[1]]) 
# Source: spark<?> [?? x 1]
  C        
  <list>   
1 <dbl [2]>
2 <dbl [2]>

我也尝试过使用“sdf_select”，但没有成功：

> dfnew %>% sdf_select("C"[[1]][1])
# Source: spark<?> [?? x 1]
  C        
  <list>   
1 <dbl [2]>
2 <dbl [2]>

在 PySpark 中，您可以显式访问元素，例如 col("C")[1]; 在 Scala 中，您可以使用 getItem 或 element_at；在 SparkR 中，您还可以使用 element_at。但是有人知道 SparklyR 设置中的解决方案吗？提前感谢您的帮助。

4

1 回答 1

1

想到了以下解决方案。

library(tidyverse)

df = tibble(group = 1:5) %>%
  mutate(C = map(group, ~array(c(1,2),c(3,4)))) 

df
# # A tibble: 5 x 2
# group C            
# <int> <list>       
#   1     1 <dbl [3 x 4]>
#   2     2 <dbl [3 x 4]>
#   3     3 <dbl [3 x 4]>
#   4     4 <dbl [3 x 4]>
#   5     5 <dbl [3 x 4]>

df$C
# [[1]]
# [,1] [,2] [,3] [,4]
# [1,]    1    2    1    2
# [2,]    2    1    2    1
# [3,]    1    2    1    2
# 
# [[2]]
# [,1] [,2] [,3] [,4]
# [1,]    1    2    1    2
# [2,]    2    1    2    1
# [3,]    1    2    1    2
# 
# [[3]]
# [,1] [,2] [,3] [,4]
# [1,]    1    2    1    2
# [2,]    2    1    2    1
# [3,]    1    2    1    2
# 
# [[4]]
# [,1] [,2] [,3] [,4]
# [1,]    1    2    1    2
# [2,]    2    1    2    1
# [3,]    1    2    1    2
# 
# [[5]]
# [,1] [,2] [,3] [,4]
# [1,]    1    2    1    2
# [2,]    2    1    2    1
# [3,]    1    2    1    2



df %>% pull(C) %>% map(~.x[1,])
# [[1]]
# [1] 1 2 1 2
# 
# [[2]]
# [1] 1 2 1 2
# 
# [[3]]
# [1] 1 2 1 2
# 
# [[4]]
# [1] 1 2 1 2
# 
# [[5]]
# [1] 1 2 1 2

df %>% pull(C) %>% map(~.x[,2])
# [[1]]
# [1] 2 1 2
# 
# [[2]]
# [1] 2 1 2
# 
# [[3]]
# [1] 2 1 2
# 
# [[4]]
# [1] 2 1 2
# 
# [[5]]
# [1] 2 1 2

df %>% pull(C) %>% map(~.x[1:2,])
# [[1]]
# [,1] [,2] [,3] [,4]
# [1,]    1    2    1    2
# [2,]    2    1    2    1
# 
# [[2]]
# [,1] [,2] [,3] [,4]
# [1,]    1    2    1    2
# [2,]    2    1    2    1
# 
# [[3]]
# [,1] [,2] [,3] [,4]
# [1,]    1    2    1    2
# [2,]    2    1    2    1
# 
# [[4]]
# [,1] [,2] [,3] [,4]
# [1,]    1    2    1    2
# [2,]    2    1    2    1
# 
# [[5]]
# [,1] [,2] [,3] [,4]
# [1,]    1    2    1    2
# [2,]    2    1    2    1

我想这就是你要找的。当然，这也适用于任何大小的任何数组。

于 2021-09-12T00:21:47.360 回答