如何使用Sparklyr总结分类变量级别 [英] How to use Sparklyr to summarize Categorical Variable Level

问题描述

对于数据集中的每个分类变量，我想获取每个级别的计数和摘要统计信息。我可以使用 dlookr R包并使用其 diagnose_category（）函数。由于没有工作包，因此我使用dplyr重新创建了该函数。

For each categorical variable in dataset, I want to get counts and summary stats for each level. I can do this using dlookr R package using their diagnose_category() function. Since at work I don't have that package I recreated the function using dplyr.

在sparklye中，我能够一次获取单个变量的计数。需要帮助来扩展所有分类变量。

In sparklye I am able to get counts for a single variable at a time. Need help to extend it all categorical variable.

需要帮助：

通过SparklyR实现该功能

Implement the function via SparklyR

表1：所需的最终输出：

Table 1: Final output needed:

# A tibble: 20 x 6
   variables levels        N  freq ratio  rank
   <chr>     <ord>     <int> <int> <dbl> <int>
 1 cut       Ideal     53940 21551 40.0      1
 2 cut       Premium   53940 13791 25.6      2
 3 cut       Very Good 53940 12082 22.4      3
 4 cut       Good      53940  4906  9.10     4
 5 cut       Fair      53940  1610  2.98     5
 6 color     G         53940 11292 20.9      1
 7 color     E         53940  9797 18.2      2
 8 color     F         53940  9542 17.7      3
 9 color     H         53940  8304 15.4      4
10 color     D         53940  6775 12.6      5
11 color     I         53940  5422 10.1      6
12 color     J         53940  2808  5.21     7
13 clarity   SI1       53940 13065 24.2      1
14 clarity   VS2       53940 12258 22.7      2
15 clarity   SI2       53940  9194 17.0      3
16 clarity   VS1       53940  8171 15.1      4
17 clarity   VVS2      53940  5066  9.39     5
18 clarity   VVS1      53940  3655  6.78     6
19 clarity   IF        53940  1790  3.32     7
20 clarity   I1        53940   741  1.37     8

R代码：

# Categorical Variable Profile
# Table based on dlookr package, diagnose_category() function
# variables : variable names
# types: the data type of the variable
# levels: level names
# N : Number of observation
# freq : Number of observation at the level
# ratio : Percentage of observation at the level
# rank : Rank of occupancy ratio of levels    

library(ggplot2)
library(dplyr)
library(tidyr)
library(purrr)
library(tibble)
library(stringr)

# Helper Function
cat_level_summary <- function(df,x) {
  count(df,x, sort = TRUE) %>% 
    transmute(levels = x, N = sum(n), freq = n,
              ratio = n / sum(n) * 100, rank = row_number())
}  

# Loading
diamonds_tbl <- diamonds

# Main Code
CategoricalVariableProfile <- diamonds_tbl %>%
  select_if(~!is.numeric(.)) %>% 
  map(~cat_level_summary(data.frame(x=.x), x)) %>%
      do.call(rbind.data.frame, .) %>%
      rownames_to_column(., "variables")%>%
      mutate(variables = str_match(variables, ".*(?=\\.)")[, 1] )

火花代码：

#Spark data Table
diamonds_tbl <- copy_to(sc, diamonds, "diamonds", overwrite = TRUE)

CategoricalVariableProfile <- diamonds_tbl %>% 
                              group_by(cut) %>%
                              summarize(count = n()) %>%
                              sdf_register("CategoricalVariableProfile")

推荐答案

使用 sdf_gather 整理数据：

long <- diamonds_tbl %>% 
  select(cut, color, clarity) %>% 
  sdf_gather("variable", "level", "cut", "color", "clarity")

通过变量和级别进行汇总：

counts <- long %>% group_by(variable, level) %>% summarise(freq = n())

最后应用所需的窗口函数：

And finally apply required window functions:

result <- counts %>%
  arrange(-freq) %>% 
  mutate(
    rank = rank(),
    total = sum(freq, na.rm = TRUE),
    ratio = freq / total * 100)

哪个会给你

result

# Source:     spark<?> [?? x 6]
# Groups:     variable
# Ordered by: -freq
   variable level      freq  rank total ratio
   <chr>    <chr>     <dbl> <int> <dbl> <dbl>
 1 cut      Ideal     21551     1 53940 40.0 
 2 cut      Premium   13791     2 53940 25.6 
 3 cut      Very Good 12082     3 53940 22.4 
 4 cut      Good       4906     4 53940  9.10
 5 cut      Fair       1610     5 53940  2.98
 6 clarity  SI1       13065     1 53940 24.2 
 7 clarity  VS2       12258     2 53940 22.7 
 8 clarity  SI2        9194     3 53940 17.0 
 9 clarity  VS1        8171     4 53940 15.1 
10 clarity  VVS2       5066     5 53940  9.39
# … with more rows

遵循优化计划

optimizedPlan(result)

<jobj[165]>
  org.apache.spark.sql.catalyst.plans.logical.Project
  Project [variable#524, level#525, freq#1478L, rank#1479, total#1480L, ((cast(freq#1478L as double) / cast(total#1480L as double)) * 100.0) AS ratio#1481]
+- Window [rank(_w1#1493L) windowspecdefinition(variable#524, _w1#1493L ASC NULLS FIRST, specifiedwindowframe(RowFrame, unboundedpreceding$(), currentrow$())) AS rank#1479], [variable#524], [_w1#1493L ASC NULLS FIRST]
   +- Window [sum(freq#1478L) windowspecdefinition(variable#524, specifiedwindowframe(RowFrame, unboundedpreceding$(), unboundedfollowing$())) AS total#1480L], [variable#524]
      +- Project [variable#524, level#525, freq#1478L, -freq#1478L AS _w1#1493L]
         +- Sort [-freq#1478L ASC NULLS FIRST], true
            +- Aggregate [variable#524, level#525], [variable#524, level#525, count(1) AS freq#1478L]
               +- Generate explode(map(cut, cut#19, color, color#20, clarity, clarity#21)), [0, 1, 2], false, [variable#524, level#525]
                  +- Project [cut#19, color#20, clarity#21]
                     +- InMemoryRelation [carat#18, cut#19, color#20, clarity#21, depth#22, table#23, price#24, x#25, y#26, z#27], StorageLevel(disk, memory, deserialized, 1 replicas)
                           +- Scan ExistingRDD[carat#18,cut#19,color#20,clarity#21,depth#22,table#23,price#24,x#25,y#26,z#27]

并查询（不包括 sdf_gather 组件）：

and query (sdf_gather component not included):

dbplyr::remote_query(result)

<SQL> SELECT `variable`, `level`, `freq`, `rank`, `total`, `freq` / `total` * 100.0 AS `ratio`
FROM (SELECT `variable`, `level`, `freq`, rank() OVER (PARTITION BY `variable` ORDER BY -`freq`) AS `rank`, sum(`freq`) OVER (PARTITION BY `variable`) AS `total`
FROM (SELECT *
FROM (SELECT `variable`, `level`, count(*) AS `freq`
FROM `sparklyr_tmp_ded2576b9f1`
GROUP BY `variable`, `level`) `dsbksdfhtf`
ORDER BY -`freq`) `obyrzsxeus`) `ekejqyjrfz`

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