[R] right_join() function does not produce the expected outcome

[abduazizR]

Describe the bug, including details regarding any error messages, version, and platform.

Hi,

I noticed something strange today when I was using arrow datasets. I cannot give a reproducible example but you can get the idea from the code below. I have ccaei as an arrow dataset. When I try to use right_join() with an R tibble before using collect(), it gives me wrong numbers (the number of distinct ENROLID is less than that present in outpatients). I get the correct number when I use right_join() after using collect(), although this is computationally inefficient. Could you help me with this?

This gives a really weird number

ccaei |>  
  filter(ADMDATE >= as_date("2016-10-01")) |> 
  filter(!is.na(ENROLID)) |> 
  select(ENROLID, ADMDATE) |> 
  right_join(outpatients) |> 
  collect() |> count(ENROLID)

This makes sense

ccaei |>  
  filter(ADMDATE >= as_date("2016-10-01")) |> 
  filter(!is.na(ENROLID)) |> 
  select(ENROLID, ADMDATE) |> 
  collect() |> 
    right_join(outpatients) |> 
  count(ENROLID)

Not sure where the mistake came from.

Component(s)

R

[thisisnic]

Hi @abduazizR , thanks for reporting this! Just to check, which version of Arrow are you using?

I understand that your data may be confidential, but is it possible to create a reproducible example using in-built datasets instead?

[abduazizR]

Hi Nic,

Thanks for responding. I am using the latest version of Arrow 10.0.1

In the example I posted, ccaei is a dataset I built using multiple parquet files. I will attach an example, but it does not show exactly the problem I faced.

library(tidyverse)
library(arrow)
#> 
#> Attaching package: 'arrow'
#> The following object is masked from 'package:utils':
#> 
#>     timestamp
library(duckdb)
#> Loading required package: DBI

data1 <- tibble(
  id = 1:100,
  x = rnorm(100)
)

data2 <- tibble(
  id = 52:69,
  z = rbinom(18, size = 18, prob = 0.5)
)

data2_arrow <- data2 |> as_arrow_table()

# The weired right_join output
data2_arrow |> 
  right_join(data1) |> collect() |> 
  # The ids from data 1 are missing
  tail(10)
#> # A tibble: 10 × 3
#>       id     z      x
#>    <int> <int>  <dbl>
#>  1    NA    NA -0.369
#>  2    NA    NA  0.470
#>  3    NA    NA -0.372
#>  4    NA    NA  0.505
#>  5    NA    NA  1.47 
#>  6    NA    NA -0.858
#>  7    NA    NA -0.330
#>  8    NA    NA  2.02 
#>  9    NA    NA  1.53 
#> 10    NA    NA -0.399

# The expected right_join output 
data2 |> 
  right_join(data1) |> 
  tail(10)
#> Joining, by = "id"
#> # A tibble: 10 × 3
#>       id     z      x
#>    <int> <int>  <dbl>
#>  1    91    NA -0.369
#>  2    92    NA  0.470
#>  3    93    NA -0.372
#>  4    94    NA  0.505
#>  5    95    NA  1.47 
#>  6    96    NA -0.858
#>  7    97    NA -0.330
#>  8    98    NA  2.02 
#>  9    99    NA  1.53 
#> 10   100    NA -0.399

^{Created on 2022-12-11 with reprex v2.0.2}

With my data, right_join is acting as inner_join. As a result, I don't even have rows with NAs in the ID column.

Thank you.

[thisisnic]

Thanks for the reprex there, this looks similar to an issue we had back in ARROW-15838 though there has been work done to fix it - I'm suspecting we might need to do something extra to make this work for right_join().

I'll take a look into what's going on in the example in the reprex, which hopefully should also fix the original problem you had, though it's not guaranteed - if you do find a reprex that captures that, feel free to add that here.

I can confirm that I can reproduce the error from the reprex in the latest dev version of Arrow.

[abduazizR]

Thanks for the response. Can I rely on left_join() for now until the issue with right_join() gets resolved?

[thisisnic]

@abduazizR It looks like that issue is restricted to right_join() only - see the reprex below:

library(arrow)
library(dplyr)

tbl <- tibble::tibble(x = 1:10, y = 1:10)
tbl2 <- tibble::tibble(y = 3:5, z = c("a", "b", "c"))

# right join using dplyr
tbl2 %>%
  right_join(tbl)
#> Joining, by = "y"
#> # A tibble: 10 × 3
#>        y z         x
#>    <int> <chr> <int>
#>  1     3 a         3
#>  2     4 b         4
#>  3     5 c         5
#>  4     1 <NA>      1
#>  5     2 <NA>      2
#>  6     6 <NA>      6
#>  7     7 <NA>      7
#>  8     8 <NA>      8
#>  9     9 <NA>      9
#> 10    10 <NA>     10

# right join using Arrow
tbl2 %>%
  arrow_table() %>%
  right_join(tbl) %>%
  collect()
#> # A tibble: 10 × 3
#>        y z         x
#>    <int> <chr> <int>
#>  1     3 a         3
#>  2     4 b         4
#>  3     5 c         5
#>  4    NA <NA>      1
#>  5    NA <NA>      2
#>  6    NA <NA>      6
#>  7    NA <NA>      7
#>  8    NA <NA>      8
#>  9    NA <NA>      9
#> 10    NA <NA>     10

# left join using dplyr
tbl %>%
  left_join(tbl2)
#> Joining, by = "y"
#> # A tibble: 10 × 3
#>        x     y z    
#>    <int> <int> <chr>
#>  1     1     1 <NA> 
#>  2     2     2 <NA> 
#>  3     3     3 a    
#>  4     4     4 b    
#>  5     5     5 c    
#>  6     6     6 <NA> 
#>  7     7     7 <NA> 
#>  8     8     8 <NA> 
#>  9     9     9 <NA> 
#> 10    10    10 <NA>

# left join using Arrow
tbl %>%
  arrow_table() %>%
  left_join(tbl2) %>%
  collect()
#> # A tibble: 10 × 3
#>        x     y z    
#>    <int> <int> <chr>
#>  1     3     3 a    
#>  2     4     4 b    
#>  3     5     5 c    
#>  4     1     1 <NA> 
#>  5     2     2 <NA> 
#>  6     6     6 <NA> 
#>  7     7     7 <NA> 
#>  8     8     8 <NA> 
#>  9     9     9 <NA> 
#> 10    10    10 <NA>

[annakrystalli]

Hello!

I was wondering if this has been resolved as I'm still coming up against behaviour that differs from dplyr behaviour in both left_join() and right_join() (see reprex below) in arrow version 12.0.1.1.

What appears to me to be the difference is that while dplyr matches NA values by default, arrow does not seem to.

Am I missing sth?

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(arrow)
#> 
#> Attaching package: 'arrow'
#> The following object is masked from 'package:utils':
#> 
#>     timestamp

tbl1 <- tibble::tibble(
  a = 1:3,
  b = c("a", "b", NA),
  d = c(letters[4:6])
)

tbl2 <- tibble::tibble(
  b = c("b", NA),
  c = c("a should be 2", "a should be 3")
)

# Left join tibbles, NAs matched
left_join(tbl2, tbl1)
#> Joining with `by = join_by(b)`
#> # A tibble: 2 × 4
#>   b     c                 a d    
#>   <chr> <chr>         <int> <chr>
#> 1 b     a should be 2     2 e    
#> 2 <NA>  a should be 3     3 f

# Left join arrow table & tibble, NAs NOT matched
left_join(as_arrow_table(tbl2), tbl1) %>% collect()
#> # A tibble: 2 × 4
#>   b     c                 a d    
#>   <chr> <chr>         <int> <chr>
#> 1 b     a should be 2     2 e    
#> 2 <NA>  a should be 3    NA <NA>
# Left join arrow table & arrow table, NAs NOT matched
left_join(as_arrow_table(tbl2), as_arrow_table(tbl1)) %>% collect()
#> # A tibble: 2 × 4
#>   b     c                 a d    
#>   <chr> <chr>         <int> <chr>
#> 1 b     a should be 2     2 e    
#> 2 <NA>  a should be 3    NA <NA>


# Same with right_join. It appears that the NAs are the problem as arrow doesn't seem to count them as a match
right_join(tbl1, tbl2)
#> Joining with `by = join_by(b)`
#> # A tibble: 2 × 4
#>       a b     d     c            
#>   <int> <chr> <chr> <chr>        
#> 1     2 b     e     a should be 2
#> 2     3 <NA>  f     a should be 3
right_join(as_arrow_table(tbl1), as_arrow_table(tbl2)) %>% collect()
#> # A tibble: 2 × 4
#>       a b     d     c            
#>   <int> <chr> <chr> <chr>        
#> 1     2 b     e     a should be 2
#> 2    NA <NA>  <NA>  a should be 3
right_join(as_arrow_table(tbl1), tbl2) %>% collect()
#> # A tibble: 2 × 4
#>       a b     d     c            
#>   <int> <chr> <chr> <chr>        
#> 1    NA <NA>  <NA>  a should be 3
#> 2     2 b     e     a should be 2

^{Created on 2023-09-26 with reprex v2.0.2}

Session info

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