count number of unique elements in each columns with dplyr in sparklyr

Question

---

I'm trying to count the number of unique elements in each column in the spark dataset s.

However It seems that spark doesn't recognize tally() k<-collect(s%>%group_by(grouping_type)%>%summarise_each(funs(tally(distinct(.))))) Error: org.apache.spark.sql.AnalysisException: undefined function TALLY

It seems that spark doesn't recognize simple r functions either, like "unique" or "length". I can run the code on local data, but when I try to run the exact same code on spark table it doesn't work.

```

d<-data.frame(cbind(seq(1,10,1),rep(1,10)))
d$group<-rep(c("a","b"),each=5)
d%>%group_by(group)%>%summarise_each(funs(length(unique(.))))
A tibble: 2 × 3
  group    X1    X2
  <chr> <int> <int>
1     a     5     1
2     b     5     1
k<-collect(s%>%group_by(grouping_type)%>%summarise_each(funs(length(unique(.)))))
Error: org.apache.spark.sql.AnalysisException: undefined function UNIQUE;

```

Answer 1

library(sparklyr)
library(dplyr)
#I am on Spark V. 2.1

#Building example input (local)
d <- data.frame(cbind(seq(1, 10, 1), rep(1,10)))
d$group <- rep(c("a","b"), each = 5)
d

#Spark tbl 
sdf <- sparklyr::sdf_copy_to(sc, d)

# The Answer
sdf %>% 
    group_by(group) %>% 
    summarise_all(funs(n_distinct)) %>%
    collect()

#Output
  group    X1    X2
  <chr> <dbl> <dbl>
1     b     5     1
2     a     5     1

NB: Given that we are using sparklyr I went for dplyr::n_distinct(). Minor: dplyr::summarise_each is deprecated. Thus, dplyr::summarise_all.

Answer 2

Remember when you are writing sparlyr you are really transpiling to spark-sql, so you may need to use spark-sql verbs from time to time. This is one of those times where spark-sql verbs like count and distinct come in handy.

library(sparkylr)

sc <- spark_connect()
iris_spk <- copy_to(sc, iris)

# for instance this does not work in R, but it does in sparklyr
iris_spk %>%
  summarise(Species = distinct(Species))
# or
iris_spk %>%
  summarise(Species = approx_count_distinct(Species))

# this does what you are looking for
iris_spk %>% 
    group_by(species) %>%
    summarise_all(funs(n_distinct))

# for larger data sets this is much faster
iris_spk %>% 
    group_by(species) %>%
    summarise_all(funs(approx_count_distinct))