Change column data type for coefficient using sparklyr

Question

I am new to Spark and currently use it with the R API through a vibrant package. I created a Spark data frame from a bush request. The data types are not specified correctly in the source table, and I am trying to use the reset data type using functions from the package dplyr. Below is the code I tried:

prod_dev <- sdf_load_table(...)
num_var <-  c("var1", "var2"....)
cat_var <-  c("var_a","var_b", ...)

pos1 <- which(colnames(prod_dev) %in% num_var)
pos2 <- which(colnames(prod_dev) %in% cat_var)

prod_model_tbl <- prod_dev %>% 
                mutate(age = 2016- as.numeric(substr(dob_yyyymmdd,1,4))) %>%
                mutate(msa_fg = ifelse(is.na(msacode2000), 0, 1)) %>% 
                mutate(csa_fg = ifelse(is.na(csacode), 0, 1)) %>%
                mutate_each(funs(factor), pos2) %>%
                mutate_each(funs(as.numeric), pos1)

The code will work if prod_dev is an R data frame. But using it in a Spark Data frame does not seem to produce the correct result:

> head(prod_model_tbl)


    Source:   query [?? x 99]
    Database: spark connection master=yarn-client app=sparklyr_test local=FALSE

    Error: org.apache.spark.sql.AnalysisException: undefined function     FACTOR; line 97 pos 2248 at org.apache.spark.sql.hive.HiveFunctionRegistry....

Can someone please advise how to make the necessary changes to the Spark Data Frame?

Answer 1

R . :

df <- data.frame(x=c(1, NA), y=c("-1", "2"))

copy_to(sc, df, "df", overwrite=TRUE) %>% 
  mutate(x_char = as.character(x)) %>% 
  mutate(y_numeric = as.numeric(y))

Source:   query [2 x 4]
Database: spark connection master=...

      x     y x_char y_numeric
  <dbl> <chr>  <chr>     <dbl>
1     1    -1    1.0        -1
2   NaN     2   <NA>         2

, Spark R factor.

Spark SQL double ML Transformers, SQL, . factor/as.factor. SparkR ML, , sparklyr ( , , ml_create_dummy_variables).