I need to read a file of type ".csv" using the library "sparklyr", in which the numeric values appear with commas. The idea is to be able to read using "spark_read_csv()" directly.
I am using:
library(sparklyr)
library(dplyr)
f<-data.frame(DNI=c("22-e","EE-4","55-W"),
DD=c("33,2","33.2","14,55"),CC=c("2","44,4","44,9"))
write.csv(f,"aff.csv")
sc <- spark_connect(master = "local", spark_home = "/home/tomas/spark-2.1.0-bin-hadoop2.7/", version = "2.1.0")
df <- spark_read_csv(sc, name = "data", path = "/home/tomas/Documentos/Clusterapp/aff.csv", header = TRUE, delimiter = ",")
tbl <- sdf_copy_to(sc = sc, x =df , overwrite = T)
The problem, read the numbers as factor
To manipulate string inside a spark df you can use regexp_replace function as mentioned here:
https://spark.rstudio.com/guides/textmining/
For you problem it would work out like this:
tbl <- sdf_copy_to(sc = sc, x =df, overwrite = T)
tbl0<-tbl%>%
mutate(DD=regexp_replace(DD,",","."),CC=regexp_replace(CC,",","."))%>%
mutate_at(vars(c("DD","CC")),as.numeric)
to check your result:
> glimpse(tbl0)
Observations: ??
Variables: 3
$ DNI <chr> "22-e", "EE-4", "55-W"
$ DD <dbl> 33.20, 33.20, 14.55
$ CC <dbl> 2.0, 44.4, 44.9